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How Open Science Can Help Researchers Prepare for the Next Pandemic
NVIDIA is collaborating with organizations including Google DeepMind and the European Molecular Biology Laboratory’s European Bioinformatics Institute to release an open dataset of viral protein structures — giving scientists a head start against diseases.
Google DeepMind's latest AI tool promises a new era for genetics research, but experts warn to take its predictions with caution
AlphaGenome Atlas could make it easier for geneticists to access powerful AI, but its predictions aren't always accurate.
Why AI is speeding up scientific research but not lab experiments
A new report by Google, Google DeepMind and MIT finds that researchers are using AI…a lot. It’s just not helping as much in the lab
The king and AI: UK monarch Charles meets artificial intelligence leaders as safety concerns swirl
LONDON (AP) — King Charles III on Thursday urged senior leaders from OpenAI, Anthropic, Google DeepMind and Nvidia to make sure that artificial intelligence remains under control and in the service of people and the planet. The gathering in Scotland was convened to discuss how AI can benefit society, and comes at a pivotal moment […]
OpenAI says it’s working with Anthropic, Google on AI safety
OpenAI is working on steps to address artificial intelligence safety issues with its top competitors Anthropic PBC and Google DeepMind, escalating industry efforts to respond to a groundswell of concern
Google DeepMind’s AlphaGenome Takes Aim at One of Genetics’ Biggest Problems
Google DeepMind has released a huge new dataset that could make it easier for scientists to study how changes in our DNA affect the way the body works. Called AlphaGenome Atlas, it contains predictions for more than 9 billion possible single-letter changes in the human genome. The dataset is about one petabyte in size and […]
No, AI Probably Won’t Cure Cancer Anytime Soon, Scientists Say
Eric Topol is a prominent cardiologist known for his research into deploying AI and other technologies to improve healthcare. He considers himself a Demis Hassabis superfan, and when I spoke to him, he enthusiastically recounted scoring a blurb from the Google DeepMind chair for “Super Agers,” ...
Google DeepMind’s new AI model can control a robot’s entire body
The new model can help robots tie up trash bags and unscrew lightbulbs, too.
Google DeepMind dismantles Nobel-winning AlphaFold team in strategy shift
Landmark project that solved protein folding gives way to a wider race to build AI systems for scientific discovery
Google AI researcher says he resigned over military contract
Alex Turner, a research scientist at Google DeepMind, said he tried to change Google executives' minds about working with the Pentagon.
Google AI researcher says he resigned over military contract
Alex Turner, a research scientist at Google DeepMind, said he tried to change Google executives' minds about working with the Pentagon.
Google DeepMind CEO issues stark warning about AGI
Google DeepMind's Demis Hassabis advocated for a US-led coalition to govern AI development, highlighting AGI's impending global impact.
200 economists and executives warn on AI's job impact: read the letter
Executives and researchers from OpenAI, Google DeepMind, and Anthropic all signed the 88-word letter released on Monday.
How should European scientists respond to the US AI kill switch?
Earlier in June, Europe’s worst fears of technological dependence were confirmed when the White House ordered the AI firm Anthropic to stop non-US citizens using its leading models. This kill-switch moment crystalised anxieties in Brussels that the continent, lagging the US on the creation of leading-edge AI models, is now a technological of vassal of Washington. The shock has specific implications for European scientists and universities. Some governments are now worried that with AI being integrated into labs, Donald Trump now has his finger hovering over an off switch for European science. “Having restrictions on this tool, it can have an impact on our own research,” said France’s research minister, Philippe Baptiste, at the VivaTech conference in Paris on June 18.Baptiste, himself a former academic, said that colleagues in mathematics had recently warned him they were now cut off from leading-edge US models, risking their work. “This model is not available anymore now because of the US restriction, and for the first time this had an impact on mathematics,” a field where large language models (LLMs) are making a significant contribution, said Baptiste. So, should European scientists avoid relying on US models in the future? Or would this be a self-harming overreaction? And how is AI being used in science, anyway? What happened?On June 12, it emerged that Washington, citing cybersecurity concerns, had ordered Anthropic to stop non-US citizens using two models: Claude Mythos 5, and Claude Fable 5, a version of Mythos but with guardrails to prevent abuse. Anthropic said Washington’s concerns were overblown. But forced to follow the order, yet with no way to check who was using the models, the company suddenly shut down access for everyone, US citizens included. There’s still no agreement on how to restore broad access. But last week, Bloomberg reported that select US companies, and the US government, still had access to Mythos despite the ban, but the EU’s cybersecurity agency Enisa had been shut out. Class division of researchersIn the longer term, access outside the US should be restored, said Marcel Salathé, co-director of the AI centre at the Federal Polytechnic School of Lausanne (EPFL), because a permanent restriction would be financially ruinous to US AI firms. “Even Trump will eventually budge to economic pressure,” he said.However, US AI firms might end up operating rather like