Google DeepMind has launched a new research institute focused on one of artificial intelligence’s biggest unanswered questions: what happens to society if artificial general intelligence, or AGI, becomes a reality?&nbsp;The DeepMind Institute will bring together researchers from Google DeepMind and outside organisations to examine the potential effects of advanced AI on the economy, science and everyday life. The launch comes as the AI industry faces a growing debate over safety, regulation and how quickly frontier systems should advance. Google DeepMind says the institute is intended to encourage research and discussion rather than promote a single view of the future.

<blockquote class="twitter-tweet">For 20+ years <a href="https://x.com/ShaneLegg?ref_src=twsrc%5Etfw">@ShaneLegg</a> and I&#39;ve discussed AGI’s potential impact on the economy, science &amp; society. With the DeepMind Institute, we&#39;re expanding interdisciplinary research on key questions for the AI era. We hope it spurs the discussions needed to get the next steps right:…&mdash; Demis Hassabis (@demishassabis) <a href="https://x.com/demishassabis/status/2100230524383981702?ref_src=twsrc%5Etfw">September 16, 2026</a></blockquote> <script async src="https://platform.x.com/widgets.js" charset="utf-8"></script>

<h2>What will the DeepMind Institute study?</h2>The institute will examine questions that go beyond building more capable AI models.Its research areas include economic policy in an AGI-driven world, how AI systems explain their reasoning, access to advanced AI across different regions and the potential impact of AI on human wellbeing.&nbsp;The initiative is being led by Shane Legg, Google DeepMind’s co-founder and chief AGI scientist, as managing editor. Google senior vice president James Manyika and DeepMind co-founder and chair Demis Hassabis are also involved in its leadership. Hassabis said he and Legg had discussed AGI’s possible impact on the economy, science and society for more than 20 years.He said the new institute would expand that work through interdisciplinary research and help generate the discussion needed as AI develops.<h2>DeepMind says researchers may disagree</h2>One notable feature of the institute is its acknowledgement that there may not be a single answer to how advanced AI should be developed or managed. In its introductory material, Hassabis, Manyika and Legg said Google, Google DeepMind and researchers elsewhere could reach different conclusions as evidence changes.&nbsp;The aim, therefore, is not simply to predict the future. It is to study the possible consequences of AGI and give researchers, policymakers and the wider public more information about the choices ahead. Google DeepMind already describes AGI as a potential major transformation for society, while its research has explored possible pathways from human-level AGI towards more advanced forms of machine intelligence.<h2>Why is this happening now?</h2>The timing is significant.&nbsp;Anthropic CEO Dario Amodei has recently called for the frontier AI industry to slow the pace at which model capabilities improve, arguing that safety work needs to keep up with rapidly advancing systems. Hassabis has supported greater cooperation around AI safety, while OpenAI CEO Sam Altman has also backed the idea of “pacing the frontier”. The debate has now spread beyond AI companies, with governments and policymakers discussing how advanced AI should be governed. Google DeepMind’s new institute does not amount to a decision to slow AGI development. Instead, it signals that one of the world’s leading AI labs wants a broader research effort examining what AGI could change and how society should prepare for it.

Google DeepMind has launched a new institute to study how artificial general intelligence could affect society, science and the economy. Led by Shane Legg with Demis Hassabis and James Manyika involved, the initiative will bring together internal and external researchers.

Google DeepMind has launched a new research institute focused on one of artificial intelligence’s biggest unanswered questions: what happens to society if artificial general intelligence, or AGI, becomes a reality? The DeepMind Institute will bring together researchers from Google DeepMind and outside organisations to examine the potential effects of advanced AI on the economy, science and everyday life. The launch comes as the AI industry faces a growing debate over safety, regulation and how quickly frontier systems should advance. Google DeepMind says the institute is intended to encourage research and discussion rather than promote a single view of the future.

Google DeepMind has launched a new institute to study how artificial general intelligence could affect society, science and the economy. Led by Shane Legg with Demis Hassabis and James Manyika involved, the initiative will bring together internal and external researchers.

Google DeepMind launches new institute to study how AGI could change society

Google DeepMind has created an AI-powered map of about 9 billion possible single-letter changes in human DNA, giving scientists a new way to search for genetic mutations that could matter for disease and biology.&nbsp;Called AlphaGenome Atlas, the platform contains precomputed predictions for every possible single-nucleotide variant in the human genome. DeepMind says testing billions of possible changes individually in laboratories would be practically impossible. The company has made the resource available for academic research through a free website, alongside its AlphaGenome API.

