New York City is taking a much tougher approach to artificial intelligence in schools. The city is banning student-facing AI tools from 2-K through eighth grade, meaning younger pupils will not be allowed to use generative AI, chatbots or AI tutors in school.&nbsp;The policy comes as parents and educators debate whether children should be relying on AI for learning. City officials are also introducing tighter limits on classroom screen use. The move is being closely watched because New York City has the largest public school system in the US.&nbsp;<h2>AI ban will cover younger students</h2>Under the new policy, students from early childhood programmes through eighth grade will face the strongest restrictions. High school students will be allowed to use AI, but access will be limited. Possible uses include literacy and career-readiness programmes, as well as selected pilot programmes.The policy also takes aim at AI companion chatbots designed to simulate relationships or provide emotional support.The exact challenge will be enforcement. Students can access many AI services outside school, making it difficult for schools to control what happens beyond the classroom.&nbsp;<h2>Teachers can still use AI</h2>The rules do not amount to a complete AI ban across New York City Public Schools.&nbsp;Teachers will be allowed to use approved AI tools for tasks such as lesson planning, translation and drafting communications. However, teachers will not be allowed to use AI for certain sensitive activities, including grading, behaviour monitoring, counselling or developing special education plans. The city's education department has said its approach should be age-appropriate and keep human judgement at the centre of education. Its earlier guidance divided AI use according to different grade bands, including K-5, 6-8 and 9-12.<h2>Why New York City changed its AI policy</h2>The decision follows backlash over the city's earlier AI framework released in March.&nbsp;That framework used a traffic-light system and allowed some student uses of AI for research, exploration and creative work. Parents and teachers argued that the approach did not go far enough. Schools Chancellor Kamar Samuels later acknowledged that officials had “missed the mark” in communicating the policy and promised to consider community feedback.&nbsp;The city is now forming a task force involving educators, elected officials, experts and union representatives. Its work is expected to shape the longer-term AI policy. With more than 900,000 students, New York City's decision could become an important test case for school districts across the US as they decide how much AI access children should have.

New York City is banning student-facing AI tools from 2-K through eighth grade, while allowing limited use in high school. Teachers can still use approved AI for tasks such as lesson planning and translation, as the city takes a tougher approach to AI in education.

New York City is taking a much tougher approach to artificial intelligence in schools. The city is banning student-facing AI tools from 2-K through eighth grade, meaning younger pupils will not be allowed to use generative AI, chatbots or AI tutors in school. The policy comes as parents and educators debate whether children should be relying on AI for learning. City officials are also introducing tighter limits on classroom screen use. The move is being closely watched because New York City has the largest public school system in the US. AI ban will cover younger students Under the new policy, students from early childhood programmes through eighth grade will face the strongest restrictions. High school students will be allowed to use AI, but access will be limited. Possible uses include literacy and career-readiness programmes, as well as selected pilot programmes. The policy also takes aim at AI companion chatbots designed to simulate relationships or provide emotional support. The exact challenge will be enforcement. Students can access many AI services outside school, making it difficult for schools to control what happens beyond the classroom. Teachers can still use AI The rules do not amount to a complete AI ban across New York City Public Schools. Teachers will be allowed to use approved AI tools for tasks such as lesson planning, translation and drafting communications. However, teachers will not be allowed to use AI for certain sensitive activities, including grading, behaviour monitoring, counselling or developing special education plans. The city's education department has said its approach should be age-appropriate and keep human judgement at the centre of education. Its earlier guidance divided AI use according to different grade bands, including K-5, 6-8 and 9-12. Why New York City changed its AI policy The decision follows backlash over the city's earlier AI framework released in March. That framework used a traffic-light system and allowed some student uses of AI for research, exploration and creative work. Parents and teachers argued that the approach did not go far enough. Schools Chancellor Kamar Samuels later acknowledged that officials had “missed the mark” in communicating the policy and promised to consider community feedback. The city is now forming a task force involving educators, elected officials, experts and union representatives. Its work is expected to shape the longer-term AI policy. With more than 900,000 students, New York City's decision could become an important test case for school districts across the US as they decide how much AI access children should have.

New York City is banning student-facing AI tools from 2-K through eighth grade, while allowing limited use in high school. Teachers can still use approved AI for tasks such as lesson planning and translation, as the city takes a tougher approach to AI in education.