banks, doing due diligence on users of their leading-edge AI models, he went on.European scientists are unlikely to fall foul of such checks, he said. But having a Chinese postgraduate in their lab, or doing a type of research the US disapproved of, could potentially result in access being denied. “It will create a sort of class division of researchers,” he said. More than frontier modelsHowever, successfully deploying AI in science is about far more than just having access to leading LLMs such as Mythos.Instead, many science-specific tools, most famously Alphafold, which predicts protein folding structures, require “far less” computing power to train than frontier models, well within the capabilities of European supercomputers, according to a recent analysis by the Arq Foundation, a Brussel-based AI think tank. Instead, tools such as Alphafold are possible because certain fields, like protein folding, weather prediction or astronomy, have large, clean, standardised datasets that can be used to train predictive models. Rather than compute, “data is the central constraint for AI for science,” the report says. For example, a less powerful physics model trained on CERN’s data “will be more valuable than the best model without certain data,” said Bengüsu Özcan, author of the report. European advantage?Europe could therefore steal a march by using its scientific data and infrastructure to create models US digital giants cannot. “They might actually match or go even beyond what these big [US] companies are doing,” said Özcan.Indeed, some European start-ups hope to create automated testing labs to create the kind of standardised data that could speed up the discovery of new materials. Materials scientists say there’s currently too little data to train models. AI scientistsHowever, even if the bottleneck for AI in science is often data, rather than compute, being at the mercy of the US for LLM access could still become a problem for European scientists. A new class of “AI scientists” based on leading LLMs is also emerging, Özcan’s report says.Unlike domain-specific tools like Alphafold, these try to automate day-to-day scientific work, by providing an overarching tool that coordinates specific models to synthesise existing literature, generate hypotheses or run analysis of data. The problem for Europe, however, is that these tools are sometimes based on US leading models. Google Deepmind’s AI Co-Scientist, for example, is built on Google’s Gemini LLM. Systematic failureThese scientific automation tools are still in their early stages, and still exhibit “systematic failures on scientific tasks,” Özcan’s report says. One of the problems is that the messy, day-to-day process of scientific discovery is largely not documented, meaning LLMs don’t have a good source of data to learn how scientists actually work. Meanwhile, some tasks, such as literature synthesis, don’t typically require access to leading edge models.However, the performance of AI scientific assistants could improve as companies such as Google pour resources into developing them. Rely on US“Unfortunately, for scientific assistants, there is not much we can do right now except rely on US frontier AI,” said Antoine Bosselut, a natural language processing expert based at EPFL. Chinese-built open models, which can run on local computers, lessening the risk of a shutdown, are touted as one alternative to US dependence. There’s also Mistral, once seen as Europe’s main AI hope, but the Paris-based company has seen its recent models fall behind in performance. But the Chinese alternatives are generally not as capable as US frontier AI models, said Bosselut, unless paired with fiddly, custom tools that allows them to act as independent agents. “Most scientists do not have the time,” he said. Don’t jump ship yetAlthough the kill switch moment is seen a warning shot for European scientists, it doesn’t mean labs should abandon US AI tools immediately. “I do not think the right response is simply to abandon US-based models and systems under the banner of sovereignty or independence,” said Mattias Björnmalm, secretary general of the Cesaer university association. “That would be an overreaction.”Related articlesEuropean sovereignty in AI requires ‘ugly trade-offs,’ say experts Robotic lab start-up makes case for €500M EU-funded materials testing facilityAI in science: is it useful? Instead, European researchers need to ask themselves whether they’re becoming dependent on technology that could be turned off in Washington, said Özcan. “They might not see this today, but they should really start asking this question,” she said. European researchers need a plan B, said Salathé. “I would recommend to every researcher, very proactively explore alternatives.”Own toolsIn addition, Björnmalm and others want European research institutions to develop their own scientific AI tools.Instead of signing AI deals with US digital giants, European universities should be using this money to create their own models, which may end up being superior for research, said Sabine Leonelli, an expert on the use of AI in science at the Technical University of Munich. “Universities in Europe seem to be happy to sign commercial contracts with US companies on the assumption [their AI tools] are better,” she said. But US frontier LLMs are typically opaque, and have been developed without independent scientific input, she said. “This is not good enough for science.”Last year, Swiss researchers released Apertus, an LLM designed to be completely transparent, from its development methods to its training data. The idea is that this makes scientific tools built on top of the model far more reproducible than with closed, commercial alternatives.“Europe needs more of this: serious capacity-building that gives researchers, universities and companies credible options,” said Björnmalm.
