Ask a pharmaceutical regulator about an AI system and the first question is whether you can reconstruct what it did. Ask an engineering team about the same system and the first question is almost always how accurate it is. Silky Sandhu has sat in both conversations, and she thinks the regulator is asking the better question.&nbsp;Sandhu is a senior manager and data engineer at a top-5 global pharmaceutical company, where she leads the technical architecture of an AI-enabled product disposition platform. Disposition is the decision that releases a manufactured drug batch toward patients. Software that participates in it falls under 21 CFR Part 11 electronic records requirements and ALCOA+ data integrity principles, where a wrong output is not a degraded user experience but a regulatory event.&nbsp;"A highly accurate model with no audit trail is unusable in GxP," she says. "A moderately accurate model with full traceability, explainability, and human override is deployable, because the human remains accountable and the system's contribution is fully documented and reconstructable." Her shorthand is blunter: the audit trail is the product, not the prediction.&nbsp;What the platform replaced makes the stakes concrete. Quality assurance had required two sequential human reviewers to independently check batch documentation against standard operating procedures before a batch could be released. The system now performs the first-level review itself, using AI to extract structured data from certificates of analysis and conformance, running deterministic checks against SOP criteria, and surfacing discrepancies to a qualified reviewer. One approval layer was removed, halving the approval touchpoints per batch, while the reviewer's work shifted from compiling data to exercising judgment, the only part that ever required a person.&nbsp;The architecture that made that acceptable is a dual-layer confidence model, and Sandhu argues it is the pattern that earns regulatory trust rather than a technical convenience. Deterministic automation checks validate what explicit rules can catch. AI anomaly detection surfaces what rules alone would miss. Both feed the same immutable audit trail, so every flag is traceable to its source, timestamped and attributable. The ordering matters more than it appears. "The automation checks give you confidence in the AI, not the other way around," she says. "When a regulator asks how you know the AI is working correctly, you point to the deterministic layer that independently validates the same data."The AI never decides. That division is what let an autonomous component into a workflow where black-box recommendations are unacceptable regardless of how well they perform.&nbsp;Also read: <a href="https://www.wionews.com/world/ai-is-the-new-cybersecurity-threat-how-automated-agents-and-ai-bots-are-helping-hackers-1793869821339">AI is the new cybersecurity threat: How automated agents and AI bots are helping hackers</a>&nbsp;Getting there required inverting the usual build order. Before any AI code was written, Sandhu ran formal risk assessments across several dimensions: the assessment her documentation calls RARA, which classifies every system function by its impact on patient safety; a data integrity risk assessment, or DIRA, mapping risk across every hop from source system to disposition decision; and a dedicated AI risk assessment covering explainability, training data bias, drift detection, and graceful degradation when model confidence falls below threshold. By validation, every AI function already carried a documented risk level, acceptance criteria and a fallback path. "If you cannot articulate what happens when the model is wrong before you deploy it, you are not ready to deploy it," she says.&nbsp;She is equally direct about a gap she thinks the industry is ignoring. AI-enabled applications introduce attack surfaces that pharmaceutical cybersecurity frameworks were not written for: model inversion, adversarial inputs, data poisoning, prompt injection. "What happens if someone feeds a manipulated certificate of analysis to your document extraction model?" she asks. "What if adversarial input causes the anomaly detector to miss a genuine discrepancy?" Her answer was a defence-in-depth architecture assessed against the NIST Cybersecurity Framework and OWASP ASVS, spanning network isolation, encrypted secrets management with automated rotation, role-based access control enforcing separation of duties so a reviewer cannot self-approve a disposition, and immutable audit logging that not even administrators can alter. Model outputs are bounded by the deterministic layer, which overrides AI recommendations that fall outside acceptable parameters.&nbsp;Underneath sits ordinary engineering: containerised services on AWS ECS, Aurora PostgreSQL, data pipelines on Databricks with Unity Catalogue, a Django API, a React frontend, and automated delivery with infrastructure as code. The platform reconciles multiple enterprise systems, including SAP, quality management, laboratory and inspection systems, processing hundreds of thousands of records on schedule. Sandhu led a cross-functional team of 20 engineers to deliver it, and separately architected an automation framework for the Annual Product Quality Review, a recurring obligation under 21 CFR 211.180(e), replacing weeks of manual spreadsheet assembly per product per year. A paper proposing a risk-based validation strategy for that work under the FDA's Computer Software Assurance guidance is under submission to a peer-reviewed pharmaceutical science journal.&nbsp;Her route to pharmaceutical manufacturing ran through precision agriculture data platforms at Bayer Crop Science and large-scale data engineering at Amazon, each with a heavier consequence for being wrong than the last.The hardest part, she says, was none of the technology. "It was aligning quality assurance, regulatory affairs, IT validation, and engineering teams around a shared understanding of what the system does and does not decide." Technical teams consistently underestimate that. “The technology is the straightforward part; the validation strategy and stakeholder alignment take three times longer and determine whether the system is adopted or shelved.” Which returns to the sentence that organizes the whole design. The system highlights, the human decides, and the audit trail captures both.&nbsp;

Sandhu is a senior manager and data engineer at a top-5 global pharmaceutical company, where she leads the technical architecture of an AI-enabled product disposition platform. 

Ask a pharmaceutical regulator about an AI system and the first question is whether you can reconstruct what it did. Ask an engineering team about the same system and the first question is almost always how accurate it is. Silky Sandhu has sat in both conversations, and she thinks the regulator is asking the better question. Sandhu is a senior manager and data engineer at a top-5 global pharmaceutical company, where she leads the technical architecture of an AI-enabled product disposition platform. Disposition is the decision that releases a manufactured drug batch toward patients. Software that participates in it falls under 21 CFR Part 11 electronic records requirements and ALCOA+ data integrity principles, where a wrong output is not a degraded user experience but a regulatory event. "A highly accurate model with no audit trail is unusable in GxP," she says. "A moderately accurate model with full traceability, explainability, and human override is deployable, because the human remains accountable and the system's contribution is fully documented and reconstructable." Her shorthand is blunter: the audit trail is the product, not the prediction. What the platform replaced makes the stakes concrete. Quality assurance had required two sequential human reviewers to independently check batch documentation against standard operating procedures before a batch could be released. The system now performs the first-level review itself, using AI to extract structured data from certificates of analysis and conformance, running deterministic checks against SOP criteria, and surfacing discrepancies to a qualified reviewer. One approval layer was removed, halving the approval touchpoints per batch, while the reviewer's work shifted from compiling data to exercising judgment, the only part that ever required a person. The architecture that made that acceptable is a dual-layer confidence model, and Sandhu argues it is the pattern that earns regulatory trust rather than a technical convenience. Deterministic automation checks validate what explicit rules can catch. AI anomaly detection surfaces what rules alone would miss. Both feed the same immutable audit trail, so every flag is traceable to its source, timestamped and attributable. The ordering matters more than it appears. "The automation checks give you confidence in the AI, not the other way around," she says. "When a regulator asks how you know the AI is working correctly, you point to the deterministic layer that independently validates the same data." The AI never decides. That division is what let an autonomous component into a workflow where black-box recommendations are unacceptable regardless of how well they perform. Also read: AI is the new cybersecurity threat: How automated agents and AI bots are helping hackers Getting there required inverting the usual build order. Before any AI code was written, Sandhu ran formal risk assessments across several dimensions: the assessment her documentation calls RARA, which classifies every system function by its impact on patient safety; a data integrity risk assessment, or DIRA, mapping risk across every hop from source system to disposition decision; and a dedicated AI risk assessment covering explainability, training data bias, drift detection, and graceful degradation when model confidence falls below threshold. By validation, every AI function already carried a documented risk level, acceptance criteria and a fallback path. "If you cannot articulate what happens when the model is wrong before you deploy it, you are not ready to deploy it," she says. She is equally direct about a gap she thinks the industry is ignoring. AI-enabled applications introduce attack surfaces that pharmaceutical cybersecurity frameworks were not written for: model inversion, adversarial inputs, data poisoning, prompt injection. "What happens if someone feeds a manipulated certificate of analysis to your document extraction model?" she asks. "What if adversarial input causes the anomaly detector to miss a genuine discrepancy?" Her answer was a defence-in-depth architecture assessed against the NIST Cybersecurity Framework and OWASP ASVS, spanning network isolation, encrypted secrets management with automated rotation, role-based access control enforcing separation of duties so a reviewer cannot self-approve a disposition, and immutable audit logging that not even administrators can alter. Model outputs are bounded by the deterministic layer, which overrides AI recommendations that fall outside acceptable parameters. Underneath sits ordinary engineering: containerised services on AWS ECS, Aurora PostgreSQL, data pipelines on Databricks with Unity Catalogue, a Django API, a React frontend, and automated delivery with infrastructure as code. The platform reconciles multiple enterprise systems, including SAP, quality management, laboratory and inspection systems, processing hundreds of thousands of records on schedule. Sandhu led a cross-functional team of 20 engineers to deliver it, and separately architected an automation framework for the Annual Product Quality Review, a recurring obligation under 21 CFR 211.180(e), replacing weeks of manual spreadsheet assembly per product per year. A paper proposing a risk-based validation strategy for that work under the FDA's Computer Software Assurance guidance is under submission to a peer-reviewed pharmaceutical science journal. Her route to pharmaceutical manufacturing ran through precision agriculture data platforms at Bayer Crop Science and large-scale data engineering at Amazon, each with a heavier consequence for being wrong than the last. The hardest part, she says, was none of the technology. "It was aligning quality assurance, regulatory affairs, IT validation, and engineering teams around a shared understanding of what the system does and does not decide." Technical teams consistently underestimate that. “The technology is the straightforward part; the validation strategy and stakeholder alignment take three times longer and determine whether the system is adopted or shelved.” Which returns to the sentence that organizes the whole design. The system highlights, the human decides, and the audit trail captures both.

Sandhu is a senior manager and data engineer at a top-5 global pharmaceutical company, where she leads the technical architecture of an AI-enabled product disposition platform.&nbsp;

Inside fight to put AI in drug release decisions - Where regulators trust audit trail, not accuracy

Enterprises are moving from paper-based and spreadsheet-heavy purchasing to intelligent procurement, empowered by growing technology, including artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA), in order to transform the entire source-to-pay (S2P) lifecycle. Several firms have shifted from using AI merely to analyse and spend toward deploying it as an operator, as agents now create purchase orders, route multi-level approvals, and flag supplier anomalies before any involvement of humans. Interestingly, teams in the firms deploying AI typically cut cycle times by 40 to 60 per cent and improve productivity and efficiency of the result more precisely without consuming extra effort and time. While AI, automation and cloud platforms are becoming increasingly accessible, many organisations continue to operate with legacy systems, fragmented data and processes that were not designed for today’s pace of business. For large enterprises, the challenge is no longer simply about investing in newer technology. It is about connecting existing systems, improving the quality of data and redesigning workflows so that technology can deliver measurable improvements in productivity, efficiency and decision-making. This shift is particularly visible in functions such as procurement, where organisations are looking to move beyond manual processes and disconnected systems towards more intelligent, integrated and user-friendly experiences. At the same time, the growing use of AI is raising new questions around governance, security, human oversight and how businesses can translate experimentation into tangible operational value. In a conversation with WION, Trishna Patel, Head, Ewiz Procure, Powerweave, and Reena Ravindran, VP – Delivery, Powerweave, discuss the major challenges enterprises experience in modernising legacy environments and the changing expectations around procurement. They also focused on how AI can deliver measurable business outcomes, and the importance of balancing automation with human judgement and responsible technology adoption.&nbsp;1. Many enterprises continue to operate with legacy systems and fragmented data. What are the biggest challenges they face when modernising their technology infrastructure?&nbsp;Trishna Patel, Head- Ewiz Procureat Powerweave: One of the biggest misconceptions is that modernisation means replacing legacy technology. In reality, the bigger issue is often the fragmentation of data, processes and workflows around it. Large enterprises have invested heavily in ERP and other core platforms, but many still operate with disconnected systems, inconsistent master data and manual processes outside those platforms. Modernisation is therefore as much a data and operating-model challenge as a technology one.&nbsp;The starting point should be the business problem: where is the process breaking down, where is data unreliable, and where are manual interventions limiting visibility or efficiency?&nbsp;"We see this often in procurement. With ewiz procure, we work alongside systems such as SAP, Oracle and Coupa. We first understand the business objective and process gaps, then fix the underlying data and governance, automate the right workflows, and introduce AI where it can add meaningful value. That is where enterprise modernisation is heading: not replacement for replacement’s sake, but making existing investments work harder through cleaner data, better-connected processes and more intelligent automation," Trishna said.&nbsp;2. With AI becoming more accessible, how can businesses move beyond experimentation and translate AI investments into measurable operational outcomes?&nbsp;Reena Ravindran, VP-Delivery at Powerweave: AI will create real value when businesses stop treating it as a standalone technology experiment and start embedding it into the way work actually gets done. The right question is not how many AI pilots an organisation has launched, but which business metric has improved because of them. To get there, AI needs to sit inside a defined workflow where it can influence decisions, automate repetitive steps and improve cycle times. That also requires the right foundation: reliable data, clear process ownership, team training, change management and controlled evaluation. Without those elements, even a strong AI capability can struggle to deliver sustained operational value. We have seen this in practice. In one chemical manufacturing deployment, the results came not from AI alone, but from redesigning the procurement workflow around structured data, automation, system integration and digital decision support. PR-to-PO processing time was reduced by 87%, while supplier onboarding time dropped by 95%. I think that is the real shift enterprises need to make. AI should not sit beside the process as another tool. It needs to be embedded into the workflow, supported by good data, governance and adoption. That is what turns experimentation into measurable business outcomes. 3. How are customer expectations evolving in procurement, particularly among manufacturers, distributors and wholesalers?&nbsp;Trishna Patel, Head- Ewiz Procure at Powerweave: Procurement users are no longer comparing their experience only with other B2B systems. They are comparing it with the best digital experiences they use every day, raising expectations around speed, visibility, self-service and ease of use. They expect every interaction with a procurement tool to feel seamless and intuitive, whether they are searching for a product, raising a request, approving a purchase or onboarding a supplier. With tools like ChatGPT shaping user behaviour, that experience is also becoming more conversational. Users increasingly expect to describe what they need in natural language and be guided to the right action without navigating complex menus. For manufacturers, distributors and wholesalers, the challenge is to hide complexity rather than expose it. Data, workflows, approvals and intelligence need to work together behind the scenes while the user gets a simple, connected experience. The competitive advantage will increasingly come from removing friction across the procurement journey while improving control, visibility and compliance. 4. How does Powerweave measure the impact of its solutions for customers? Could you share examples of business challenges addressed and outcomes achieved?&nbsp;Reena Ravindran, VP-Delivery at Powerweave: Technology should never be the outcome. The outcome is the only reason to deploy technology in the first place. We therefore establish a baseline before measuring impact. In ewiz procure, customers have seen 8–12% savings on indirect procurement, sourcing cycles around 30% faster and 27% lower process costs. In one procurement data cleaning project, we helped uncover €40M vendor-consolidation opportunity for a global manufacturer. The opportunity was already there. We just helped them uncover it. In awspromostack, AI has reduced order-entry effort by 40% and art and booking effort by 60%, while storefront and product-listing creation is 80% faster. Our operations process more than 40,000 orders a day at over 98% accuracy. Across these examples, the principle is consistent: we measure what changed in the business, not how much technology was deployed 5. With customers across multiple geographies, how does Powerweave adapt its solutions to different markets, regulatory environments and business requirements?&nbsp;Trishna Patel, Head- Ewiz Procure, Powerweave: Global technology does not mean identical technology everywhere. The foundation can be common, but the context cannot. Regulations, workflows, data structures and customer expectations vary significantly by market. The real capability lies in knowing what should be standardised and what must remain local. Snowkap is a good example. An Indian business may operate under BRSR and BRSR Core while also responding to frameworks such as CSRD, GRI, TCFD or IFRS when serving global markets. The same principle applies to procurement. ewiz procure works with the customer’s existing ERP, S2P and supplier landscape, while adapting workflows, data structures, approvals and governance to local business requirements rather than forcing a single operating model everywhere. Our approach is therefore to build adaptable technology around the customer’s reality. Scale comes from a strong common foundation; relevance comes from understanding the market, regulations and operating model in which that foundation has to work. 6. As AI becomes more deeply integrated into business operations, what responsibilities do technology leaders have in ensuring its adoption is practical, secure and aligned with business objectives?&nbsp;Reena Ravindran, VP-Delivery, Powerweave: The responsibility of a technology leader is no longer simply to ask whether AI can be deployed. It is to decide where AI can create measurable business value, how it should fit into existing workflows, and where human judgement must remain. AI is highly effective at processing information, identifying patterns and automating repetitive work. But relationships, commercial judgement, risk and strategic decisions often depend on context that cannot be captured in data alone. That is why we believe in progressive adoption: automate administrative work first, augment human decision-making next, and introduce greater autonomy only where the controls, data and context support it. Security and governance are equally fundamental. That means clear access controls, data governance, auditability, defined decision rights and human oversight, alongside strong enterprise security standards. Powerweave's platforms and operations are ISO 27001 certified and SOC 2 Type II audited. Responsible AI ultimately comes down to four things: a clear business purpose, reliable data, appropriate controls and clear accountability for the outcome.&nbsp; &nbsp;

The shift is particularly visible in functions such as procurement, where organisations are looking to move beyond manual processes and disconnected systems towards more intelligent, integrated and user-friendly experiences. 

Enterprises are moving from paper-based and spreadsheet-heavy purchasing to intelligent procurement, empowered by growing technology, including artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA), in order to transform the entire source-to-pay (S2P) lifecycle. Several firms have shifted from using AI merely to analyse and spend toward deploying it as an operator, as agents now create purchase orders, route multi-level approvals, and flag supplier anomalies before any involvement of humans. Interestingly, teams in the firms deploying AI typically cut cycle times by 40 to 60 per cent and improve productivity and efficiency of the result more precisely without consuming extra effort and time. While AI, automation and cloud platforms are becoming increasingly accessible, many organisations continue to operate with legacy systems, fragmented data and processes that were not designed for today’s pace of business. For large enterprises, the challenge is no longer simply about investing in newer technology. It is about connecting existing systems, improving the quality of data and redesigning workflows so that technology can deliver measurable improvements in productivity, efficiency and decision-making. This shift is particularly visible in functions such as procurement, where organisations are looking to move beyond manual processes and disconnected systems towards more intelligent, integrated and user-friendly experiences. At the same time, the growing use of AI is raising new questions around governance, security, human oversight and how businesses can translate experimentation into tangible operational value. In a conversation with WION, Trishna Patel, Head, Ewiz Procure, Powerweave, and Reena Ravindran, VP – Delivery, Powerweave, discuss the major challenges enterprises experience in modernising legacy environments and the changing expectations around procurement. They also focused on how AI can deliver measurable business outcomes, and the importance of balancing automation with human judgement and responsible technology adoption. 1. Many enterprises continue to operate with legacy systems and fragmented data. What are the biggest challenges they face when modernising their technology infrastructure? Trishna Patel, Head- Ewiz Procureat Powerweave: One of the biggest misconceptions is that modernisation means replacing legacy technology. In reality, the bigger issue is often the fragmentation of data, processes and workflows around it. Large enterprises have invested heavily in ERP and other core platforms, but many still operate with disconnected systems, inconsistent master data and manual processes outside those platforms. Modernisation is therefore as much a data and operating-model challenge as a technology one. The starting point should be the business problem: where is the process breaking down, where is data unreliable, and where are manual interventions limiting visibility or efficiency? "We see this often in procurement. With ewiz procure, we work alongside systems such as SAP, Oracle and Coupa. We first understand the business objective and process gaps, then fix the underlying data and governance, automate the right workflows, and introduce AI where it can add meaningful value. That is where enterprise modernisation is heading: not replacement for replacement’s sake, but making existing investments work harder through cleaner data, better-connected processes and more intelligent automation," Trishna said. 2. With AI becoming more accessible, how can businesses move beyond experimentation and translate AI investments into measurable operational outcomes? Reena Ravindran, VP-Delivery at Powerweave: AI will create real value when businesses stop treating it as a standalone technology experiment and start embedding it into the way work actually gets done. The right question is not how many AI pilots an organisation has launched, but which business metric has improved because of them. To get there, AI needs to sit inside a defined workflow where it can influence decisions, automate repetitive steps and improve cycle times. That also requires the right foundation: reliable data, clear process ownership, team training, change management and controlled evaluation. Without those elements, even a strong AI capability can struggle to deliver sustained operational value. We have seen this in practice. In one chemical manufacturing deployment, the results came not from AI alone, but from redesigning the procurement workflow around structured data, automation, system integration and digital decision support. PR-to-PO processing time was reduced by 87%, while supplier onboarding time dropped by 95%. I think that is the real shift enterprises need to make. AI should not sit beside the process as another tool. It needs to be embedded into the workflow, supported by good data, governance and adoption. That is what turns experimentation into measurable business outcomes. 3. How are customer expectations evolving in procurement, particularly among manufacturers, distributors and wholesalers? Trishna Patel, Head- Ewiz Procure at Powerweave: Procurement users are no longer comparing their experience only with other B2B systems. They are comparing it with the best digital experiences they use every day, raising expectations around speed, visibility, self-service and ease of use. They expect every interaction with a procurement tool to feel seamless and intuitive, whether they are searching for a product, raising a request, approving a purchase or onboarding a supplier. With tools like ChatGPT shaping user behaviour, that experience is also becoming more conversational. Users increasingly expect to describe what they need in natural language and be guided to the right action without navigating complex menus. For manufacturers, distributors and wholesalers, the challenge is to hide complexity rather than expose it. Data, workflows, approvals and intelligence need to work together behind the scenes while the user gets a simple, connected experience. The competitive advantage will increasingly come from removing friction across the procurement journey while improving control, visibility and compliance. 4. How does Powerweave measure the impact of its solutions for customers? Could you share examples of business challenges addressed and outcomes achieved? Reena Ravindran, VP-Delivery at Powerweave: Technology should never be the outcome. The outcome is the only reason to deploy technology in the first place. We therefore establish a baseline before measuring impact. In ewiz procure, customers have seen 8–12% savings on indirect procurement, sourcing cycles around 30% faster and 27% lower process costs. In one procurement data cleaning project, we helped uncover €40M vendor-consolidation opportunity for a global manufacturer. The opportunity was already there. We just helped them uncover it. In awspromostack, AI has reduced order-entry effort by 40% and art and booking effort by 60%, while storefront and product-listing creation is 80% faster. Our operations process more than 40,000 orders a day at over 98% accuracy. Across these examples, the principle is consistent: we measure what changed in the business, not how much technology was deployed 5. With customers across multiple geographies, how does Powerweave adapt its solutions to different markets, regulatory environments and business requirements? Trishna Patel, Head- Ewiz Procure, Powerweave: Global technology does not mean identical technology everywhere. The foundation can be common, but the context cannot. Regulations, workflows, data structures and customer expectations vary significantly by market. The real capability lies in knowing what should be standardised and what must remain local. Snowkap is a good example. An Indian business may operate under BRSR and BRSR Core while also responding to frameworks such as CSRD, GRI, TCFD or IFRS when serving global markets. The same principle applies to procurement. ewiz procure works with the customer’s existing ERP, S2P and supplier landscape, while adapting workflows, data structures, approvals and governance to local business requirements rather than forcing a single operating model everywhere. Our approach is therefore to build adaptable technology around the customer’s reality. Scale comes from a strong common foundation; relevance comes from understanding the market, regulations and operating model in which that foundation has to work. 6. As AI becomes more deeply integrated into business operations, what responsibilities do technology leaders have in ensuring its adoption is practical, secure and aligned with business objectives? Reena Ravindran, VP-Delivery, Powerweave: The responsibility of a technology leader is no longer simply to ask whether AI can be deployed. It is to decide where AI can create measurable business value, how it should fit into existing workflows, and where human judgement must remain. AI is highly effective at processing information, identifying patterns and automating repetitive work. But relationships, commercial judgement, risk and strategic decisions often depend on context that cannot be captured in data alone. That is why we believe in progressive adoption: automate administrative work first, augment human decision-making next, and introduce greater autonomy only where the controls, data and context support it. Security and governance are equally fundamental. That means clear access controls, data governance, auditability, defined decision rights and human oversight, alongside strong enterprise security standards. Powerweave's platforms and operations are ISO 27001 certified and SOC 2 Type II audited. Responsible AI ultimately comes down to four things: a clear business purpose, reliable data, appropriate controls and clear accountability for the outcome.