No AI until high school: New York City schools introduce sweeping new rules

For years, India has repeated the phrase “data is the new oil” to describe the value of the information generated by more than a billion people. But the technology boom of 2026 is creating a different comparison: RAM may be the new oil.&nbsp;Computer memory has become dramatically more expensive as AI data centres compete for supplies. Tom’s Hardware recently reported that some DDR5 memory kits had risen by roughly 500% over 12 months, while a 128GB DDR5-6400 kit reached $3,399. The impact is no longer limited to PC enthusiasts. Zoho founder Sridhar Vembu recently warned that rising memory costs, combined with higher AI costs, are making business increasingly difficult.&nbsp;<h2>Why is RAM suddenly so expensive?</h2>The answer is AI. Modern AI systems need huge amounts of fast memory. Data centres running models from companies such as OpenAI, Google and Anthropic require enormous quantities of DRAM and high-bandwidth memory. Memory manufacturers are therefore directing more capacity towards the lucrative AI and server market. That leaves less supply for conventional computers, smartphones and other devices. Apple CEO Tim Cook has described the situation as a “hundred-year flood” in memory pricing and said price increases had become unavoidable. Apple has already raised prices on several Macs and iPads.&nbsp;<h2>Why this could affect almost every business</h2>The important point is that RAM is no longer simply a component inside a laptop. Almost every modern business depends on computing. A dental clinic may use computers to manage appointments and patient records. A bank needs servers. A factory needs connected machines. A small company may rely entirely on cloud software. All of those systems ultimately depend on computer hardware and data-centre infrastructure. When memory becomes more expensive, the additional cost can move through the supply chain. Hardware makers can raise prices. Cloud providers can face higher infrastructure costs. Software companies may then have to decide whether to absorb those costs or pass them to customers. That is why Vembu’s warning matters beyond Zoho.&nbsp;<h2>Could the RAM crisis last for years?</h2>There are reasons to worry about a prolonged squeeze. Memory companies including Samsung, SK hynix and Micron are expanding production, but much of the new capacity is being directed towards AI-related memory. Industry forecasts also suggest that supply could remain tight for years rather than months. This creates a difficult choice for companies: accept lower profits, raise prices or redesign products and software to use less memory. That last option could become increasingly important. Vembu has argued that programming languages and compilers need to become more memory-efficient because developers can no longer assume that memory is effectively free.&nbsp;The comparison with oil is therefore more than a catchy phrase.Oil powered the physical economy. RAM is becoming one of the resources that powers the digital one. And if AI continues consuming memory at today's pace, the next technology shock may not come from a lack of intelligence. It may come from a lack of space for that intelligence to run.

RAM prices have surged as AI data centres consume more memory, with some DDR5 products up around 500% year-on-year. Zoho’s Sridhar Vembu says the squeeze is making business harder, raising fears that higher computing costs could spread across the wider economy.

For years, India has repeated the phrase “data is the new oil” to describe the value of the information generated by more than a billion people. But the technology boom of 2026 is creating a different comparison: RAM may be the new oil. Computer memory has become dramatically more expensive as AI data centres compete for supplies. Tom’s Hardware recently reported that some DDR5 memory kits had risen by roughly 500% over 12 months, while a 128GB DDR5-6400 kit reached $3,399. The impact is no longer limited to PC enthusiasts. Zoho founder Sridhar Vembu recently warned that rising memory costs, combined with higher AI costs, are making business increasingly difficult. Why is RAM suddenly so expensive? The answer is AI. Modern AI systems need huge amounts of fast memory. Data centres running models from companies such as OpenAI, Google and Anthropic require enormous quantities of DRAM and high-bandwidth memory. Memory manufacturers are therefore directing more capacity towards the lucrative AI and server market. That leaves less supply for conventional computers, smartphones and other devices. Apple CEO Tim Cook has described the situation as a “hundred-year flood” in memory pricing and said price increases had become unavoidable. Apple has already raised prices on several Macs and iPads. Why this could affect almost every business The important point is that RAM is no longer simply a component inside a laptop. Almost every modern business depends on computing. A dental clinic may use computers to manage appointments and patient records. A bank needs servers. A factory needs connected machines. A small company may rely entirely on cloud software. All of those systems ultimately depend on computer hardware and data-centre infrastructure. When memory becomes more expensive, the additional cost can move through the supply chain. Hardware makers can raise prices. Cloud providers can face higher infrastructure costs. Software companies may then have to decide whether to absorb those costs or pass them to customers. That is why Vembu’s warning matters beyond Zoho. Could the RAM crisis last for years? There are reasons to worry about a prolonged squeeze. Memory companies including Samsung, SK hynix and Micron are expanding production, but much of the new capacity is being directed towards AI-related memory. Industry forecasts also suggest that supply could remain tight for years rather than months. This creates a difficult choice for companies: accept lower profits, raise prices or redesign products and software to use less memory. That last option could become increasingly important. Vembu has argued that programming languages and compilers need to become more memory-efficient because developers can no longer assume that memory is effectively free. The comparison with oil is therefore more than a catchy phrase. Oil powered the physical economy. RAM is becoming one of the resources that powers the digital one. And if AI continues consuming memory at today's pace, the next technology shock may not come from a lack of intelligence. It may come from a lack of space for that intelligence to run.

