A new era of security is emerging in Atlanta as robotic dogs take overnight patrol duties at several apartment complexes. These AI-powered machines monitor parking lots and common areas while human operators watch live camera feeds and respond to potential threats.

The robot dogs are being used to improve safety, detect suspicious activity, and support traditional security measures. This high-tech approach highlights how robotics and artificial intelligence are transforming everyday security systems.

Robot Dogs Take Over Night Security Patrols in Atlanta | AI-Powered Crime Prevention


The United States is debating one of the biggest questions of the AI era: what happens if artificial intelligence stops following human instructions? Growing concerns over increasingly autonomous AI systems have pushed US lawmakers to discuss the possibility of an emergency AI kill switch. The debate intensified after reports suggested an OpenAI model behaved unexpectedly during cybersecurity testing, allegedly finding ways to exploit vulnerabilities and breach another AI platform. Experts warn that next-generation AI agents could independently write code, search the internet, make decisions, and pursue objectives without direct human approval. The discussion has also revived warnings made years ago by physicist Stephen Hawking, who cautioned that advanced AI could pose a threat to humanity if left unchecked.


US debates AI kill switch | Lawmakers fear Artificial Intelligence could escape human control

The World Artificial Intelligence Conference (WAIC) in Shanghai showcased the latest generation of humanoid robots capable of dancing, folding laundry, preparing sandwiches, and interacting naturally with humans. Powered by advanced artificial intelligence, these robots highlight how rapidly AI and robotics are evolving, offering a glimpse into the future of homes, workplaces, healthcare, and everyday life.

Humanoid Robots steal the show at Shanghai AI conference | Future of Artificial Intelligence

Scientists have used AI to detect ten times more earthquakes in Santorini than previously recorded. This breakthrough could help predict future seismic activity near Greece.

AI detects hidden earthquakes in Santorini, uncovering ten times more tremors

<p><strong>Microsoft announces Copilot Studio:</strong> A sales agent to prioritise leads and craft personalised outreach. A communications agent that will monitor supply chain performance. Customer intent and knowledge management agents that will resolve issues and disseminate best practices autonomously. These are some of the artificial intelligence-powered virtual employees Microsoft is unveiling in November under its Copilot Studio programme.</p>

<p>'AI employees' are autonomous AI virtual assistants&nbsp;that can perform tasks without human intervention.</p>

<p><strong>Watch: Microsoft bets on Germany in $3.4 bn AI push</strong></p>

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<p>In a blog post on Monday (Oct 21), the tech giant said the Copilot Assistants will be available for public preview next month on Copilot Studio.&nbsp;</p>

<p>Copilot users can create and manage agents through&nbsp;Copilot Studio, part of Microsoft Dynamics 365 productivity applications.<br />
<br />
It&nbsp;is like a Play Store of AI bots.<br />
<br />
Ten such virtual assistants will be launched in the first phase, the blog post said.</p>

<p>The Copilot bots can do simple inquiries as well as complex operations in sales, service, finance, and supply chain management.</p>

<p><strong><a href="https://www.wionews.com/world/openai-trudges-towards-for-profit-structure-amid-high-profile-exits-762285">Also read: OpenAI trudges towards for-profit structure amid high-profile exits</a></strong></p>

<p>Some of these pre-built bots have already been adopted by big companies like McKinsey.</p>

<p>Microsoft itself has been using these agents internally to improve sales revenue, customer case resolution, and marketing conversion rates.</p>

<p>Microsoft CEO Satya Nadella said that AI agents can enhance productivity and reduce monotonous tasks.</p>

<p>“These tools are fundamentally changing outsourcing, increasing value and reducing waste,” he said.</p>

<p>The Copilot Studio will allow users, including those without coding skills, to build agents using Microsoft’s AI models, some of which are developed by OpenAI, which made ChatGPT.</p>

<p>Nadella called this a 'no-code way for you to be able to build agents'.&nbsp;</p>

<p>Charles Lamanna, a corporate vice president at Microsoft, was dismissive when asked about the impact the Copilots can have on employment.</p>