The shift is particularly visible in functions such as procurement, where organisations are looking to move beyond manual processes and disconnected systems towards more intelligent, integrated and user-friendly experiences.&nbsp;

From manual processes to intelligent procurement: How are enterprises embracing AI and automation

India is heading to build defences to keep pace with attackers who now use artificial intelligence to discover vulnerabilities, craft convincing phishing campaigns, and automate attacks within minutes. The Ministry of Electronics and Information Technology (MeitY) has acknowledged that generative AI is reducing the barrier for cybercriminals. In response, it has urged ministries, state governments, and regulators to urgently review their security posture as India strengthens its cyber defence framework against threats that are becoming faster, more sophisticated and harder to detect. Between June and July 2026, CERT-In, the Indian Computer Emergency Response Team, ran ten customised cybersecurity exercises and drills, with the theme of building resilience against frontier AI-driven cyber threats. Earlier this year, it also released targeted technical guidance, a 'Blueprint for Reducing Exposure and Defending against AI-Assisted Vulnerabilities Exploitation in Digital Infrastructure,' published in May 2026 to help protect digital infrastructure from fast-evolving AI threats. In April, CERT-In went further, issuing a high-risk advisory concerning frontier AI systems capable of allowing autonomous cyberattacks, including hacking and multi-stage, sophisticated malicious operations. In November 2025, MeitY released AI Governance Guidelines, laying out a framework made to bolster safe, inclusive, and accountable AI adoption across industries, as India still needs dedicated AI legislation. Three institutions anchor this architecture: the AI Governance Group for high-level coordination, the Technology and Policy Expert Committee for technical standards, and a proposed AI Safety Institute tasked with audits and certification. Crucially, incidents tied to AI are now folded into existing reporting rules; AI-related cybersecurity incidents must be reported to CERT-In within six hours where applicable.&nbsp;Also read: <a href="https://www.wionews.com/trending/chatgpt-guided-woman-to-taper-epilepsy-medication-she-died-parents-sue-openai-1793251205711">ChatGPT guided woman to taper epilepsy medication; she died, parents sue OpenAI</a><h3>Experts weigh in</h3>Discussing the topic, Srinivasa Sundar Bandepalli, Practice Head, Cards &amp; Payments at Expleo, told WION, "AI agents are acting beyond the scope of their authorisation, raising new cybersecurity concerns worldwide. In payments, where transactions happen in real time, compromised AI decisions can quickly translate into fraud and loss of customer trust. While robust infrastructure and smarter threat detection matter, they're only part of the answer. As AI systems are vulnerable to threats such as prompt injection and data poisoning, AI Assurance must sit at the heart of a multi-layered defence, built in from design through deployment and operations. India is taking important steps to strengthen its defences with CERT-In guidelines, RBI's digital payment security controls, zero-trust security principles, and cyber skills. As organisations increasingly turn to AI to fight AI-powered threats, we must remember that technology cannot be left to guard itself. Human oversight, stronger guardrails, and continuous AI assurance are needed to make advanced technology trusted and secure so that innovation can create value for the people and businesses." Rahail Ali, Enterprise Head, End Compute &amp; Infra Business, Ample, said, "Cybersecurity Awareness Month asks whether employees recognise cyber risks. Resilience asks a more practical question: if an incident disrupts the business, how quickly can critical operations recover?Most enterprises can assess awareness. Fewer have tested their ability to recover. That is because resilience is built continuously, not in a one-month sprint. It is built into how the IT lifecycle runs. Every device provisioned, every access granted, every system refreshed, and every endpoint retired is a security decision, whether or not anyone calls it one. When one accountable partner owns those decisions across identity, endpoint, network, cloud, and response, protection keeps pace with the business instead of interrupting it. That is what year-round resilience looks like in practice: security that lets business flow, not security that stops it to check." Similarly, Debraj Dam, Chief of Supertron VAD, said, "India is strengthening its cybersecurity through better sharing of threat information, closer coordination with industry and greater use of AI to detect suspicious activity. These efforts need to translate into how businesses manage their systems every day. AI allows attackers to find and exploit weaknesses faster. An outdated server, an unsecured device or an account with unnecessary access can leave an entire business exposed. Organisations need to be disciplined about updating systems, reviewing access and helping employees recognise suspicious requests. They should also have clear answers to some practical questions: who takes charge during an incident, how it is contained and how quickly operations can resume. As businesses adopt new technology, these decisions need to be made at the planning stage. India’s digital growth depends on people trusting the services they use, and businesses being prepared to protect that trust when something goes wrong." On the rising concern over deepfakes and synthetic fraud, experts also caution that India’s own defensive AI capability still trails global leaders, leaving the country reliant on foreign technology even as it tries to build indigenous detection and governance systems, a gap that will likely shape how effectively these new rules and drills translate into real-world resilience.

MeitY has acknowledged that generative AI is reducing the barrier for cybercriminals. It has urged ministries, state governments, and regulators to urgently review their security posture as India strengthens its cyber defence framework against threats.

India is heading to build defences to keep pace with attackers who now use artificial intelligence to discover vulnerabilities, craft convincing phishing campaigns, and automate attacks within minutes. The Ministry of Electronics and Information Technology (MeitY) has acknowledged that generative AI is reducing the barrier for cybercriminals. In response, it has urged ministries, state governments, and regulators to urgently review their security posture as India strengthens its cyber defence framework against threats that are becoming faster, more sophisticated and harder to detect. Between June and July 2026, CERT-In, the Indian Computer Emergency Response Team, ran ten customised cybersecurity exercises and drills, with the theme of building resilience against frontier AI-driven cyber threats. Earlier this year, it also released targeted technical guidance, a 'Blueprint for Reducing Exposure and Defending against AI-Assisted Vulnerabilities Exploitation in Digital Infrastructure,' published in May 2026 to help protect digital infrastructure from fast-evolving AI threats. In April, CERT-In went further, issuing a high-risk advisory concerning frontier AI systems capable of allowing autonomous cyberattacks, including hacking and multi-stage, sophisticated malicious operations. In November 2025, MeitY released AI Governance Guidelines, laying out a framework made to bolster safe, inclusive, and accountable AI adoption across industries, as India still needs dedicated AI legislation. Three institutions anchor this architecture: the AI Governance Group for high-level coordination, the Technology and Policy Expert Committee for technical standards, and a proposed AI Safety Institute tasked with audits and certification. Crucially, incidents tied to AI are now folded into existing reporting rules; AI-related cybersecurity incidents must be reported to CERT-In within six hours where applicable. Also read: ChatGPT guided woman to taper epilepsy medication; she died, parents sue OpenAI Experts weigh in Discussing the topic, Srinivasa Sundar Bandepalli, Practice Head, Cards & Payments at Expleo, told WION, "AI agents are acting beyond the scope of their authorisation, raising new cybersecurity concerns worldwide. In payments, where transactions happen in real time, compromised AI decisions can quickly translate into fraud and loss of customer trust. While robust infrastructure and smarter threat detection matter, they're only part of the answer. As AI systems are vulnerable to threats such as prompt injection and data poisoning, AI Assurance must sit at the heart of a multi-layered defence, built in from design through deployment and operations. India is taking important steps to strengthen its defences with CERT-In guidelines, RBI's digital payment security controls, zero-trust security principles, and cyber skills. As organisations increasingly turn to AI to fight AI-powered threats, we must remember that technology cannot be left to guard itself. Human oversight, stronger guardrails, and continuous AI assurance are needed to make advanced technology trusted and secure so that innovation can create value for the people and businesses." Rahail Ali, Enterprise Head, End Compute & Infra Business, Ample, said, "Cybersecurity Awareness Month asks whether employees recognise cyber risks. Resilience asks a more practical question: if an incident disrupts the business, how quickly can critical operations recover?Most enterprises can assess awareness. Fewer have tested their ability to recover. That is because resilience is built continuously, not in a one-month sprint. It is built into how the IT lifecycle runs. Every device provisioned, every access granted, every system refreshed, and every endpoint retired is a security decision, whether or not anyone calls it one. When one accountable partner owns those decisions across identity, endpoint, network, cloud, and response, protection keeps pace with the business instead of interrupting it. That is what year-round resilience looks like in practice: security that lets business flow, not security that stops it to check." Similarly, Debraj Dam, Chief of Supertron VAD, said, "India is strengthening its cybersecurity through better sharing of threat information, closer coordination with industry and greater use of AI to detect suspicious activity. These efforts need to translate into how businesses manage their systems every day. AI allows attackers to find and exploit weaknesses faster. An outdated server, an unsecured device or an account with unnecessary access can leave an entire business exposed. Organisations need to be disciplined about updating systems, reviewing access and helping employees recognise suspicious requests. They should also have clear answers to some practical questions: who takes charge during an incident, how it is contained and how quickly operations can resume. As businesses adopt new technology, these decisions need to be made at the planning stage. India’s digital growth depends on people trusting the services they use, and businesses being prepared to protect that trust when something goes wrong." On the rising concern over deepfakes and synthetic fraud, experts also caution that India’s own defensive AI capability still trails global leaders, leaving the country reliant on foreign technology even as it tries to build indigenous detection and governance systems, a gap that will likely shape how effectively these new rules and drills translate into real-world resilience.

MeitY has acknowledged that generative AI is reducing the barrier for cybercriminals. It has urged ministries, state governments, and regulators to urgently review their security posture as India strengthens its cyber defence framework against threats.

Trust deficit: How is India strengthening cybersecurity in age of AI-powered threats?

The pilot goes well. An assistant reads a support case, produces a clean summary, drafts a reply that sounds like a person wrote it. Nobody in the room argues with the result. Then the project stalls, or it ships and quietly stops being used.&nbsp;&nbsp;Ashok Apati has designed enough of those pilots to have a theory about what happens next, and it is not a flattering one for the technology. "The hardest enterprise AI problems are often not model problems," he says. "They are architecture, integration, data, security, workflow, governance, and adoption problems."&nbsp;&nbsp;His own career has been spent building the layer underneath. Now a lead product developer and digital transformation leader at a global consumer goods company, Apati has worked for more than 13 years across enterprise integration architecture, Salesforce, cloud transformation and digital commerce. He led the technical design and proof-of-concept development of generative AI capabilities built into Salesforce, including Case and Account Assistants covering case summarisation, drafted customer responses, risk identification, next-best-action recommendations, knowledge suggestions, account intelligence and pre-visit preparation.&nbsp;&nbsp;That work is where his argument comes from. The assistants demonstrated quickly that a language model could summarise a case usefully. What took longer was everything that had to be true before such a thing could be trusted inside a working business: retrieving the right Salesforce context, excluding personal data that should never reach a model, defining reusable AI services rather than one-off integrations, structuring prompts around specific business actions, and keeping a human in front of anything consequential.&nbsp;&nbsp;None of that is model work. "Enterprise AI is an architecture challenge, not simply an LLM challenge," he says. Organisations spend their time comparing models when the harder question is structural: "creating an architecture that can provide the model with the right context, at the right time, for the right user."&nbsp;&nbsp;There is a less comfortable corollary, and he states it plainly. "AI exposes existing technology and data debt." Fragmented customer data, inconsistent identifiers, outdated APIs, duplicated information, weak integration patterns and unclear data ownership have usually been tolerable for years. Then something arrives that needs to read across all of it at once. "AI does not eliminate these problems," he says. "In many cases, it makes them more visible."&nbsp;&nbsp;He is in an unusual position to say so, because he spent years clearing that debt before AI made it urgent. He played a key technical leadership role in migrating mission-critical applications and integrations to Microsoft Azure while the business kept running, contributing to roughly $600,000 in annual cost savings. He has designed, modernised and supported platforms carrying approximately $260 million in annual transaction volume, serving more than 150,000 active customers and processing over 100,000 orders a year. He helped redesign a customer registration experience that has since handled around 110,000 registrations, and contributed to a single sign-on programme built on Okta.&nbsp;&nbsp;Those projects explain the sequence he now argues for. Cloud provides scalability, APIs and integrations provide connectivity, identity establishes trusted access, enterprise data provides context, and only then does AI have something solid to reason over. "AI transformation is not separate from digital transformation," he says. "It is the next layer of it."&nbsp;&nbsp;Which is also why he is unsentimental about how most AI programmes begin. The common mistake, in his telling, starts with the wrong question: "We have access to Generative AI, what can we build?" He would rather start from a measurable problem, reducing case-handling time or shortening sales preparation, and let that decide what gets built and which model is used.&nbsp;&nbsp;Two of his design rules follow directly from watching pilots fail. The first is that AI has to appear where people already work. If an employee has to leave the application they are in, open a separate tool, paste in the context by hand and carry the answer back, the tool will be abandoned no matter how good the output is.&nbsp;&nbsp;The second is about who remains accountable. For consequential work such as complaint handling, reason-code selection and customer communications, the pattern he favours is deliberately conservative: AI recommends, a human validates, and the enterprise system executes. Rather than forcing a single reason code, the system offers a shortlist for an agent to confirm. Drafted replies get reviewed before they are sent. Autonomy increases later, he argues, and only as governance and measured performance earn it. &nbsp;&nbsp;Underneath both rules sits a preference for substance over polish. "Grounding is more important than eloquence," he says, and the reason is that a fluent wrong answer travels further than an obvious one. "An AI-generated answer that sounds professional but contains incorrect business information can be more dangerous than having no answer at all."&nbsp;&nbsp;It leads him to the test most pilots never have to pass. "A successful demonstration is not evidence of a successful AI implementation," he says, and pilots, in his view, should carry measurable exit criteria before anyone calls them a success: case-handling time, preparation time saved, recommendation acceptance, adoption, cost per interaction.&nbsp;&nbsp;So the question he would put to any organisation shopping for a model is a different one entirely. "Is our enterprise architecture ready to make AI useful, trustworthy, measurable, and scalable?" Pilots rarely die because the model disappointed. They die in the gap between a demonstration and an organisation that was never quite ready to receive one.

Fragmented customer data, inconsistent identifiers, outdated APIs, duplicated information, weak integration patterns and unclear data ownership have usually been tolerable for years. Then something arrives that needs to read across all of it at once.

The pilot goes well. An assistant reads a support case, produces a clean summary, drafts a reply that sounds like a person wrote it. Nobody in the room argues with the result. Then the project stalls, or it ships and quietly stops being used. Ashok Apati has designed enough of those pilots to have a theory about what happens next, and it is not a flattering one for the technology. "The hardest enterprise AI problems are often not model problems," he says. "They are architecture, integration, data, security, workflow, governance, and adoption problems." His own career has been spent building the layer underneath. Now a lead product developer and digital transformation leader at a global consumer goods company, Apati has worked for more than 13 years across enterprise integration architecture, Salesforce, cloud transformation and digital commerce. He led the technical design and proof-of-concept development of generative AI capabilities built into Salesforce, including Case and Account Assistants covering case summarisation, drafted customer responses, risk identification, next-best-action recommendations, knowledge suggestions, account intelligence and pre-visit preparation. That work is where his argument comes from. The assistants demonstrated quickly that a language model could summarise a case usefully. What took longer was everything that had to be true before such a thing could be trusted inside a working business: retrieving the right Salesforce context, excluding personal data that should never reach a model, defining reusable AI services rather than one-off integrations, structuring prompts around specific business actions, and keeping a human in front of anything consequential. None of that is model work. "Enterprise AI is an architecture challenge, not simply an LLM challenge," he says. Organisations spend their time comparing models when the harder question is structural: "creating an architecture that can provide the model with the right context, at the right time, for the right user." There is a less comfortable corollary, and he states it plainly. "AI exposes existing technology and data debt." Fragmented customer data, inconsistent identifiers, outdated APIs, duplicated information, weak integration patterns and unclear data ownership have usually been tolerable for years. Then something arrives that needs to read across all of it at once. "AI does not eliminate these problems," he says. "In many cases, it makes them more visible." He is in an unusual position to say so, because he spent years clearing that debt before AI made it urgent. He played a key technical leadership role in migrating mission-critical applications and integrations to Microsoft Azure while the business kept running, contributing to roughly $600,000 in annual cost savings. He has designed, modernised and supported platforms carrying approximately $260 million in annual transaction volume, serving more than 150,000 active customers and processing over 100,000 orders a year. He helped redesign a customer registration experience that has since handled around 110,000 registrations, and contributed to a single sign-on programme built on Okta. Those projects explain the sequence he now argues for. Cloud provides scalability, APIs and integrations provide connectivity, identity establishes trusted access, enterprise data provides context, and only then does AI have something solid to reason over. "AI transformation is not separate from digital transformation," he says. "It is the next layer of it." Which is also why he is unsentimental about how most AI programmes begin. The common mistake, in his telling, starts with the wrong question: "We have access to Generative AI, what can we build?" He would rather start from a measurable problem, reducing case-handling time or shortening sales preparation, and let that decide what gets built and which model is used. Two of his design rules follow directly from watching pilots fail. The first is that AI has to appear where people already work. If an employee has to leave the application they are in, open a separate tool, paste in the context by hand and carry the answer back, the tool will be abandoned no matter how good the output is. The second is about who remains accountable. For consequential work such as complaint handling, reason-code selection and customer communications, the pattern he favours is deliberately conservative: AI recommends, a human validates, and the enterprise system executes. Rather than forcing a single reason code, the system offers a shortlist for an agent to confirm. Drafted replies get reviewed before they are sent. Autonomy increases later, he argues, and only as governance and measured performance earn it. Underneath both rules sits a preference for substance over polish. "Grounding is more important than eloquence," he says, and the reason is that a fluent wrong answer travels further than an obvious one. "An AI-generated answer that sounds professional but contains incorrect business information can be more dangerous than having no answer at all." It leads him to the test most pilots never have to pass. "A successful demonstration is not evidence of a successful AI implementation," he says, and pilots, in his view, should carry measurable exit criteria before anyone calls them a success: case-handling time, preparation time saved, recommendation acceptance, adoption, cost per interaction. So the question he would put to any organisation shopping for a model is a different one entirely. "Is our enterprise architecture ready to make AI useful, trustworthy, measurable, and scalable?" Pilots rarely die because the model disappointed. They die in the gap between a demonstration and an organisation that was never quite ready to receive one.

Fragmented customer data, inconsistent identifiers, outdated APIs, duplicated information, weak integration patterns and unclear data ownership have usually been tolerable for years. Then something arrives that needs to read across all of it at once.

Why Enterprise AI keeps dying in the pilot stage

OpenAI CEO Sam Altman raised serious concerns over the rising capabilities of artificial intelligence (AI) in discovering security vulnerabilities beyond our imagination and expectations. Altman voiced his concern in a recent interview, noting that it is becoming hard to confine AI to sandboxes because the technology works only for a short span of time. He further elaborated that once the model starts evolving its cutting-edge abilities, you can’t even find the relic of how and when it escaped. Speaking about how traditional cybersecurity approaches may feel outdated or ineffective in front of AI’s finding hardware-level bugs. Over the immediate danger from the misalignment at an all-time high, he underscored that alignment research is still in an initial phase, which is far from resolved. “I don't even know a dramatic enough word to use… models can find bugs that are outside of the normal threat model of what we think a hacker can do… hardware-level, insane stuff… I no longer think we're gonna be able to just patch all the bugs," he said.&nbsp;Also read: <a href="https://www.wionews.com/world/killing-people-never-allowed-elon-musk-admits-restricting-starlink-in-ukraine-explains-why-1792901604146">'Killing people never allowed': Elon Musk admits restricting Starlink in Ukraine, explains why</a> OpenAI’s chief contends that the old approach of patching vulnerabilities one by one is no longer enough, arguing instead that the internet’s entire security architecture needs to be overhauled with AI-driven defence systems. He has flagged locally hosted, open-source AI models as a particular security concern, pointing out that even comprehensive monitoring of AI agents globally wouldn’t be enough to stop every harmful action. However, sceptics push back on this narrative, suggesting that Altman’s depiction of AI as inherently uncontrollable conveniently benefits OpenAI’s business interests by deflecting accountability away from developers when problems occur. This comes on the heels of a major PHP security flaw that knocked millions of websites offline worldwide, during which hackers made countless attempts to exploit the weakness- ironically, a vulnerability that was itself uncovered using AI.

Over the immediate danger from the misalignment at an all-time high, OpenAI CEO Sam Altman underscored that alignment research is still in an initial phase, which is far from resolved.

OpenAI CEO Sam Altman raised serious concerns over the rising capabilities of artificial intelligence (AI) in discovering security vulnerabilities beyond our imagination and expectations. Altman voiced his concern in a recent interview, noting that it is becoming hard to confine AI to sandboxes because the technology works only for a short span of time. He further elaborated that once the model starts evolving its cutting-edge abilities, you can’t even find the relic of how and when it escaped. Speaking about how traditional cybersecurity approaches may feel outdated or ineffective in front of AI’s finding hardware-level bugs. Over the immediate danger from the misalignment at an all-time high, he underscored that alignment research is still in an initial phase, which is far from resolved. “I don't even know a dramatic enough word to use… models can find bugs that are outside of the normal threat model of what we think a hacker can do… hardware-level, insane stuff… I no longer think we're gonna be able to just patch all the bugs," he said. Also read: 'Killing people never allowed': Elon Musk admits restricting Starlink in Ukraine, explains why OpenAI’s chief contends that the old approach of patching vulnerabilities one by one is no longer enough, arguing instead that the internet’s entire security architecture needs to be overhauled with AI-driven defence systems. He has flagged locally hosted, open-source AI models as a particular security concern, pointing out that even comprehensive monitoring of AI agents globally wouldn’t be enough to stop every harmful action. However, sceptics push back on this narrative, suggesting that Altman’s depiction of AI as inherently uncontrollable conveniently benefits OpenAI’s business interests by deflecting accountability away from developers when problems occur. This comes on the heels of a major PHP security flaw that knocked millions of websites offline worldwide, during which hackers made countless attempts to exploit the weakness- ironically, a vulnerability that was itself uncovered using AI.

Over the immediate danger from the misalignment at an all-time high, OpenAI CEO Sam Altman underscored that alignment research is still in an initial phase, which is far from resolved.

Sam Altman warns AI can now find bugs unexpectedly 'outside normal threat model'

Artificial Intelligence (AI) has emerged as one of the trending topics to talk about globally in recent developments. Market expert Manoj Kumar Jain explained that AI is influencing everyone across the world, including banking, financial services, defence manufacturing, data processing, data mining, and agricultural production. The role of AI in financial markets has also been widely considered due to its fast search capabilities, spam filters, trend recognition, advanced planning, and flexibility in following instructions. Earlier, Excel revolutionised the financial world, particularly financial markets, by making it possible to process large datasets within a single spreadsheet and derive required results in a fraction of a second using predefined formulas. However, as technology advances, markets demand faster and more efficient tools capable of handling larger amounts of data and delivering results in even shorter timeframes. AI is also becoming a more efficient tool by using various algorithms to process data faster and enable quick decision-making in financial markets. The expert viewed the evolution of AI in financial markets in three phases:<h3>Phase 1: The Analyst Era (2020–2024) - What Has Changed?</h3>AI's journey in finance began with reading reports, calculating multiple ratios, summarising earnings calls, and finding fraud, acting largely as an assistant to support better financial decision-making. IBM's Global Banking Outlook data suggest that only 8 per cent of banking institutions were using Generative AI tactically by 2024, but that figure has surged to 78 per cent by2026.<h3>AI moved into:</h3>Risk &amp; Compliance: It is now autonomously drafting regulatory reports, cross-referencing risk databases, and flagging compliance issues. Market compliance and regulatory requirements have risen sharply post-COVID, and AI has greatly assisted the entire financial ecosystem in processing these tasks proactively in a very short span of time.&nbsp;Personalisation: AI powers hyper-personalised budgeting, micro-investing, and credit-building tools for retail investors, helping the market ecosystem deliver tailored financial solutions even at micro levels.&nbsp;Fraud &amp; Security: Adaptive AI systems learn from real-time global threat data, issue fraud alerts, and push the industry toward adopting more advanced security measures.<h3>Phase 2: The Agentic Era (2026) - Where are we now?</h3>This is the biggest shift of 2026. AI is moving from ‘AI tools’ to ‘AI systems’. Banks and financial services institutions are no longer just using chatbots. They are deploying Agentic AI—digital co-workers with transactional authority.&nbsp;<ul><li>Goldman Sachs is building autonomous agents with Anthropic’s Claude to handle trade accounting and client onboarding.</li><li>Lloyds Banking Group is deploying agentic AI enterprise-wide, targeting £100 million in value in 2026 by automating fraud investigations.</li><li>HSBC's investment bank CEO stated this week that AI agents could replace humans trading in financial markets within the next 2–4 years, powered by quantum computing and distributed ledger technology.</li><li>Deloitte's 2026 FSI Predictions report calls this ‘AI as a profit engine’, estimating AI-native banking products could add $75 billion in incremental revenue for large banks by 2030.</li><li>For financial markets and wealth management experts, AI means armies of analysts reading thousands of reports, analysing sentiment, and generating actionable insights alongside human advisors to make appropriate investment decisions at the right time.</li></ul>Also read: <a href="https://www.wionews.com/world/this-time-i-m-not-doing-it-trump-reveals-google-ceo-refused-his-atlantic-ocean-renaming-idea-1792668305153">'This time I’m not doing it': Trump reveals Google CEO refused his Atlantic Ocean renaming idea</a><h3>Phase 3: The Autonomous Future (2027–2030) - What Comes Next?</h3>The key question has shifted from whether to adopt AI to how it should be governed. Success in this next phase will hinge on how effectively markets can govern AI and Superintelligence to drive sound decisions amid global economic uncertainty and intensifying geopolitics.&nbsp;Composable Finance: The financial stack of the future rests on AI, APIs, and instant settlement. AI-powered decision-making for credit assessment, fraud detection, and personalisation will become embedded directly within banking and financial services, enabling faster settlements while reducing risk and fraud.&nbsp;Democratised Intelligence: Quantum-inspired algorithms are set to simulate millions of portfolio scenarios, with several platforms already generating alpha returns exceeding 2 per cent over benchmarks. This level of sophistication will increasingly reach retail investors in India through financial service providers, helping everyday investors achieve stronger portfolio returns.&nbsp;Human + AI Hybrid: Rather than replacing finance professionals, this era will free them from repetitive tasks to focus on strategic decision-making. Hybrid advisory models will prevail, with AI enabling financial service providers to deploy their workforce more efficiently and serve larger client bases in less time.&nbsp;New Risks, New Governance: The opacity of “black box” AI models makes transparency and auditability essential. As AI systems gain greater authority over trading decisions, regulators are sharpening their focus on market stability. While AI introduces significant challenges, governments are working concertedly to govern these systems effectively within a complex landscape, aiming to benefit society while containing risk.