RAM prices have surged as AI data centres consume more memory, with some DDR5 products up around 500% year-on-year. Zoho’s Sridhar Vembu says the squeeze is making business harder, raising fears that higher computing costs could spread across the wider economy.

RAM is becoming the new oil: How a 500% memory price surge could make everything more expensive

Integrating artificial intelligence into the S-400 Triumf could radically enhance its lethality. By using neural networks for threat prioritisation and trajectory prediction, AI reduces operator workload by 40 per cent to flawlessly destroy targets.

Integrating artificial intelligence into the S-400 Triumf could radically enhance its lethality. By using neural networks for threat prioritisation and trajectory prediction, AI reduces operator workload by 40 per cent to flawlessly destroy targets.

Can AI make the S-400 deadlier?

Artificial intelligence is moving beyond pilot projects and proofs-of-concept into the very fabric of enterprise strategy, reshaping how businesses innovate, compete and create value. Industry leaders stated that the technology is no longer just a productivity tool; it has become a strategic capability driving industry-specific transformation, richer customer experiences and smarter business process orchestration. It also transforms enterprise innovation by shifting from simple task automation to autonomous, goal-driven systems that reason, plan, and execute complex workflows. Companies leverage generative models, multi-agent frameworks, and predictive analytics to reshape research and development, streamline operations, and hyper-personalise customer engagement.<h3>Enterprises' investment in AI</h3>According to Praveen Ojha, Chief Technologist at EPAM Systems India Pvt. Ltd., enterprises are concentrating AI investment where it delivers the strongest competitive edge. He noted that AI now blends reasoning, prediction and content generation with enterprise data, enabling companies to redesign business models and boost workforce productivity. Sectors like healthcare and financial services are already seeing tangible applications, from clinical research and pharmacovigilance to content generation and regulatory mapping, while multimodal and agentic AI increasingly power more intuitive, automated workflows. Praveen Ojha said, "Enterprises are focusing AI investment on a few key areas where it creates the most competitive differentiation: industry-specific transformation, customer experience and business process orchestration. AI is combining reasoning, prediction and content generation with enterprise data to help organisations redesign business models, personalise customer experiences, augment workforce productivity through intelligent automation and create new sources of value through AI-enabled products and services. In healthcare and life sciences, AI is modernising clinical research through AI-powered evidence generation, data hub development, MDM (Medical Decision Making) modernisation and pharmacovigilance." "In financial services, it's being applied to content generation, MCC (Merchant Category Code) validation and BIAN (Banking Industry Architecture Network) mapping. Alongside these use cases, multimodal AI is enabling richer, more intuitive customer experiences by combining text, voice, image, video and sensor data, while agentic AI is increasingly orchestrating workflows, supporting decisions and executing complex business processes with human oversight. As these capabilities scale, data centre operations must evolve too. Supporting multimodal and agentic AI at scale requires intelligent infrastructure that can dynamically allocate compute and orchestrate workloads with minimal manual intervention," he added. Ojha emphasised that true competitive advantage doesn't come from foundation models alone, but from combining them with proprietary data and deep domain expertise. Many organisations, he said, are now deploying "digital workers", AI agents managing end-to-end functional responsibilities under human oversight. As these systems scale, he added, supporting infrastructure must also evolve to dynamically allocate compute and orchestrate workloads with minimal manual intervention, ensuring enterprises can convert AI capability into measurable business outcomes. "Capturing this value requires rethinking enterprise innovation. Many are increasingly adopting digital workers - AI agents that handle end-to-end responsibilities within a function, with humans providing oversight. Competitive advantage doesn’t come from access to foundation models alone. Instead, it comes from combining those models with proprietary data, institutional knowledge and deep domain expertise. Beyond efficiency gains, organisations have started looking at AI enabling new products, better customer experiences and new sources of business value. Ultimately, leadership in this next phase will belong to organisations that can turn AI into real outcomes through engineering capabilities and multidisciplinary teams that can deploy AI at production scale," Ojha said.