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"Google stunned the world by defeating Go legend Lee Se-dol yesterday, and it wasn't a fluke - AlphaGo, the AI program developed by Google's DeepMind unit, has just won the second game of a five-game Go match being held in Seoul, South Korea. AlphaGo prevailed in a gripping battle that saw Lee resign after hanging on in the final period of byo-yomi ("second-reading" in Japanese) overtime, which gave him fewer than 60 seconds to carry out each move."

"DeepMind is trying to combine deep learning and algorithms, creating the one algorithm to rule them all: a deep learning model that can learn how to emulate any algorithm, generating an algorithm-equivalent model that can work with real-world data."

""It marks an exciting moment for the field," said Demis Hassabis, DeepMind's founder and chief executive. "These algorithms are now becoming mature enough and powerful enough to be applicable to really challenging scientific problems." Advertisement Venki Ramakrishnan, the president of the Royal Society, called the work "a stunning advance" that had occurred "decades before many people in the field would have predicted"."

"According to tweets from DeepMind founder Demis Hassabis, however, this time AlphaGo really did make mistakes. The AI "thought it was doing well, but got confused on move 87," Hassabis said, later clarifying that it made a mistake on move 79 but only realized its error by 87. AlphaGo adjusts its playing style based on its evaluation of how the game is progressing."

"A company owned by Google has been given access to the healthcare data of up to 1.6 million patients from three hospitals run by a major London NHS trust. DeepMind, the tech giant's London-based company most famous for its innovative use of artificial intelligence, is being provided with the patient information as part of an agreement with the Royal Free NHS trust, which runs the Barnet, Chase Farm and Royal Free hospitals. It includes information about people who are HIV-positive as well as details of drug overdoses, abortions and patient data from the past five years, according to a report by the New Scientist."

"The arcane nature of "protein folding", a mind-boggling form of molecular origami, is rarely discussed outside scientific circles, but it is a problem of profound importance. The machinery of biology is built from proteins and it a protein's shape defines its function. Understand how proteins fold up and researchers could usher in a new era of scientific and medical progress."

"AI tool GNoME finds 2.2 million new crystals, including 380,000 stable materials that could power future technologies Modern technologies from computer chips and batteries to solar panels rely on inorganic crystals. To enable new technologies, crystals must be stable otherwise they can decompose, and behind each new, stable crystal can be months of painstaking experimentation. Today, in a paper published in Nature, we share the discovery of 2.2 million new crystals - equivalent to nearly 800 years' worth of knowledge. We introduce Graph Networks for Materials Exploration (GNoME), our new deep learning tool that dramatically increases the speed and efficiency of discovery by predicting the stability of new materials."

"On the other side of the table will be his opponent: Alphago, a programme built by Google subsidiary DeepMind which became, in October, the first machine to beat a professional human Go player, the European champion Fan Hui. That match proved that Alphago could hold its own against the best; this one will demonstrate whether "the best" have to relinquish that title entirely."

"The groundbreaking artificial intelligence system, developed by the AI-outfit DeepMind with Moorfields eye hospital NHS foundation trust and University College London, was capable of correctly referring patients with more than 50 different eye diseases for further treatment with 94% accuracy, matching or beating world-leading eye specialists."

"It is also self-improving. The 10-year-old grading software leverages deep learning algorithms to "compare notes" with human teachers' scores, suggestions, and comments. An engineer involved in the project compared its capabilities to those of AlphaGo, the record-breaking AI Go player developed by Google subsidiary DeepMind."

"This type of approach can speed up learning times and improve the efficiency of algorithms, says Max Jaderberg at Google's AI company DeepMind. The company used a similar technique last year to teach an AI to explore a virtual maze. Its algorithm learned much more quickly than conventional reinforcement learning approaches. "Our agent is far quicker and requires a lot less experience from the world to train, making it much more data efficient," he says."

"In a paper published in Nature, we introduce AlphaFold 3, a revolutionary model that can predict the structure and interactions of all life's molecules with unprecedented accuracy. For the interactions of proteins with other molecule types we see at least a 50% improvement compared with existing prediction methods, and for some important categories of interaction we have doubled prediction accuracy."

"Writing in the journal Science, Summerfield and colleagues from Google DeepMind report how they built the "Habermas Machine" - an AI system named after the German philosopher Jürgen Habermas. The system works by taking written views of individuals within a group and using them to generate a set of group statements designed to be acceptable to all. Group members can then rate these statements, a process that not only trains the system but allows the statement with the greatest endorsement to be selected."

"Imagine sitting down with an AI model for a spoken two-hour interview. A friendly voice guides you through a conversation that ranges from your childhood, your formative memories, and your career to your thoughts on immigration policy. Not long after, a virtual replica of you is able to embody your values and preferences with stunning accuracy. That's now possible, according to a new paper from a team including researchers from Stanford and Google DeepMind, which has been published on arXiv and has not yet been peer-reviewed."

"First, the problems were manually translated into formal mathematical language for our systems to understand. In the official competition, students submit answers in two sessions of 4.5 hours each. Our systems solved one problem within minutes and took up to three days to solve the others. AlphaProof solved two algebra problems and one number theory problem by determining the answer and proving it was correct. This included the hardest problem in the competition, solved by only five contestants at this year's IMO. AlphaGeometry 2 proved the geometry problem, while the two combinatorics problems remained unsolved."

"The really significant thing about AlphaGo is that it (and its creators) cannot explain its moves. And yet it plays a very difficult game expertly. So it's displaying a capability eerily similar to what we call intuition - "knowledge obtained without conscious reasoning". Up to now, we have regarded that as an exclusively human prerogative. It's what Newton was on about when he wrote "Hypotheses non fingo" in the second edition of his Principia: "I don't make hypotheses," he's saying, "I just know.""

"The models work by dividing maps into what Google calls "supersegments" "