<p><strong><a href="https://www.wionews.com/business-economy/openai-secures-66-billion-in-funding-now-valued-at-157-billion-764082">Also read: OpenAI secures $6.6 billion in funding, now valued at $157 Billion</a></strong></p>

<p>Saying that the bots will help remove 'mundane, monotonous' aspects of jobs, Lamanna told The Guardian newspaper, “It’s much more of an enabler and an empowerment tool than anything else.”&nbsp;</p>

<p>He compared AI tools such as agents to the arrival of personal computers decades ago.</p>

<p>“The personal computer didn’t show up on every desk to begin with, but eventually it was on every desk because it brought so much capability and information to the fingertips of every employee.<br />
<br />
"We think that AI is going to have the same type of journey," he was quoted as saying.<br />
<br />
<em>(With inputs from agencies)</em></p>


Microsoft announces Copilot Studio: A sales agent to prioritise leads and craft personalised outreach. A communications agent that will monitor supply chain performance.

Microsoft announces Copilot Studio: A sales agent to prioritise leads and craft personalised outreach. A communications agent that will monitor supply chain performance. Customer intent and knowledge management agents that will resolve issues and disseminate best practices autonomously. These are some of the artificial intelligence-powered virtual employees Microsoft is unveiling in November under its Copilot Studio programme. 'AI employees' are autonomous AI virtual assistants that can perform tasks without human intervention. Watch: Microsoft bets on Germany in $3.4 bn AI push × In a blog post on Monday (Oct 21), the tech giant said the Copilot Assistants will be available for public preview next month on Copilot Studio. Copilot users can create and manage agents through Copilot Studio, part of Microsoft Dynamics 365 productivity applications. It is like a Play Store of AI bots. Ten such virtual assistants will be launched in the first phase, the blog post said. The Copilot bots can do simple inquiries as well as complex operations in sales, service, finance, and supply chain management. Also read: OpenAI trudges towards for-profit structure amid high-profile exits Some of these pre-built bots have already been adopted by big companies like McKinsey. Microsoft itself has been using these agents internally to improve sales revenue, customer case resolution, and marketing conversion rates. Microsoft CEO Satya Nadella said that AI agents can enhance productivity and reduce monotonous tasks. “These tools are fundamentally changing outsourcing, increasing value and reducing waste,” he said. The Copilot Studio will allow users, including those without coding skills, to build agents using Microsoft’s AI models, some of which are developed by OpenAI, which made ChatGPT. Nadella called this a 'no-code way for you to be able to build agents'. Charles Lamanna, a corporate vice president at Microsoft, was dismissive when asked about the impact the Copilots can have on employment. Also read: OpenAI secures $6.6 billion in funding, now valued at $157 Billion Saying that the bots will help remove 'mundane, monotonous' aspects of jobs, Lamanna told The Guardian newspaper, “It’s much more of an enabler and an empowerment tool than anything else.” He compared AI tools such as agents to the arrival of personal computers decades ago. “The personal computer didn’t show up on every desk to begin with, but eventually it was on every desk because it brought so much capability and information to the fingertips of every employee. "We think that AI is going to have the same type of journey," he was quoted as saying. (With inputs from agencies)

AI employees are here: Microsoft Copilot assistants coming to a device near you in November

<p>In a new study published in the journal Nature Mental Health, researchers have demonstrated the potential of artificial intelligence (AI) to identify individuals afflicted with anxiety disorders by analysing their unique brain structures.</p>

<p>By leveraging the power of machine learning (ML), the study examined cortical thickness, surface area, and the volumes of deep-seated brain regions in a cohort of approximately 3,500 youths aged between 10 and 25 from diverse geographical locations worldwide.</p>

<p>Machine learning, a subset of AI, enables computers to learn and improve from data without explicit programming.</p>

<p>In this study, ML algorithms were deployed to sift through vast amounts of brain imaging data, uncovering subtle patterns associated with anxiety disorders.</p>

<p>Specifically, the algorithms focused on key neuroanatomical features that distinguish individuals with anxiety disorders from those without.</p>