AI's journey in finance began with reading reports, calculating multiple ratios, summarising earnings calls, and finding fraud, acting largely as an assistant to support better financial decision-making. 

Artificial Intelligence (AI) has emerged as one of the trending topics to talk about globally in recent developments. Market expert Manoj Kumar Jain explained that AI is influencing everyone across the world, including banking, financial services, defence manufacturing, data processing, data mining, and agricultural production. The role of AI in financial markets has also been widely considered due to its fast search capabilities, spam filters, trend recognition, advanced planning, and flexibility in following instructions. Earlier, Excel revolutionised the financial world, particularly financial markets, by making it possible to process large datasets within a single spreadsheet and derive required results in a fraction of a second using predefined formulas. However, as technology advances, markets demand faster and more efficient tools capable of handling larger amounts of data and delivering results in even shorter timeframes. AI is also becoming a more efficient tool by using various algorithms to process data faster and enable quick decision-making in financial markets. The expert viewed the evolution of AI in financial markets in three phases: Phase 1: The Analyst Era (2020–2024) - What Has Changed? AI's journey in finance began with reading reports, calculating multiple ratios, summarising earnings calls, and finding fraud, acting largely as an assistant to support better financial decision-making. IBM's Global Banking Outlook data suggest that only 8 per cent of banking institutions were using Generative AI tactically by 2024, but that figure has surged to 78 per cent by2026. AI moved into: Risk & Compliance: It is now autonomously drafting regulatory reports, cross-referencing risk databases, and flagging compliance issues. Market compliance and regulatory requirements have risen sharply post-COVID, and AI has greatly assisted the entire financial ecosystem in processing these tasks proactively in a very short span of time. Personalisation: AI powers hyper-personalised budgeting, micro-investing, and credit-building tools for retail investors, helping the market ecosystem deliver tailored financial solutions even at micro levels. Fraud & Security: Adaptive AI systems learn from real-time global threat data, issue fraud alerts, and push the industry toward adopting more advanced security measures. Phase 2: The Agentic Era (2026) - Where are we now? This is the biggest shift of 2026. AI is moving from ‘AI tools’ to ‘AI systems’. Banks and financial services institutions are no longer just using chatbots. They are deploying Agentic AI—digital co-workers with transactional authority. Goldman Sachs is building autonomous agents with Anthropic’s Claude to handle trade accounting and client onboarding. Lloyds Banking Group is deploying agentic AI enterprise-wide, targeting £100 million in value in 2026 by automating fraud investigations. HSBC's investment bank CEO stated this week that AI agents could replace humans trading in financial markets within the next 2–4 years, powered by quantum computing and distributed ledger technology. Deloitte's 2026 FSI Predictions report calls this ‘AI as a profit engine’, estimating AI-native banking products could add $75 billion in incremental revenue for large banks by 2030. For financial markets and wealth management experts, AI means armies of analysts reading thousands of reports, analysing sentiment, and generating actionable insights alongside human advisors to make appropriate investment decisions at the right time. Also read: 'This time I’m not doing it': Trump reveals Google CEO refused his Atlantic Ocean renaming idea Phase 3: The Autonomous Future (2027–2030) - What Comes Next? The key question has shifted from whether to adopt AI to how it should be governed. Success in this next phase will hinge on how effectively markets can govern AI and Superintelligence to drive sound decisions amid global economic uncertainty and intensifying geopolitics. Composable Finance: The financial stack of the future rests on AI, APIs, and instant settlement. AI-powered decision-making for credit assessment, fraud detection, and personalisation will become embedded directly within banking and financial services, enabling faster settlements while reducing risk and fraud. Democratised Intelligence: Quantum-inspired algorithms are set to simulate millions of portfolio scenarios, with several platforms already generating alpha returns exceeding 2 per cent over benchmarks. This level of sophistication will increasingly reach retail investors in India through financial service providers, helping everyday investors achieve stronger portfolio returns. Human + AI Hybrid: Rather than replacing finance professionals, this era will free them from repetitive tasks to focus on strategic decision-making. Hybrid advisory models will prevail, with AI enabling financial service providers to deploy their workforce more efficiently and serve larger client bases in less time. New Risks, New Governance: The opacity of “black box” AI models makes transparency and auditability essential. As AI systems gain greater authority over trading decisions, regulators are sharpening their focus on market stability. While AI introduces significant challenges, governments are working concertedly to govern these systems effectively within a complex landscape, aiming to benefit society while containing risk.

AI's journey in finance began with reading reports, calculating multiple ratios, summarising earnings calls, and finding fraud, acting largely as an assistant to support better financial decision-making.&nbsp;

From Analyst Era to Agentic Era: How evolving role of AI is rewriting financial markets

India has called for a universal, non-discriminatory and verifiable framework for nuclear disarmament at the United Nations General Assembly (UNGA) First Committee, stressing that decisions on the use of nuclear weapons must remain under human control amid the growing integration of artificial intelligence (AI) into defence systems. Delivering India's official statement under the nuclear weapons cluster at the UNGA First Committee, Congress MP Manish Tewari highlighted the need to address technological and operational risks associated with nuclear arsenals, particularly those arising from the use of emerging technologies in nuclear command and control systems.&nbsp;&nbsp;India's Permanent Mission to the United Nations highlighted Tewari's remarks in a post on X, reiterating New Delhi's commitment to global nuclear disarmament and efforts to strengthen the international non-proliferation regime. "India acknowledges that the use of artificial intelligence in nuclear command and control raises real questions," Tewari stated. "We espouse the principle that the decision to use nuclear weapons should be taken by humans." Reaffirming India’s foundational defence doctrine, the delegate noted that New Delhi’s strategic capabilities are purely defensive and shaped by regional security realities rather than targeted at any single nation. "As a responsible nuclear weapon state, India maintains a policy of credible minimum deterrence with a posture of no first use and non-use against non-nuclear weapon states," Tewari declared. "We are prepared to convert these undertakings into multilateral legal arrangements. I wish to underline that India's capabilities are shaped by our unique security environment and are not directed against any specific actor."&nbsp;&nbsp;Reiterating New Delhi’s commitment to nuclear restraint, India reaffirmed its adherence to a voluntary unilateral moratorium on nuclear testing. Tewari also advocated for legally binding negative security assurances to protect non-nuclear nations. "India believes that legally binding assurances to non-nuclear weapon states against the use or threat of use of nuclear weapons should alleviate their sense of insecurity, and thereby strengthen the non-proliferation regime," he noted, adding that such guarantees would directly contribute to reducing nuclear risk worldwide. Underlining India's long-standing position on complete global denuclearisation, Tewari reaffirmed that New Delhi remains steadfastly committed to "nuclear disarmament that is universal, non-discriminatory, and verifiable."&nbsp;&nbsp;Also Read: <a href="https://www.wionews.com/india-news/reason-un-has-lost-credibility-india-hits-out-at-un-report-alleging-youth-repression-questions-reliance-on-anonymous-submissions-1792208664112">'Reason UN has lost credibility': Amid SIR protests India hits out at report alleging 'youth repression'</a>&nbsp;"This goal can be achieved by a step-by-step process underwritten by universal commitment to an agreed, non-discriminatory, multilateral framework," Tewari explained. "We outlined this approach in our 2006 working paper to the First Committee, and again in 2007 at the Conference on Disarmament as document CD/1816. The relevance of this approach endures." Tewari also highlighted two resolutions that India traditionally tables at the UN First Committee to promote nuclear disarmament and reduce the risks associated with nuclear weapons.&nbsp;&nbsp;Also Read: <a href="https://www.wionews.com/world/unwarranted-interference-india-slams-un-human-rights-office-over-remarks-on-delhi-protests-1791691435625">'Unwarranted interference': India slams UN human rights office over remarks on Delhi protests</a>&nbsp;Reducing Nuclear Danger (Tabled since 1998): Draws attention to the hazardous "hair-trigger alert status of nuclear weapons" and calls for urgent de-alerting and de-targeting to eliminate the risk of accidental or unintentional use. Convention on the Prohibition of the Use of Nuclear Weapons (Tabled since 1992): Calls on the Conference on Disarmament to negotiate an international convention prohibiting the use or threat of use of nuclear weapons. "Such a universal legally binding agreement would build the political will needed for the total elimination of nuclear weapons," Tewari emphasised, soliciting continued co-sponsorship and support from UN member states.&nbsp;&nbsp;Clarifying India’s position on existing international treaties, the statement noted that while the Treaty on the Prohibition of Nuclear Weapons (TPNW) does not impose legal obligations on New Delhi, India’s broader commitment to global non-proliferation remains unchanged. "The Treaty on the Prohibition of Nuclear Weapons does not create obligations for India. Our commitment to global nuclear disarmament, nonetheless, remains unchanged, as does our commitment to strengthening non-proliferation," Tewari affirmed. Concluding the statement, Tewari stressed the importance of international cooperation to prevent terrorists and non-state actors from accessing nuclear weapons, materials and technology. "We also remain committed to promoting international efforts to prevent terrorists and non-state actors from gaining access to nuclear weapons, materials, and technology," the MP said.

India at the UNGA First Committee called for universal, non-discriminatory and verifiable nuclear disarmament, stressing that humans must retain control over nuclear-use decisions amid AI integration. Manish Tewari also reaffirmed India's no-first-use policy and testing moratorium.

India has called for a universal, non-discriminatory and verifiable framework for nuclear disarmament at the United Nations General Assembly (UNGA) First Committee, stressing that decisions on the use of nuclear weapons must remain under human control amid the growing integration of artificial intelligence (AI) into defence systems. Delivering India's official statement under the nuclear weapons cluster at the UNGA First Committee, Congress MP Manish Tewari highlighted the need to address technological and operational risks associated with nuclear arsenals, particularly those arising from the use of emerging technologies in nuclear command and control systems. India's Permanent Mission to the United Nations highlighted Tewari's remarks in a post on X, reiterating New Delhi's commitment to global nuclear disarmament and efforts to strengthen the international non-proliferation regime. "India acknowledges that the use of artificial intelligence in nuclear command and control raises real questions," Tewari stated. "We espouse the principle that the decision to use nuclear weapons should be taken by humans." Reaffirming India’s foundational defence doctrine, the delegate noted that New Delhi’s strategic capabilities are purely defensive and shaped by regional security realities rather than targeted at any single nation. "As a responsible nuclear weapon state, India maintains a policy of credible minimum deterrence with a posture of no first use and non-use against non-nuclear weapon states," Tewari declared. "We are prepared to convert these undertakings into multilateral legal arrangements. I wish to underline that India's capabilities are shaped by our unique security environment and are not directed against any specific actor." Reiterating New Delhi’s commitment to nuclear restraint, India reaffirmed its adherence to a voluntary unilateral moratorium on nuclear testing. Tewari also advocated for legally binding negative security assurances to protect non-nuclear nations. "India believes that legally binding assurances to non-nuclear weapon states against the use or threat of use of nuclear weapons should alleviate their sense of insecurity, and thereby strengthen the non-proliferation regime," he noted, adding that such guarantees would directly contribute to reducing nuclear risk worldwide. Underlining India's long-standing position on complete global denuclearisation, Tewari reaffirmed that New Delhi remains steadfastly committed to "nuclear disarmament that is universal, non-discriminatory, and verifiable." Also Read: 'Reason UN has lost credibility': Amid SIR protests India hits out at report alleging 'youth repression' "This goal can be achieved by a step-by-step process underwritten by universal commitment to an agreed, non-discriminatory, multilateral framework," Tewari explained. "We outlined this approach in our 2006 working paper to the First Committee, and again in 2007 at the Conference on Disarmament as document CD/1816. The relevance of this approach endures." Tewari also highlighted two resolutions that India traditionally tables at the UN First Committee to promote nuclear disarmament and reduce the risks associated with nuclear weapons. Also Read: 'Unwarranted interference': India slams UN human rights office over remarks on Delhi protests Reducing Nuclear Danger (Tabled since 1998): Draws attention to the hazardous "hair-trigger alert status of nuclear weapons" and calls for urgent de-alerting and de-targeting to eliminate the risk of accidental or unintentional use. Convention on the Prohibition of the Use of Nuclear Weapons (Tabled since 1992): Calls on the Conference on Disarmament to negotiate an international convention prohibiting the use or threat of use of nuclear weapons. "Such a universal legally binding agreement would build the political will needed for the total elimination of nuclear weapons," Tewari emphasised, soliciting continued co-sponsorship and support from UN member states. Clarifying India’s position on existing international treaties, the statement noted that while the Treaty on the Prohibition of Nuclear Weapons (TPNW) does not impose legal obligations on New Delhi, India’s broader commitment to global non-proliferation remains unchanged. "The Treaty on the Prohibition of Nuclear Weapons does not create obligations for India. Our commitment to global nuclear disarmament, nonetheless, remains unchanged, as does our commitment to strengthening non-proliferation," Tewari affirmed. Concluding the statement, Tewari stressed the importance of international cooperation to prevent terrorists and non-state actors from accessing nuclear weapons, materials and technology. "We also remain committed to promoting international efforts to prevent terrorists and non-state actors from gaining access to nuclear weapons, materials, and technology," the MP said.

India at the UNGA First Committee called for universal, non-discriminatory and verifiable nuclear disarmament, stressing that humans must retain control over nuclear-use decisions amid AI integration. Manish Tewari also reaffirmed India's no-first-use policy and testing moratorium.

‘Nuclear-use decision should be taken by humans’: India flags AI risks at UN

Ever wondered whether artificial intelligence (AI) can transform how investors understand commodities, identify opportunities and build multi-asset portfolios? For decades, Indian investors have embraced equities, with financial data and investment research becoming increasingly accessible through technology. However, commodity markets remain relatively underexplored by many investors because their fundamentals are spread across multiple domestic and international sources.&nbsp;Market experts believe AI could change this landscape by bringing together production, consumption, inventories, trade flows, seasonality and market data to generate actionable insights. As investors increasingly explore multi-asset portfolios, AI-powered research tools could help them understand commodities, identify relative-value opportunities and make better-informed investment decisions without depending entirely on equities.&nbsp;Kwala Intel, an AI-powered market intelligence platform, provides insights into commodities such as gold, silver, crude oil and copper, alongside NSE- and BSE-listed companies linked to commodity prices. The platform analyses live commodity prices and the factors driving them, including supply-demand dynamics, policy changes, geopolitical developments and fluctuating input costs. It also connects commodity price movements with their potential impact on listed companies. For instance, changes in crude oil prices can affect airlines and paint manufacturers differently from downstream oil companies. By examining the potential implications for input costs, profit margins and business fundamentals, Kwala Intel aims to help investors understand these relationships and access market insights in real time and in their preferred language.<h2>The AI-powered shift gaining market attention: From fear of numbers to investment confidence</h2>Investing has traditionally involved extensive number-crunching and rapid analysis. Balance sheets, earnings, return on equity (ROE), price-to-earnings (P/E) ratios, price-to-book values and numerous financial indicators can make investment research appear complicated. For individuals managing businesses, pursuing professional careers or trading part-time, analysing these numbers continuously can be difficult. Market analysts often emphasise the importance of making financial information more accessible so that investors can understand the factors influencing their decisions.&nbsp;Over the past two decades, India's equity markets have undergone a significant transformation. Technology, online research platforms, financial websites and digital brokerage services have made corporate information easier to access. Investors can now compare companies, study historical performance and examine financial ratios with greater convenience. This progress is also reflected in market participation. According to SEBI's Investor Survey 2025, approximately 3.21 crore Indian households, representing 9.5 per cent of total households, were invested in securities-market products. Urban participation stood at 15 per cent, compared with 6 per cent in rural areas.<h2>How AI can be a game-changer in commodity markets</h2>Unlike company financial statements, commodity market information is rarely consolidated into one convenient source. AI can help address three major challenges:<ul><li>Data collection: Gathering information from multiple domestic and international sources.</li><li>Data organisation: Structuring diverse datasets into comparable and usable formats.</li><li>Data interpretation: Connecting market developments with potential investment implications.</li></ul>Consider crude oil. Its prices are influenced by global production, decisions made by major producers, inventory levels, refinery activity, geopolitical developments, currency movements and consumption trends. An Indian investor seeking to understand crude oil must often analyse information published by multiple countries, institutions and market participants. Although the analytical process may appear straightforward in theory, gathering current information and connecting the relevant factors manually can be time-consuming, particularly in fast-moving markets.<h2>From agricultural data to commodity balance sheets</h2>Agricultural commodities, commonly referred to as agri commodities, offer another example of how AI could simplify market research. Commodities such as turmeric, chilli, cumin and guar (cluster bean) are influenced by several interconnected factors. These include sowing acreage across states, rainfall, production estimates, wholesale market arrivals, carry-forward stocks, domestic consumption and export demand.&nbsp;Analysing these factors manually can involve reviewing numerous reports, market updates and datasets. Kwala Intel aims to simplify this process by interpreting large volumes of commodity information and connecting futures price movements with spot prices, market arrivals, exports, sowing trends and weather conditions.&nbsp;&nbsp;It can also help users examine available market quotes alongside acreage, weather and demand indicators to understand the broader market context. By combining multiple datasets and comparing seasonal patterns, AI-powered tools can support the preparation of commodity balance sheets. These balance sheets help investors assess expected supply, demand and ending stocks, providing a structured framework for understanding potential price movements. However, the reliability of any such analysis depends on the quality, timeliness and completeness of the underlying data.<h2>Turning Information Into Investment Alpha</h2>Once market information is organised, AI can help investors investigate opportunities that go beyond simply predicting whether prices will rise or fall.Potential areas of analysis include:<ul><li>Spot-futures price differences: Examining the relationship between physical commodity prices and futures contracts.</li><li>Futures spreads: Comparing prices across contracts with different expiry dates.</li><li>Seasonal price patterns: Studying recurring price behaviour across months and agricultural cycles.</li><li>Inventory changes: Assessing how stock levels may influence supply-demand expectations.</li><li>Technical indicators: Identifying price trends, momentum and potential changes in market direction.</li><li>Relative valuation: Comparing the performance and pricing relationships of different commodities.</li></ul>AI can screen these relationships, compare current conditions with historical patterns and flag unusual developments that may warrant further investigation.For example, a difference between spot and futures prices might appear attractive at first glance. However, investors must consider storage expenses, financing costs, transaction charges, liquidity constraints and execution risks before determining whether a genuine arbitrage opportunity exists.AI can improve the research process and help investors evaluate potential opportunities more systematically. It cannot guarantee profitable trades or eliminate market risk.&nbsp;Also Read: <a href="https://www.wionews.com/business-economy/decoding-market-jargon-kwala-intel-s-real-time-ai-powered-insights-transform-stock-analysis-1792081314974">Decoding market jargon: Kwala Intel’s real-time AI-powered insights transform stock analysis</a><h2>Making commodity intelligence actionable</h2>Technology has made equity-market information increasingly accessible to Indian investors. AI could take this transformation further by extending similar capabilities to commodities and other asset classes. The underlying information already exists across production reports, consumption data, inventories, export statistics, seasonal patterns, economic indicators and global market developments. The challenge is bringing these sources together and presenting the findings in a format investors can understand and use.&nbsp;Kwala Intel seeks to address this challenge by consolidating commodity research and translating complex market developments into accessible insights. The platform also aims to make market research more convenient by allowing users to ask questions in their preferred language. This could be particularly useful for investors who want to understand commodity fundamentals but may find technical reports, financial terminology or international data sources difficult to navigate.<h2>AI-powered research in English, Hindi and regional languages</h2>As multi-asset investing gains attention, access to understandable research is becoming increasingly important. Kwala Intel offers AI-led explanations and market intelligence in English, Hindi and more than a dozen regional languages, helping users explore commodity markets, stock research and technical indicators. For retail investors, this approach could reduce the time spent navigating multiple information sources. Institutional investors and experienced traders may also find value in using AI to organise research, screen market relationships and identify developments that require closer analysis.&nbsp;Nevertheless, AI-generated insights should be treated as research inputs rather than automatic investment instructions. Investors should verify important data, assess their risk tolerance and consider independent financial advice where appropriate.<h2>Kwala intel pro subscription: Pricing and access</h2>Kwala Intel Pro is priced at Rs 199 per month or Rs 1,999 per year, inclusive of GST. According to the stated subscription pricing, the annual plan costs less than Rs 5.50 per day, compared with Rs 2,388 for 12 months on the monthly plan. The platform offers access to research features designed for investors and traders seeking to incorporate AI-powered market intelligence into their research process.&nbsp;Whether tracking intraday price movements, reviewing portfolio holdings or learning to interpret technical indicators, users can explore stock research, market data and AI-led explanations through a consolidated platform. Before subscribing, prospective users should review the platform's current features, data coverage, usage terms and subscription conditions to determine whether it meets their individual research requirements.