<blockquote class="twitter-tweet">We’re launching AlphaGenome Atlas: an AI-powered searchable database mapping the predicted impact of all 9 billion possible single-letter DNA changes. Here’s how it could help researchers better understand our biology 🧵 <a href="https://t.co/SABFZW5SiR">pic.twitter.com/SABFZW5SiR</a>&mdash; Google DeepMind (@GoogleDeepMind) <a href="https://x.com/GoogleDeepMind/status/2097325048109384166?ref_src=twsrc%5Etfw">September 8, 2026</a></blockquote> <script async src="https://platform.x.com/widgets.js" charset="utf-8"></script>

<h2>What does AlphaGenome Atlas actually do?</h2>Think of it as a giant search map for DNA. The Atlas uses predictions from AlphaGenome, DeepMind’s AI model designed to predict how genetic variants affect biological processes. It covers both the part of DNA that produces proteins and the much larger non-coding portion that helps control gene activity.&nbsp;DeepMind says the Atlas is a 1-petabyte dataset, more than 30 times larger than the AlphaFold Database.&nbsp;It also introduces an AlphaGenome Variant Impact (AVI) score. The score gives researchers a single way to rank potentially important genetic changes and investigate what biological processes they may disrupt.&nbsp;<h2>Why could 9 billion DNA changes matter?</h2>The human genome contains an enormous number of possible variations. Finding the few changes that actually matter for a disease can be like searching for a needle in a haystack. DeepMind says researchers have already used AlphaGenome Atlas in rare-disease research and studies of common traits.&nbsp;In one example, researchers working with the Broad Institute used the AVI score to prioritise a variant linked to DNM1, a gene associated with epileptic encephalopathy. Experimental work then validated the predicted effect.&nbsp;Another study using data from more than 54,000 UK Biobank participants identified 22% more non-coding genetic associations by grouping variants according to their predicted molecular effects.<h2>What happens next?</h2>DeepMind says AlphaGenome Atlas is a starting point rather than an endpoint. As its AI models improve, the company expects the predictions to become more comprehensive and precise. The goal is not to replace laboratory research. Instead, the Atlas could help scientists decide which genetic changes deserve closer investigation first. That could speed up research into genetic disorders, disease mechanisms and potential therapeutic targets while leaving the crucial experimental validation to scientists.

Google DeepMind has launched AlphaGenome Atlas, an AI-powered database mapping predictions for about 9 billion possible single-letter DNA changes. The tool helps researchers rank potentially important genetic variants and could accelerate research into rare diseases and human biology.


Google DeepMind has created an AI-powered map of about 9 billion possible single-letter changes in human DNA, giving scientists a new way to search for genetic mutations that could matter for disease and biology. Called AlphaGenome Atlas, the platform contains precomputed predictions for every possible single-nucleotide variant in the human genome. DeepMind says testing billions of possible changes individually in laboratories would be practically impossible. The company has made the resource available for academic research through a free website, alongside its AlphaGenome API.

Google DeepMind has launched AlphaGenome Atlas, an AI-powered database mapping predictions for about 9 billion possible single-letter DNA changes. The tool helps researchers rank potentially important genetic variants and could accelerate research into rare diseases and human biology.

Google DeepMind maps 9 billion DNA mutations: How AI could help solve rare diseases

Google is entering a new phase in the artificial intelligence race after two of its most influential AI leaders changed roles within days of each other. Jeff Dean, one of Google's earliest employees and the architect behind many of its core technologies, is leaving the company after 27 years. At the same time, Demis Hassabis is stepping down as CEO of Google DeepMind to become Alphabet's Chief Scientist. The changes come as Google faces mounting pressure from rivals such as OpenAI and Anthropic in the race to build the next generation of AI systems.

<blockquote class="twitter-tweet">Tomorrow will be my last day at Google after 27 years, and watching it grow from 25 people to 190,000+ has been an amazing journey. Below is a note I shared with many people internally at Google today. An excerpt is: It has been an absolute pleasure to work with you and to help… <a href="https://t.co/o3aRNLkssA">pic.twitter.com/o3aRNLkssA</a>&mdash; Jeff Dean (@JeffDean) <a href="https://x.com/JeffDean/status/2085083442669318443?ref_src=twsrc%5Etfw">August 5, 2026</a></blockquote> <script async src="https://platform.x.com/widgets.js" charset="utf-8"></script>