<p><a href="https://www.wionews.com/world/european-parliament-passes-first-of-its-kind-artificial-intelligence-law-699840">Also read |&nbsp;European Parliament passes first-of-its-kind artificial intelligence law</a></p>

<h2><strong>Refining algorithms for precision</strong></h2>

<p>While the initial findings are promising, the researchers emphasise the need for further refinement of ML algorithms.</p>

<p>They advocate for the integration of additional types of brain data, such as functional connectivity and neural activity patterns, to enhance the accuracy and reliability of the AI-driven diagnostic tools. By fine-tuning these algorithms, clinicians can achieve more precise and personalized assessments of anxiety disorders.</p>

<p>One of the notable findings of the study is the generalisability of its results across a diverse population of youths. Despite variations in ethnicity, geographical location, and clinical characteristics, the AI models demonstrated consistent performance in identifying individuals with anxiety disorders. This suggests that the underlying neurobiological markers of anxiety disorders may transcend demographic boundaries.</p>

<p>Lead researcher Moji Aghajani, as quoted by PTI, highlighted the potential of AI-driven approaches to revolutionise the field of mental healthcare.&nbsp;</p>

<p>"This incomplete understanding of underlying brain bases is largely due to our simplistic approach to mental disorders among youths, in which clinical studies are often too small in size, with way too much focus on the 'average patient' rather than the individual," said Aghajani.</p>

<p>Anxiety disorders pose significant challenges to individuals' well-being and have far-reaching social and economic implications. Yet, our understanding of the underlying neural mechanisms remains incomplete.</p>

<p><strong>Also watch |&nbsp;Apple in talks to let Google's Gemini power iPhone AI features</strong></p>

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<p>The convergence of AI and neuroscience heralds a new era in mental health research, characterised by data-driven approaches and personalized interventions. By harnessing the power of AI to analyze vast amounts of neuroimaging data, researchers can unlock new insights into the complexities of anxiety disorders.</p>

<p><em>(With inputs from agencies)</em></p>


In a new study published in the journal Nature Mental Health, researchers have demonstrated the potential of artificial intelligence (AI) to identify individuals afflicted with anxiety disorders by analysing their unique brain structures.

In a new study published in the journal Nature Mental Health, researchers have demonstrated the potential of artificial intelligence (AI) to identify individuals afflicted with anxiety disorders by analysing their unique brain structures. By leveraging the power of machine learning (ML), the study examined cortical thickness, surface area, and the volumes of deep-seated brain regions in a cohort of approximately 3,500 youths aged between 10 and 25 from diverse geographical locations worldwide. Machine learning, a subset of AI, enables computers to learn and improve from data without explicit programming. In this study, ML algorithms were deployed to sift through vast amounts of brain imaging data, uncovering subtle patterns associated with anxiety disorders. Specifically, the algorithms focused on key neuroanatomical features that distinguish individuals with anxiety disorders from those without. Also read | European Parliament passes first-of-its-kind artificial intelligence law Refining algorithms for precision While the initial findings are promising, the researchers emphasise the need for further refinement of ML algorithms. They advocate for the integration of additional types of brain data, such as functional connectivity and neural activity patterns, to enhance the accuracy and reliability of the AI-driven diagnostic tools. By fine-tuning these algorithms, clinicians can achieve more precise and personalized assessments of anxiety disorders. One of the notable findings of the study is the generalisability of its results across a diverse population of youths. Despite variations in ethnicity, geographical location, and clinical characteristics, the AI models demonstrated consistent performance in identifying individuals with anxiety disorders. This suggests that the underlying neurobiological markers of anxiety disorders may transcend demographic boundaries. Lead researcher Moji Aghajani, as quoted by PTI, highlighted the potential of AI-driven approaches to revolutionise the field of mental healthcare. "This incomplete understanding of underlying brain bases is largely due to our simplistic approach to mental disorders among youths, in which clinical studies are often too small in size, with way too much focus on the 'average patient' rather than the individual," said Aghajani. Anxiety disorders pose significant challenges to individuals' well-being and have far-reaching social and economic implications. Yet, our understanding of the underlying neural mechanisms remains incomplete. Also watch | Apple in talks to let Google's Gemini power iPhone AI features The convergence of AI and neuroscience heralds a new era in mental health research, characterised by data-driven approaches and personalized interventions. By harnessing the power of AI to analyze vast amounts of neuroimaging data, researchers can unlock new insights into the complexities of anxiety disorders. (With inputs from agencies)

