David La retweeted
You know what's cool... a quadrillion tokens.
We processed almost 1,000,000,000,000,000 tokens last month, more than double the amount from May. 📈
Google is processing 980 trillion+ monthly tokens across our products and APIs (up from 480T in May) 🤯
No slowdown in sight, intelligence is everywhere.
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David La retweeted
Big news for data science in higher ed! 🚀Colab now offers 1-year Pro subscriptions free of charge for verified US students/faculty, interactive Slideshow Mode for lectures, & an AI toggle per notebook. Enhance teaching & learning in the upcoming academic year! Read all about it here: goo.gle/40pxnl1. #GoogleColab #EdTech.
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Major congratulations to my previous postdoc advisor David Baker @UWproteindesign and my current coworkers Demis Hassabis and John Jumper @GoogleDeepMind!
BREAKING NEWS
The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Chemistry with one half to David Baker “for computational protein design” and the other half jointly to Demis Hassabis and John M. Jumper “for protein structure prediction.”
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David La retweeted
BREAKING NEWS
The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Chemistry with one half to David Baker “for computational protein design” and the other half jointly to Demis Hassabis and John M. Jumper “for protein structure prediction.”
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David La retweeted
BREAKING NEWS
The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”
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David La retweeted
Our AI for chip design method AlphaChip has transformed the way we design microchips. ⚡
From helping to design state-of-the-art TPUs for building AI models to CPUs in data centers - its widespread impact can be seen across Alphabet and beyond.
Find out more → dpmd.ai/3ZDRtYY
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David La retweeted
We’re presenting AlphaProteo: an AI system for designing novel proteins that bind more successfully to target molecules. 🧬
It could help scientists better understand how biological systems function, save time in research, advance drug design and more. 🧵 dpmd.ai/3XuMqbX
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David La retweeted
Introducing Ithaca, the first deep neural network for textual restoration, as well as geographical and chronological attribution of ancient Greek inscriptions.
Out today in @nature, Ithaca aims to assist historians & better understand ancient history: dpmd.ai/Ithaca-blog 1/
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David La retweeted
On the cover of @Nature today, our work ITHACA on restoring, locating and dating ancient texts using deep neural networks! So proud of our team @iassael @Sicilyepigraphy @BrendanShilling @_Mahyar_ @NandoDF @ionandrou @itpavlopoulos & all our collaborators
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David La retweeted
Today in @nature, with @EPFL, the first deep reinforcement learning system that can keep nuclear fusion plasma stable inside its tokamaks, opening new avenues to advance nuclear fusion research.
Paper: dpmd.ai/fusion-paper
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David La retweeted
OK, this is one I’ve been waiting to share for a *long* time – the first ever demonstration of deep reinforcement learning on a nuclear fusion research device! nature.com/articles/s41586-0…
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David La retweeted
Introducing #AlphaCode: a system that can compete at average human level in competitive coding competitions like @codeforces. An exciting leap in AI problem-solving capabilities, combining many advances in machine learning!
Read more: dpmd.ai/Alpha-Code 1/
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Aversarial example in the human mind?
This deceiving grid tricks you into thinking there's a curved line somewhere, but you can't find it. The purposefully placed gray lines of squares in a curved formation will induce your peripheral vision to interpolate curved lines [read more: buff.ly/3oKqWXa]
David La retweeted
Can machine learning explain the world at the quantum level? New in @ScienceMagazine, with the code also released, our team shows that neural networks can radically improve density functionals with #DM21.
Read more: dpmd.ai/density-functionals
Code: dpmd.ai/density-functionals-… 1/
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David La retweeted
Ramanujan is my favourite mathematician. I’ve always loved his intuitive style, seeing hidden patterns in numbers noone else could. It inspired us to wonder about the potential of using AI as a tool to help the intuition of today’s top mathematicians: dpmd.ai/maths-blog
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David La retweeted
A new paper in @Nature details how machine learning was used to make significant new discoveries in pure mathematics by guiding the intuition of some of the world’s top mathematicians from @Sydney_Uni and @OxUniMaths: dpmd.ai/maths-blog
Paper: dpmd.ai/nature-maths 1/
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David La retweeted
Thrilled to announce the launch of a new Alphabet company @IsomorphicLabs. Our mission is to reimagine the drug discovery process from first principles with an AI-first approach, to accelerate biomedical breakthroughs and find cures for diseases. Details: isomorphiclabs.com/blog
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David La retweeted
The #AlphaFold source code has been updated and now accounts for multi-chain protein complexes - providing a significant improvement in accuracy for predicting protein interactions: dpmd.ai/af-multimer-os
Generate predictions from your browser via: dpmd.ai/alphafold-colab
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David La retweeted
We’ve acquired the MuJoCo physics simulator (mujoco.org) and are making it free for all, to support research everywhere. MuJoCo is a fast, powerful, easy-to-use, and soon to be open-source simulation tool, designed for robotics research: dpmd.ai/mujoco-blog
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David La retweeted
Many single protein chains can be accurately predicted using AlphaFold, but multi-chain protein complexes remain challenging. Introducing AlphaFold-Multimer, a model that can predict the structure of multi-chain protein complexes with increased accuracy dpmd.ai/alphafold_multimer 1/
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