<h2>Jeff Dean starts a new AI company</h2>Dean joined Google in 1999 when the company had only around 25 employees. During his career, he helped build technologies including MapReduce, Bigtable and Google Brain, and later played a major role in Gemini and Google's AI research. Announcing his departure on X, Dean said it had been "an amazing journey" watching Google grow into a company serving billions of users worldwide. He is now launching Discovery Loop, a startup focused on building AI systems that can automate scientific and engineering research. The company is co-founded by Google veterans Sanjay Ghemawat, Oriol Vinyals and Quoc Le, with backing from Khosla Ventures. Google will remain an investor and cloud infrastructure partner. According to WIRED, Alphabet CEO Sundar Pichai attempted to persuade the team to stay, but the founders ultimately chose the flexibility of building an independent company.<h2>Hassabis moves to a broader research role</h2>While Dean is leaving Google, Demis Hassabis is staying within Alphabet but giving up his operational responsibilities. He has been appointed Alphabet's Chief Scientist and Chair of Google DeepMind, while former DeepMind CTO Koray Kavukcuoglu will take over as CEO and report directly to Sundar Pichai. The move allows Hassabis to focus on long-term AI research, scientific discovery and the pursuit of artificial general intelligence (AGI), rather than overseeing day-to-day product development.&nbsp;The twin announcements mark one of the biggest leadership reshuffles in Google's AI history. Dean's departure is particularly significant because Discovery Loop brings together four of Google's best-known AI researchers. Their goal is to develop AI systems capable of generating scientific hypotheses, running experiments and accelerating breakthroughs in biology, materials science, chip design and drug discovery. For Google, the challenge is now clear: continue competing with OpenAI, Anthropic and other AI leaders while replacing decades of engineering experience and accelerating Gemini's development. How successfully the company navigates this transition could shape the next chapter of the global AI race.

Google's AI leadership has undergone a major shake-up. Jeff Dean is leaving after 27 years to launch Discovery Loop with top Google researchers, while Demis Hassabis is stepping down as DeepMind CEO to become Alphabet's Chief Scientist amid growing AI competition.

Google is entering a new phase in the artificial intelligence race after two of its most influential AI leaders changed roles within days of each other. Jeff Dean, one of Google's earliest employees and the architect behind many of its core technologies, is leaving the company after 27 years. At the same time, Demis Hassabis is stepping down as CEO of Google DeepMind to become Alphabet's Chief Scientist. The changes come as Google faces mounting pressure from rivals such as OpenAI and Anthropic in the race to build the next generation of AI systems.

Google's AI leadership has undergone a major shake-up. Jeff Dean is leaving after 27 years to launch Discovery Loop with top Google researchers, while Demis Hassabis is stepping down as DeepMind CEO to become Alphabet's Chief Scientist amid growing AI competition.

Google AI shake-up: Jeff Dean leaves Google after 27 years to launch AI startup with top researchers

Google DeepMind has said its artificial intelligence system AlphaProof Nexus has solved several long-standing mathematical problems, including some that remained unsolved for more than 50 years. The development has drawn attention across the AI community, especially as debates grow around whether such breakthroughs signal progress towards artificial general intelligence (AGI).According to researchers, the system solved nine open problems related to mathematician Paul Erdős, along with 44 open conjectures and a 15-year-old problem in algebraic geometry, showing how AI is increasingly being used in advanced scientific research.<h2>How Google’s AI solved complex maths problems</h2>The system, called AlphaProof Nexus, works by combining AI reasoning with a formal verification tool known as Lean. Instead of just generating answers, the AI produces step-by-step proofs, which are then checked automatically by the system.This approach helps ensure that:Every logical step is verified Incorrect assumptions are rejected The final proof meets strict mathematical standardsResearchers said the cost of solving each problem was only a few hundred dollars in computing power, highlighting how efficient such systems can be.<h2>Why ‘hallucinations’ remain a concern</h2>One of the key challenges in AI-generated mathematics is the issue of hallucinations, where systems produce results that appear correct but contain hidden errors.Experts explain that AI models may:Invent new statements that are not proven Skip difficult parts of a problem Present incomplete proofs as finishedTo address this, Google’s system uses Lean to verify every step, reducing the risk of such errors and making the results more reliable.<h2>How this compares with other AI developments</h2>The announcement comes shortly after OpenAI said one of its models solved a well-known mathematical problem. However, OpenAI relied on external experts to verify the result, while DeepMind used automated verification.This difference highlights two approaches in the AI industry:Human-reviewed proofs Machine-verified proofs Researchers believe combining both methods may improve future results.<h2>Does this mean AI is reaching AGI?</h2> Despite the progress, Demis Hassabis has said that current AI systems are still far from achieving AGI. He explained that solving specific problems does not mean AI can match human intelligence across all areas. True AGI would require: Creativity across different fields Independent reasoning Ability to generate original ideas According to Hassabis, today’s systems are powerful tools but do not yet show the level of understanding seen in human experts.<h2>What this means for research and industry</h2> The development shows how AI is becoming a useful assistant in scientific research. Instead of replacing mathematicians, these systems can help:Explore complex problems faster Check proofs more efficiently Support researchers in difficult areasExperts say this could change how mathematics and science are conducted in the future. As AI models continue to improve, their role in research is expected to expand. However, the debate around safety, accuracy and long-term capabilities will remain important. For now, while AI can solve complex mathematical problems, it is still seen as a specialised tool rather than a system with general intelligence