&nbsp;Also read: <a href="https://www.wionews.com/trending/how-repeated-paper-leaks-are-breaking-students-before-they-begin-their-journey-explained-1786101519100">How repeated paper leaks are breaking students before they begin their journey? EXPLAINED</a><h3>AI into workflows and innovation cycles</h3>Echoing this shift, Mahesh Sharma, SVP of Data &amp; AI Engineering at Bread Financial, noted that the AI conversation has moved from experimentation to full-scale transformation. Organisations, he explained, are embedding AI into everyday workflows and innovation cycles rather than treating it as a standalone tool. Sharma outlined three defining shifts: reaching critical mass in AI adoption to unlock real impact, using AI to shorten the gap between idea and execution, and empowering agentic AI to plan and execute complex, long-running tasks, freeing humans to focus on strategic judgment and creativity. "The conversation around AI is shifting from experimentation to transformation. Forward-looking organisations no longer view AI as a standalone tool; they are reimagining how work gets done by embedding AI into workflows, customer experiences and innovation cycles to unlock new forms of value creation. The future will be shaped by three important shifts. First, organisations must rethink how they use AI to solve business problems. Access alone does not create impact. Value emerges when adoption reaches critical mass, and AI is incorporated into daily work.&nbsp; Second, AI is dramatically shortening the distance between idea and execution. Organisations can hypothesise faster, validate assumptions with greater confidence, and accelerate the journey from concept to implementation. As a result, teams can innovate with greater speed and precision. Third, Agentic AI will fundamentally reshape how work is performed. Agents will increasingly plan, coordinate and execute complex, long-running activities based on desired business outcomes. This shift will enable new operating models where humans focus on strategic judgment, creativity and oversight, while AI handles orchestration and execution at scale," Sharma said. "Organisations must be deliberate in how they pursue this transformation. Leaders must define the problem they want to solve, identify where AI can create meaningful value, and establish clear accountability for outcomes. The organisations that create lasting value will be those that view AI not merely as a productivity tool, but as a strategic capability that equips people to solve complex problems, make better-informed decisions, and convert ideas into sustainable business outcomes," Sharma added. Reacting to the use of AI in enterprises, Dinesh Parikh, Vice President - Product Management, Newgen Software, said, "For years, enterprises viewed AI primarily as a tool for generating insights. Today, we're in a far more consequential phase where AI is an active participant in how work gets done. The conversation is no longer about intelligence in isolation; it's about intelligence in motion. The most innovative organisations are embedding AI directly into the fabric of their operations, enabling systems to understand and orchestrate actions across people, processes, and technology. This shift is redefining enterprise agility, helping businesses move from reactive decision-making to continuous, adaptive execution, where AI can drive multi-step intelligence and actions while keeping humans in the loop for oversight, judgment, and accountability. What's particularly exciting is the emergence of AI as a force multiplier for human potential. Rather than replacing expertise, it amplifies it, freeing employees from routine work and enabling them to focus on creativity, strategy, and problem-solving. The enterprises that lead the next decade won't be those with the most AI pilots. They'll be the ones that successfully orchestrate AI, automation, data, and human ingenuity into a connected system of innovation, transforming intelligence from a capability into a competitive advantage." &nbsp;

Enterprises are concentrating AI investment where it delivers the strongest competitive edge. He noted that AI now blends reasoning, prediction and content generation with enterprise data, enabling companies to redesign business models and boost workforce productivity. 

