Google DeepMind News and Discussions

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Yuli Ban
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"Highly accurate protein structure prediction with AlphaFold"
Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort, the structures of around 100,000 unique proteins have been determined, but this represents a small fraction of the billions of known protein sequences. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the 3-D structure that a protein will adopt based solely on its amino acid sequence, the structure prediction component of the ‘protein folding problem’, has been an important open research problem for more than 50 years. Despite recent progress, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even where no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14), demonstrating accuracy competitive with experiment in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.
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Yuli Ban
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And remember my friend, future events such as these will affect you in the future
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Yuli Ban
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Re: Google DeepMind News and Discussions

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DeepMind says it will release the structure of every protein known to science
Back in December 2020, DeepMind took the world of biology by surprise when it solved a 50-year grand challenge with AlphaFold, an AI tool that predicts the structure of proteins. Last week the London-based company published full details of that tool and released its source code.

Now the firm has announced that it has used its AI to predict the shapes of nearly every protein in the human body, as well as the shapes of hundreds of thousands of other proteins found in 20 of the most widely studied organisms, including yeast, fruit flies, and mice. The breakthrough could allow biologists from around the world to understand diseases better and develop new drugs.
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Generally capable agents emerge from open-ended play
In recent years, artificial intelligence agents have succeeded in a range of complex game environments. For instance, AlphaZero beat world-champion programs in chess, shogi, and Go after starting out with knowing no more than the basic rules of how to play. Through reinforcement learning (RL), this single system learnt by playing round after round of games through a repetitive process of trial and error. But AlphaZero still trained separately on each game — unable to simply learn another game or task without repeating the RL process from scratch. The same is true for other successes of RL, such as Atari, Capture the Flag, StarCraft II, Dota 2, and Hide-and-Seek. DeepMind’s mission of solving intelligence to advance science and humanity led us to explore how we could overcome this limitation to create AI agents with more general and adaptive behaviour. Instead of learning one game at a time, these agents would be able to react to completely new conditions and play a whole universe of games and tasks, including ones never seen before.

Today, we published "Open-Ended Learning Leads to Generally Capable Agents," a preprint detailing our first steps to train an agent capable of playing many different games without needing human interaction data. We created a vast game environment we call XLand, which includes many multiplayer games within consistent, human-relatable 3D worlds. This environment makes it possible to formulate new learning algorithms, which dynamically control how an agent trains and the games on which it trains. The agent’s capabilities improve iteratively as a response to the challenges that arise in training, with the learning process continually refining the training tasks so the agent never stops learning. The result is an agent with the ability to succeed at a wide spectrum of tasks — from simple object-finding problems to complex games like hide and seek and capture the flag, which were not encountered during training. We find the agent exhibits general, heuristic behaviours such as experimentation, behaviours that are widely applicable to many tasks rather than specialised to an individual task. This new approach marks an important step toward creating more general agents with the flexibility to adapt rapidly within constantly changing environments.

starspawn0:
This is one of the tasks I wrote once before it would be nice to see brain data applied to. In an old post of mine, I wondered: could one use brain data to build a game-playing agent that can do decently on a new game out-of-the-box? You see, humans can be shown a new game, and if they have some game-playing experience, can do an ok job in the first try -- e.g. they won't die immediately; won't run into enemies; will predict where the enemies are moving, using physical commonsense reasoning; infer what a goal might be; and so on. That's a much, much harder problem than training an agent to solve any particular game. It requires something closer to AGI than we've seen in game-playing AIs in the past.

I would say this is as much a breakthrough and shock as GPT-3 (and GPT-2). Scale this up and use more real-world tasks (instead of games), and you could probably make something that genuinely seems intelligent, if put in a robot body and allowed to interact with the world. Add in some language capability, and you're going to have something that needs to be watched carefully!

....

The success of this work will lead to even larger attempts by other groups. The perceived risk in attempting something like this is now a lot lower. Before this work, some teams might have had the same idea, but then thought, "Ahh... probably won't work. And if we try, we'll have wasted large numbers of hours and millions of dollars, with little to show for it, except marginally better game-playing agents. Could we really make this work?..." and the doubt and skepticism set in.
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Yuli Ban
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Re: Google DeepMind News and Discussions

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And remember my friend, future events such as these will affect you in the future
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Yuli Ban
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Re: Google DeepMind News and Discussions

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And remember my friend, future events such as these will affect you in the future
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Yuli Ban
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Re: Google DeepMind News and Discussions

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And remember my friend, future events such as these will affect you in the future
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