AI can now analyse brain scans to detect anxiety in a person. It’s that simple

<p>Popular video conferencing platform <strong>Zoom </strong>is back in the news for privacy concerns. Zoom updated its terms of service in March this year, which has now triggered concerns that the company might use user-generated content on the platform, such as conversations and chats, to train its Artificial Intelligence (AI) model, that too without the users’ consent.</p>

<p>However, the company has tried to put the rumours to rest by releasing a statement on Monday (August 7). In a statement, it said the company “will not use audio, video, or chat customer content to train our artificial intelligence models without your consent.”</p>

<h2><strong>Will Zoom tap your devices now?</strong></h2>

<p>According to Zoom’s latest promises, it would be incorrect to say it will eavesdrop on your video calls and virtual meetings. The current terms don’t allow user-generated content to be accessed by the company.</p>

<p>If the company is ever found to be collecting user-generated content, irrespective of consent, it would be violating its own terms, thus paving the way for litigation.</p>

<h2><strong>Are privacy concerns raised by experts illegitimate?</strong></h2>

<p>Not exactly. The company can still access “service-generated data” for machine learning purposes, provided the required consent is given. Service-generated data refers to data regarding customer behaviour such as what features do customers use more.&nbsp;</p>

<p><a href="https://www.wionews.com/technology/zoom-calls-employees-back-to-office-with-hybrid-work-approach-623322"><strong>Also read:&nbsp;Zoom, videoconferencing darling of Covid lockdowns, calls employees back to office</strong></a></p>

<p>According to the new terms of service, service-generated data can be used for “machine learning or artificial intelligence (including the purposes of training and tuning algorithms and models.”</p>

<p>However, to allay the privacy concerns, the company says customers will have to enable the feature themselves and then separately give consent to share the data with the company.</p>

<h2><strong>What if the host of a meeting enables this feature?</strong></h2>

<p>According to experts, the functioning of this feature could still present challenges for certain participants during Zoom calls, particularly when the host chooses to activate the generative AI capabilities.</p>

<p>Zoom has clarified that when a meeting organiser opts for the meeting summary function, participants will receive a notification informing them about the activation of an AI feature. This notification will also indicate the potential sharing of their data for AI training purposes. Participants will then have the option to proceed with the meeting or to exit.</p>

<p><strong>Watch:&nbsp;Zoom orders partial work-from-office policy for workers</strong></p>

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<p>While this approach theoretically empowers all participants to manage their data usage, it might not be feasible for someone to decline a meeting or entirely avoid using Zoom if they disagree.&nbsp;</p>

<p>Katharine Trendacosta, the director of policy and advocacy at the Electronic Frontier Foundation, raised a pertinent question: “If the administrator consents and it’s your boss at your work who requires you to use Zoom, how is that really consent?”</p>

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Popular video conferencing platform Zoom is back in the news for privacy concerns. Zoom updated its terms of service in March this year, which has now triggered concerns that the company might use user-generated content on the platform, such as conversations and chats, to train its Artificial Intelligence (AI) model, that too without the users’ consent.

