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Google DeepMind

@GoogleDeepMind

Nov 20

Introducing AlphaQubit: our AI-based system that can more accurately identify errors inside quantum computers. 🖥️⚡

This research is a joint venture with @GoogleQuantumAI, published today in @Nature → goo.gle/3ZflWMn

The possibilities in quantum computing are compelling. ♾️

They can solve certain problems in a few hours, which would take a classical computer billions of years. This can help lead to advances in areas like drug discovery to material design.

But building a stable quantum system is a challenge.

Qubits are units of information that underpin quantum computing. These can be disrupted by microscopic defects in hardware, heat, vibration, and more.

Quantum error correction solves this by grouping multiple noisy qubits together to create redundancy, into something called a “logical qubit”. Using consistency checks, a decoder then protects the information stored in this.

In our experiments, our decoder AlphaQubit made the fewest errors.

Read 7 tweets

Google DeepMind

@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/4eTg28Z

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

Google DeepMind

@GoogleDeepMind

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

Google DeepMind

@GoogleDeepMind

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

Google DeepMind

@GoogleDeepMind

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’.

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.

Read 7 tweets

Google DeepMind

@GoogleDeepMind

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.

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