Artificial intelligence is moving beyond pilot projects and proofs-of-concept into the very fabric of enterprise strategy, reshaping how businesses innovate, compete and create value. Industry leaders stated that the technology is no longer just a productivity tool; it has become a strategic capability driving industry-specific transformation, richer customer experiences and smarter business process orchestration. It also transforms enterprise innovation by shifting from simple task automation to autonomous, goal-driven systems that reason, plan, and execute complex workflows. Companies leverage generative models, multi-agent frameworks, and predictive analytics to reshape research and development, streamline operations, and hyper-personalise customer engagement. Enterprises' investment in AI According to Praveen Ojha, Chief Technologist at EPAM Systems India Pvt. Ltd., enterprises are concentrating AI investment where it delivers the strongest competitive edge. He noted that AI now blends reasoning, prediction and content generation with enterprise data, enabling companies to redesign business models and boost workforce productivity. Sectors like healthcare and financial services are already seeing tangible applications, from clinical research and pharmacovigilance to content generation and regulatory mapping, while multimodal and agentic AI increasingly power more intuitive, automated workflows. Praveen Ojha said, "Enterprises are focusing AI investment on a few key areas where it creates the most competitive differentiation: industry-specific transformation, customer experience and business process orchestration. AI is combining reasoning, prediction and content generation with enterprise data to help organisations redesign business models, personalise customer experiences, augment workforce productivity through intelligent automation and create new sources of value through AI-enabled products and services. In healthcare and life sciences, AI is modernising clinical research through AI-powered evidence generation, data hub development, MDM (Medical Decision Making) modernisation and pharmacovigilance." "In financial services, it's being applied to content generation, MCC (Merchant Category Code) validation and BIAN (Banking Industry Architecture Network) mapping. Alongside these use cases, multimodal AI is enabling richer, more intuitive customer experiences by combining text, voice, image, video and sensor data, while agentic AI is increasingly orchestrating workflows, supporting decisions and executing complex business processes with human oversight. As these capabilities scale, data centre operations must evolve too. Supporting multimodal and agentic AI at scale requires intelligent infrastructure that can dynamically allocate compute and orchestrate workloads with minimal manual intervention," he added. Ojha emphasised that true competitive advantage doesn't come from foundation models alone, but from combining them with proprietary data and deep domain expertise. Many organisations, he said, are now deploying "digital workers", AI agents managing end-to-end functional responsibilities under human oversight. As these systems scale, he added, supporting infrastructure must also evolve to dynamically allocate compute and orchestrate workloads with minimal manual intervention, ensuring enterprises can convert AI capability into measurable business outcomes. "Capturing this value requires rethinking enterprise innovation. Many are increasingly adopting digital workers - AI agents that handle end-to-end responsibilities within a function, with humans providing oversight. Competitive advantage doesn’t come from access to foundation models alone. Instead, it comes from combining those models with proprietary data, institutional knowledge and deep domain expertise. Beyond efficiency gains, organisations have started looking at AI enabling new products, better customer experiences and new sources of business value. Ultimately, leadership in this next phase will belong to organisations that can turn AI into real outcomes through engineering capabilities and multidisciplinary teams that can deploy AI at production scale," Ojha said. Also read: How repeated paper leaks are breaking students before they begin their journey? EXPLAINED AI into workflows and innovation cycles Echoing this shift, Mahesh Sharma, SVP of Data & AI Engineering at Bread Financial, noted that the AI conversation has moved from experimentation to full-scale transformation. Organisations, he explained, are embedding AI into everyday workflows and innovation cycles rather than treating it as a standalone tool. Sharma outlined three defining shifts: reaching critical mass in AI adoption to unlock real impact, using AI to shorten the gap between idea and execution, and empowering agentic AI to plan and execute complex, long-running tasks, freeing humans to focus on strategic judgment and creativity. "The conversation around AI is shifting from experimentation to transformation. Forward-looking organisations no longer view AI as a standalone tool; they are reimagining how work gets done by embedding AI into workflows, customer experiences and innovation cycles to unlock new forms of value creation. The future will be shaped by three important shifts. First, organisations must rethink how they use AI to solve business problems. Access alone does not create impact. Value emerges when adoption reaches critical mass, and AI is incorporated into daily work. Second, AI is dramatically shortening the distance between idea and execution. Organisations can hypothesise faster, validate assumptions with greater confidence, and accelerate the journey from concept to implementation. As a result, teams can innovate with greater speed and precision. Third, Agentic AI will fundamentally reshape how work is performed. Agents will increasingly plan, coordinate and execute complex, long-running activities based on desired business outcomes. This shift will enable new operating models where humans focus on strategic judgment, creativity and oversight, while AI handles orchestration and execution at scale," Sharma said. "Organisations must be deliberate in how they pursue this transformation. Leaders must define the problem they want to solve, identify where AI can create meaningful value, and establish clear accountability for outcomes. The organisations that create lasting value will be those that view AI not merely as a productivity tool, but as a strategic capability that equips people to solve complex problems, make better-informed decisions, and convert ideas into sustainable business outcomes," Sharma added. Reacting to the use of AI in enterprises, Dinesh Parikh, Vice President - Product Management, Newgen Software, said, "For years, enterprises viewed AI primarily as a tool for generating insights. Today, we're in a far more consequential phase where AI is an active participant in how work gets done. The conversation is no longer about intelligence in isolation; it's about intelligence in motion. The most innovative organisations are embedding AI directly into the fabric of their operations, enabling systems to understand and orchestrate actions across people, processes, and technology. This shift is redefining enterprise agility, helping businesses move from reactive decision-making to continuous, adaptive execution, where AI can drive multi-step intelligence and actions while keeping humans in the loop for oversight, judgment, and accountability. What's particularly exciting is the emergence of AI as a force multiplier for human potential. Rather than replacing expertise, it amplifies it, freeing employees from routine work and enabling them to focus on creativity, strategy, and problem-solving. The enterprises that lead the next decade won't be those with the most AI pilots. They'll be the ones that successfully orchestrate AI, automation, data, and human ingenuity into a connected system of innovation, transforming intelligence from a capability into a competitive advantage."