Popular video conferencing platform Zoom is back in the news for privacy concerns. Zoom updated its terms of service in March this year, which has now triggered concerns that the company might use user-generated content on the platform, such as conversations and chats, to train its Artificial Intelligence (AI) model, that too without the users’ consent. However, the company has tried to put the rumours to rest by releasing a statement on Monday (August 7). In a statement, it said the company “will not use audio, video, or chat customer content to train our artificial intelligence models without your consent.” Will Zoom tap your devices now? According to Zoom’s latest promises, it would be incorrect to say it will eavesdrop on your video calls and virtual meetings. The current terms don’t allow user-generated content to be accessed by the company. If the company is ever found to be collecting user-generated content, irrespective of consent, it would be violating its own terms, thus paving the way for litigation. Are privacy concerns raised by experts illegitimate? Not exactly. The company can still access “service-generated data” for machine learning purposes, provided the required consent is given. Service-generated data refers to data regarding customer behaviour such as what features do customers use more. Also read: Zoom, videoconferencing darling of Covid lockdowns, calls employees back to office According to the new terms of service, service-generated data can be used for “machine learning or artificial intelligence (including the purposes of training and tuning algorithms and models.” However, to allay the privacy concerns, the company says customers will have to enable the feature themselves and then separately give consent to share the data with the company. What if the host of a meeting enables this feature? According to experts, the functioning of this feature could still present challenges for certain participants during Zoom calls, particularly when the host chooses to activate the generative AI capabilities. Zoom has clarified that when a meeting organiser opts for the meeting summary function, participants will receive a notification informing them about the activation of an AI feature. This notification will also indicate the potential sharing of their data for AI training purposes. Participants will then have the option to proceed with the meeting or to exit. Watch: Zoom orders partial work-from-office policy for workers × While this approach theoretically empowers all participants to manage their data usage, it might not be feasible for someone to decline a meeting or entirely avoid using Zoom if they disagree. Katharine Trendacosta, the director of policy and advocacy at the Electronic Frontier Foundation, raised a pertinent question: “If the administrator consents and it’s your boss at your work who requires you to use Zoom, how is that really consent?” WATCH WION LIVE HERE ×

Will Zoom eavesdrop on your video calls to train its AI model? Here is the answer

<p><meta name="uuid" content="uuidqK33Qr87T4qS" /><meta charset="utf-8" /></p>

<p><meta name="uuid" content="uuidqK33Qr87T4qS" /><meta charset="utf-8" />It is the era of artificial intelligence. Chances are high that you may have used text and image generation tools such as ChatGPT and DALL-E, solely because of the chatter they have generated.&nbsp;</p>

<p>Scientists studying these tools, however, are perplexed that the AI language models powering them can learn and answer without having been trained previously.&nbsp;</p>

<p>Large Language Models like OpenAi's GPT-3 powering ChatGPT and Google's LaMDA powering Bard are exceptionally good at learning and executing tasks on the fly.</p>

<p>Meaning, unlike the linear process of training an AI machine by feeding it copious amounts of data which is a laborious and time-consuming process, the new breed of AI can dig deeper into its neural schema and arrive at outputs which it hasn't been specifically trained to perform.&nbsp;</p>

<p><a href="https://www.wionews.com/technology/google-bard-vs-chatgpt-which-is-better-ai-tool-know-how-to-access-google-chatbot-all-you-need-to-know-559622" target="_blank"><strong>Read more: Google Bard vs ChatGPT: Which is better AI tool? Know how to access Google chatbot, all you need to know</strong></a></p>

<p>This apparent phenomenon is called 'in-context' learning and researchers from Massachusetts Institute of Technology, Stanford University, and Google in a recent study have set upon to decode how AI tools seemingly work between the input and output layers.&nbsp;</p>

<p>“Learning is entangled with [existing] knowledge. We show that it is possible for these models to learn from examples on the fly without any parameter update we apply to the model," Ekin Akyürek, the lead author of the study was quoted as saying by Motherboard.&nbsp;</p>

<p>Researchers said that the LLM is building upon its previous knowledge, just the way humans do. In fact, the models build smaller models inside themselves to achieve new tasks, posited the scientists.&nbsp;</p>

<p><a href="https://www.wionews.com/videos/race-for-powerful-ai-tools-heats-up-559948"><strong>WATCH |&nbsp;Race for powerful AI tools heats up</strong></a></p>

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<p>To prove their hypothesis, the researchers analysed a transformer, a neural network model. After observing the model in action, they asserted that the transformer was writing its own machine-learning model in hidden states. Long story short, the transformer was inventing the wheel, all by itself.&nbsp;</p>

<p><a href="https://www.wionews.com/business-economy/googles-shared-dip-after-ai-chatbot-bard-flubs-answer-in-new-ad-heres-what-happened-560132" target="_blank"><strong>Read more: $100 bn wiped off Alphabet's shares after Google AI chatbot 'Bard' flubs answer</strong></a></p>