Google DeepMind has said its artificial intelligence system AlphaProof Nexus has solved several long-standing mathematical problems, including some that remained unsolved for more than 50 years.


Google DeepMind has said its artificial intelligence system AlphaProof Nexus has solved several long-standing mathematical problems, including some that remained unsolved for more than 50 years. The development has drawn attention across the AI community, especially as debates grow around whether such breakthroughs signal progress towards artificial general intelligence (AGI). According to researchers, the system solved nine open problems related to mathematician Paul Erdős, along with 44 open conjectures and a 15-year-old problem in algebraic geometry, showing how AI is increasingly being used in advanced scientific research. How Google’s AI solved complex maths problems The system, called AlphaProof Nexus, works by combining AI reasoning with a formal verification tool known as Lean. Instead of just generating answers, the AI produces step-by-step proofs, which are then checked automatically by the system. This approach helps ensure that: Every logical step is verified Incorrect assumptions are rejected The final proof meets strict mathematical standards Researchers said the cost of solving each problem was only a few hundred dollars in computing power, highlighting how efficient such systems can be. Why ‘hallucinations’ remain a concern One of the key challenges in AI-generated mathematics is the issue of hallucinations, where systems produce results that appear correct but contain hidden errors. Experts explain that AI models may: Invent new statements that are not proven Skip difficult parts of a problem Present incomplete proofs as finished To address this, Google’s system uses Lean to verify every step, reducing the risk of such errors and making the results more reliable. How this compares with other AI developments The announcement comes shortly after OpenAI said one of its models solved a well-known mathematical problem. However, OpenAI relied on external experts to verify the result, while DeepMind used automated verification. This difference highlights two approaches in the AI industry: Human-reviewed proofs Machine-verified proofs Researchers believe combining both methods may improve future results. Does this mean AI is reaching AGI? Despite the progress, Demis Hassabis has said that current AI systems are still far from achieving AGI. He explained that solving specific problems does not mean AI can match human intelligence across all areas. True AGI would require: Creativity across different fields Independent reasoning Ability to generate original ideas According to Hassabis, today’s systems are powerful tools but do not yet show the level of understanding seen in human experts. What this means for research and industry The development shows how AI is becoming a useful assistant in scientific research. Instead of replacing mathematicians, these systems can help: Explore complex problems faster Check proofs more efficiently Support researchers in difficult areas Experts say this could change how mathematics and science are conducted in the future. As AI models continue to improve, their role in research is expected to expand. However, the debate around safety, accuracy and long-term capabilities will remain important. For now, while AI can solve complex mathematical problems, it is still seen as a specialised tool rather than a system with general intelligence

Google DeepMind has said its artificial intelligence system AlphaProof Nexus has solved several long-standing mathematical problems, including some that remained unsolved for more than 50 years.

Google AI solves decades-old maths problems, but DeepMind CEO says AGI is still far away

Scientists at&nbsp;Google's artificial intelligence arm DeepMind have developed a breakthrough tool that can predict&nbsp;whether genetic mutations are likely to cause harm - helping&nbsp;research into rare diseases.&nbsp;&nbsp;

A human on average has 9,000 genetic mutations throughout their genome. While most are harmless, some can cause&nbsp;cancer,&nbsp;cystic fibrosis or&nbsp;damage brain development.

Up until now, only four million mutations have been observed in humans by scientists, out of a possible 71 million such mutations. Additionally, only two per cent have been classified as&nbsp;disease-causing or benign.&nbsp;

<h2>How does the AI tool work?</h2>

The Google AI tool, named, AlphaMissense, makes predictions about&nbsp;so-called missense mutations, where a single letter is misspelt in the genetic (DNA) code. Upon reviewing the mutation, the AI tool was able to predict&nbsp;89 per cent of them, with 90 per cent accuracy.