Enterprises are concentrating AI investment where it delivers the strongest competitive edge. He noted that AI now blends reasoning, prediction and content generation with enterprise data, enabling companies to redesign business models and boost workforce productivity.&nbsp;

How is AI transforming the future of enterprise innovation? EXPLAINED

Aliens have long been a fascinating subject for humans. Innumerable movies, TV series and books are proof of this allure. Our search for extra-terrestrial has even&nbsp;taken us to other planets, albeit remotely. This search has progressed leaps and bounds in the last few years, but it is still in its natal stages. Global space agencies like the National Aeronautics and Space Administration (NASA) and China National Space Administration (CNSA) have in recent years sent rovers to Mars to aid this search remotely. However, the accuracy of these random searches remains low.

To remedy this, the Search for Extraterrestrial Intelligence (SETI) Institute has been exploring the use of artificial intelligence (AI) for finding extraterrestrial life on Mars and other icy worlds.

According to a report on Space, a recent study from SETI&nbsp;states that AI could be used to detect microbial life in the depths of the icy oceans on other planets.

In a paper published in Nature Astronomy, the team details how they trained a machine-learning model to scan data for signs of microbial life or other unusual features that could be indicative of alien life.&nbsp;

Also read |&nbsp;<a href="https://www.wionews.com/trending/heres-how-ai-can-help-modern-day-goldilocks-get-a-good-nights-sleep-578610">Here's how Artificial Intelligence can help modern-day Goldilocks get a good night's sleep</a>

Using a machine learning algorithm called convolutional neural networks (CNNs) a multidisciplinary team of scientists led by SETI's Kim Warren-Rhodes has mapped sparse lifeforms on Earth.&nbsp;Warren-Rhodes worked alongside experts from other prestigious institutions: Michael Phillips of Johns Hopkins Applied Physics Lab&nbsp;and Freddie Kalaitzis of the University&nbsp;of Oxford.

The system developed by them used statistical ecology and AI-detected biosignatures with up to 87.5 per cent accuracy, compared to only 10 per cent for random searches. As per the researchers, it&nbsp;can&nbsp;potentially reduce the search area by up to 97 per cent, making it easier for scientists to locate potential chemical traces of life.&nbsp;

Also read |&nbsp;<a href="https://www.wionews.com/technology/up-up-and-away-elon-musks-spacex-to-try-and-launch-starship-its-most-powerful-rocket-ever-on-monday-582601">Up, Up, and Away!: Elon Musk’s SpaceX to try and launch ‘Starship,’ its most powerful rocket ever on Monday</a>

For testing their system,&nbsp;they initially&nbsp;focused on the sparse lifeforms that dwell in salt domes, rocks, and crystals at Salar de Pajonales at the boundary of the Chilean Atacama Desert and Altiplano.