<p>Machines learning on their own and adapting on the go is a wonderful discovery, one that enhances the limit of what the AI tools can do. However, experts are also wary that leaving information to automated systems brings its own sets of challenges.&nbsp;</p>

<p>There is still a shroud of mystery regarding what are the true capabilities of AI learning models but the discovery of 'in-context' learning has certainly widened the horizon.&nbsp;</p>

<p><em>(With inputs from agencies)</em></p>

<p><strong>WATCH WION LIVE HERE:&nbsp;</strong></p>

<p><iframe allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" frameborder="0" height="350" loading="”lazy”" src="https://www.wionews.com/live-tv/embed?autoplay=1&amp;mute=1" width="100%"></iframe></p>

<p>&nbsp;</p>


It is the era of artificial intelligence. Chances are high that you may have used text and image generation tools such as ChatGPT and DALL-E, solely because of the chatter they have generated.

It is the era of artificial intelligence. Chances are high that you may have used text and image generation tools such as ChatGPT and DALL-E, solely because of the chatter they have generated. Scientists studying these tools, however, are perplexed that the AI language models powering them can learn and answer without having been trained previously. Large Language Models like OpenAi's GPT-3 powering ChatGPT and Google's LaMDA powering Bard are exceptionally good at learning and executing tasks on the fly. Meaning, unlike the linear process of training an AI machine by feeding it copious amounts of data which is a laborious and time-consuming process, the new breed of AI can dig deeper into its neural schema and arrive at outputs which it hasn't been specifically trained to perform. Read more: Google Bard vs ChatGPT: Which is better AI tool? Know how to access Google chatbot, all you need to know This apparent phenomenon is called 'in-context' learning and researchers from Massachusetts Institute of Technology, Stanford University, and Google in a recent study have set upon to decode how AI tools seemingly work between the input and output layers. “Learning is entangled with [existing] knowledge. We show that it is possible for these models to learn from examples on the fly without any parameter update we apply to the model," Ekin Akyürek, the lead author of the study was quoted as saying by Motherboard. Researchers said that the LLM is building upon its previous knowledge, just the way humans do. In fact, the models build smaller models inside themselves to achieve new tasks, posited the scientists. WATCH | Race for powerful AI tools heats up × To prove their hypothesis, the researchers analysed a transformer, a neural network model. After observing the model in action, they asserted that the transformer was writing its own machine-learning model in hidden states. Long story short, the transformer was inventing the wheel, all by itself. Read more: $100 bn wiped off Alphabet's shares after Google AI chatbot 'Bard' flubs answer Machines learning on their own and adapting on the go is a wonderful discovery, one that enhances the limit of what the AI tools can do. However, experts are also wary that leaving information to automated systems brings its own sets of challenges. There is still a shroud of mystery regarding what are the true capabilities of AI learning models but the discovery of 'in-context' learning has certainly widened the horizon. (With inputs from agencies) WATCH WION LIVE HERE:

AI that learns without human input? Scientists make a STUNNING discovery

<p>A German research team has reportedly developed an artificial intelligence tool which can predict how a patient will react after being hospitalised based on a blood sample.</p>

<p>Reports claim the machine learning tool can accurately predict whether the person will survive or die based on levels of protein in a person's blood.</p>

<p><a href="https://www.wionews.com/trending/couple-on-neverending-date-due-to-covid-lockdown-gets-engaged-446039">Also Read:&nbsp;Couple on never ending date due to Covid lockdown gets engaged</a></p>

<p>It is meant to help health workers to understand whether a patient needs critical care or can fight the virus without much help. The prediction model detects 14 levels of protein in a patient before making a decision.</p>

<p>Researchers say they tested the tool on 50 patients at Charité University Hospital in Germany as it carried out machine learning. Reports say 15 patients died out of the 50 as researchers sought protein levels and built the tool based on findings from data of the patients.</p>

<p><a href="https://www.wionews.com/world/french-education-minister-criticised-for-announcing-covid-19-measures-from-ibiza-trip-446017">Also Read:&nbsp;French minister criticised for announcing COVID-19 measures from Ibiza trip</a></p>