When the researchers set the programme's precision to 90 per cent, it predicted 57 per cent of&nbsp;missense mutations were probably harmless and 32 per cent&nbsp;were probably harmful while remaining uncertain about the rest.&nbsp;

<a href="https://www.wionews.com/science/hey-google-why-am-i-sad-tech-giant-developing-ai-chatbot-that-offers-life-advice-626471">Also read | 'Hey Google, why am I sad?': Tech giant developing AI chatbot that offers life advice</a>

To teach the AI system, scientists fed DNA data from humans and&nbsp;closely associated primates which enabled it to learn about the missense mutations that are relatively common and, therefore benign and others which are rare and potentially dangerous.

While the scientists taught the system, it familiarised itself by studying millions of protein sequences and learning what a “healthy” protein looks like.

Upon being fed a mutation, the AI tool generates a score which is assigned to each&nbsp;mutation, indicating the risk of it causing disease (otherwise referred to as pathogenic).&nbsp;

“This is very similar to human language.&nbsp;If we substitute a word in an English sentence, a person familiar with English can immediately see whether the word substitution will change the meaning of the sentence or not,"&nbsp;said Jun Cheng of Google DeepMind.

The database compiled by&nbsp;AlphaMissense has now been made public and available to scientists, accompanying a study that was published in the journal Science.&nbsp;

<a href="https://www.wionews.com/videos/gravitas-all-about-googles-ai-tool-to-help-journalists-617938">Watch |&nbsp;Gravitas: All about Google's AI tool to help journalists</a>

<div class="ui-state-default" contenteditable="false">
<div class="twitter-block" draggable="false">
<div class="videoWrapper trancodeVideo"><iframe allowfullscreen="" autoplay="1" class="includeVideo" frameborder="0" height="350" src="https://www.wionews.com/videos/gravitas-all-about-googles-ai-tool-to-help-journalists-617938/embed" style="width:100%;max-width:560px;" width="560"></iframe></div>
</div>
<a class="close-mark close-div" href="#" id="jqclosemark">×</a></div>

<h2>Researchers remain hopeful&nbsp;</h2>

Scientists are of the view that the breakthrough could lead to the development of new treatments and assessing the risk of a deadly mutation before it reaches a point of no return.&nbsp;

"We should emphasise that the predictions were never really trained or never really intended to be used for clinical diagnosis alone," said Jun.&nbsp;

"However, we do think that our predictions can potentially be helpful to increase the diagnosed rate of rare diseases, and also potentially to help us find new disease-causing genes."

While the AI tool is able to predict if a genetic mutation is harmful, the onus remains on the human doctors to understand the reason and ultimately use the information to operate on the patient.&nbsp;

(With inputs from agencies)

WATCH WION LIVE HERE:&nbsp;

<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>

Scientists at Google's artificial intelligence arm DeepMind have developed a breakthrough tool that can predict whether genetic mutations are likely to cause harm - helping research into rare diseases.

Scientists at Google's artificial intelligence arm DeepMind have developed a breakthrough tool that can predict whether genetic mutations are likely to cause harm - helping research into rare diseases. A human on average has 9,000 genetic mutations throughout their genome. While most are harmless, some can cause cancer, cystic fibrosis or damage brain development. Up until now, only four million mutations have been observed in humans by scientists, out of a possible 71 million such mutations. Additionally, only two per cent have been classified as disease-causing or benign. How does the AI tool work? The Google AI tool, named, AlphaMissense, makes predictions about so-called missense mutations, where a single letter is misspelt in the genetic (DNA) code. Upon reviewing the mutation, the AI tool was able to predict 89 per cent of them, with 90 per cent accuracy. When the researchers set the programme's precision to 90 per cent, it predicted 57 per cent of missense mutations were probably harmless and 32 per cent were probably harmful while remaining uncertain about the rest. Also read | 'Hey Google, why am I sad?': Tech giant developing AI chatbot that offers life advice To teach the AI system, scientists fed DNA data from humans and closely associated primates which enabled it to learn about the missense mutations that are relatively common and, therefore benign and others which are rare and potentially dangerous. While the scientists taught the system, it familiarised itself by studying millions of protein sequences and learning what a “healthy” protein looks like. Upon being fed a mutation, the AI tool generates a score which is assigned to each mutation, indicating the risk of it causing disease (otherwise referred to as pathogenic). “This is very similar to human language. If we substitute a word in an English sentence, a person familiar with English can immediately see whether the word substitution will change the meaning of the sentence or not," said Jun Cheng of Google DeepMind. The database compiled by AlphaMissense has now been made public and available to scientists, accompanying a study that was published in the journal Science. Watch | Gravitas: All about Google's AI tool to help journalists × Researchers remain hopeful Scientists are of the view that the breakthrough could lead to the development of new treatments and assessing the risk of a deadly mutation before it reaches a point of no return. "We should emphasise that the predictions were never really trained or never really intended to be used for clinical diagnosis alone," said Jun. "However, we do think that our predictions can potentially be helpful to increase the diagnosed rate of rare diseases, and also potentially to help us find new disease-causing genes." While the AI tool is able to predict if a genetic mutation is harmful, the onus remains on the human doctors to understand the reason and ultimately use the information to operate on the patient. (With inputs from agencies) WATCH WION LIVE HERE:

Breakthrough? Google develops AI tool capable of predicting harmful genetic mutations

Researchers at Google's DeepMind and the European Molecular Biology Laboratory (EMBL) have&nbsp;unveiled the most exhaustive database yet of the proteins that form the building blocks of life,

The database of 20,000 proteins expressed by the human genome, freely and openly available online.&nbsp;Observers believe the&nbsp;breakthrough&nbsp;would "fundamentally change biological research".

They also included more than 350,000 proteins from 20 organisms such as bacteria, yeast and mice that scientists rely on for research.

Every cell in every living organism is triggered to perform its function by proteins that deliver constant instructions to maintain health and ward off infection.

Unlike the genome, the complete sequence of human genes that encode cellular life,&nbsp;the human proteome is constantly changing in response to genetic instructions and environmental stimuli.&nbsp;

Understanding how proteins operate, the shape in which they end up, or "fold" into,&nbsp;within cells has fascinated scientists for decades.&nbsp;

But determining each protein's precise function through direct experimentation is painstaking.

Fifty years of research have until now yielded only 17 percent of the human proteome's amino acids, the subunits of proteins.

To create the database, scientists used a state-of-the-art machine learning programme that was able to accurately predict the shape of proteins based on their amino acid sequences.&nbsp;

Instead of spending months using multi-million dollar equipment, they trained their AlphaFold system on a database of 170,000 known protein structures.&nbsp;

The AI then used an algorithm to make accurate predictions of the shape of 58 percent of all proteins within the human proteome.

This more than doubled the number of high-accuracy human protein structures that researchers had identified during 50 years of direct experimentation, essentially overnight.&nbsp;

The potential applications are enormous, from researching genetic diseases and combating anti-microbial resistance to engineering more drought-resistant crops.

'Protein-folding problem'&nbsp;

Paul Nurse, winner of the 2001 Nobel Prize for Medicine and director of the Francis Crick Institute, said Thursday's release was "a great leap for biological innovation".

"With this resource freely and openly available, the scientific community will be able to draw on collective knowledge to accelerate discovery, ushering in a new era for AI-enabled biology," he said.

John McGeehan, director for the Centre for Enzyme Innovation at the University of Portsmouth, whose team is developing enzymes capable of consuming single-use plastic waste, said AlphaFold had revolutionised the field. &nbsp;

"What took us months and years to do, AlphaFold was able to do in a weekend. I feel like we have just jumped at least a year ahead of where we were yesterday," he said.&nbsp;

The ability to predict a protein's shape from its amino acid sequence using a computer rather than experimentation is already helping scientists in a number of research fields.

AlphaFold is already being used in research into cures for diseases that disproportionately affect poorer countries.

One US-based team is using the AI prediction to study ways of overcoming strains of drug-resistant bacteria.

Another group is using the database to better understand how SARS-CoV-2, the virus that causes Covid-19, bonds with human cells.

Venki Ramakrishnan, winner of the 2009 Nobel Prize for Chemistry, said Thursday's research, published in the journal Nature, was a "stunning advance" in biological research.&nbsp;

He said AlphaFold had essentially solved the so-called "protein-folding problem", which argued that the 3D structure of a given protein should be determinable from its amino acid sequence, and which had puzzled scientists for half a century.

Given that the number of shapes a protein could theoretically take is astronomically large, the protein-fold problem was partly one of processing power.&nbsp;

The task was so daunting that in 1969 US molecular biologist Cyril Levinthal famously theorised that it would take longer than the age of the known universe to enumerate all possible protein configurations using brute calculation.&nbsp;

But with AlphaFold capable of performing a mind-dizzying number of calculations every second, the problem stood no chance when faced with AI and algorithms.

