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@DeepMind

22 Jul

@emblebi

Today with @emblebi, we're launching the #AlphaFold Protein Structure Database, which offers the most complete and accurate picture of the human proteome, doubling humanity’s accumulated knowledge of high-accuracy human protein structures - for free: dpmd.ai/alphafolddb 1/

@uniprot

We’re also sharing the proteomes of 20 other biologically-significant organisms, totalling over 350k structures. Soon we plan to expand to over 100 million, covering almost every sequenced protein known to science & the @uniprot reference database.

dpmd.ai/alphafold-blog 2/

@DNDi

We’re excited to see how this will enable and accelerate research for scientists around the world. We've already seen promising signals from early collaborators using #AlphaFold in their own work, including @DNDi, @CEI_UoP, @UCSF & @CUBoulder: dpmd.ai/alphafold-case… 3/

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@DeepMind

6 Jul

Many models bake in domain knowledge to control how input data is processed. This means models must be redesigned to handle new types of data.

Introducing the Perceiver, an architecture that works on many kinds of data - in some cases all at once: dpmd.ai/perceiver (1/)

Like Transformers, Perceivers process inputs using attention. But unlike Transformers, they first map inputs to a small latent space where processing is cheap & doesn’t depend on the input size. This allows us to build deep networks even when using large inputs like images. (2/)

Perceivers can learn a different attention pattern for each type of data (shown for images and video), making it easy for them to adapt to new data and unexplored problems where researchers may not know what kinds of patterns they should be looking for. (3/)

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DeepMind

@DeepMind

14 May

In a new paper, our team tackles a fundamental AI problem: how can we simultaneously parse the world into objects and properties, while simultaneously inducing the rules explaining how objects change over time: dpmd.ai/3fmrxsn (1/)

@LittleBimble

Work by @LittleBimble with @pfau, @pushmeet, Matko Bosnjak, Lars Buesing, Kevin Ellis, and Marek Sergot. (2/)

This system combines the Apperception Engine with a binary neural network to learn a provably 100% accurate model of non-trivial environments (e.g. Sokoban) from noisy raw pixel data. (3/)

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DeepMind

@DeepMind

7 May

Multimodal transformers achieve impressive results on many tasks like Visual Question Answering and Image Retrieval, but what contributes most to their success? dpmd.ai/3h8u23Z (1/)

@jalayrac

This work explores how different architecture variations, pretraining datasets, and losses impact multimodal transformers’ performance on image retrieval: dpmd.ai/3eENAtF

(By Lisa Anne Hendricks, John Mellor, Rosalia Schneider, @jalayrac & @aidanematzadeh) (2/)

Multimodal transformers outperform simpler dual encoder architectures when the amount of data is held constant. Interestingly, larger datasets don’t always improve performance. (3/)

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DeepMind

@DeepMind

10 Dec 20

@wojczarnecki

For #NeurIPS2020, we spoke with @wojczarnecki about Spinning Tops, advice he wish he received as a student, and his goals for next year! #PeopleBehindThePapers

AI has been extremely successful in real world games (GO, DOTA, StarCraft) with results coming from relatively simple multi-agent algorithms. In this paper, we hypothesise that they share a common geometry - Spinning Tops. Learn more: bit.ly/3qI8RrD #NeurIPS2020

I’ve always loved biology. During my masters I decided to take a handful of neurophysiology courses - which I found to be interesting. But eventually I realised that my true strengths were in mathematical sciences. A career in ML and AI became a natural way to combine the two.

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DeepMind

@DeepMind

1 Dec 20

Yesterday we shared the news that #AlphaFold has been recognised as a solution to the ‘protein folding problem’ by #CASP14, the biennial Critical Assessment of Protein Structure Prediction. But what exactly is protein folding, and why is it important? A thread… (1/6)

Proteins are the building blocks of life - they underpin the biological processes in every living thing. If you could unravel a protein you would see that it’s like a string of beads made of a sequence of different chemicals known as amino acids. (2/6)

Interactions between these amino acids make the protein fold, as it finds its shape out of almost limitless possibilities. For decades, scientists have been trying to find a method to reliably determine a protein’s structure just from its sequence of amino acids. (3/6)

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