Warren-Rhodes and his team collected over 8,000 images and 1,000 samples from Salar de Pajonales to search for photosynthetic microbes that may represent a biosignature on NASA's "ladder of life detection" for finding life beyond Earth.&nbsp;

The team also used drone imagery to simulate Mars Reconnaissance Orbiter's High-Resolution Imaging Experiment camera's Martian terrain images to examine the region.

They found that microbial life in the region is concentrated in biological hotspots that strongly relate to the availability of water.

Researchers suggest that the machine learning tools developed can be used in robotic planetary missions like NASA's Perseverance Rover. The tools can guide rovers towards areas with a higher probability of having traces of alien life, even if they are rare or hidden.&nbsp;

"With these models, we can design tailor-made roadmaps and algorithms to guide rovers to places with the highest probability of harbouring past or present life  — no matter how hidden or rare," explained Warren-Rhodes.&nbsp;

(With inputs from agencies)

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Aliens have long been a fascinating subject for humans. Innumerable movies, TV series and books are proof of this allure. Our search for extra-terrestrial has even taken us to other planets, albeit remotely.

Aliens have long been a fascinating subject for humans. Innumerable movies, TV series and books are proof of this allure. Our search for extra-terrestrial has even taken us to other planets, albeit remotely. This search has progressed leaps and bounds in the last few years, but it is still in its natal stages. Global space agencies like the National Aeronautics and Space Administration (NASA) and China National Space Administration (CNSA) have in recent years sent rovers to Mars to aid this search remotely. However, the accuracy of these random searches remains low. To remedy this, the Search for Extraterrestrial Intelligence (SETI) Institute has been exploring the use of artificial intelligence (AI) for finding extraterrestrial life on Mars and other icy worlds. According to a report on Space, a recent study from SETI states that AI could be used to detect microbial life in the depths of the icy oceans on other planets. In a paper published in Nature Astronomy, the team details how they trained a machine-learning model to scan data for signs of microbial life or other unusual features that could be indicative of alien life. Also read | Here's how Artificial Intelligence can help modern-day Goldilocks get a good night's sleep Using a machine learning algorithm called convolutional neural networks (CNNs) a multidisciplinary team of scientists led by SETI's Kim Warren-Rhodes has mapped sparse lifeforms on Earth. Warren-Rhodes worked alongside experts from other prestigious institutions: Michael Phillips of Johns Hopkins Applied Physics Lab and Freddie Kalaitzis of the University of Oxford. The system developed by them used statistical ecology and AI-detected biosignatures with up to 87.5 per cent accuracy, compared to only 10 per cent for random searches. As per the researchers, it can potentially reduce the search area by up to 97 per cent, making it easier for scientists to locate potential chemical traces of life. Also read | Up, Up, and Away!: Elon Musk’s SpaceX to try and launch ‘Starship,’ its most powerful rocket ever on Monday For testing their system, they initially focused on the sparse lifeforms that dwell in salt domes, rocks, and crystals at Salar de Pajonales at the boundary of the Chilean Atacama Desert and Altiplano. Warren-Rhodes and his team collected over 8,000 images and 1,000 samples from Salar de Pajonales to search for photosynthetic microbes that may represent a biosignature on NASA's "ladder of life detection" for finding life beyond Earth. The team also used drone imagery to simulate Mars Reconnaissance Orbiter's High-Resolution Imaging Experiment camera's Martian terrain images to examine the region. They found that microbial life in the region is concentrated in biological hotspots that strongly relate to the availability of water. Researchers suggest that the machine learning tools developed can be used in robotic planetary missions like NASA's Perseverance Rover. The tools can guide rovers towards areas with a higher probability of having traces of alien life, even if they are rare or hidden. "With these models, we can design tailor-made roadmaps and algorithms to guide rovers to places with the highest probability of harbouring past or present life  — no matter how hidden or rare," explained Warren-Rhodes. (With inputs from agencies) WATCH WION LIVE HERE You can now write for wionews.com and be a part of the community. Share your stories and opinions with us here.

Research reveals how Artificial Intelligence can help look for alien lifeforms on Mars and other planets