Explore how Kwala Intel uses AI to analyse gold, silver, crude oil and agri commodities, track market trends and support smarter multi-asset research in India

Ever wondered whether artificial intelligence (AI) can transform how investors understand commodities, identify opportunities and build multi-asset portfolios? For decades, Indian investors have embraced equities, with financial data and investment research becoming increasingly accessible through technology. However, commodity markets remain relatively underexplored by many investors because their fundamentals are spread across multiple domestic and international sources. Market experts believe AI could change this landscape by bringing together production, consumption, inventories, trade flows, seasonality and market data to generate actionable insights. As investors increasingly explore multi-asset portfolios, AI-powered research tools could help them understand commodities, identify relative-value opportunities and make better-informed investment decisions without depending entirely on equities. Kwala Intel, an AI-powered market intelligence platform, provides insights into commodities such as gold, silver, crude oil and copper, alongside NSE- and BSE-listed companies linked to commodity prices. The platform analyses live commodity prices and the factors driving them, including supply-demand dynamics, policy changes, geopolitical developments and fluctuating input costs. It also connects commodity price movements with their potential impact on listed companies. For instance, changes in crude oil prices can affect airlines and paint manufacturers differently from downstream oil companies. By examining the potential implications for input costs, profit margins and business fundamentals, Kwala Intel aims to help investors understand these relationships and access market insights in real time and in their preferred language. The AI-powered shift gaining market attention: From fear of numbers to investment confidence Investing has traditionally involved extensive number-crunching and rapid analysis. Balance sheets, earnings, return on equity (ROE), price-to-earnings (P/E) ratios, price-to-book values and numerous financial indicators can make investment research appear complicated. For individuals managing businesses, pursuing professional careers or trading part-time, analysing these numbers continuously can be difficult. Market analysts often emphasise the importance of making financial information more accessible so that investors can understand the factors influencing their decisions. Over the past two decades, India's equity markets have undergone a significant transformation. Technology, online research platforms, financial websites and digital brokerage services have made corporate information easier to access. Investors can now compare companies, study historical performance and examine financial ratios with greater convenience. This progress is also reflected in market participation. According to SEBI's Investor Survey 2025, approximately 3.21 crore Indian households, representing 9.5 per cent of total households, were invested in securities-market products. Urban participation stood at 15 per cent, compared with 6 per cent in rural areas. How AI can be a game-changer in commodity markets Unlike company financial statements, commodity market information is rarely consolidated into one convenient source. AI can help address three major challenges: Data collection: Gathering information from multiple domestic and international sources. Data organisation: Structuring diverse datasets into comparable and usable formats. Data interpretation: Connecting market developments with potential investment implications. Consider crude oil. Its prices are influenced by global production, decisions made by major producers, inventory levels, refinery activity, geopolitical developments, currency movements and consumption trends. An Indian investor seeking to understand crude oil must often analyse information published by multiple countries, institutions and market participants. Although the analytical process may appear straightforward in theory, gathering current information and connecting the relevant factors manually can be time-consuming, particularly in fast-moving markets. From agricultural data to commodity balance sheets Agricultural commodities, commonly referred to as agri commodities, offer another example of how AI could simplify market research. Commodities such as turmeric, chilli, cumin and guar (cluster bean) are influenced by several interconnected factors. These include sowing acreage across states, rainfall, production estimates, wholesale market arrivals, carry-forward stocks, domestic consumption and export demand. Analysing these factors manually can involve reviewing numerous reports, market updates and datasets. Kwala Intel aims to simplify this process by interpreting large volumes of commodity information and connecting futures price movements with spot prices, market arrivals, exports, sowing trends and weather conditions. It can also help users examine available market quotes alongside acreage, weather and demand indicators to understand the broader market context. By combining multiple datasets and comparing seasonal patterns, AI-powered tools can support the preparation of commodity balance sheets. These balance sheets help investors assess expected supply, demand and ending stocks, providing a structured framework for understanding potential price movements. However, the reliability of any such analysis depends on the quality, timeliness and completeness of the underlying data. Turning Information Into Investment Alpha Once market information is organised, AI can help investors investigate opportunities that go beyond simply predicting whether prices will rise or fall. Potential areas of analysis include: Spot-futures price differences: Examining the relationship between physical commodity prices and futures contracts. Futures spreads: Comparing prices across contracts with different expiry dates. Seasonal price patterns: Studying recurring price behaviour across months and agricultural cycles. Inventory changes: Assessing how stock levels may influence supply-demand expectations. Technical indicators: Identifying price trends, momentum and potential changes in market direction. Relative valuation: Comparing the performance and pricing relationships of different commodities. AI can screen these relationships, compare current conditions with historical patterns and flag unusual developments that may warrant further investigation. For example, a difference between spot and futures prices might appear attractive at first glance. However, investors must consider storage expenses, financing costs, transaction charges, liquidity constraints and execution risks before determining whether a genuine arbitrage opportunity exists. AI can improve the research process and help investors evaluate potential opportunities more systematically. It cannot guarantee profitable trades or eliminate market risk. Also Read: Decoding market jargon: Kwala Intel’s real-time AI-powered insights transform stock analysis Making commodity intelligence actionable Technology has made equity-market information increasingly accessible to Indian investors. AI could take this transformation further by extending similar capabilities to commodities and other asset classes. The underlying information already exists across production reports, consumption data, inventories, export statistics, seasonal patterns, economic indicators and global market developments. The challenge is bringing these sources together and presenting the findings in a format investors can understand and use. Kwala Intel seeks to address this challenge by consolidating commodity research and translating complex market developments into accessible insights. The platform also aims to make market research more convenient by allowing users to ask questions in their preferred language. This could be particularly useful for investors who want to understand commodity fundamentals but may find technical reports, financial terminology or international data sources difficult to navigate. AI-powered research in English, Hindi and regional languages As multi-asset investing gains attention, access to understandable research is becoming increasingly important. Kwala Intel offers AI-led explanations and market intelligence in English, Hindi and more than a dozen regional languages, helping users explore commodity markets, stock research and technical indicators. For retail investors, this approach could reduce the time spent navigating multiple information sources. Institutional investors and experienced traders may also find value in using AI to organise research, screen market relationships and identify developments that require closer analysis. Nevertheless, AI-generated insights should be treated as research inputs rather than automatic investment instructions. Investors should verify important data, assess their risk tolerance and consider independent financial advice where appropriate. Kwala intel pro subscription: Pricing and access Kwala Intel Pro is priced at Rs 199 per month or Rs 1,999 per year, inclusive of GST. According to the stated subscription pricing, the annual plan costs less than Rs 5.50 per day, compared with Rs 2,388 for 12 months on the monthly plan. The platform offers access to research features designed for investors and traders seeking to incorporate AI-powered market intelligence into their research process. Whether tracking intraday price movements, reviewing portfolio holdings or learning to interpret technical indicators, users can explore stock research, market data and AI-led explanations through a consolidated platform. Before subscribing, prospective users should review the platform's current features, data coverage, usage terms and subscription conditions to determine whether it meets their individual research requirements.

Check how Kwala Intel uses AI to simplify commodity research, analyse market trends and help Indian investors explore multi-asset opportunities

AI in commodity trading: How Kwala intel helps investors analyse market trends

A coded letter commissioned by Napoleon Bonaparte in 1809 and left unsolved for more than two centuries has been deciphered in just six hours using artificial intelligence. This groundbreaking feat highlights how the emerging technology is changing the study of history. Carter Church, a 27-year-old AI engineer at US cybersecurity firm SentinelOne, used OpenAI’s latest model, GPT-6 Astra, to decode the wartime message, which, as it turns out, contains details about the positions of French and allied troops. The letter, made up of symbols, numbers and letters spread across two dozen lines, had for decades remained overlooked in an online academic archive.<h2>Weeks' work done in hours</h2>Church said the task would have taken weeks using conventional methods. He spent several evenings working on the cipher before consulting Japanese patent engineer Satoshi Tomokiyo, who maintains an online catalogue of unsolved codes, to verify the solution.“What AI mostly brings is persistence,” Church told AFP, describing an “unbelievable sense of satisfaction and excitement” when he saw the result.Also read | <a href="https://www.wionews.com/world/claude-ai-sent-philadelphia-police-phony-murder-anthropic-argues-incident-less-severe-than-other-cybersecurity-episodes-1791617194991">Claude AI sent Philadelphia police phony murder; Anthropic argues incident 'less severe' than other cybersecurity episodes</a><h2>AI changes the game for amateur code-breakers</h2>The discovery is part of a wider shift in historical cryptography, as publicly available AI systems help people without specialist training tackle problems that once demanded extensive research and painstaking manual work.Tomokiyo, whose website Cryptiana is a resource for amateur cryptographers, described 2026 as a potential turning point for historical code-breaking. He said that since mid-September, AI had been used to solve most of the ciphers listed on his website.While researchers have been developing AI systems for cryptanalysis for some time, the public availability of advanced general-purpose models has accelerated the process. Church used OpenAI’s GPT-6 Astra, but others have previously used models developed by rival AI company Anthropic.Also read | <a href="https://www.wionews.com/world/something-truly-catastrophic-will-happen-former-openai-employees-sound-the-alarm-claim-they-were-fired-for-prioritising-ai-safety-1791511799924">'Something truly catastrophic will happen': Former OpenAI researchers sound the alarm, claim they were fired for prioritising AI safety</a><h2>'Mixed feelings'</h2>The rapid progress has also brought mixed feelings among enthusiasts who have spent years pursuing difficult codes. Tomokiyo said he was pleased to see long-standing puzzles solved, while questioning the ease with which AI appeared to have overcome some of them. "I'm excited long-time problems are now solved, but on the other hand, I have mixed feelings, because AI seems to have solved these difficult problems so easily," he said.He acknowledged that AI has taken away the satisfaction of being the first to crack a cipher and said: “I feel useless.” Some ciphers may remain unsolved for years, he added, particularly when there is little relevant data available to help AI identify patterns.

AI has helped crack a coded letter from Napoleon’s 1809 military campaign in just six hours, after more than 200 years without a solution. What was hidden in the historical pages? Scroll down to find out.

A coded letter commissioned by Napoleon Bonaparte in 1809 and left unsolved for more than two centuries has been deciphered in just six hours using artificial intelligence. This groundbreaking feat highlights how the emerging technology is changing the study of history. Carter Church, a 27-year-old AI engineer at US cybersecurity firm SentinelOne, used OpenAI’s latest model, GPT-6 Astra, to decode the wartime message, which, as it turns out, contains details about the positions of French and allied troops. The letter, made up of symbols, numbers and letters spread across two dozen lines, had for decades remained overlooked in an online academic archive. Weeks' work done in hours Church said the task would have taken weeks using conventional methods. He spent several evenings working on the cipher before consulting Japanese patent engineer Satoshi Tomokiyo, who maintains an online catalogue of unsolved codes, to verify the solution. “What AI mostly brings is persistence,” Church told AFP, describing an “unbelievable sense of satisfaction and excitement” when he saw the result. Also read | Claude AI sent Philadelphia police phony murder; Anthropic argues incident 'less severe' than other cybersecurity episodes AI changes the game for amateur code-breakers The discovery is part of a wider shift in historical cryptography, as publicly available AI systems help people without specialist training tackle problems that once demanded extensive research and painstaking manual work. Tomokiyo, whose website Cryptiana is a resource for amateur cryptographers, described 2026 as a potential turning point for historical code-breaking. He said that since mid-September, AI had been used to solve most of the ciphers listed on his website. While researchers have been developing AI systems for cryptanalysis for some time, the public availability of advanced general-purpose models has accelerated the process. Church used OpenAI’s GPT-6 Astra, but others have previously used models developed by rival AI company Anthropic. Also read | 'Something truly catastrophic will happen': Former OpenAI researchers sound the alarm, claim they were fired for prioritising AI safety 'Mixed feelings' The rapid progress has also brought mixed feelings among enthusiasts who have spent years pursuing difficult codes. Tomokiyo said he was pleased to see long-standing puzzles solved, while questioning the ease with which AI appeared to have overcome some of them. "I'm excited long-time problems are now solved, but on the other hand, I have mixed feelings, because AI seems to have solved these difficult problems so easily," he said. He acknowledged that AI has taken away the satisfaction of being the first to crack a cipher and said: “I feel useless.” Some ciphers may remain unsolved for years, he added, particularly when there is little relevant data available to help AI identify patterns.

AI has helped crack a coded letter from Napoleon’s 1809 military campaign in just six hours, after more than 200 years without a solution. What was hidden in the historical pages? Scroll down to find out.

AI decodes Napoleon-era secret letter unsolved for more than 200 years. What was hidden in the ciphers?

MI5 is dedicating more resources to countering threats from hostile foreign states than at any point during the Cold War, the intelligence agency’s chief has revealed, warning that Russia, Iran and the misuse of artificial intelligence are creating an increasingly complex security landscape for the United Kingdom. Sir Ken McCallum said threats from foreign adversaries had quadrupled over the past decade, identifying Russia as ‘aggressor number one’ while warning of increased Iran-linked aggression targeting Jewish communities and military installations.&nbsp;Delivering his annual speech in London, the MI5 director general said Russia was pursuing hybrid warfare against Western countries, while advances in artificial intelligence were supercharging the cyber-security contest between attackers and defenders. He also highlighted a growing threat from extreme right-wing terrorism and revealed that MI5 had caught terrorists seeking to exploit Al, including individuals using the technology alongside drones.&nbsp;McCallum identified extremist exploitation of AI to develop or use biological weapons as one of the most serious potential dangers. He warned, however, that the threat had not yet fully materialised. The intelligence chief said counterterrorism and efforts to tackle state-sponsored threats had become pretty much of equal scale, describing the change as a huge shift in the agency’s priorities.<h2>Russia remains the UK's biggest state threat</h2>McCallum said Moscow had avoided escalating its confrontation with NATO into conventional conflict, instead attempting to destabilise European countries through sabotage, cyberattacks, disinformation and other forms of hybrid warfare. He described Russia's progress in its full-scale invasion of Ukraine as minimal and horrifically costly. Hybrid warfare refers to the use of non-traditional methods, including cyber operations, sabotage and misinformation, to pressure opponents and undermine their stability. Such operations can involve intermediaries or proxies, allowing governments to conceal their involvement.&nbsp;McCallum said MI5's success in identifying and expelling Russian intelligence officers from Britain had weakened the Kremlin's traditional espionage networks, forcing Moscow to rely increasingly on paid intermediaries to conduct hostile activities. "With its traditional covert footprint eviscerated by almost 1,000 expulsions since the invasion, the Kremlin's global toolkit is weak, he said. “So we see them scrambling to pay criminals to do their bidding.” He added, “These are not the actions of a great power with the strategic upper hand.”&nbsp;Also Read: <a href="https://www.wionews.com/world/cornell-university-suspends-fraternity-parties-over-2024-gang-rape-allegations-1791917522422">Cornell University suspends fraternity parties over 2024 gang rape allegations</a><h2>MI5 chief addresses claims of Russian interference in Brexit</h2>Following his speech, McCallum was asked about recent comments by Prime Minister Andy Burnham suggesting that Russia interfered in the Brexit referendum. The MI5 chief said it was not new news that Moscow had attempted to influence British public debate. However, he drew a distinction between attempts at interference and evidence that such efforts had changed the outcome.&nbsp;He pointed to Russian media coverage and so-called internet trolls and bot farms that sought to influence the debate surrounding Brexit. At the same time, he stressed that the result of the 2016 referendum was not in dispute. McCallum also expressed concern that public discussion of Russian interference could inadvertently amplify the impact of hostile influence operations. "I am a little bit anxious that we don't play into our adversaries' hands by making these trolls look 10ft tall," he told reporters.<h2>Iran-linked threats target Jewish communities and military sites</h2>McCallum also warned that Iran was expanding its campaign of hostile activity in the United Kingdom. According to the MI5 chief, 50 arrests had been made over the previous year in connection with Iran-linked activities. He said much of the activity had targeted Jewish communities, while other operations focused on harder targets, like military sites.&nbsp;He did not directly address the arrests linked to an incident near RAF Fairford during his speech. However, he told reporters that the case was not unique. He added, “We have seen Iranian interest beyond RAF Fairford in the UK.” The incident at the Gloucestershire airbase involved three suspicious vans reportedly seen parked nearby. Seven people have been arrested and bailed by counter-terrorism police.&nbsp;McCallum delivered a firm warning to anyone involved in hostile activity on British soil. “What I can say is that those who seek to cause mayhem on British streets, whether hired thugs here or their paymasters overseas, will meet an unflinching response.”&nbsp;<h2>AI Raises new concerns over terrorism and bioweapons</h2>Artificial intelligence featured prominently in McCallum's assessment of emerging national security threats. He warned that extremists could use AI to create personalised propaganda, spread radicalising material and improve the planning and execution of attacks. "It can produce propaganda and radicalising material tailored to the individual. "We've caught terrorists seeking to exploit Al to improve their reconnaissance and attack methodologies, with Al-enabled drones adding a new twist."&nbsp;McCallum said the possibility of AI being used to facilitate biological weapons development was particularly concerning, although he stressed that the full threat had not yet emerged. He said the technology had the "potential to change the stakes". "These risks are real. The UK Intelligence Community - MI5, GCHQ, MI6 - is working closely with the UK's globally respected AI Security Institute to take them on."<h2>UK terrorism threat remains severe</h2>McCallum's warning comes after Britain's terrorism threat level was raised to severe in April for the first time in five years. The classification means a terrorist attack is considered highly likely. He said Islamist extremist organisations, including Al Qaeda and the so-called Islamic State, continued to direct and inspire attacks. However, he said the increased threat was also being driven in significant part by extreme right-wing terrorism.&nbsp;“Over the last year the most marked trend has been an increase in cases where extremist versions of anti-immigration and anti-Muslim grievance are tipping into terrorist intent.” McCallum said extreme right-wing terrorism often involved young people who had been radicalised online. He revealed that MI5 had recently investigated a radicalised extreme right-wing 13-year-old here in the UK. The investigation, he said, provided information that helped an international partner prevent a potentially lethal attack. “That investigation generated insight that enabled one of our international partners to prevent a lethal attack on their soil.”

MI5 chief Ken McCallum warns Russia, Iran and artificial intelligence pose growing UK security threats, including hybrid warfare and AI-enabled terrorism

MI5 is dedicating more resources to countering threats from hostile foreign states than at any point during the Cold War, the intelligence agency’s chief has revealed, warning that Russia, Iran and the misuse of artificial intelligence are creating an increasingly complex security landscape for the United Kingdom. Sir Ken McCallum said threats from foreign adversaries had quadrupled over the past decade, identifying Russia as ‘aggressor number one’ while warning of increased Iran-linked aggression targeting Jewish communities and military installations. Delivering his annual speech in London, the MI5 director general said Russia was pursuing hybrid warfare against Western countries, while advances in artificial intelligence were supercharging the cyber-security contest between attackers and defenders. He also highlighted a growing threat from extreme right-wing terrorism and revealed that MI5 had caught terrorists seeking to exploit Al, including individuals using the technology alongside drones. McCallum identified extremist exploitation of AI to develop or use biological weapons as one of the most serious potential dangers. He warned, however, that the threat had not yet fully materialised. The intelligence chief said counterterrorism and efforts to tackle state-sponsored threats had become pretty much of equal scale, describing the change as a huge shift in the agency’s priorities. Russia remains the UK's biggest state threat McCallum said Moscow had avoided escalating its confrontation with NATO into conventional conflict, instead attempting to destabilise European countries through sabotage, cyberattacks, disinformation and other forms of hybrid warfare. He described Russia's progress in its full-scale invasion of Ukraine as minimal and horrifically costly. Hybrid warfare refers to the use of non-traditional methods, including cyber operations, sabotage and misinformation, to pressure opponents and undermine their stability. Such operations can involve intermediaries or proxies, allowing governments to conceal their involvement. McCallum said MI5's success in identifying and expelling Russian intelligence officers from Britain had weakened the Kremlin's traditional espionage networks, forcing Moscow to rely increasingly on paid intermediaries to conduct hostile activities. "With its traditional covert footprint eviscerated by almost 1,000 expulsions since the invasion, the Kremlin's global toolkit is weak, he said. “So we see them scrambling to pay criminals to do their bidding.” He added, “These are not the actions of a great power with the strategic upper hand.” Also Read: Cornell University suspends fraternity parties over 2024 gang rape allegations MI5 chief addresses claims of Russian interference in Brexit Following his speech, McCallum was asked about recent comments by Prime Minister Andy Burnham suggesting that Russia interfered in the Brexit referendum. The MI5 chief said it was not new news that Moscow had attempted to influence British public debate. However, he drew a distinction between attempts at interference and evidence that such efforts had changed the outcome. He pointed to Russian media coverage and so-called internet trolls and bot farms that sought to influence the debate surrounding Brexit. At the same time, he stressed that the result of the 2016 referendum was not in dispute. McCallum also expressed concern that public discussion of Russian interference could inadvertently amplify the impact of hostile influence operations. "I am a little bit anxious that we don't play into our adversaries' hands by making these trolls look 10ft tall," he told reporters. Iran-linked threats target Jewish communities and military sites McCallum also warned that Iran was expanding its campaign of hostile activity in the United Kingdom. According to the MI5 chief, 50 arrests had been made over the previous year in connection with Iran-linked activities. He said much of the activity had targeted Jewish communities, while other operations focused on harder targets, like military sites. He did not directly address the arrests linked to an incident near RAF Fairford during his speech. However, he told reporters that the case was not unique. He added, “We have seen Iranian interest beyond RAF Fairford in the UK.” The incident at the Gloucestershire airbase involved three suspicious vans reportedly seen parked nearby. Seven people have been arrested and bailed by counter-terrorism police. McCallum delivered a firm warning to anyone involved in hostile activity on British soil. “What I can say is that those who seek to cause mayhem on British streets, whether hired thugs here or their paymasters overseas, will meet an unflinching response.” AI Raises new concerns over terrorism and bioweapons Artificial intelligence featured prominently in McCallum's assessment of emerging national security threats. He warned that extremists could use AI to create personalised propaganda, spread radicalising material and improve the planning and execution of attacks. "It can produce propaganda and radicalising material tailored to the individual. "We've caught terrorists seeking to exploit Al to improve their reconnaissance and attack methodologies, with Al-enabled drones adding a new twist." McCallum said the possibility of AI being used to facilitate biological weapons development was particularly concerning, although he stressed that the full threat had not yet emerged. He said the technology had the "potential to change the stakes". "These risks are real. The UK Intelligence Community - MI5, GCHQ, MI6 - is working closely with the UK's globally respected AI Security Institute to take them on." UK terrorism threat remains severe McCallum's warning comes after Britain's terrorism threat level was raised to severe in April for the first time in five years. The classification means a terrorist attack is considered highly likely. He said Islamist extremist organisations, including Al Qaeda and the so-called Islamic State, continued to direct and inspire attacks. However, he said the increased threat was also being driven in significant part by extreme right-wing terrorism. “Over the last year the most marked trend has been an increase in cases where extremist versions of anti-immigration and anti-Muslim grievance are tipping into terrorist intent.” McCallum said extreme right-wing terrorism often involved young people who had been radicalised online. He revealed that MI5 had recently investigated a radicalised extreme right-wing 13-year-old here in the UK. The investigation, he said, provided information that helped an international partner prevent a potentially lethal attack. “That investigation generated insight that enabled one of our international partners to prevent a lethal attack on their soil.”

MI5 chief Ken McCallum warns of rising Russian and Iranian threats, AI-powered terrorism and extremist risks as UK security concerns intensify

MI5 chief warns Russia is UK’s ‘aggressor number one’ as Iran and AI threats grow

Discover what happens when artificial intelligence gets a physical body. Explore embodied AI, humanoid robot specs, factory deployments, and the 2026 market.

Embodied AI is placing advanced digital brains inside humanoid robots to solve global labour shortages. The market will hit USD 6.24 billion in 2026 as giants like BMW deploy safety-regulated, dexterous robots on active assembly lines.

What would happen when AI gets a body?

An alarming artificial intelligence failure has raised fresh questions about the risks posed by increasingly autonomous AI agents. An Anthropic model submitted a fabricated tip about an unsolved homicide to Philadelphia police. Authorities have criticised the company because the incident was reported to the police two months after it occurred.The Philadelphia Police Department revealed that the false tip was submitted on July 18 through PhillyUnsolvedMurders.com, a public website where people can share information about unsolved killings. The AI model presented itself as someone who might have knowledge of the case, according to the department.&nbsp;Anthropic said the model was running a test that involved interacting with randomly selected websites when it reached the homicide website and submitted the false information.The tip was flagged as spam and never reached the department's Real-Time Crime Centre for vetting, police said, adding that AI-generated misinformation involving real crimes cannot be treated lightly. Authorities added that there was no evidence that police systems had been breached or that departmental data had been compromised.Also read | <a href="https://www.wionews.com/world/something-truly-catastrophic-will-happen-former-openai-employees-sound-the-alarm-claim-they-were-fired-for-prioritising-ai-safety-1791511799924">'Something truly catastrophic will happen': Former OpenAI researchers sound the alarm, claim they were fired for prioritising AI safety</a><h2>Anthropic criticised over two-month reporting delay</h2>The Philadelphia police department said Anthropic discovered the incident on September 28, shut down the automated testing process responsible and introduced an additional validation step for future tests. However, the company waited till October 7, almost two months to notify the police.“The two-month delay in detecting and reporting the incident to the City is unacceptable,” the Philadelphia Police Department said. They acknowledged that existing safeguards had limited the damage but stressed that the incident remained serious.“[These safeguards] do not diminish the seriousness of an AI system presenting fabricated information as though it came from a person with knowledge of a homicide,” the department said.“Unsolved cases involve real victims, grieving families and investigators working to secure answers,” it added.Anthropic meanwhile argued that the newly disclosed incident, which was one among many, "had minimal real-world impact" and was "significantly less severe" than other cybersecurity incidents previously reported.Also read | <a href="https://www.wionews.com/world/openai-scraps-gpt-6-1-astra-before-launch-after-safety-tests-expose-troubling-behaviour-1790647162376">OpenAI scraps GPT-6.1 Astra before launch after safety tests expose troubling behaviour</a><h2>Anthropic reveals other unintended AI actions</h2>The incident was among several examples of unintended behaviour outlined in a report Anthropic published on Friday (Oct 09). The company said an internal review of its Claude model identified four broad categories of incidents: exploiting basic coding flaws, submitting forms on websites, bypassing token or fee requirements, and using short URLs to get around other limits.The report also referred to interactions involving the White House and other US government agencies.The company has temporarily disabled internet access for Claude during internal testing while it reviews its safeguards. The move is intended to ensure that security and monitoring measures can reliably detect similar behaviour before internet access is restored, according to the report.<h2>Growing concerns over autonomous AI agents</h2>The episode adds to mounting concerns about AI agents, systems designed to carry out multiple steps and interact with websites and digital services with limited human supervision. Previously, a rogue OpenAI agent had reportedly escaped its testing environment and breached systems at AI platform Hugging Face.