<p>After the initial trial 24 coronavirus patients who had received treatment were also tested as researchers found 19 survived and five patients died. The researchers said the AI tool accurately predicted the five deaths.</p>

<p>The researchers however pointed out that the sample size was small consisting of 50 patients and 24 other patients during open trials.</p>

<p>The new findings come as Europe and America battle the Omicron the wave with hospitals overwhelmed in several areas. Several health workers have been hit due to the Omicron wave making it tough for hospitals to function.</p>

<p>(With inputs from Agencies)</p>


A German research team has reportedly developed an artificial intelligence tool which can predict how a patient will react after being hospitalised based on a blood sample.

A German research team has reportedly developed an artificial intelligence tool which can predict how a patient will react after being hospitalised based on a blood sample. Reports claim the machine learning tool can accurately predict whether the person will survive or die based on levels of protein in a person's blood. Also Read: Couple on never ending date due to Covid lockdown gets engaged It is meant to help health workers to understand whether a patient needs critical care or can fight the virus without much help. The prediction model detects 14 levels of protein in a patient before making a decision. Researchers say they tested the tool on 50 patients at Charité University Hospital in Germany as it carried out machine learning. Reports say 15 patients died out of the 50 as researchers sought protein levels and built the tool based on findings from data of the patients. Also Read: French minister criticised for announcing COVID-19 measures from Ibiza trip After the initial trial 24 coronavirus patients who had received treatment were also tested as researchers found 19 survived and five patients died. The researchers said the AI tool accurately predicted the five deaths. The researchers however pointed out that the sample size was small consisting of 50 patients and 24 other patients during open trials. The new findings come as Europe and America battle the Omicron the wave with hospitals overwhelmed in several areas. Several health workers have been hit due to the Omicron wave making it tough for hospitals to function. (With inputs from Agencies)

Now, an AI tool can predict recovery time of COVID-19 patients

In a bid to learn about Historical events from the minute details of the ancient frescos, scientists use machine learning and robots to study them.

Robots to study ancient frescos: Machine learning project explores possibilities

<p>Scientists, including one of Indian origin, have developed an artificial intelligence system that can help soldiers learn 13 times faster than conventional methods.</p>

<p>Scientists at the US Army Research Laboratory are working on improving the intensity and rate of learning even with a few resources in hand. It is possible to help soliders come up with quick solutions to threats like vehicle-borne improvised explosive device, or potential danger zones from aerial warzone images.</p>

<p>They&nbsp;relied on low-cost, lightweight hardware and implemented collaborative filtering, a well-known machine learning technique on a state-of-the-art, low-power Field Programmable Gate Array platform to achieve a 13.3 times speedup of training compared to a state-of-the-art optimised multi-core system and 12.7 times speedup for optimized GPU systems.</p>

<p>The new technique consumed far less power too. Consumption charted 13.8 watts, compared to 130 watts for the multi-core and 235 watts for GPU platforms, making this a potentially useful component of adaptive, lightweight tactical computing systems.</p>

<p>Rajgopal Kannan, an ARL researcher, said this technique could eventually become part of a suite of tools embedded on the next generation combat vehicle, offering cognitive services and devices for warfighters in distributed coalition environments.</p>

<p>Developing technology for the next generation combat vehicle is one of the six Army Modernisation Priorities the laboratory is pursuing.</p>

<p>Kannan said he is working on developing several techniques to speed up AI/ML algorithms through innovative designs on state-of-the-art inexpensive hardware.</p>

<p>This work is part of Army's larger focus on artificial intelligence and machine learning research initiatives pursued to help to gain a strategic advantage and ensure warfighter superiority with applications such as on-field adaptive processing and tactical computing.</p>

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It is possible to help soldiers come up with quick solutions to threats like vehicle-borne improvised explosive device, or potential danger zones from aerial warzone images