"It has occurred long before many people in the field would have predicted," Ramakrishnan said.&nbsp;

"It will be exciting to see the many ways in which it will fundamentally change biological research."

(With inputs from agencies)

Researchers at Google's DeepMind and the European Molecular Biology Laboratory (EMBL) have unveiled the most exhaustive database yet of the proteins that form the building blocks of life, The database of 20,000 proteins expressed by the human genome, freely and openly available online.

Researchers at Google's DeepMind and the European Molecular Biology Laboratory (EMBL) have unveiled the most exhaustive database yet of the proteins that form the building blocks of life, The database of 20,000 proteins expressed by the human genome, freely and openly available online. Observers believe the breakthrough would "fundamentally change biological research". They also included more than 350,000 proteins from 20 organisms such as bacteria, yeast and mice that scientists rely on for research. Every cell in every living organism is triggered to perform its function by proteins that deliver constant instructions to maintain health and ward off infection. Unlike the genome, the complete sequence of human genes that encode cellular life, the human proteome is constantly changing in response to genetic instructions and environmental stimuli. Understanding how proteins operate, the shape in which they end up, or "fold" into, within cells has fascinated scientists for decades. But determining each protein's precise function through direct experimentation is painstaking. Fifty years of research have until now yielded only 17 percent of the human proteome's amino acids, the subunits of proteins. To create the database, scientists used a state-of-the-art machine learning programme that was able to accurately predict the shape of proteins based on their amino acid sequences. Instead of spending months using multi-million dollar equipment, they trained their AlphaFold system on a database of 170,000 known protein structures. The AI then used an algorithm to make accurate predictions of the shape of 58 percent of all proteins within the human proteome. This more than doubled the number of high-accuracy human protein structures that researchers had identified during 50 years of direct experimentation, essentially overnight. The potential applications are enormous, from researching genetic diseases and combating anti-microbial resistance to engineering more drought-resistant crops. 'Protein-folding problem' Paul Nurse, winner of the 2001 Nobel Prize for Medicine and director of the Francis Crick Institute, said Thursday's release was "a great leap for biological innovation". "With this resource freely and openly available, the scientific community will be able to draw on collective knowledge to accelerate discovery, ushering in a new era for AI-enabled biology," he said. John McGeehan, director for the Centre for Enzyme Innovation at the University of Portsmouth, whose team is developing enzymes capable of consuming single-use plastic waste, said AlphaFold had revolutionised the field. "What took us months and years to do, AlphaFold was able to do in a weekend. I feel like we have just jumped at least a year ahead of where we were yesterday," he said. The ability to predict a protein's shape from its amino acid sequence using a computer rather than experimentation is already helping scientists in a number of research fields. AlphaFold is already being used in research into cures for diseases that disproportionately affect poorer countries. One US-based team is using the AI prediction to study ways of overcoming strains of drug-resistant bacteria. Another group is using the database to better understand how SARS-CoV-2, the virus that causes Covid-19, bonds with human cells. Venki Ramakrishnan, winner of the 2009 Nobel Prize for Chemistry, said Thursday's research, published in the journal Nature, was a "stunning advance" in biological research. He said AlphaFold had essentially solved the so-called "protein-folding problem", which argued that the 3D structure of a given protein should be determinable from its amino acid sequence, and which had puzzled scientists for half a century. Given that the number of shapes a protein could theoretically take is astronomically large, the protein-fold problem was partly one of processing power. The task was so daunting that in 1969 US molecular biologist Cyril Levinthal famously theorised that it would take longer than the age of the known universe to enumerate all possible protein configurations using brute calculation. But with AlphaFold capable of performing a mind-dizzying number of calculations every second, the problem stood no chance when faced with AI and algorithms. "It has occurred long before many people in the field would have predicted," Ramakrishnan said. "It will be exciting to see the many ways in which it will fundamentally change biological research." (With inputs from agencies)

Google's DeepMind creates ‘transformative’ map of human proteins drawn by Artificial Intelligence

Google&#39;s artificial intelligence (AI) program, AlphaGo, beat Chinese Go master Ke Jie for a second time on Thursday, taking an unassailable 2-0 lead in a best of three series meant to test the limits of computers in taking on humans at complex tasks.

Go is a highly complex board game dating back thousands of years that involves two contestants placing black and white stones on a grid. It is popular in Asian countries and most top-ranked players hail from China, Japan and South Korea.