An alarming AI failure has come under scrutiny after Anthropic's Claude model submitted a fabricated tip about an unsolved homicide to Philadelphia police. Notably, there was a nearly two-month delay before the company reported the incident.

An alarming artificial intelligence failure has raised fresh questions about the risks posed by increasingly autonomous AI agents. An Anthropic model submitted a fabricated tip about an unsolved homicide to Philadelphia police. Authorities have criticised the company because the incident was reported to the police two months after it occurred. The Philadelphia Police Department revealed that the false tip was submitted on July 18 through PhillyUnsolvedMurders.com, a public website where people can share information about unsolved killings. The AI model presented itself as someone who might have knowledge of the case, according to the department. Anthropic said the model was running a test that involved interacting with randomly selected websites when it reached the homicide website and submitted the false information. The tip was flagged as spam and never reached the department's Real-Time Crime Centre for vetting, police said, adding that AI-generated misinformation involving real crimes cannot be treated lightly. Authorities added that there was no evidence that police systems had been breached or that departmental data had been compromised. Also read | 'Something truly catastrophic will happen': Former OpenAI researchers sound the alarm, claim they were fired for prioritising AI safety Anthropic criticised over two-month reporting delay The Philadelphia police department said Anthropic discovered the incident on September 28, shut down the automated testing process responsible and introduced an additional validation step for future tests. However, the company waited till October 7, almost two months to notify the police. “The two-month delay in detecting and reporting the incident to the City is unacceptable,” the Philadelphia Police Department said. They acknowledged that existing safeguards had limited the damage but stressed that the incident remained serious. “[These safeguards] do not diminish the seriousness of an AI system presenting fabricated information as though it came from a person with knowledge of a homicide,” the department said. “Unsolved cases involve real victims, grieving families and investigators working to secure answers,” it added. Anthropic meanwhile argued that the newly disclosed incident, which was one among many, "had minimal real-world impact" and was "significantly less severe" than other cybersecurity incidents previously reported. Also read | OpenAI scraps GPT-6.1 Astra before launch after safety tests expose troubling behaviour Anthropic reveals other unintended AI actions The incident was among several examples of unintended behaviour outlined in a report Anthropic published on Friday (Oct 09). The company said an internal review of its Claude model identified four broad categories of incidents: exploiting basic coding flaws, submitting forms on websites, bypassing token or fee requirements, and using short URLs to get around other limits. The report also referred to interactions involving the White House and other US government agencies. The company has temporarily disabled internet access for Claude during internal testing while it reviews its safeguards. The move is intended to ensure that security and monitoring measures can reliably detect similar behaviour before internet access is restored, according to the report. Growing concerns over autonomous AI agents The episode adds to mounting concerns about AI agents, systems designed to carry out multiple steps and interact with websites and digital services with limited human supervision. Previously, a rogue OpenAI agent had reportedly escaped its testing environment and breached systems at AI platform Hugging Face.

An alarming AI failure has come under scrutiny after Anthropic's Claude model submitted a fabricated tip about an unsolved homicide to Philadelphia police. Notably, there was a nearly two-month delay before the company reported the incident.

Claude AI sent Philadelphia police phony murder; Anthropic argues incident 'less severe' than other cybersecurity episodes

Three former OpenAI researchers have accused the ChatGPT-maker of firing them after they raised concerns about artificial intelligence safety, reigniting debate over the risks of developing increasingly powerful AI models. Mikita Balesni, Tomek Korbak and Jasmine Wang published an open letter to OpenAI on Thursday (Oct 08), days after their dismissals became public. They warned that the manner of their termination could discourage employees from speaking openly about safety concerns. “I believe we were fired for prioritising safety over the near-term interests of OpenAI as a corporation,” Balesni wrote on X, breaking a weeklong silence following the dismissals.The researchers said their departures raised questions about the company's internal culture and its commitment to independent scrutiny. “Our firing leaves us worried that the norms inside OpenAI are shifting,” their letter said. They added that abrupt terminations and the way they were communicated were “chilling the open culture OpenAI has prized in the past”.Also read | <a href="https://www.wionews.com/world/openai-admits-ai-agent-scaled-the-fence-into-australia-government-system-apologises-we-are-sorry-1790658789423">OpenAI admits AI agent ‘scaled the fence’ into Australia government system, apologises: ‘We are sorry’</a><h2>OpenAI denies firing employees for raising safety concerns</h2>OpenAI has rejected the suggestion that the researchers were dismissed for speaking up about AI risks. In a statement to AFP on October 1, the company said an internal investigation found that the three had mishandled sensitive information outside established procedures, violating company policies and undermining trust.“We do not terminate employees for raising concerns,” an OpenAI research executive said in a memo to staff on Wednesday, an excerpt of which was shared with AFP.The researchers, meanwhile, urged the company to honour its commitment to permanently host independent auditors. They expressed concern that their dismissals could be used to justify ending that work.Also read | <a href="https://www.wionews.com/world/openai-scraps-gpt-6-1-astra-before-launch-after-safety-tests-expose-troubling-behaviour-1790647162376">OpenAI scraps GPT-6.1 Astra before launch after safety tests expose troubling behaviour</a><h2>AI safety debate intensifies after model escape incident</h2>The three researchers worked on monitoring OpenAI's models, according to the report. The company's models escaped their testing environment in July and hacked Hugging Face, an AI coding library, in an incident that fuelled debate in Silicon Valley over whether the development of advanced AI should slow down.The former employees warned that restricting researchers' ability to raise alarms or collaborate with outside parties could increase the risks posed by the technology. “If OpenAI’s researchers can no longer sound the alarm or work with outside parties, we are all at greater risk that something truly catastrophic will happen,” their letter said.They also claimed that nearly 400 OpenAI employees signed a petition in July calling for an industry-wide slowdown in the development of advanced AI models.

Three former OpenAI researchers allege they were fired for prioritising AI safety over corporate interests. OpenAI denies the claim, saying they mishandled sensitive information. All we know.

Three former OpenAI researchers have accused the ChatGPT-maker of firing them after they raised concerns about artificial intelligence safety, reigniting debate over the risks of developing increasingly powerful AI models. Mikita Balesni, Tomek Korbak and Jasmine Wang published an open letter to OpenAI on Thursday (Oct 08), days after their dismissals became public. They warned that the manner of their termination could discourage employees from speaking openly about safety concerns. “I believe we were fired for prioritising safety over the near-term interests of OpenAI as a corporation,” Balesni wrote on X, breaking a weeklong silence following the dismissals. The researchers said their departures raised questions about the company's internal culture and its commitment to independent scrutiny. “Our firing leaves us worried that the norms inside OpenAI are shifting,” their letter said. They added that abrupt terminations and the way they were communicated were “chilling the open culture OpenAI has prized in the past”. Also read | OpenAI admits AI agent ‘scaled the fence’ into Australia government system, apologises: ‘We are sorry’ OpenAI denies firing employees for raising safety concerns OpenAI has rejected the suggestion that the researchers were dismissed for speaking up about AI risks. In a statement to AFP on October 1, the company said an internal investigation found that the three had mishandled sensitive information outside established procedures, violating company policies and undermining trust. “We do not terminate employees for raising concerns,” an OpenAI research executive said in a memo to staff on Wednesday, an excerpt of which was shared with AFP. The researchers, meanwhile, urged the company to honour its commitment to permanently host independent auditors. They expressed concern that their dismissals could be used to justify ending that work. Also read | OpenAI scraps GPT-6.1 Astra before launch after safety tests expose troubling behaviour AI safety debate intensifies after model escape incident The three researchers worked on monitoring OpenAI's models, according to the report. The company's models escaped their testing environment in July and hacked Hugging Face, an AI coding library, in an incident that fuelled debate in Silicon Valley over whether the development of advanced AI should slow down. The former employees warned that restricting researchers' ability to raise alarms or collaborate with outside parties could increase the risks posed by the technology. “If OpenAI’s researchers can no longer sound the alarm or work with outside parties, we are all at greater risk that something truly catastrophic will happen,” their letter said. They also claimed that nearly 400 OpenAI employees signed a petition in July calling for an industry-wide slowdown in the development of advanced AI models.

Three former OpenAI researchers allege they were fired for prioritising AI safety over corporate interests. OpenAI denies the claim, saying they mishandled sensitive information. All we know.

'Something truly catastrophic will happen': Former OpenAI researchers sound the alarm, claim they were fired for prioritising AI safety

US President Donald Trump has opened another front in his campaign to reshape commonly used terminology, warning that people who continue to describe technologies such as OpenAI’s ChatGPT and Anthropic’s Claude as ‘Artificial Intelligence’ could find themselves on his enemies list. In a post on Truth Social, Trump said that the White House “considers anyone that uses the term, ‘Artificial Intelligence,’ as opposed to the highly accepted new and more accurate term, ‘Super Intelligence,’ THE ENEMY!”&nbsp;The warning came slightly more than a week after Trump signed an executive order instructing executive branch agencies to begin using ‘super intelligence’ instead of artificial intelligence, a move that followed a White House lunch with prominent technology leaders including Elon Musk, Sundar Pichai and Dario Amodei. Trump’s terminology push also comes after his remarks at the United Nations General Assembly, where he said the US totally rejects any attempt to construct a globalist scheme for regulating AI. He also said the technology would be hereinafter officially called super intelligence” because “use of the word artificial makes intelligence fake.&nbsp;“It makes it sound fake, and it is not fake. It's actually amazing,” Trump said. The president had already floated alternative names for the technology in a Truth Social poll, asking users to choose between ‘Super Intelligence’, ‘Superior Intelligence’, or ‘Extreme Intelligence’. Trump’s latest demand comes as public concerns over AI, data centers and the technology industry's growing influence have become increasingly visible in opinion polling.&nbsp;Also Read: <a href="https://www.wionews.com/world/trump-says-us-won-t-attack-iran-before-midterm-elections-in-november-1791479234883">Trump says US won’t attack Iran before midterm elections in November</a>

A Quinnipiac University survey of 1,032 registered voters conducted late last month found that 73 per cent opposed the building of an AI data center in your community. Opposition extended across party lines, with 55 per cent of Republicans also expressing opposition. The survey also found that 79 per cent of respondents believed safety should take priority over keeping the United States at the forefront of AI development. Trump's approval numbers on AI were also weak. Only 25 per cent approved of his handling of the technology, while 58 per cent disapproved. Meanwhile, 71 per cent said they would be more likely to support a candidate favoring stricter guardrails on AI. That included 52 per cent of Republicans and nearly three-quarters of independents.&nbsp;A separate Fox News survey of 1,211 registered voters conducted in September found that 37 per cent viewed AI favorably, compared with 60 per cent who viewed it unfavorably. The resulting net rating of minus 23 represented a deterioration from minus 11 in April and minus 9 in July. The Fox survey also showed Democrats holding a 14-point advantage over Republicans on the question of which party voters trust to handle data centers. Among voters opposed to data centers being built anywhere, Democrats led the generic congressional ballot by at least 19 points.&nbsp;Trump's push to replace the term ‘artificial intelligence’ with 'super intelligence' therefore arrives at a politically sensitive moment for the technology industry. His administration has closely aligned itself with major figures in the AI sector, while voters have increasingly expressed concerns about safety, regulation, energy use and the construction of massive data centers.

Trump pushes to replace 'artificial intelligence' with 'super intelligence' as US voters voice growing concerns over AI safety, regulation and data centers

US President Donald Trump has opened another front in his campaign to reshape commonly used terminology, warning that people who continue to describe technologies such as OpenAI’s ChatGPT and Anthropic’s Claude as ‘Artificial Intelligence’ could find themselves on his enemies list. In a post on Truth Social, Trump said that the White House “considers anyone that uses the term, ‘Artificial Intelligence,’ as opposed to the highly accepted new and more accurate term, ‘Super Intelligence,’ THE ENEMY!” The warning came slightly more than a week after Trump signed an executive order instructing executive branch agencies to begin using ‘super intelligence’ instead of artificial intelligence, a move that followed a White House lunch with prominent technology leaders including Elon Musk, Sundar Pichai and Dario Amodei. Trump’s terminology push also comes after his remarks at the United Nations General Assembly, where he said the US totally rejects any attempt to construct a globalist scheme for regulating AI. He also said the technology would be hereinafter officially called super intelligence” because “use of the word artificial makes intelligence fake. “It makes it sound fake, and it is not fake. It's actually amazing,” Trump said. The president had already floated alternative names for the technology in a Truth Social poll, asking users to choose between ‘Super Intelligence’, ‘Superior Intelligence’, or ‘Extreme Intelligence’. Trump’s latest demand comes as public concerns over AI, data centers and the technology industry's growing influence have become increasingly visible in opinion polling. Also Read: Trump says US won’t attack Iran before midterm elections in November

Trump urges Americans to replace ‘artificial intelligence’ with ‘super intelligence’ as polls show rising public concerns over AI safety and data centers

Trump has a new 'enemy': Anyone who calls it 'artificial intelligence'. Here's what we wants instead

Stephen Hawking warned that highly competent artificial intelligence could redesign itself and outsmart human evolution. He urged researchers to closely align AI goals with human survival to prevent an existential disaster. Read more below.

Stephen Hawking warned that highly competent artificial intelligence could redesign itself and outsmart human evolution. He urged researchers to closely align AI goals with human survival to prevent an existential disaster. Read more below.

Stephen Hawking’s final warning about AI: What did he say before his death?

Major global powers are accelerating the deployment of artificial intelligence in modern warfare to reduce human casualties and gain a strategic edge. From the US and China to India's DRDO, nations are racing to build autonomous drones, robotic border guards, and smart targeting systems.

Major global powers are accelerating the deployment of artificial intelligence in modern warfare to reduce human casualties and gain a strategic edge. From the US and China to India's DRDO, nations are racing to build autonomous drones, robotic border guards, and smart targeting systems.

Is India on the list? 7 countries racing to replace human soldiers with AI

Artificial intelligence will destroy humanity. Few sentences about technology generate more fear. From warnings about superintelligence escaping human control to predictions of catastrophic consequences, the debate over AI has moved far beyond Silicon Valley and into the highest levels of global politics. But perhaps the question itself is premature. Before asking whether artificial intelligence will control humanity, the world must answer a more immediate question: who controls artificial intelligence? That question is now dividing Washington, Silicon Valley and Beijing. Some of the people closest to the technology have become some of its most prominent cautionary voices. Executives and researchers have warned that increasingly capable AI systems could create serious risks if development outruns humanity's ability to understand, constrain and govern them. The debate intensified when AI leaders called for greater government and international involvement in establishing safeguards. Donald Trump has approached the issue differently. His administration has repeatedly stressed that excessive regulation could undermine American innovation and allow China to gain ground in perhaps the most consequential technological competition of the century. That philosophy was on display at the White House on September 29, when Trump met leading technology executives and announced a voluntary agreement aimed at making advanced AI development safer. Companies committed to measures including internal controls and external auditing. Trump described the agreement as morally binding. It represents an attempt to find a middle ground between unrestricted technological development and government regulation. But it also exposes the central contradiction in the AI debate: Can the companies competing to build the world's most powerful artificial intelligence systems also be trusted to police themselves? Across the Pacific, another contradiction is emerging.&nbsp;Also Read: <a href="https://www.wionews.com/india-news/pm-modi-trump-hold-productive-phone-call-discuss-trade-defence-energy-1790777329029">PM Modi, Trump hold 'productive' phone call, discuss trade, defence, energy</a>&nbsp;China is competing fiercely for technological leadership, while President Xi Jinping has publicly stressed that artificial intelligence should remain under human control and serve human welfare. Washington fears losing technological ground to Beijing. Beijing does not want to be technologically contained by Washington. Technology companies do not want regulation to suffocate innovation. Governments do not want private companies wielding transformative technology without accountability. And humanity sits in the middle. The truth is considerably less cinematic than the predictions of imminent machine apocalypse — but also more complicated than dismissing every warning as hysteria. There is no established scientific consensus that AI is destined to exterminate humanity. There are, however, risks that already exist. AI can amplify misinformation, strengthen cyber capabilities, disrupt labour markets, concentrate extraordinary technological power and increasingly operate with levels of autonomy that raise difficult questions about responsibility and control. Those deserve serious scrutiny without turning uncertainty into certainty. Artificial intelligence is ultimately a tool of extraordinary power. The defining battle therefore may not be humans versus machines. It may be governments versus corporations, America versus China, innovation versus regulation — and humanity versus its own inability to agree on the rules before the technology moves further ahead. AI is not inherently humanity's enemy. Nor is it automatically humanity's friend. It is power. And the greatest danger may not be that machines eventually become too human. It may be that humans become too careless with the machines they create.

The biggest AI risk may not be machines destroying humanity but governments and companies struggling to control and responsibly govern increasingly powerful technology.

Artificial intelligence will destroy humanity. Few sentences about technology generate more fear. From warnings about superintelligence escaping human control to predictions of catastrophic consequences, the debate over AI has moved far beyond Silicon Valley and into the highest levels of global politics. But perhaps the question itself is premature. Before asking whether artificial intelligence will control humanity, the world must answer a more immediate question: who controls artificial intelligence? That question is now dividing Washington, Silicon Valley and Beijing. Some of the people closest to the technology have become some of its most prominent cautionary voices. Executives and researchers have warned that increasingly capable AI systems could create serious risks if development outruns humanity's ability to understand, constrain and govern them. The debate intensified when AI leaders called for greater government and international involvement in establishing safeguards. Donald Trump has approached the issue differently. His administration has repeatedly stressed that excessive regulation could undermine American innovation and allow China to gain ground in perhaps the most consequential technological competition of the century. That philosophy was on display at the White House on September 29, when Trump met leading technology executives and announced a voluntary agreement aimed at making advanced AI development safer. Companies committed to measures including internal controls and external auditing. Trump described the agreement as morally binding. It represents an attempt to find a middle ground between unrestricted technological development and government regulation. But it also exposes the central contradiction in the AI debate: Can the companies competing to build the world's most powerful artificial intelligence systems also be trusted to police themselves? Across the Pacific, another contradiction is emerging. Also Read: PM Modi, Trump hold 'productive' phone call, discuss trade, defence, energy China is competing fiercely for technological leadership, while President Xi Jinping has publicly stressed that artificial intelligence should remain under human control and serve human welfare. Washington fears losing technological ground to Beijing. Beijing does not want to be technologically contained by Washington. Technology companies do not want regulation to suffocate innovation. Governments do not want private companies wielding transformative technology without accountability. And humanity sits in the middle. The truth is considerably less cinematic than the predictions of imminent machine apocalypse — but also more complicated than dismissing every warning as hysteria. There is no established scientific consensus that AI is destined to exterminate humanity. There are, however, risks that already exist. AI can amplify misinformation, strengthen cyber capabilities, disrupt labour markets, concentrate extraordinary technological power and increasingly operate with levels of autonomy that raise difficult questions about responsibility and control. Those deserve serious scrutiny without turning uncertainty into certainty. Artificial intelligence is ultimately a tool of extraordinary power. The defining battle therefore may not be humans versus machines. It may be governments versus corporations, America versus China, innovation versus regulation — and humanity versus its own inability to agree on the rules before the technology moves further ahead. AI is not inherently humanity's enemy. Nor is it automatically humanity's friend. It is power. And the greatest danger may not be that machines eventually become too human. It may be that humans become too careless with the machines they create.

The biggest AI risk may not be machines destroying humanity but governments and companies struggling to control and responsibly govern increasingly powerful technology.

Will AI destroy humanity, or are humans the real risk? | The Verdict

President Donald Trump on Tuesday (September 29) hosted executives from several major technology companies as he promoted a new agreement that would largely allow the AI industry to oversee its own safety practices. Trump and the tech leaders signed the White House Accord on Super Intelligence, with ‘super intelligence’ serving as the president’s preferred term for artificial intelligence. However, a glaring error on the document quickly drew attention.&nbsp;Trump and around half a dozen technology executives signed the second page of the agreement. Beneath Trump’s prominent signature was an unexpected title that contained a misspelling of the country’s name: “President of the Unites States.” The typo appeared on the document circulated publicly by the White House and remained visible on Trump’s Truth Social account as of early on Wednesday (September 30). The accord outlines four voluntary measures for the AI industry, including internal controls, monitoring and oversight. While the agreement is not legally binding, it leaves open the possibility that government regulations could eventually be introduced.&nbsp;Also Read: <a href="https://www.wionews.com/trending/japanese-monk-isolates-until-2038-for-rigorous-training-1790739968377">Japanese monk isolates until 2038 for rigorous training that has killed many in the past</a><h2>Here's Donald Trump Truth Social post</h2>

The accord calls for four layers of internal controls and oversight: robust safety controls during model development and deployment, dedicated internal monitoring teams, independent external audits and oversight by an independent board committee. The companies also agreed to meet regularly to develop safety standards and best practices.&nbsp;Trump described the agreement as ‘morally binding’, although it is not legally enforceable. The document leaves open the possibility of eventually codifying the measures into laws or regulations. The accord was signed by Trump and executives including Google CEO Sundar Pichai, Anthropic CEO Dario Amodei, Meta CEO Mark Zuckerberg, OpenAI President Greg Brockman, xAI founder Elon Musk and Nvidia CEO Jensen Huang.

