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May 11, 2020 7 tweets 5 min read Read on X
We’re back with more suggestions from our researchers for ways to expand your knowledge of AI.

Today’s #AtHomeWithAI recommendations are from research scientist Kimberly Stachenfeld (@neuro_kim) (1/7) Image
She recommends “The Scientist in the Crib” [longer listen] by @AlisonGopnik, Andrew Meltzoff, & Patricia K. Kuhl for those who are interested in what early learning tells us about the mind.

Listen along here: adbl.co/2Wwp5pE #AtHomeWithAI
Want to explore intelligence by using an approach that integrates cognitive science, neuroscience, computer science and AI?

Kimberly suggests the Brains, Minds & Machines Summer Course, offered & taught by @MBLScience and @MIT_CBMM here: bit.ly/351M6V5 #AtHomeWithAI
Interested in computational systems neuroscience? @neuro_kim recommends the lecture series from @MBLScience to learn more about circuits and system properties of the brain.

Watch them here: bit.ly/2VzCez2 #AtHomeWithAI
@neuro_kim says Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems [longer read] by Peter Dayan & L.F. Abbott is a must read for anyone looking for an introduction to the topic.

Read here: bit.ly/2xLt2yv #AtHomeWithAI
Described as “a classic for anyone who wants to understand the roots of DL”, Kimberly recommends “The Appeal of Parallel Distributed Processing” [longer read] by James McClelland, the late David Rumelhart, & Geoffrey Hinton.

Read it here: stanford.io/2VCHbHr #AtHomeWithAI
Have your own list of favourite resources? Let us know below! #AtHomeWithAI

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More from @GoogleDeepMind

Oct 23
Our latest generative technology is now powering MusicFX DJ in @LabsDotGoogle - and we’ve also updated Music AI Sandbox, a suite of experimental music tools which can streamline creation. 🎵

This will make it easier than ever to make music in real-time with AI. ✨goo.gle/4eTg28ZImage
MusicFX DJ lets you input multiple prompts and include details on instruments, genres and vibes to create music. 🎛️

We’ve updated and improved the interface using feedback from @YouTube’s Music AI Incubator.
Two key innovations lie at the core of MusicFX DJ.

🔘 We adapted our models to perform real-time streaming by training them to generate the next 2 seconds of music, based on the previous 10 seconds.

🔘 “Style embedding” is steered by the player, which is a mix of text prompt embeddings set by the slider values
Read 6 tweets
Sep 5
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
Protein binders are promising tools in drug development and biotech.

They’ve demonstrated potential in:
🌀 binding cancer targets
🌀 blocking viral infections
🌀 modulating immune response

But traditional ways of identifying effective protein binders involve extensive lab work.
AlphaProteo was trained on vast amounts of protein data from @PDBeurope and millions of predicted structures from #AlphaFold.

This meant it was able to learn how molecules bind to each other in intricate ways. dpmd.ai/3XuMqbX
Read 6 tweets
Aug 8
Meet our AI-powered robot that’s ready to play table tennis. 🤖🏓

It’s the first agent to achieve amateur human level performance in this sport. Here’s how it works. 🧵
Robotic table tennis has served as a benchmark for this type of research since the 1980s.

The robot has to be good at low level skills, such as returning the ball, as well as high level skills, like strategizing and long-term planning to achieve a goal.
To train the robot, we gathered a dataset of initial table tennis ball states - which included information about position, speed, and spin.

The system practiced using this library and learned different skills, like forehand topspin, backhand targeting, and returning serves.
Read 9 tweets
Aug 2
AI systems can be powerful but opaque "black boxes" - even to researchers who train them. ⬛

Enter Gemma Scope: a set of open tools made up of sparse autoencoders to help decode the inner workings of Gemma 2 models, and better address safety issues. → dpmd.ai/gemma-scope
Language models turn your text input into a series of ‘activations’ - which map the relationships between the words you’ve entered to help it write its answer. 💬

Activations at different layers in its neural network represent increasingly advanced concepts, known as ‘features’. Image
Activations are made up of neurons, which “fire” for many unrelated features - making them hard to decipher.

Each feature seems to be a specific combination of neurons - but how can we find the meaningful combinations of neurons?

This is where sparse autoencoders can help.Image
Read 7 tweets
Jul 31
We’re welcoming a new 2 billion parameter model to the Gemma 2 family. 🛠️

It offers best-in-class performance for its size and can run efficiently on a wide range of hardware.

Developers can get started with 2B today → dpmd.ai/4d0MKEH
We’re also introducing ShieldGemma: a series of state-of-the-art safety classifiers designed to filter harmful content. 🛡️

These target hate speech, harassment, sexually explicit material and more, both in the input and output stages.
Finally, we’re announcing Gemma Scope, a set of tools to help researchers examine how Gemma 2 makes decisions. 🔍

It's a comprehensive, open suite of sparse autoencoders - specialized neural networks that zoom into the model’s inner workings and make them more interpretable.
Read 4 tweets
Jul 25
We’re presenting the first AI to solve International Mathematical Olympiad problems at a silver medalist level.🥈

It combines AlphaProof, a new breakthrough model for formal reasoning, and AlphaGeometry 2, an improved version of our previous system. 🧵 dpmd.ai/imo-silver
Our system had to solve this year's six IMO problems, involving algebra, combinatorics, geometry & number theory. We then invited mathematicians @wtgowers and Dr Joseph K Myers to oversee scoring.

It solved 4️⃣ problems to gain 28 points - equivalent to earning a silver medal. ↓ Colored graph showing our AI system’s performance relative to human competitors earning bronze, silver and gold at IMO 2024. Our system earned 28 out of 42 total points, achieving the same level as a silver medalist in the competition and nearly reaching the gold-medal threshold starting at 29 points.
For non-geometry, it uses AlphaProof, which can create proofs in Lean. 🧮

It couples a pre-trained language model with the AlphaZero reinforcement learning algorithm, which previously taught itself to master games like chess, shogi and Go. dpmd.ai/imo-silverProcess infographic of AlphaProof’s reinforcement learning training loop: Around one million informal math problems are translated into a formal math language by a formalizer network. Then a solver network searches for proofs or disproofs of the problems, progressively training itself via the AlphaZero algorithm to solve more challenging problems.
Read 8 tweets

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