Advice on getting a frontier AI job, from a Google DeepMind engineer
Google DeepMind distinguished engineer Vladimir Feinberg has some blunt advice for those seeking a job at a top AI lab — "work like a dog."
Nobel-winning AlphaFold pioneer leaves Google DeepMind for Anthropic
John Jumper, a key member of the team at Google DeepMind for nearly a decade, is decamping for Anthropic.
Google I/O showed how the path for AI-driven science is shifting
Two years ago, an AI tool won Google DeepMind a Nobel. Researchers are now climbing toward a new goal.
Google launches Gemini for Science AI tools | ETIH EdTech News
AI in education and edtech research shift as Google launches Gemini for Science, with Google Labs tools for literature insights, hypothesis generation, computational discovery, and Science Skills in Google Antigravity. ETIH edtech news looks at Google DeepMind, Google Cloud, NotebookLM, Co-Scientist
Google Reveals Gemini For Science, An AI Research Tool And Science Skills Platform
Google introduced Gemini for Science, a new collection of AI-powered scientific tools and experiments designed to accelerate discovery and support researchers across disciplines. The initiative combines multiple Google DeepMind technologies, including Co-Scientist, AlphaEvolve, Empirical Research Assistance (ERA), and NotebookLM, to streamline key parts of the scientific method.
How Google Is Moving The Needle On AI, Tony Stark-Style
Google DeepMind's new AI-powered pointer may change how humans interact with computers altogether.
DeepMind details Googlebook ‘Magic Pointer’ with demos you can try, also coming to Gemini in Chrome
The Magic Pointer on Googlebook was built with Google DeepMind. The research team behind this underlying capability...
Coachella Uses Google DeepMind AI to Test the Future of Live Entertainment
Coachella is experimenting with AI-built artist tools, immersive digital worlds, and 3D performance archives that could shape future fan experiences.
DeepMind moved faster by acting like a startup, Demis Hassabis says
Google DeepMind CEO Demis Hassabis shared how his AI lab has caught up with its rivals in the last two to three years.
Google DeepMind Partnerships in India: scaling AI in science and education
Google DeepMind announces new AI partnerships in India to advance scientific research, empower students with interactive Gemini-powered learning, and support national goals in agriculture and renewable energy.
We're creating cutting-edge AI science tools for Google DeepMind—and 3 million researchers across 190+ countries
Five years ago, our AlphaFold AI system solved the 50-year grand challenge of protein structure prediction. But that's not the whole story.
Google DeepMind launches AI tool to help identify genetic drivers of disease
AlphaGenome can analyse up to 1m letters of DNA code at once and could pave way for new treatments
Google DeepMind launches AI tool to help identify genetic drivers of disease
AlphaGenome can analyse up to 1m letters of DNA code at once and could pave way for new treatments
Former Google, Apple Researchers Raising $50 Million for New Visual AI Startup
Andrew Dai, a veteran AI researcher who recently left Google DeepMind after 14 years, is launching a new startup focused on AI models that understand and process text, images, video and audio simultaneously, Dai said. The new startup, Elorian, is in talks with investors to raise a seed round of ...
Google DeepMind CEO Demis Hassabis says some AI startups are wildly overpriced — and a correction is coming
The Google DeepMind CEO warns that some early AI startups are overvalued and the current pace of funding won't last.
Google DeepMind to build materials science lab after signing deal with UK
Big Tech group will work with Sir Keir Starmer’s government to enhance AI use across public sector
AI to accelerate national renewal and growth as Google DeepMind backs UK tech and science sectors
A new partnership with Google DeepMind will help to turn cutting-edge AI into real benefits for working people.
Google DeepMind agrees to sweeping research collaboration with the U.K. government
The partnership will focus on nuclear fusion, discovering new materials, AI safety, and the societal impacts of AI.
Google DeepMind CEO Demis Hassabis says AI scaling 'must be pushed to the maximum'
Google DeepMind CEO Demis Hassabis says AI scaling is key to reaching AGI, a still theoretical version of AI that reasons as well as humans.