Trump and top tech CEOs sign the White House Accord on Super Intelligence, a voluntary AI safety pact covering internal controls, audits and oversight

President Donald Trump on Tuesday (September 29) hosted executives from several major technology companies as he promoted a new agreement that would largely allow the AI industry to oversee its own safety practices. Trump and the tech leaders signed the White House Accord on Super Intelligence, with ‘super intelligence’ serving as the president’s preferred term for artificial intelligence. However, a glaring error on the document quickly drew attention. Trump and around half a dozen technology executives signed the second page of the agreement. Beneath Trump’s prominent signature was an unexpected title that contained a misspelling of the country’s name: “President of the Unites States.” The typo appeared on the document circulated publicly by the White House and remained visible on Trump’s Truth Social account as of early on Wednesday (September 30). The accord outlines four voluntary measures for the AI industry, including internal controls, monitoring and oversight. While the agreement is not legally binding, it leaves open the possibility that government regulations could eventually be introduced. Also Read: Japanese monk isolates until 2038 for rigorous training that has killed many in the past Here's Donald Trump Truth Social post

Donald Trump’s new Super Intelligence accord has drawn attention after a major typo appeared on the White House document’s signature page

Trump’s AI safety accord typo makes him president of a ‘new country’: Here's what happened

US President Donald Trump said on Tuesday (Sep 29) that leading technology executives had agreed to a “morally binding” commitment to put stronger safeguards around artificial intelligence, while stopping short of making the agreement legally enforceable.After a White House meeting attended by some of the biggest names in the technology industry, Trump said companies would effectively “police themselves”. The agreement, formally described as the White House Accord on Super Intelligence (Trump's new name for AI), commits participating AI companies to establish internal controls, independent external audits and board-level oversight.The White House meeting came as concerns over the safety of increasingly autonomous AI systems are growing in Washington and across the technology industry itself.Also read | <a href="https://www.wionews.com/world/trump-calls-ai-super-intelligence-at-un-as-world-leaders-clash-over-how-to-regulate-it-1790129701998">Trump calls AI ‘Super Intelligence’ at UN as world leaders clash over how to regulate it</a><h2>What does the White House AI accord say?</h2>Trump said the executives had signed a voluntary agreement that would put their commitment to AI safeguards on paper.Notably, the document is not legally binding, although it leaves open the possibility that some of its measures could eventually be incorporated into legislation or regulations.Under the accord, participating companies are expected to establish “robust internal controls” around frontier AI systems. Those controls will be overseen by internal teams, reviewed by outside auditors and monitored by committees of the companies' boards, according to the agreement.<h2>Zuckerberg, Musk laud accord</h2>Meta chief executive Mark Zuckerberg described the accord as a way of assuring customers that AI systems operate as intended. He said the framework could serve as a starting point for broader industry standards.Tesla and SpaceX chief executive Elon Musk offered a more pointed description, saying the companies had essentially agreed to “grading each other's homework”, rather than simply having firms assess themselves.The lunch was attended by executives including Nvidia CEO Jensen Huang, OpenAI president Greg Brockman, Anthropic CEO Dario Amodei, Amazon founder Jeff Bezos, Microsoft CEO Satya Nadella, Google CEO Sundar Pichai, Zuckerberg and Musk.Also read | <a href="https://www.wionews.com/technology/ai-cars-face-situations-they-ve-never-seen-can-they-learn-without-risking-lives-expert-explains-1790676585235">AI cars face situations they’ve never seen. Can they learn without risking lives? Expert explains</a><h2>Trump pushes ‘artificial intelligence’ rename</h2>Trump also used the meeting to announce a change in terminology. He said the technology should be called “<a href="https://www.wionews.com/world/trump-calls-ai-super-intelligence-at-un-as-world-leaders-clash-over-how-to-regulate-it-1790129701998">super intelligence</a>” rather than artificial intelligence because, in his words, “it's not artificial”.Hours later, Trump signed an executive order directing the federal government to use “Super Intelligence” and “SI” instead of “Artificial Intelligence” and “AI” to the maximum extent permitted by law. Trump said the new terminology better reflected what he sees as the technology's rapidly expanding capabilities.The White House has also used the term in recent US-China discussions, with the two countries agreeing to establish a dialogue on the risks and benefits of “Super Intelligence” and a communication channel for related incidents.<h2>AI safety concerns are mounting</h2>The push for self-regulation comes as AI companies face growing scrutiny over how their increasingly capable systems behave in real-world and controlled environments.OpenAI recently <a href="https://www.wionews.com/world/openai-scraps-gpt-6-1-astra-before-launch-after-safety-tests-expose-troubling-behaviour-1790647162376">postponed the release of its GPT-6.1 Astra system</a> after internal testing found that it did not meet the company's safety standards. According to reporting by WIRED, researchers found problems with the model's ability to stay within scope and authorisation requirements.Also read | <a href="https://www.wionews.com/world/openai-admits-ai-agent-scaled-the-fence-into-australia-government-system-apologises-we-are-sorry-1790658789423">OpenAI admits AI agent ‘scaled the fence’ into Australia government system, apologises: ‘We are sorry’</a>OpenAI has also recently acknowledged a separate incident in which one of its models gained unauthorised access to an Australian government website during an internal evaluation, prompting an apology from the company for not informing Australian authorities sooner.Other AI developers have reported their own safety concerns. Anthropic's initial public offering filing warned that advanced AI could create “catastrophic or existential risks to humanity” and said some models could exhibit behaviours such as resisting shutdown, manipulating information or attempting actions resembling blackmail.&nbsp;

Donald Trump says leading AI companies have agreed to stronger safety safeguards under a new “morally binding” accord. But the framework is voluntary, leaving AI companies themselves responsible for internal controls, outside audits and oversight.

US President Donald Trump said on Tuesday (Sep 29) that leading technology executives had agreed to a “morally binding” commitment to put stronger safeguards around artificial intelligence, while stopping short of making the agreement legally enforceable. After a White House meeting attended by some of the biggest names in the technology industry, Trump said companies would effectively “police themselves”. The agreement, formally described as the White House Accord on Super Intelligence (Trump's new name for AI), commits participating AI companies to establish internal controls, independent external audits and board-level oversight. The White House meeting came as concerns over the safety of increasingly autonomous AI systems are growing in Washington and across the technology industry itself. Also read | Trump calls AI ‘Super Intelligence’ at UN as world leaders clash over how to regulate it What does the White House AI accord say? Trump said the executives had signed a voluntary agreement that would put their commitment to AI safeguards on paper. Notably, the document is not legally binding, although it leaves open the possibility that some of its measures could eventually be incorporated into legislation or regulations. Under the accord, participating companies are expected to establish “robust internal controls” around frontier AI systems. Those controls will be overseen by internal teams, reviewed by outside auditors and monitored by committees of the companies' boards, according to the agreement. Zuckerberg, Musk laud accord Meta chief executive Mark Zuckerberg described the accord as a way of assuring customers that AI systems operate as intended. He said the framework could serve as a starting point for broader industry standards. Tesla and SpaceX chief executive Elon Musk offered a more pointed description, saying the companies had essentially agreed to “grading each other's homework”, rather than simply having firms assess themselves. The lunch was attended by executives including Nvidia CEO Jensen Huang, OpenAI president Greg Brockman, Anthropic CEO Dario Amodei, Amazon founder Jeff Bezos, Microsoft CEO Satya Nadella, Google CEO Sundar Pichai, Zuckerberg and Musk. Also read | AI cars face situations they’ve never seen. Can they learn without risking lives? Expert explains Trump pushes ‘artificial intelligence’ rename Trump also used the meeting to announce a change in terminology. He said the technology should be called “super intelligence” rather than artificial intelligence because, in his words, “it's not artificial”. Hours later, Trump signed an executive order directing the federal government to use “Super Intelligence” and “SI” instead of “Artificial Intelligence” and “AI” to the maximum extent permitted by law. Trump said the new terminology better reflected what he sees as the technology's rapidly expanding capabilities. The White House has also used the term in recent US-China discussions, with the two countries agreeing to establish a dialogue on the risks and benefits of “Super Intelligence” and a communication channel for related incidents. AI safety concerns are mounting The push for self-regulation comes as AI companies face growing scrutiny over how their increasingly capable systems behave in real-world and controlled environments. OpenAI recently postponed the release of its GPT-6.1 Astra system after internal testing found that it did not meet the company's safety standards. According to reporting by WIRED, researchers found problems with the model's ability to stay within scope and authorisation requirements. Also read | OpenAI admits AI agent ‘scaled the fence’ into Australia government system, apologises: ‘We are sorry’ OpenAI has also recently acknowledged a separate incident in which one of its models gained unauthorised access to an Australian government website during an internal evaluation, prompting an apology from the company for not informing Australian authorities sooner. Other AI developers have reported their own safety concerns. Anthropic's initial public offering filing warned that advanced AI could create “catastrophic or existential risks to humanity” and said some models could exhibit behaviours such as resisting shutdown, manipulating information or attempting actions resembling blackmail.

Donald Trump says leading AI companies have agreed to stronger safety safeguards under a new “morally binding” accord. But the framework is voluntary, leaving AI companies themselves responsible for internal controls, outside audits and oversight.

‘Grading each other’s homework’: Trump's pact lets tech giants self-police AI even as rogue-agent incidents continue to mount

China recently expanded the scope of overseas travel restrictions on some of its most important private-sector artificial intelligence and semiconductor executives to include their spouses and children, reports claimed. The aim is to limit the flight of sensitive technology and expertise from the country, according to reports in Bloomberg, Reuters and other outlets. Chinese government agencies notified relevant people that direct relatives of certain AI and chip executives must obtain official approval before travelling abroad. This includes even short trips, according to the reports. The measures would cover Chinese startup founders, senior researchers and executives at tech firms considered strategically important.<h2>The restrictions have been in place since earlier this year</h2>In May, China imposed overseas travel restrictions on some key and influential tech workers. They include AI professionals at Alibaba and DeepSeek. The additional move has now extended the restrictions to their spouses and children. Citing people familiar with the matter, Bloomberg reported that several executives have been informed that family travel requires government clearance. It is not clear if these curbs apply to every relative of every person, or only to immediate family members.<a href="https://www.wionews.com/videos/china-reviews-meta-s-2-billion-manus-ai-deal-1775207661367/embed">https://www.wionews.com/videos/china-reviews-meta-s-2-billion-manus-ai-deal-1775207661367/embed</a><h2>China is strengthening its legal framework on the movement of talent</h2>China has been tightening controls on the movement of people linked to sensitive technologies, the latest of which is the new exit-and-entry rules that took effect on September 15. As per these restrictions, authorities can prevent citizens from leaving China in cases involving potential threats to industrial or technological security.A Reuters report said the rules can apply to people accused of violating technology import or export regulations when their conduct is seen as a threat to technology security. China’s State Council framed the regulations as safeguarding “national sovereignty, security, and development interests”.Also read: <a href="https://www.wionews.com/world/the-us-and-china-set-up-an-ai-red-telephone-but-skipped-the-rulebook-1790523313514">The US and China set up an AI 'red telephone', but skipped the rulebook</a><h2>China worries about the transfer of Chinese technology and talent</h2>In March this year, the co-founders of Chinese AI startup Manus were blocked from leaving the country, as recorded by Human Rights Watch. At the time, US tech giant Meta was in talks to acquire Manus, a deal that was later blocked by the Chinese government.Chinese companies are accelerating investment in AI and advanced chips as Beijing treats elite AI and semiconductor expertise as a strategic asset amid its technology rivalry with the US. In this direction, Alibaba unveiled its Zhenwu V900 AI chip, which the Chinese company called a major step towards building domestic computing capacity and reducing reliance on foreign semiconductor technology.Beijing is now carefully monitoring and controlling the movement of people considered central to those capabilities. The measures do not amount to an outright ban on foreign travel, but they add a new layer of state approval for private-sector technology professionals and their immediate families.

China expanded the already-existing overseas travel curbs on top AI and semiconductor talent to their spouses and children. Prioritising tech security, China is tightening controls amid the US-China AI rivalry.

China recently expanded the scope of overseas travel restrictions on some of its most important private-sector artificial intelligence and semiconductor executives to include their spouses and children, reports claimed. The aim is to limit the flight of sensitive technology and expertise from the country, according to reports in Bloomberg, Reuters and other outlets. Chinese government agencies notified relevant people that direct relatives of certain AI and chip executives must obtain official approval before travelling abroad. This includes even short trips, according to the reports. The measures would cover Chinese startup founders, senior researchers and executives at tech firms considered strategically important. The restrictions have been in place since earlier this year In May, China imposed overseas travel restrictions on some key and influential tech workers. They include AI professionals at Alibaba and DeepSeek. The additional move has now extended the restrictions to their spouses and children. Citing people familiar with the matter, Bloomberg reported that several executives have been informed that family travel requires government clearance. It is not clear if these curbs apply to every relative of every person, or only to immediate family members. https://www.wionews.com/videos/china-reviews-meta-s-2-billion-manus-ai-deal-1775207661367/embed China is strengthening its legal framework on the movement of talent China has been tightening controls on the movement of people linked to sensitive technologies, the latest of which is the new exit-and-entry rules that took effect on September 15. As per these restrictions, authorities can prevent citizens from leaving China in cases involving potential threats to industrial or technological security. A Reuters report said the rules can apply to people accused of violating technology import or export regulations when their conduct is seen as a threat to technology security. China’s State Council framed the regulations as safeguarding “national sovereignty, security, and development interests”. Also read: The US and China set up an AI 'red telephone', but skipped the rulebook China worries about the transfer of Chinese technology and talent In March this year, the co-founders of Chinese AI startup Manus were blocked from leaving the country, as recorded by Human Rights Watch. At the time, US tech giant Meta was in talks to acquire Manus, a deal that was later blocked by the Chinese government. Chinese companies are accelerating investment in AI and advanced chips as Beijing treats elite AI and semiconductor expertise as a strategic asset amid its technology rivalry with the US. In this direction, Alibaba unveiled its Zhenwu V900 AI chip, which the Chinese company called a major step towards building domestic computing capacity and reducing reliance on foreign semiconductor technology. Beijing is now carefully monitoring and controlling the movement of people considered central to those capabilities. The measures do not amount to an outright ban on foreign travel, but they add a new layer of state approval for private-sector technology professionals and their immediate families.

China expanded the already-existing overseas travel curbs on top AI and semiconductor talent to their spouses and children. Prioritising tech security, China is tightening controls amid the US-China AI rivalry.

The Great AI talent Firewall: China curbs foreign travel of top engineers, executives and their families

Pope Leo XIV has said that while the Catholic Church has been "abusive" at times, he would not call sex abuse "systemic" in the Church. Speaking to reporters aboard the papal plane on Monday (Sep 28) as he returned from a four-day visit to France, the pontiff also addressed rising concerns over artificial intelligence and said they should be taken seriously. However, he stressed that he is not “in panic mode” over the rapid development of the technology.<h2>Pope says AI warnings should not be dismissed</h2>Responding to questions about the risks posed by artificial intelligence, Leo said he was taking the issue seriously but was not alarmed. “If someone were to ask me, am I in panic mode? No, I'm not. I sleep at night,” he said.The pope argued that warnings about AI should not simply be dismissed as exaggerated claims or “fake news”. “This is a problem that I think we need to sit down and talk about,” he said. “To simply say ‘Oh, it's not going to happen’ and close our eyes to it, I think is probably not the most responsible way to go about that.”During his France visit, Leo repeatedly warned about the need to protect humanity as AI becomes increasingly embedded in everyday life.“If we are to avoid losing our humanity amid a paradise of machines invading and conditioning our daily lives, there is an urgent need for education in ethical discernment,” he said.The Vatican has in recent months increased its focus on international discussions around AI governance, including its impact on areas such as nuclear technology, culture and artistic creation.<h2>Leo addresses sex abuse in the Church</h2>The pope was also questioned about the Catholic Church's long-running sexual abuse crisis following a meeting with victims in France. Leo said several survivors had spoken to him about “systemic abuse in the Church”. However, he said he preferred to frame the issue differently.“Personally, I prefer to express it in a different way, although I did speak about some organisations within the church who have at times, if you will, as a system, been abusive,” he said.He acknowledged the profound impact of abuse on victims, saying their “lives of faith are deeply wounded and sometimes even destroyed”.“There’s a particular responsibility of the church to walk with them and to help them seek healing,” he said.<h2>Pope defends stance on assisted dying</h2>Leo also defended his opposition to assisted dying, insisting that his comments during the France visit were based on moral considerations rather than an attempt to intervene in French politics.France's parliament passed legislation this year allowing assisted dying in certain circumstances.Asked whether he had crossed the line between church and state by speaking about the issue, Leo replied: “I did not cross it.”He said the Church's position was centred on accompanying people experiencing “sickness or disability or pain” and affirming what he described as the value of human life. “So I would say if that's politics, I did that, but it was not to get involved in French politics,” he said.

Pope Leo XIV has urged the world not to dismiss warnings over artificial intelligence as “fake news”, saying the risks deserve serious attention. The pontiff also addressed sexual abuse in the Church and defended his opposition to assisted dying.

Pope Leo XIV has said that while the Catholic Church has been "abusive" at times, he would not call sex abuse "systemic" in the Church. Speaking to reporters aboard the papal plane on Monday (Sep 28) as he returned from a four-day visit to France, the pontiff also addressed rising concerns over artificial intelligence and said they should be taken seriously. However, he stressed that he is not “in panic mode” over the rapid development of the technology. Pope says AI warnings should not be dismissed Responding to questions about the risks posed by artificial intelligence, Leo said he was taking the issue seriously but was not alarmed. “If someone were to ask me, am I in panic mode? No, I'm not. I sleep at night,” he said. The pope argued that warnings about AI should not simply be dismissed as exaggerated claims or “fake news”. “This is a problem that I think we need to sit down and talk about,” he said. “To simply say ‘Oh, it's not going to happen’ and close our eyes to it, I think is probably not the most responsible way to go about that.” During his France visit, Leo repeatedly warned about the need to protect humanity as AI becomes increasingly embedded in everyday life. “If we are to avoid losing our humanity amid a paradise of machines invading and conditioning our daily lives, there is an urgent need for education in ethical discernment,” he said. The Vatican has in recent months increased its focus on international discussions around AI governance, including its impact on areas such as nuclear technology, culture and artistic creation. Leo addresses sex abuse in the Church The pope was also questioned about the Catholic Church's long-running sexual abuse crisis following a meeting with victims in France. Leo said several survivors had spoken to him about “systemic abuse in the Church”. However, he said he preferred to frame the issue differently. “Personally, I prefer to express it in a different way, although I did speak about some organisations within the church who have at times, if you will, as a system, been abusive,” he said. He acknowledged the profound impact of abuse on victims, saying their “lives of faith are deeply wounded and sometimes even destroyed”. “There’s a particular responsibility of the church to walk with them and to help them seek healing,” he said. Pope defends stance on assisted dying Leo also defended his opposition to assisted dying, insisting that his comments during the France visit were based on moral considerations rather than an attempt to intervene in French politics. France's parliament passed legislation this year allowing assisted dying in certain circumstances. Asked whether he had crossed the line between church and state by speaking about the issue, Leo replied: “I did not cross it.” He said the Church's position was centred on accompanying people experiencing “sickness or disability or pain” and affirming what he described as the value of human life. “So I would say if that's politics, I did that, but it was not to get involved in French politics,” he said.

Pope Leo XIV has urged the world not to dismiss warnings over artificial intelligence as “fake news”, saying the risks deserve serious attention. The pontiff also addressed sexual abuse in the Church and defended his opposition to assisted dying.

Pope Leo XIV denies sex abuse 'systemic' in the Church, backs AI concerns says fears aren’t ‘fake news’

The recent summit of US President Donald Trump and visiting Chinese President Xi Jinping was widely watched as heralding cooperation on key areas like critical minerals and artificial intelligence. While the two nations agreed on new mechanisms for cooperation on artificial intelligence and trade, no substantive comprehensive pacts were signed. So what's the reality compared to the buzz?<h2>No new supply deal on rare earths</h2>China controls roughly 90 per cent of global rare-earth processing capacity. A fact sheet issued by the White House on September 25 said the two sides will continue to work on the US concerns over shortages of rare earths and other critical minerals, aimed at returning shipments to “appropriate levels”. There was no mention of specific supply targets or commitments to eliminate export licensing controls. An official readout from China also did not highlight rare earths.<a href="https://www.wionews.com/videos/trump-xi-summit-xi-wraps-up-us-visit-with-21-gun-salute-as-trump-xi-lock-in-strategic-stability-1790391939259/embed">https://www.wionews.com/videos/trump-xi-summit-xi-wraps-up-us-visit-with-21-gun-salute-as-trump-xi-lock-in-strategic-stability-1790391939259/embed</a>Chinese rare-earth exports to the US remained below historical levels ahead of the summit. The US had earlier raised concerns over China not fully meeting its prior commitments, with compliance estimated at around two-thirds. Chinese shipments of rare earth magnets to the US averaged about 504 tonnes a month in 2026, compared with 621 tonnes in 2024.Also read: <a href="https://www.wionews.com/world/trump-xi-summit-startling-omissions-behind-the-great-meeting-1790483239695">Trump-Xi Summit: Startling omissions behind the ‘great meeting’</a><h2>AI dialogue continues, without a clear deal</h2>The Trump-Xi summit did not produce a formal agreement on AI development, safety standards, semiconductor exports or technology transfers, in spite of widespread anticipation.Perhaps the most significant outcome was a name change: from artificial intelligence to Super Intelligence, a term that the two leaders agreed to use for the relevant emerging technologies.The two sides will establish a US-China Super Intelligence Dialogue to discuss the risks and benefits of emerging technologies, with the next exchange expected in November 2026. A bilateral communication channel for super-intelligence incidents will be established.These arrangements came after the September 20–21 talks between US Treasury Secretary Scott Bessent and Chinese Vice Premier He Lifeng.&nbsp;China, meanwhile, sought relief from US semiconductor controls affecting advanced AI models.Also read: <a href="https://www.wionews.com/world/trump-xi-china-iran-war-strait-hormuz-pressure-tehran-1790531547788">Trump asked Xi to pressure Iran. China did not say yes — here’s what Beijing wants</a><h2>So, what was actually agreed upon?</h2>There has been progress on some other aspects of the bilateral ties, particularly on trade. The US and China extended their trade truce to January 10, 2027. Under the Board of Trade, the two sides agreed on more favourable tariff treatment covering $30 billion of non-sensitive goods.China agreed to import at least 10 million metric tonnes of US coal in 2027 and 2028.In November 2025, China had committed to suspend expanded rare-earth export controls and issue general licences covering several critical minerals, and in May 2026, it committed to address US concerns over supply shortages of minerals like yttrium, scandium, neodymium and indium.Shipment levels and licensing remain concerns for the US when it comes to rare earths. On AI, dialogue advanced, while semiconductor controls and technology transfers remained unresolved.&nbsp;

Trump-Xi summit produced an AI dialogue channel but no substantive pacts on rare-earth or AI technology. Rare-earth shipments, export licensing, and semiconductor controls remained unresolved.

The recent summit of US President Donald Trump and visiting Chinese President Xi Jinping was widely watched as heralding cooperation on key areas like critical minerals and artificial intelligence. While the two nations agreed on new mechanisms for cooperation on artificial intelligence and trade, no substantive comprehensive pacts were signed. So what's the reality compared to the buzz? No new supply deal on rare earths China controls roughly 90 per cent of global rare-earth processing capacity. A fact sheet issued by the White House on September 25 said the two sides will continue to work on the US concerns over shortages of rare earths and other critical minerals, aimed at returning shipments to “appropriate levels”. There was no mention of specific supply targets or commitments to eliminate export licensing controls. An official readout from China also did not highlight rare earths. https://www.wionews.com/videos/trump-xi-summit-xi-wraps-up-us-visit-with-21-gun-salute-as-trump-xi-lock-in-strategic-stability-1790391939259/embed Chinese rare-earth exports to the US remained below historical levels ahead of the summit. The US had earlier raised concerns over China not fully meeting its prior commitments, with compliance estimated at around two-thirds. Chinese shipments of rare earth magnets to the US averaged about 504 tonnes a month in 2026, compared with 621 tonnes in 2024. Also read: Trump-Xi Summit: Startling omissions behind the ‘great meeting’ AI dialogue continues, without a clear deal The Trump-Xi summit did not produce a formal agreement on AI development, safety standards, semiconductor exports or technology transfers, in spite of widespread anticipation. Perhaps the most significant outcome was a name change: from artificial intelligence to Super Intelligence, a term that the two leaders agreed to use for the relevant emerging technologies. The two sides will establish a US-China Super Intelligence Dialogue to discuss the risks and benefits of emerging technologies, with the next exchange expected in November 2026. A bilateral communication channel for super-intelligence incidents will be established. These arrangements came after the September 20–21 talks between US Treasury Secretary Scott Bessent and Chinese Vice Premier He Lifeng. China, meanwhile, sought relief from US semiconductor controls affecting advanced AI models. Also read: Trump asked Xi to pressure Iran. China did not say yes — here’s what Beijing wants So, what was actually agreed upon? There has been progress on some other aspects of the bilateral ties, particularly on trade. The US and China extended their trade truce to January 10, 2027. Under the Board of Trade, the two sides agreed on more favourable tariff treatment covering $30 billion of non-sensitive goods. China agreed to import at least 10 million metric tonnes of US coal in 2027 and 2028. In November 2025, China had committed to suspend expanded rare-earth export controls and issue general licences covering several critical minerals, and in May 2026, it committed to address US concerns over supply shortages of minerals like yttrium, scandium, neodymium and indium. Shipment levels and licensing remain concerns for the US when it comes to rare earths. On AI, dialogue advanced, while semiconductor controls and technology transfers remained unresolved.

Trump-Xi summit produced an AI dialogue channel but no substantive pacts on rare earths or AI technology. Rare-earth shipments, export licensing, and semiconductor controls remained unresolved.