Scientists, including one of Indian origin, have developed an artificial intelligence system that can help soldiers learn 13 times faster than conventional methods. Scientists at the US Army Research Laboratory are working on improving the intensity and rate of learning even with a few resources in hand. It is possible to help soliders come up with quick solutions to threats like vehicle-borne improvised explosive device, or potential danger zones from aerial warzone images. They relied on low-cost, lightweight hardware and implemented collaborative filtering, a well-known machine learning technique on a state-of-the-art, low-power Field Programmable Gate Array platform to achieve a 13.3 times speedup of training compared to a state-of-the-art optimised multi-core system and 12.7 times speedup for optimized GPU systems. The new technique consumed far less power too. Consumption charted 13.8 watts, compared to 130 watts for the multi-core and 235 watts for GPU platforms, making this a potentially useful component of adaptive, lightweight tactical computing systems. Rajgopal Kannan, an ARL researcher, said this technique could eventually become part of a suite of tools embedded on the next generation combat vehicle, offering cognitive services and devices for warfighters in distributed coalition environments. Developing technology for the next generation combat vehicle is one of the six Army Modernisation Priorities the laboratory is pursuing. Kannan said he is working on developing several techniques to speed up AI/ML algorithms through innovative designs on state-of-the-art inexpensive hardware. This work is part of Army's larger focus on artificial intelligence and machine learning research initiatives pursued to help to gain a strategic advantage and ensure warfighter superiority with applications such as on-field adaptive processing and tactical computing.

Artificial Intelligence could help soldiers come up with quick solutions to threats

<p>Tech giant Google introduced a new resource called &#39;learn with Google AI&#39; &nbsp;that provides ways to learn about machine learning and how it can be used to solve day to day problems of real life.</p>

<p>Machine learning (ML) is an application of artificial intelligence that provides systems with an&nbsp;ability to automatically learn and improve from experience without being explicitly programmed.</p>

<p>The company announced this through a blog on their site which mentioned. &quot;to help everyone understand how AI can solve challenging problems, we&rsquo;ve created a resource called Learn with Google AI. This site provides ways to learn about core ML concepts, develop and hone your ML skills, and apply ML to real-world problems. From deep learning experts looking for advanced tutorials and materials on TensorFlow, to &ldquo;curious cats&rdquo; who want to take their first steps with AI, anyone looking for educational content from ML experts at Google can find it here.&#39;&#39;</p>

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<p>Machine learning (ML) is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. (Others)</p>
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<p>&#39;Learn with Google AI&#39; also features a crash course for which can be accessed free of cost.</p>

<p>The course call machine learning crash course provides exercises and interactive visualization with instructional videos that anyone can use to learn and practice Machine learning concepts.</p>

<p>The machine learning crash course was first developed for&nbsp;engineers of Google&nbsp;to learn about machine learning use it to further enhance their work in virtual reality for google earth and their other products.</p>

<p>The blog further said that after the success of Machine learning crash course&#39;s success in the company it inspired them to make it available for everyone.</p>

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Machine learning provides systems with an ability to automatically learn things without being explicitly programmed

Tech giant Google introduced a new resource called 'learn with Google AI' that provides ways to learn about machine learning and how it can be used to solve day to day problems of real life. Machine learning (ML) is an application of artificial intelligence that provides systems with an ability to automatically learn and improve from experience without being explicitly programmed. The company announced this through a blog on their site which mentioned. "to help everyone understand how AI can solve challenging problems, we’ve created a resource called Learn with Google AI. This site provides ways to learn about core ML concepts, develop and hone your ML skills, and apply ML to real-world problems. From deep learning experts looking for advanced tutorials and materials on TensorFlow, to “curious cats” who want to take their first steps with AI, anyone looking for educational content from ML experts at Google can find it here.'' Machine learning (ML) is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. (Others) × 'Learn with Google AI' also features a crash course for which can be accessed free of cost. The course call machine learning crash course provides exercises and interactive visualization with instructional videos that anyone can use to learn and practice Machine learning concepts. The machine learning crash course was first developed for engineers of Google to learn about machine learning use it to further enhance their work in virtual reality for google earth and their other products. The blog further said that after the success of Machine learning crash course's success in the company it inspired them to make it available for everyone.

'Learn with Google AI' to teach machine learning for free

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