Ke, the 19-year-old world no. 1, was visibly frustrated, tugging his hair and laying his head on the table during the final moments of the second match against AlphaGo on Thursday.

&quot;Last year, I think the way AlphaGo played was pretty close to human beings, but today I think he plays like the God of Go,&quot; Ke said after the game. Following his defeat in the first match of the series on Tuesday, Ke said he would not compete against AI again due to its rapid improvement.

The victory over the world&#39;s top player - which many thought would take decades to achieve - comes after the AI program from Google&#39;s DeepMind unit bested a South Korean Go professional in a similar exhibition match last year.

This week&#39;s event, held in the eastern river town of Wuzhen and attended by local officials and Google parent Alphabet&#39;s top brass, is a feather in the cap for the US&nbsp;search giant as it woos Beijing for better market access.

Since AlphaGo&#39;s defeat of Lee Sedol just over a year ago, AI has shot up the agenda for China&#39;s top policy makers, making its first appearance this year in Premier Li Keqiang&#39;s annual work report, a document laying out China&#39;s top policy priorities.

Market boost

For Google, AlphaGo&#39;s triumph in China also offers a marketing boost in a country where its main services have long been blocked, and local rivals to its search engine, email and video sites have thrived since it largely exited China in 2010.

Despite Mandarin and English livestreams of the match being broadcast on Youtube, which is blocked in China, the match was widely reported via local news outlets and social media.

Ke, who has some 3.5 million followers on Twitter-like service Weibo, shared details of the Google event and his trip to Wuzhen, which is also the seat of the country&#39;s top annual government internet conference.

&quot;Ke Jie pushed AlphaGo right to the limit,&quot; tweeted DeepMind founder Demis Hassabis following the match. Twitter Inc is also blocked in China.

(Reuters)

Many thought it would take decades for an AI to achieve this

Google's artificial intelligence (AI) program, AlphaGo, beat Chinese Go master Ke Jie for a second time on Thursday, taking an unassailable 2-0 lead in a best of three series meant to test the limits of computers in taking on humans at complex tasks. Go is a highly complex board game dating back thousands of years that involves two contestants placing black and white stones on a grid. It is popular in Asian countries and most top-ranked players hail from China, Japan and South Korea. Ke, the 19-year-old world no. 1, was visibly frustrated, tugging his hair and laying his head on the table during the final moments of the second match against AlphaGo on Thursday. "Last year, I think the way AlphaGo played was pretty close to human beings, but today I think he plays like the God of Go," Ke said after the game. Following his defeat in the first match of the series on Tuesday, Ke said he would not compete against AI again due to its rapid improvement. The victory over the world's top player - which many thought would take decades to achieve - comes after the AI program from Google's DeepMind unit bested a South Korean Go professional in a similar exhibition match last year. This week's event, held in the eastern river town of Wuzhen and attended by local officials and Google parent Alphabet's top brass, is a feather in the cap for the US search giant as it woos Beijing for better market access. Since AlphaGo's defeat of Lee Sedol just over a year ago, AI has shot up the agenda for China's top policy makers, making its first appearance this year in Premier Li Keqiang's annual work report, a document laying out China's top policy priorities. Market boost For Google, AlphaGo's triumph in China also offers a marketing boost in a country where its main services have long been blocked, and local rivals to its search engine, email and video sites have thrived since it largely exited China in 2010. Despite Mandarin and English livestreams of the match being broadcast on Youtube, which is blocked in China, the match was widely reported via local news outlets and social media. Ke, who has some 3.5 million followers on Twitter-like service Weibo, shared details of the Google event and his trip to Wuzhen, which is also the seat of the country's top annual government internet conference. "Ke Jie pushed AlphaGo right to the limit," tweeted DeepMind founder Demis Hassabis following the match. Twitter Inc is also blocked in China. (Reuters)

Google's AlphaGo clinches series win over Chinese Go master

deepmind

Deepmind, News on Deepmind, stories on Deepmind, articles on Deepmind, Deepmind stories at Wion, Deepmind news, Deepmind latest news

deepmind

Google DeepMind launches new institute to study how AGI could change society

DeepMind launches giant AI-powered DNA map to help solve genetic mysteries

Jeff Dean leaves Google after 27 years to launch AI startup with top researchers

DeepMind’s AI breakthrough: What it means for science and research

Breakthrough? Google develops AI tool capable of predicting harmful genetic mutations

Google's DeepMind creates ‘transformative’ map of human proteins drawn by Artificial Intelligence

Google's AlphaGo clinches series win over Chinese Go master