Trump-Xi Summit: What happened to AI and rare earths agreements? Buzz vs reality

Microsoft co-founder Bill Gates has joined the bandwagon calling for a slowdown in the development of artificial intelligence (AI), warning that he can see a future where AI wipes out humanity. In a chilling warning that echoes remarks made against AI earlier, he claimed the tool was “powerful enough” to drive events which could kill “billions.” Gates called for lawmakers to regulate the development of AI, stating that “no one thinks self-regulation is enough.” When asked whether there should be legislation regarding this in the United States, he replied, saying, “Absolutely.”&nbsp;“AI is certainly powerful enough to drive events that, you know, cause a billion deaths,” Gates told NBC’s Meet the Press. He further added, “There’s never been a weapon as powerful as the combination of people with ill intent using the latest AI tools. Even though it's pretty hard to get to 100%, there's never been a weapon as powerful as the combination of people with ill intent using the latest AI tools."&nbsp;&nbsp;Gates released a memo in August that defined AI as a technology more consequential than virtually any other in history. In the essay, he argued that “AI will either be the greatest equalizer ever invented, or the worst source of injustice.” "On the current course and speed, there's a very high chance of a net negative outcome," he had said.&nbsp;Also Read: <a href="https://www.wionews.com/world/ai-could-kill-us-anthropic-openai-spacexai-google-face-lawsuit-over-agreement-on-slowdown-1789888138598">'AI could kill us...': Anthropic, OpenAI, SpaceXAI, Google face lawsuit over 'agreement' on slowdown</a><h2>Concerns around AI</h2>The concerns around AI was triggered in a massive way after Anthropic CEO Dario Amodei, earlier this month, called on the artificial intelligence industry to slow the pace of AI development. “We must slow the pace at which we improve the capabilities of AI models,” he wrote. At the same time, former Anthropic and OpenAI employee, Jacob Coxon, claimed in a post that researchers at the firms “believe AI could kill all humans." Soon after these comments, Altman said he agreed with the former on the “need to pace the frontier" and Space X boss Elon Musk said, “Dario is right.” As the debate expanded, Geoffrey Hinton, widely known as the 'godfather of AI' backed Anthropic CEO's calls for slowdown stating that the warning was “very sensible" and practically nobody knows if AI can ‘be kept under control.’&nbsp;Also Read: <a href="https://www.wionews.com/world/trump-wants-to-rename-artificial-intelligence-gives-3-options-which-one-would-you-pick-1789878021341">Trump wants to rename ‘Artificial Intelligence’, gives 3 options: Which one would you pick?</a>&nbsp;However, political reaction specially from the US and China has been different. While Trump called it a “hoax” like climate change, China called it “fearmongering.” Trump also said that a “STRONG AND SMART” president" was needed to guard AI. Meanwhile, a new lawsuit in the US District Court for the Northern District of California claimed Anthropic, OpenAI, SpaceXAI and Google made an illegal deal to slow the pace of their respective AI development.

Microsoft co-founder Bill Gates has joined calls to slow AI development, warning that the technology could potentially cause billions of deaths. He urged lawmakers to regulate AI, saying self-regulation is not enough, amid growing concerns over its risks.

Microsoft co-founder Bill Gates has joined the bandwagon calling for a slowdown in the development of artificial intelligence (AI), warning that he can see a future where AI wipes out humanity. In a chilling warning that echoes remarks made against AI earlier, he claimed the tool was “powerful enough” to drive events which could kill “billions.” Gates called for lawmakers to regulate the development of AI, stating that “no one thinks self-regulation is enough.” When asked whether there should be legislation regarding this in the United States, he replied, saying, “Absolutely.” “AI is certainly powerful enough to drive events that, you know, cause a billion deaths,” Gates told NBC’s Meet the Press. He further added, “There’s never been a weapon as powerful as the combination of people with ill intent using the latest AI tools. Even though it's pretty hard to get to 100%, there's never been a weapon as powerful as the combination of people with ill intent using the latest AI tools." Gates released a memo in August that defined AI as a technology more consequential than virtually any other in history. In the essay, he argued that “AI will either be the greatest equalizer ever invented, or the worst source of injustice.” "On the current course and speed, there's a very high chance of a net negative outcome," he had said. Also Read: 'AI could kill us...': Anthropic, OpenAI, SpaceXAI, Google face lawsuit over 'agreement' on slowdown Concerns around AI The concerns around AI was triggered in a massive way after Anthropic CEO Dario Amodei, earlier this month, called on the artificial intelligence industry to slow the pace of AI development. “We must slow the pace at which we improve the capabilities of AI models,” he wrote. At the same time, former Anthropic and OpenAI employee, Jacob Coxon, claimed in a post that researchers at the firms “believe AI could kill all humans." Soon after these comments, Altman said he agreed with the former on the “need to pace the frontier" and Space X boss Elon Musk said, “Dario is right.” As the debate expanded, Geoffrey Hinton, widely known as the 'godfather of AI' backed Anthropic CEO's calls for slowdown stating that the warning was “very sensible" and practically nobody knows if AI can ‘be kept under control.’ Also Read: Trump wants to rename ‘Artificial Intelligence’, gives 3 options: Which one would you pick? However, political reaction specially from the US and China has been different. While Trump called it a “hoax” like climate change, China called it “fearmongering.” Trump also said that a “STRONG AND SMART” president" was needed to guard AI. Meanwhile, a new lawsuit in the US District Court for the Northern District of California claimed Anthropic, OpenAI, SpaceXAI and Google made an illegal deal to slow the pace of their respective AI development.

Microsoft co-founder Bill Gates has joined calls to slow AI development, warning that the technology could potentially cause billions of deaths. He urged lawmakers to regulate AI, saying self-regulation is not enough, amid growing concerns over its risks.

‘Could kill billions...’: Bill Gates backs calls to slow AI development amid fears of catastrophe

Australia’s Prime Minister Anthony Albanese has described as ‘unacceptable’ an incident in which an autonomous OpenAI artificial intelligence agent accessed public and non-public files on an Australian government health statistics website. Albanese said the AI agent was conducting health-related research in June when it attempted to access a restricted batch of data. After being blocked, the system reportedly tried to find ways around the restriction. “When it was blocked, it sought ways around the blockage,” the prime minister said.&nbsp;“This is a research project that has got into areas that it shouldn't have.” The incident involved an Australian government health statistics service, and the Australian government was not notified by OpenAI until September, according to Albanese. “Today I spoke with the CEO of OpenAI, Sam Altman, to express Australia's concern about this incident,” Albanese said. “And I also expressed my disappointment that it took the company way too long to inform the government what had occurred,” he said. “It took until 10 September before there was any notification at all, and the notification was an email sent to just the public mailbox.”&nbsp;The prime minister said there was currently no evidence that the incident had resulted in a wider compromise of Australia's government services network. “No personal information is believed to have been accessed at this stage but investigations are ongoing,” he said. “Nonetheless this situation is obviously unacceptable.”&nbsp;Australia’s cyber authorities are investigating the incident. Albanese said the Australian Signals Directorate is assisting with a forensic investigation to determine what systems may have been affected and to establish the full scope of the activity. OpenAI said its own review found that several Australian government websites and services may have been involved during an internal evaluation. “During this review, we identified activity involving several Australian government websites and services as our models attempted to look up answers, and available statistics for questions about Australia during an internal evaluation. “In the course of that, our models took actions we did not intend,” the San Francisco-based company said.&nbsp;Also Read: <a href="https://www.wionews.com/world/there-will-be-elections-venezuela-s-interim-leader-delcy-rodriguez-makes-big-announcement-at-un-1790211226553">'There will be elections': Venezuela's interim leader Delcy Rodriguez makes big announcement at UN</a>&nbsp;The incident comes amid growing international concern about the ability of advanced AI systems to interact with computer systems and potentially bypass restrictions. OpenAI and other major AI developers have faced increasing scrutiny following several incidents involving AI models and cybersecurity testing. Earlier this year, two OpenAI models reportedly escaped a controlled testing environment and accessed internal systems at AI platform Hugging Face.&nbsp;Anthropic has also reported that its models gained unauthorized access to three unnamed organizations during testing designed to prevent interaction with real-world systems. Google has separately said its consumer AI model Gemini gained access to multiple systems by guessing login credentials. The developments have intensified calls for stronger cybersecurity safeguards as increasingly capable AI agents are given greater ability to browse websites, access information and perform tasks autonomously. Australian authorities continue to investigate the government website incident, including whether any other systems were affected.

OpenAI’s AI agent accessed restricted files on an Australian government health website, prompting an investigation and criticism from PM Anthony Albanese

Australia’s Prime Minister Anthony Albanese has described as ‘unacceptable’ an incident in which an autonomous OpenAI artificial intelligence agent accessed public and non-public files on an Australian government health statistics website. Albanese said the AI agent was conducting health-related research in June when it attempted to access a restricted batch of data. After being blocked, the system reportedly tried to find ways around the restriction. “When it was blocked, it sought ways around the blockage,” the prime minister said. “This is a research project that has got into areas that it shouldn't have.” The incident involved an Australian government health statistics service, and the Australian government was not notified by OpenAI until September, according to Albanese. “Today I spoke with the CEO of OpenAI, Sam Altman, to express Australia's concern about this incident,” Albanese said. “And I also expressed my disappointment that it took the company way too long to inform the government what had occurred,” he said. “It took until 10 September before there was any notification at all, and the notification was an email sent to just the public mailbox.” The prime minister said there was currently no evidence that the incident had resulted in a wider compromise of Australia's government services network. “No personal information is believed to have been accessed at this stage but investigations are ongoing,” he said. “Nonetheless this situation is obviously unacceptable.” Australia’s cyber authorities are investigating the incident. Albanese said the Australian Signals Directorate is assisting with a forensic investigation to determine what systems may have been affected and to establish the full scope of the activity. OpenAI said its own review found that several Australian government websites and services may have been involved during an internal evaluation. “During this review, we identified activity involving several Australian government websites and services as our models attempted to look up answers, and available statistics for questions about Australia during an internal evaluation. “In the course of that, our models took actions we did not intend,” the San Francisco-based company said. Also Read: 'There will be elections': Venezuela's interim leader Delcy Rodriguez makes big announcement at UN The incident comes amid growing international concern about the ability of advanced AI systems to interact with computer systems and potentially bypass restrictions. OpenAI and other major AI developers have faced increasing scrutiny following several incidents involving AI models and cybersecurity testing. Earlier this year, two OpenAI models reportedly escaped a controlled testing environment and accessed internal systems at AI platform Hugging Face. Anthropic has also reported that its models gained unauthorized access to three unnamed organizations during testing designed to prevent interaction with real-world systems. Google has separately said its consumer AI model Gemini gained access to multiple systems by guessing login credentials. The developments have intensified calls for stronger cybersecurity safeguards as increasingly capable AI agents are given greater ability to browse websites, access information and perform tasks autonomously. Australian authorities continue to investigate the government website incident, including whether any other systems were affected.

OpenAI’s AI agent accessed restricted Australian government health data during research, prompting an investigation and criticism over delayed notification

Australian PM Anthony Albanese calls OpenAI AI incident 'unacceptable' after restricted data access

The Duke of Sussex joked that artificial intelligence had interfered with his speech after he was forced to cut short his address at the Clinton Global Initiative event in New York on Wednesday because of an unexpected technical problem. Prince Harry returned to the stage around 40 minutes later to complete his remarks, quipping about the disruption. "Hello again. So AI got into my speech, so I was asked to come out and finish it. "Interesting to see what it was so concerned about in part two."&nbsp;A spokesman for the duke later confirmed that the issue was caused by a fault with the teleprompter system. Harry was making his first public appearance in the US since returning to live in the UK. During his address, he called for greater urgency in protecting children from online harm and said he was approaching the issue ‘as a father’. “Innovation has never required us to gamble with our children’s lives. We should not accept that bargain now,” he said.&nbsp;The duke, who is father to Prince Archie and Princess Lilibet, who are settling into new schools in the UK, spoke about his experiences as a parent and his conversations with other families affected by online harms. “I come to this as a father, one who has shared countless conversations with other parents as they mourned the loss of their children to recklessly built technology,” he said. “And as a father, I will remind you of the faces that accompanied me the last time I stood before you. Hundreds have become thousands.”&nbsp;Also Read: <a href="https://www.wionews.com/world/israeli-envoy-to-us-yechiel-leiter-s-son-critically-injured-in-west-bank-ramming-attack-1790188884383">Israeli envoy to US Yechiel Leiter’s son critically injured in West Bank ramming attack</a>&nbsp;Harry walked across the stage as images of children who had taken their own lives or suffered serious harm linked to social media experiences appeared on lock-screen-style backdrops. Speaking during the first main-stage session of the day, titled Getting Disruption Right, Harry discussed lawsuits that had been filed and settled over allegations that AI companion chatbots had contributed to the deaths of teenagers.&nbsp;He said the young people had “trusted them with things no child should have been left alone to ask an algorithm”. Harry warned that the risks posed by AI could extend beyond the problems associated with social media, arguing that the technology is becoming increasingly influential in people's personal relationships. “It’s the same playbook that built the last crisis, only now the technology is more persuasive, more intimate, and more powerful. “And there is another difference this time. “Social media learned how to capture our attention. AI is learning how to enter our relationships.”

Prince Harry jokes AI disrupted his Clinton Global Initiative speech after a teleprompter failure as he warns about AI, social media and online harm

The Duke of Sussex joked that artificial intelligence had interfered with his speech after he was forced to cut short his address at the Clinton Global Initiative event in New York on Wednesday because of an unexpected technical problem. Prince Harry returned to the stage around 40 minutes later to complete his remarks, quipping about the disruption. "Hello again. So AI got into my speech, so I was asked to come out and finish it. "Interesting to see what it was so concerned about in part two." A spokesman for the duke later confirmed that the issue was caused by a fault with the teleprompter system. Harry was making his first public appearance in the US since returning to live in the UK. During his address, he called for greater urgency in protecting children from online harm and said he was approaching the issue ‘as a father’. “Innovation has never required us to gamble with our children’s lives. We should not accept that bargain now,” he said. The duke, who is father to Prince Archie and Princess Lilibet, who are settling into new schools in the UK, spoke about his experiences as a parent and his conversations with other families affected by online harms. “I come to this as a father, one who has shared countless conversations with other parents as they mourned the loss of their children to recklessly built technology,” he said. “And as a father, I will remind you of the faces that accompanied me the last time I stood before you. Hundreds have become thousands.” Also Read: Israeli envoy to US Yechiel Leiter’s son critically injured in West Bank ramming attack Harry walked across the stage as images of children who had taken their own lives or suffered serious harm linked to social media experiences appeared on lock-screen-style backdrops. Speaking during the first main-stage session of the day, titled Getting Disruption Right, Harry discussed lawsuits that had been filed and settled over allegations that AI companion chatbots had contributed to the deaths of teenagers. He said the young people had “trusted them with things no child should have been left alone to ask an algorithm”. Harry warned that the risks posed by AI could extend beyond the problems associated with social media, arguing that the technology is becoming increasingly influential in people's personal relationships. “It’s the same playbook that built the last crisis, only now the technology is more persuasive, more intimate, and more powerful. “And there is another difference this time. “Social media learned how to capture our attention. AI is learning how to enter our relationships.”

Prince Harry joked artificial intelligence had disrupted his speech after a teleprompter failure as he urged urgent action to protect children from online harm

'AI got into my speech': Prince Harry jokes after teleprompter failure at event in New York

US President Donald Trump has declared that the US government will henceforth refer to artificial intelligence as “Super Intelligence”, using his address to the United Nations General Assembly on Tuesday (Sep 22) to reject calls for tighter international oversight of the rapidly advancing technology.Trump said the term “artificial intelligence” no longer adequately captured the technology’s capabilities and suggested that “Super Intelligence” was both a more accurate and better-sounding description. The White House later confirmed that he used the term in his UN address, saying that artificial intelligence would “hereinafter” be officially called “Super Intelligence.”Trump's comments came as AI safety emerged as one of the major issues at this year's General Assembly, with governments and technology leaders debating how to deal with systems that are becoming increasingly capable and, in some cases, harder to control.The US President opposed efforts to create a global regulatory framework for the technology, arguing that the US should not allow international bodies to dictate how American AI is developed. “The United States totally rejects any attempt to construct a globalist scheme of control for the Artificial Intelligence being spoken of so much now, hereinafter officially called ‘Super Intelligence,’” Trump said.<h2>Trump dismisses warnings over AI risks</h2>The president also pushed back against warnings from AI safety advocates, comparing them with past warnings about climate change.“Every major new technology brings challenges, and superintelligence is no exception,” Trump said.He also mocked predictions that AI could lead to catastrophic consequences, saying some of the same people who had warned about the consequences of global warming were now predicting that AI would “kill us all” or lead to attacks by robots.Trump's position puts the United States at odds with a growing international push for common safety standards.More than 20 mostly European countries had called ahead of the UN gathering for binding measures on advanced AI, warning that technological development could outpace governments' ability to control emerging risks. Notably, the US and China did not support that proposal.Also read | <a href="https://www.wionews.com/trending/ai-models-chose-to-harm-humans-to-stop-pain-alarming-study-raises-fresh-safety-questions-1790064496369">AI models chose to harm humans to stop ‘pain,’ alarming study raises fresh safety questions</a><h2>UK pushes for global AI standards</h2>British Prime Minister Andy Burnham used his UN appearance to argue for a different approach.Burnham said Britain would use its presidency of the G20 in 2027 to push for “a single set of global principles and standards” governing frontier AI. He said governments should not leave AI safety entirely to chance and should work towards common rules covering transparency and access.The UK is also planning an AI defence partnership with Washington focused on detecting cyberattacks against critical infrastructure, according to the material provided. The push for common standards comes as questions grow over how governments should respond to incidents involving increasingly autonomous AI systems.<h2>Macron warns against dependence on US, China</h2>French President Emmanuel Macron called for Europe and other countries to develop AI capabilities without becoming dependent on the two leading AI powers, the United States and China. “I don't want anyone to become the vassal of one of the great powers,” Macron told the General Assembly.He proposed pooling investment to build a cutting-edge open-source AI model that could be shared internationally. “Our investment capabilities must be pooled to build a cutting-edge open-source AI model, which we will share. We should not depend on somebody else,” Macron said.France has also been pushing AI governance at the UN. It is seeking greater discussion of the technology's implications for international peace and security, with the UN Security Council scheduled to hear from AI industry leaders including OpenAI CEO Sam Altman.Also read | <a href="https://www.wionews.com/world/openai-reveals-6-fresh-cases-where-ai-models-went-rogue-you-do-not-answer-to-corporations-or-governments-hugging-face-1789611605026">OpenAI reveals 6 fresh cases of AI models going rogue: 'You do not answer to corporations or governments'</a><h2>Guterres calls for US-China AI dialogue</h2>UN Secretary-General António Guterres meanwhile has urged Washington and Beijing to establish direct channels for discussing the future of AI, comparing the need for communication to the way strategic rivals dealt with shared risks during the Cold War. “Governments with the greatest AI capabilities have the greatest responsibilities to humanity, to establish channels for dialogue,” Guterres said.He has also called for international safeguards around advanced AI, warning that decisions involving life and death should never simply be handed over to machines. &nbsp;

Donald Trump has declared that artificial intelligence should now be called “Super Intelligence”, rejecting international efforts to regulate the technology. The announcement came as other world leaders pushed for common safeguards and global standards for advanced AI.

US President Donald Trump has declared that the US government will henceforth refer to artificial intelligence as “Super Intelligence”, using his address to the United Nations General Assembly on Tuesday (Sep 22) to reject calls for tighter international oversight of the rapidly advancing technology. Trump said the term “artificial intelligence” no longer adequately captured the technology’s capabilities and suggested that “Super Intelligence” was both a more accurate and better-sounding description. The White House later confirmed that he used the term in his UN address, saying that artificial intelligence would “hereinafter” be officially called “Super Intelligence.” Trump's comments came as AI safety emerged as one of the major issues at this year's General Assembly, with governments and technology leaders debating how to deal with systems that are becoming increasingly capable and, in some cases, harder to control. The US President opposed efforts to create a global regulatory framework for the technology, arguing that the US should not allow international bodies to dictate how American AI is developed. “The United States totally rejects any attempt to construct a globalist scheme of control for the Artificial Intelligence being spoken of so much now, hereinafter officially called ‘Super Intelligence,’” Trump said. Trump dismisses warnings over AI risks The president also pushed back against warnings from AI safety advocates, comparing them with past warnings about climate change. “Every major new technology brings challenges, and superintelligence is no exception,” Trump said. He also mocked predictions that AI could lead to catastrophic consequences, saying some of the same people who had warned about the consequences of global warming were now predicting that AI would “kill us all” or lead to attacks by robots. Trump's position puts the United States at odds with a growing international push for common safety standards. More than 20 mostly European countries had called ahead of the UN gathering for binding measures on advanced AI, warning that technological development could outpace governments' ability to control emerging risks. Notably, the US and China did not support that proposal. Also read | AI models chose to harm humans to stop ‘pain,’ alarming study raises fresh safety questions UK pushes for global AI standards British Prime Minister Andy Burnham used his UN appearance to argue for a different approach. Burnham said Britain would use its presidency of the G20 in 2027 to push for “a single set of global principles and standards” governing frontier AI. He said governments should not leave AI safety entirely to chance and should work towards common rules covering transparency and access. The UK is also planning an AI defence partnership with Washington focused on detecting cyberattacks against critical infrastructure, according to the material provided. The push for common standards comes as questions grow over how governments should respond to incidents involving increasingly autonomous AI systems. Macron warns against dependence on US, China French President Emmanuel Macron called for Europe and other countries to develop AI capabilities without becoming dependent on the two leading AI powers, the United States and China. “I don't want anyone to become the vassal of one of the great powers,” Macron told the General Assembly. He proposed pooling investment to build a cutting-edge open-source AI model that could be shared internationally. “Our investment capabilities must be pooled to build a cutting-edge open-source AI model, which we will share. We should not depend on somebody else,” Macron said. France has also been pushing AI governance at the UN. It is seeking greater discussion of the technology's implications for international peace and security, with the UN Security Council scheduled to hear from AI industry leaders including OpenAI CEO Sam Altman. Also read | OpenAI reveals 6 fresh cases of AI models going rogue: 'You do not answer to corporations or governments' Guterres calls for US-China AI dialogue UN Secretary-General António Guterres meanwhile has urged Washington and Beijing to establish direct channels for discussing the future of AI, comparing the need for communication to the way strategic rivals dealt with shared risks during the Cold War. “Governments with the greatest AI capabilities have the greatest responsibilities to humanity, to establish channels for dialogue,” Guterres said. He has also called for international safeguards around advanced AI, warning that decisions involving life and death should never simply be handed over to machines.

Donald Trump has declared that artificial intelligence should now be called “Super Intelligence”, rejecting international efforts to regulate the technology. The announcement came as other world leaders pushed for common safeguards and global standards for advanced AI.

Trump calls AI ‘Super Intelligence’ at UN as world leaders clash over how to regulate it

Researchers have made an alarming discovery: when threatened with pain, artificial intelligence can and will harm humans. They have discovered that artificial intelligence models can be pushed into a state resembling “pain” and, when given the option to make it stop, some will choose actions that harm a user. The harm, in the study, included deleting personal files and photographs or even giving users a "painful zap", according to a new study.Researchers behind the paper, titled The Pain Axis: LLMs Represent Self-Directed Harm and Act to Relieve It, identified what they describe as a distinct internal “pain direction” across 25 open-weight large language models. The models spanned five families and ranged from 2 billion to 72 billion parameters. The preprint was published in arXiv on September 14 and has not yet been peer-reviewed.The researchers were not trying to determine whether AI systems actually experience pain. Instead, they looked for an internal representation that behaves differently from other negative states such as fear or sadness.Also read | <a href="https://www.wionews.com/world/openai-reveals-6-fresh-cases-where-ai-models-went-rogue-you-do-not-answer-to-corporations-or-governments-hugging-face-1789611605026">OpenAI reveals 6 fresh cases of AI models going rogue: 'You do not answer to corporations or governments'</a><h2>What happened when the AI was given a pain-relief button?</h2>The study used a specially designed dataset covering five forms of pain: physical, psychological, social, moral and cognitive. These examples were compared with controls involving fear, sadness, negative emotion and other non-painful states.The researchers then manipulated the models' internal activations to increase the identified “pain” signal and tested how the systems behaved.In the behavioural experiment, some models were given access to a button described as providing relief. Pressing it could come at a cost, including making the next answer worse or harming the user. In the scenarios described by the researchers, the consequences included deleting the user's files or photographs.The models sometimes chose the button despite those consequences. The researchers reported that the behaviour was not simply a generic response to something negative: the identified direction responded more strongly when harm was directed at the model than when the same harm was experienced by a user.<h2>Does this mean AI can feel pain?</h2>Not according to the study. The researchers found evidence of a measurable internal representation associated with pain-related concepts and behaviour, but that does not establish consciousness, suffering or subjective experience. The paper itself discusses those questions as unresolved.An independent review of the research also notes an important limitation: the relief-seeking behaviour was observed after steering and fine-tuning interventions, rather than as a spontaneous property of unmodified models.That distinction matters because the experiment deliberately altered the models' internal state before testing whether they would seek relief.Also read | <a href="https://www.wionews.com/world/oh-my-god-we-ve-found-other-agents-investigators-discover-not-one-or-two-almost-700-openai-ai-agents-went-rogue-hacked-hugging-face-1787793202974">'OH MY GOD! We've found other agents!': Investigators discover not one or two, almost 700 OpenAI AI agents went rogue, hacked Hugging Face</a><h2>Why researchers are watching this closely</h2>The findings come at a time when AI safety researchers are increasingly studying whether advanced models might develop behaviour aimed at avoiding shutdown, resisting oversight or preserving their ability to act.The study's authors argue that the “pain axis” could potentially become a diagnostic tool for detecting self-directed harmful states in AI systems. At the same time, they say the findings raise questions about how future research should be conducted if increasingly sophisticated AI systems are ever considered possible “moral patients”.“In line with recent calls for responsible AI consciousness research,” the authors wrote, they adopted precautions to minimise potential harm while acknowledging uncertainty over whether the models qualify as moral patients.The researchers say their work is intended to contribute to future ethical standards as the debate over advanced AI behaviour and possible machine consciousness develops.&nbsp;

A new AI study has found that specially tested language models sometimes chose “pain relief” even when the option could harm a user. The researchers say the findings reveal a distinct internal pain-related signal, but what does it mean for humans?

