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Google DeepMind

@GoogleDeepMind

Jun 24

We’re bringing powerful AI directly onto robots with Gemini Robotics On-Device. 🤖

It’s our first vision-language-action model to help make robots faster, highly efficient, and adaptable to new tasks and environments - without needing a constant internet connection. 🧵

What makes this new model unique?

🔵 It has the generality and dexterity of Gemini Robotics - but it can run locally on the device
🔵 It can handle a wide variety of complex, two-handed tasks out of the box
🔵 It can learn new skills with as few as 50-100 demonstrations

From humanoids to industrial bi-arm robots, the model supports multiple embodiments, even though it was pre-trained on ALOHA - while following instructions from humans. 💬

These tasks may seem easy for us but require fine motor skills, precise manipulation and more. ↓

Read 5 tweets

Google DeepMind

@GoogleDeepMind

Jun 17

Hot Gemini updates off the press. 🚀

Anyone can now use 2.5 Flash and Pro to build and scale production-ready AI applications. 🙌

We’re also launching 2.5 Flash-Lite in preview: the fastest model in the 2.5 family to respond to requests, with the lowest cost too. 🧵

2.5 Flash-Lite now supports:

🔹Thinking: improving performance and transparency through step-by-step reasoning
🔹Tool-use: including Search, code execution and 1 million token context window - similar to 2.5 Flash and Pro

⚡ 2.5 Flash-Lite is our most cost efficient model yet - and with lower latency than 2.0 Flash-Lite and Flash on a broad sample of prompts.

It also has all-around, higher quality than 2.0 Flash-Lite on coding, math, science, reasoning and multimodal benchmarks.

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Google DeepMind

@GoogleDeepMind

May 14

Introducing AlphaEvolve: a Gemini-powered coding agent for algorithm discovery.

It’s able to:

🔘 Design faster matrix multiplication algorithms
🔘 Find new solutions to open math problems
🔘 Make data centers, chip design and AI training more efficient across @Google. 🧵

Our system uses:
🔵 LLMs: To synthesize information about problems as well as previous attempts to solve them - and to propose new versions of algorithms
🔵 Automated evaluation: To address the broad class of problems where progress can be clearly and systematically measured.
🔵 Evolution: Iteratively improving the best algorithms found, and re-combining ideas from different solutions to find even better ones.

Over the past year, we’ve deployed algorithms discovered by AlphaEvolve across @Google’s computing ecosystem, including data centers, software and hardware.

It’s been able to:

🔧 Optimize data center scheduling
🔧 Assist in hardware design
🔧 Enhance AI training and inference

Read 6 tweets

Google DeepMind

@GoogleDeepMind

Apr 30

We’re helping robots self-improve with the power of LLMs. 🤖

Introducing the Summarize, Analyze, Synthesize (SAS) prompt, which analyzes how they perform tasks based on previous actions and then suggests ways for them to get better using the medium of table tennis. 🏓

Large language models like Gemini have an inherent ability to problem solve, without needing to retrain for specific jobs.

Robots can use these models to improve how they operate over time, by interacting with the world, and learning from those interactions. 🦾 goo.gle/4jVFsoE

With the SAS prompt, we can now use language models like Gemini to learn from a robot's history.

This allows the model to analyze parameter effects ⚡, and suggest ways to improve - similar to a real-life table tennis coach. 💡 goo.gle/3GvWQ54

Read 4 tweets

Google DeepMind

@GoogleDeepMind

Apr 23

We built an AI model to simulate how a fruit fly walks, flies and behaves – in partnership with @HHMIJanelia. 🪰

Our computerized insect replicates realistic motion, and can even use its eyes to control its actions.

Here’s how we developed it – and what it means for science. 🧵

To create it, we turned to MuJoCo, our open-source physics simulator – created for robotics and biomechanics – and added features such as:

▪️simulating fluid forces on the flapping wings, enabling flight
▪️adhesion actuators – mimicking the gripping force of insect feet

We then trained an artificial neural network on real fly behavior from recorded videos 🎥 and let it control the model in MuJoCo.

This enables it to learn how to move the virtual insect in the most realistic way.

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Google DeepMind

@GoogleDeepMind

Dec 16, 2024

Today, we’re announcing Veo 2: our state-of-the-art video generation model which produces realistic, high-quality clips from text or image prompts. 🎥

We’re also releasing an improved version of our text-to-image model, Imagen 3 - available to use in ImageFX through @LabsDotGoogle. → goo.gle/veo-2-imagen-3

Veo 2 is able to:
▪️ Create videos at resolutions up to 4k
▪️ Understand camera controls in prompts, such as wide shot, POV and drone shots
▪️ Better recreate real-world physics and realistic human expression

In head-to-head comparisons of outputs by human raters, it was preferred over other top video generation models. → goo.gle/veo-2

We’ve also enhanced Imagen 3’s ability to:
▪️ Produce diverse art styles: realism, fantasy, portraiture and more
▪️ More faithfully turn prompts into accurate images
▪️ Generate brighter, more compositionally balanced visuals

→ goo.gle/imagen-3-update

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