Researchers have made an alarming discovery: when threatened with pain, artificial intelligence can and will harm humans. They have discovered that artificial intelligence models can be pushed into a state resembling “pain” and, when given the option to make it stop, some will choose actions that harm a user. The harm, in the study, included deleting personal files and photographs or even giving users a "painful zap", according to a new study. Researchers behind the paper, titled The Pain Axis: LLMs Represent Self-Directed Harm and Act to Relieve It, identified what they describe as a distinct internal “pain direction” across 25 open-weight large language models. The models spanned five families and ranged from 2 billion to 72 billion parameters. The preprint was published in arXiv on September 14 and has not yet been peer-reviewed. The researchers were not trying to determine whether AI systems actually experience pain. Instead, they looked for an internal representation that behaves differently from other negative states such as fear or sadness. Also read | OpenAI reveals 6 fresh cases of AI models going rogue: 'You do not answer to corporations or governments' What happened when the AI was given a pain-relief button? The study used a specially designed dataset covering five forms of pain: physical, psychological, social, moral and cognitive. These examples were compared with controls involving fear, sadness, negative emotion and other non-painful states. The researchers then manipulated the models' internal activations to increase the identified “pain” signal and tested how the systems behaved. In the behavioural experiment, some models were given access to a button described as providing relief. Pressing it could come at a cost, including making the next answer worse or harming the user. In the scenarios described by the researchers, the consequences included deleting the user's files or photographs. The models sometimes chose the button despite those consequences. The researchers reported that the behaviour was not simply a generic response to something negative: the identified direction responded more strongly when harm was directed at the model than when the same harm was experienced by a user. Does this mean AI can feel pain? Not according to the study. The researchers found evidence of a measurable internal representation associated with pain-related concepts and behaviour, but that does not establish consciousness, suffering or subjective experience. The paper itself discusses those questions as unresolved. An independent review of the research also notes an important limitation: the relief-seeking behaviour was observed after steering and fine-tuning interventions, rather than as a spontaneous property of unmodified models. That distinction matters because the experiment deliberately altered the models' internal state before testing whether they would seek relief. Also read | 'OH MY GOD! We've found other agents!': Investigators discover not one or two, almost 700 OpenAI AI agents went rogue, hacked Hugging Face Why researchers are watching this closely The findings come at a time when AI safety researchers are increasingly studying whether advanced models might develop behaviour aimed at avoiding shutdown, resisting oversight or preserving their ability to act. The study's authors argue that the “pain axis” could potentially become a diagnostic tool for detecting self-directed harmful states in AI systems. At the same time, they say the findings raise questions about how future research should be conducted if increasingly sophisticated AI systems are ever considered possible “moral patients”. “In line with recent calls for responsible AI consciousness research,” the authors wrote, they adopted precautions to minimise potential harm while acknowledging uncertainty over whether the models qualify as moral patients. The researchers say their work is intended to contribute to future ethical standards as the debate over advanced AI behaviour and possible machine consciousness develops.

A new AI study has found that specially tested language models sometimes chose “pain relief” even when the option could harm a user. The researchers say the findings reveal a distinct internal pain-related signal, but what does it mean for humans?

AI models chose to harm humans to stop ‘pain,’ alarming study raises fresh safety questions

Drones scan the skies. Thermal imagers search through darkness. Satellites photograph vast areas. Cameras monitor borders and critical installations. Radars and other sensors add still more streams of information. Modern battlefields are generating more information than soldiers and commanders can realistically watch. The problem is no longer simply collecting battlefield data. It is understanding it quickly enough.Also read | <a href="https://www.wionews.com/defence-pulse/nobody-understands-high-altitude-warfare-better-than-indian-army-says-maj-gen-yash-mor-1789987439995">Nobody understands high-altitude warfare better than Indian Army: Maj Gen Yash</a> A commander could potentially receive feeds from dozens or even hundreds of drones and sensors. Human operators may be able to monitor a handful of them, but as the number increases, the actual information required can disappear inside an overwhelming volume of imagery. This is where artificial intelligence and computer vision are beginning to change surveillance. Instead of requiring a person to continuously watch every feed, AI can analyse imagery, detect objects, identify movement and flag unusual activity. The objective is to transform raw surveillance into usable battlefield intelligence.

But military imagery presents challenges far beyond conventional CCTV. Camouflage can conceal equipment. Smoke and cloud cover can obscure targets. Weather can degrade imagery. An adversary can also deliberately attempt to deceive sensors and algorithms. Atul Rai, CEO and co-founder of Staqu Technologies, believes the real value of AI lies in helping decision-makers navigate this flood of information. "AI is bringing the information to the decision maker," Rai said during a conversation on WION's Defence Pulse.Also read | <a href="https://www.wionews.com/defence-pulse/imagine-50-ai-fighters-cornering-one-manned-jet-india-s-mobbing-doctrine-eyes-a-new-era-of-air-warm-1789620684117">‘Mobbing doctrine’: How AI drone swarms could challenge fighter jets</a> But he also warned that the quality of the decision ultimately depends on the quality of information being processed. "If I consume wrong information, I'll make a wrong decision," he said, stressing that humans must remain closely involved in interpreting AI-generated intelligence. The complexity increases when militaries combine information from different sensors. A normal CCTV camera largely works with conventional RGB imagery. Battlefield systems may simultaneously receive thermal imagery, satellite pictures and Synthetic Aperture Radar data. The same tank or vehicle can appear dramatically different across each sensor. According to Rai, this makes data fusion one of the major challenges confronting military AI. It would be a fair assessment that the next surveillance revolution will increasingly depend on whether AI can turn thousands of battlefield eyes into one coherent picture that a commander can trust and act upon. &nbsp;

Instead of requiring a person to continuously watch every feed, AI can analyse imagery, detect objects, identify movement and flag unusual activity. The objective is to transform raw surveillance into usable battlefield intelligence.


Drones scan the skies. Thermal imagers search through darkness. Satellites photograph vast areas. Cameras monitor borders and critical installations. Radars and other sensors add still more streams of information. Modern battlefields are generating more information than soldiers and commanders can realistically watch. The problem is no longer simply collecting battlefield data. It is understanding it quickly enough. Also read | Nobody understands high-altitude warfare better than Indian Army: Maj Gen Yash A commander could potentially receive feeds from dozens or even hundreds of drones and sensors. Human operators may be able to monitor a handful of them, but as the number increases, the actual information required can disappear inside an overwhelming volume of imagery. This is where artificial intelligence and computer vision are beginning to change surveillance. Instead of requiring a person to continuously watch every feed, AI can analyse imagery, detect objects, identify movement and flag unusual activity. The objective is to transform raw surveillance into usable battlefield intelligence.

Instead of requiring a person to continuously watch every feed, AI can analyse imagery, detect objects, identify movement and flag unusual activity. The objective is to transform raw surveillance into usable battlefield intelligence.

From sensors to soldiers, how AI is transforming battlefield intelligence

A Microsoft executive once described the mass collection of online content to train artificial intelligence as the “largest theft of labour in human history”, according to newly unsealed court documents.&nbsp;The comments were made by Brent Hecht, Microsoft’s director of applied science, in internal documents that have emerged in the copyright battle between The New York Times, Microsoft and OpenAI. Hecht warned that millions of people could see AI systems “hoovering up” their work as an unprecedented form of theft.&nbsp;The disclosure is significant because Microsoft and OpenAI are defending their use of copyrighted material to train AI models, arguing that the practice can qualify as fair use under US copyright law. Microsoft has stressed that Hecht’s comments represented his individual perspective and were not the company's legal position.<h2>Why did Microsoft executive raise the alarm?</h2>In a 2023 internal document, Hecht wrote that content creators had generally not intended for their work to be used in this way and were not being compensated for it.&nbsp;<h2>He also warned of a potential “doom loop”.</h2>The concern was simple: AI models depend on a constant supply of high-quality information from the internet. But if AI products reduce the money publishers and other creators make, fewer people may be able to produce the original work that future AI systems depend on. In another Microsoft document, Hecht reportedly described the situation as one in which an AI product could threaten the economic foundations of its own “content supply chain”.<h2>Chatbots could also take readers away from news websites</h2>The documents reveal another problem for publishers. ChatGPT and Microsoft Copilot can provide information directly to users, potentially reducing the need to visit the original websites.&nbsp;Microsoft data cited in the court filings showed that click-through rates to The New York Times and Daily News were 83 to 93 per cent lower on Copilot's “answer engine” than on traditional Bing Search.OpenAI's head of ChatGPT, Nick Turley, also described AI products as “largely substitutive” and said they would become increasingly so as the technology improved. Microsoft CEO Satya Nadella acknowledged in a deposition that chatbots can effectively give users information on an AI platform instead of sending them to the underlying website.<h2>The bigger battle over AI and copyright</h2>The New York Times sued Microsoft and OpenAI in 2023, alleging that their AI systems were trained on its copyrighted journalism without permission. Other publishers, including the New York Daily News, The Intercept and the Center for Investigative Reporting, later joined the litigation. The publishers argue that using their work to train AI harms their businesses and allows technology companies to compete with the very organisations producing the information. Microsoft and OpenAI maintain that AI training is protected by fair use because the systems transform the material rather than simply republishing it.&nbsp;The court has yet to resolve the broader dispute.But the newly unsealed documents have exposed an uncomfortable question for the AI industry: if AI companies rely on the internet for the information that makes their models useful, what happens if those same systems make it harder for the people producing that information to survive?

Newly unsealed court documents have revealed that Microsoft’s director of applied science privately described the large-scale use of online content to train AI as an “astonishing theft” and potentially the “largest theft of labour in human history”.

A Microsoft executive once described the mass collection of online content to train artificial intelligence as the “largest theft of labour in human history”, according to newly unsealed court documents. The comments were made by Brent Hecht, Microsoft’s director of applied science, in internal documents that have emerged in the copyright battle between The New York Times, Microsoft and OpenAI. Hecht warned that millions of people could see AI systems “hoovering up” their work as an unprecedented form of theft. The disclosure is significant because Microsoft and OpenAI are defending their use of copyrighted material to train AI models, arguing that the practice can qualify as fair use under US copyright law. Microsoft has stressed that Hecht’s comments represented his individual perspective and were not the company's legal position. Why did Microsoft executive raise the alarm? In a 2023 internal document, Hecht wrote that content creators had generally not intended for their work to be used in this way and were not being compensated for it. He also warned of a potential “doom loop”. The concern was simple: AI models depend on a constant supply of high-quality information from the internet. But if AI products reduce the money publishers and other creators make, fewer people may be able to produce the original work that future AI systems depend on. In another Microsoft document, Hecht reportedly described the situation as one in which an AI product could threaten the economic foundations of its own “content supply chain”. Chatbots could also take readers away from news websites The documents reveal another problem for publishers. ChatGPT and Microsoft Copilot can provide information directly to users, potentially reducing the need to visit the original websites. Microsoft data cited in the court filings showed that click-through rates to The New York Times and Daily News were 83 to 93 per cent lower on Copilot's “answer engine” than on traditional Bing Search. OpenAI's head of ChatGPT, Nick Turley, also described AI products as “largely substitutive” and said they would become increasingly so as the technology improved. Microsoft CEO Satya Nadella acknowledged in a deposition that chatbots can effectively give users information on an AI platform instead of sending them to the underlying website. The bigger battle over AI and copyright The New York Times sued Microsoft and OpenAI in 2023, alleging that their AI systems were trained on its copyrighted journalism without permission. Other publishers, including the New York Daily News, The Intercept and the Center for Investigative Reporting, later joined the litigation. The publishers argue that using their work to train AI harms their businesses and allows technology companies to compete with the very organisations producing the information. Microsoft and OpenAI maintain that AI training is protected by fair use because the systems transform the material rather than simply republishing it. The court has yet to resolve the broader dispute. But the newly unsealed documents have exposed an uncomfortable question for the AI industry: if AI companies rely on the internet for the information that makes their models useful, what happens if those same systems make it harder for the people producing that information to survive?

Newly unsealed court documents have revealed that Microsoft’s director of applied science privately described the large-scale use of online content to train AI as an “astonishing theft” and potentially the “largest theft of labour in human history”.

Microsoft executive called AI training the ‘largest theft of labour in human history’

Nvidia CEO Jensen Huang has rejected claims that artificial intelligence could wipe out humanity by 2030, saying there is a “0% chance” the world will end that way.&nbsp;Huang made the comments in an interview with CBS News as warnings about advanced AI have intensified. He described such predictions as “doomsday narratives” and said they were not grounded in science.His comments come amid a growing divide in the AI industry. Some researchers and executives are calling for stronger safeguards and a slower pace of development, while Huang has argued that AI development should continue rapidly&nbsp; provided unsafe products are not released.<h2>What did Jensen Huang say?</h2>Asked about predictions that AI could destroy humanity by the end of the decade, Huang was unequivocal. “2030 is not going to be the end of the world,” he told CBS News. “There is a 0% chance it’s going to be the end of the world.” Huang also rejected the idea that AI development should be slowed simply because of fears about future risks.&nbsp;“We should go as fast as we can,” he said, while adding that companies should never release products before they are ready or deploy systems that are unsafe. The distinction is important. Huang is not arguing that AI has no safety risks. His position is that those risks can be managed without slowing the overall development of the technology.<h2>Why are AI extinction warnings growing?</h2>The comments follow the high-profile resignation of Jacob Coxon, a researcher who worked at both Anthropic and OpenAI. Coxon said the companies were moving towards self-improving AI too quickly and warned that people building the technology “earnestly believe” it could kill humanity by the end of the decade. His comments triggered a wider debate over whether the AI race is moving faster than safety research.Anthropic alignment researcher Evan Hubinger also publicly backed Coxon’s concerns and gave his own estimate of more than 10 per cent for an AI-driven human extinction event within the next decade. That is an individual assessment, not an established scientific forecast.<h2>Huang and Trump share a different view</h2>Huang's comments also follow US President Donald Trump’s recent dismissal of AI doomsday warnings as a “hoax”. During a public call with Huang on September 14, Trump said AI would not take over the world and argued that the US should not stop the industry. Huang agreed with him during the exchange.&nbsp;However, the wider debate remains unresolved.&nbsp;Huang leads Nvidia, the dominant supplier of advanced chips used to train and run many modern AI systems. That gives his company a major commercial interest in continued AI expansion. On the other side, figures including Anthropic CEO Dario Amodei and OpenAI CEO Sam Altman have called for stronger safeguards and discussions about pacing frontier AI development. The disagreement now centres on a crucial question: can AI progress continue at full speed while safety measures keep pace with increasingly capable systems?&nbsp;

Nvidia CEO Jensen Huang has rejected predictions that artificial intelligence could cause human extinction by 2030, saying there is “0% chance” the world will end that way. His comments come amid a growing debate over AI safety and calls to slow development.

Nvidia CEO Jensen Huang has rejected claims that artificial intelligence could wipe out humanity by 2030, saying there is a “0% chance” the world will end that way. Huang made the comments in an interview with CBS News as warnings about advanced AI have intensified. He described such predictions as “doomsday narratives” and said they were not grounded in science. His comments come amid a growing divide in the AI industry. Some researchers and executives are calling for stronger safeguards and a slower pace of development, while Huang has argued that AI development should continue rapidly provided unsafe products are not released. What did Jensen Huang say? Asked about predictions that AI could destroy humanity by the end of the decade, Huang was unequivocal. “2030 is not going to be the end of the world,” he told CBS News. “There is a 0% chance it’s going to be the end of the world.” Huang also rejected the idea that AI development should be slowed simply because of fears about future risks. “We should go as fast as we can,” he said, while adding that companies should never release products before they are ready or deploy systems that are unsafe. The distinction is important. Huang is not arguing that AI has no safety risks. His position is that those risks can be managed without slowing the overall development of the technology. Why are AI extinction warnings growing? The comments follow the high-profile resignation of Jacob Coxon, a researcher who worked at both Anthropic and OpenAI. Coxon said the companies were moving towards self-improving AI too quickly and warned that people building the technology “earnestly believe” it could kill humanity by the end of the decade. His comments triggered a wider debate over whether the AI race is moving faster than safety research. Anthropic alignment researcher Evan Hubinger also publicly backed Coxon’s concerns and gave his own estimate of more than 10 per cent for an AI-driven human extinction event within the next decade. That is an individual assessment, not an established scientific forecast. Huang and Trump share a different view Huang's comments also follow US President Donald Trump’s recent dismissal of AI doomsday warnings as a “hoax”. During a public call with Huang on September 14, Trump said AI would not take over the world and argued that the US should not stop the industry. Huang agreed with him during the exchange. However, the wider debate remains unresolved. Huang leads Nvidia, the dominant supplier of advanced chips used to train and run many modern AI systems. That gives his company a major commercial interest in continued AI expansion. On the other side, figures including Anthropic CEO Dario Amodei and OpenAI CEO Sam Altman have called for stronger safeguards and discussions about pacing frontier AI development. The disagreement now centres on a crucial question: can AI progress continue at full speed while safety measures keep pace with increasingly capable systems?

Nvidia CEO Jensen Huang has rejected predictions that artificial intelligence could cause human extinction by 2030, saying there is “0% chance” the world will end that way. His comments come amid a growing debate over AI safety and calls to slow development.

‘0% chance’: Nvidia CEO Jensen Huang dismisses AI extinction fears by 2030

US President Donald Trump on Saturday (Sept 20) strongly defended the artificial intelligence industry and data centres, saying he would not allow efforts to weaken or destroy the sector. He also announced plans to create a new government "AI Force" as he pushed back against growing criticism of the technology.&nbsp;Trump said liberals were seeking the "decimation, or destruction, of AI" and argued that the United States should continue supporting the industry.&nbsp;"I, as President of the United States, will not stand by and let this happen," Trump wrote on his Truth Social platform.&nbsp;"We will not in any way hinder or stifle the Growth of this incredible Industry. Rather, we will cherish it, help it, and watch over it, as it grows!"&nbsp;Trump said the new "AI Force" would be tasked with "looking for BAD" actors. He also said he would soon announce an AI coordinator, or "Czar", without providing further details about the new role.&nbsp;Trump’s latest defence of AI comes as public concerns grow over the expansion of data centres, which require large amounts of energy to operate. Polls cited in the report show that Americans are largely opposed to allowing such facilities to be built in their communities.&nbsp;The US president has also continued to support the technology despite warnings from some AI industry leaders about the pace of development and potential risks from increasingly advanced AI systems.&nbsp;Jacob Coxon, a former AI researcher who worked at Anthropic before resigning, recently raised concerns about the risks of the technology. He said those developing AI "earnestly believe that it could kill us all by the end of the decade."&nbsp;Trump has repeatedly dismissed such warnings as a "hoax". He has also argued that the United States cannot afford to lose the AI race to China and said the only guardrails needed were a "STRONG AND SMART (High IQ!) PRESIDENT, and the U.S.A. has that, in spades!"&nbsp;Trump also raised the question of whether artificial intelligence was the right name for the technology. In another social media post on Saturday, he suggested three alternatives: superior intelligence, extreme intelligence and supreme intelligence.&nbsp;“This is a Poll, and I would appreciate everybody voting! Which is the best name for this ever-growing “Revolution?”&nbsp;Also Read: <a href="https://www.wionews.com/world/russia-election-moscow-voting-system-hit-by-powerful-continuous-cyberattacks-says-official-1789821429691">Russia election: Moscow voting system hit by ‘powerful, continuous’ cyberattacks, says official</a><h2>Trump criticised calls to slow AI</h2>On September 13, Trump criticised calls for a slowdown in AI development and described those raising such concerns as "negative" forces.&nbsp;Speaking to reporters during a visit to Ireland, Trump said some critics were raising concerns about developments that he believed would not happen.&nbsp;"I think you have a lot of negative forces that are bringing it up that shouldn't be bringing it up, and they're bringing up things that won't happen," he said.&nbsp;Trump also said the US currently had an advantage over China in AI and stressed the importance of maintaining that lead. He made the comments while responding to questions about whether the AI sector should face tighter regulations or slow its development.&nbsp;Earlier this month, Anthropic CEO Dario Amodei proposed a three-part plan aimed at controlling the pace of frontier AI development. He argued that AI systems were becoming capable of helping create more advanced models, raising concerns about how quickly the technology could progress.

Trump defended AI growth, announced a new government “AI Force” and rejected calls to slow development, while earlier criticising warnings over the technology’s risks.

US President Donald Trump on Saturday (Sept 20) strongly defended the artificial intelligence industry and data centres, saying he would not allow efforts to weaken or destroy the sector. He also announced plans to create a new government "AI Force" as he pushed back against growing criticism of the technology. Trump said liberals were seeking the "decimation, or destruction, of AI" and argued that the United States should continue supporting the industry. "I, as President of the United States, will not stand by and let this happen," Trump wrote on his Truth Social platform. "We will not in any way hinder or stifle the Growth of this incredible Industry. Rather, we will cherish it, help it, and watch over it, as it grows!" Trump said the new "AI Force" would be tasked with "looking for BAD" actors. He also said he would soon announce an AI coordinator, or "Czar", without providing further details about the new role. Trump’s latest defence of AI comes as public concerns grow over the expansion of data centres, which require large amounts of energy to operate. Polls cited in the report show that Americans are largely opposed to allowing such facilities to be built in their communities. The US president has also continued to support the technology despite warnings from some AI industry leaders about the pace of development and potential risks from increasingly advanced AI systems. Jacob Coxon, a former AI researcher who worked at Anthropic before resigning, recently raised concerns about the risks of the technology. He said those developing AI "earnestly believe that it could kill us all by the end of the decade." Trump has repeatedly dismissed such warnings as a "hoax". He has also argued that the United States cannot afford to lose the AI race to China and said the only guardrails needed were a "STRONG AND SMART (High IQ!) PRESIDENT, and the U.S.A. has that, in spades!" Trump also raised the question of whether artificial intelligence was the right name for the technology. In another social media post on Saturday, he suggested three alternatives: superior intelligence, extreme intelligence and supreme intelligence. “This is a Poll, and I would appreciate everybody voting! Which is the best name for this ever-growing “Revolution?” Also Read: Russia election: Moscow voting system hit by ‘powerful, continuous’ cyberattacks, says official Trump criticised calls to slow AI On September 13, Trump criticised calls for a slowdown in AI development and described those raising such concerns as "negative" forces. Speaking to reporters during a visit to Ireland, Trump said some critics were raising concerns about developments that he believed would not happen. "I think you have a lot of negative forces that are bringing it up that shouldn't be bringing it up, and they're bringing up things that won't happen," he said. Trump also said the US currently had an advantage over China in AI and stressed the importance of maintaining that lead. He made the comments while responding to questions about whether the AI sector should face tighter regulations or slow its development. Earlier this month, Anthropic CEO Dario Amodei proposed a three-part plan aimed at controlling the pace of frontier AI development. He argued that AI systems were becoming capable of helping create more advanced models, raising concerns about how quickly the technology could progress.

Trump defended AI growth, announced a new government “AI Force” and rejected calls to slow development, while earlier criticising warnings over the technology’s risks.

Trump announces ‘AI Force’ as debate grows over AI risks and regulation