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Sep 9, 2021 10 tweets 9 min read Read on X
Introducing the '21 DeepMind x @ai_ucl Reinforcement Learning Lecture Series, a comprehensive introduction to modern RL.

Follow along with our researchers are they explore Markov Decision Processes, sample-based learning algorithms & much more: dpmd.ai/2021RLseries 1/2 Image
Also find the full series via the DeepMind @YouTube channel: dpmd.ai/DeepMindxUCL21
In the first lecture of the series, Research Scientist Hado introduces the course and explores the fascinating connection between reinforcement learning and artificial intelligence: dpmd.ai/RLseries1

#DeepMindxUCL @ai_ucl Image
In lecture two, Research Scientist Hado explains why it's important for learning agents to balance exploring and exploiting acquired knowledge at the same time: dpmd.ai/RLseries2

#DeepMindxUCL @ai_ucl Image
In the third lecture, Research Scientist Diana shows us how to solve MDPs with dynamic programming to extract accurate predictions and good control policies: dpmd.ai/RLseries3

#DeepMindxUCL @ai_ucl Image
In lecture four, Diana covers dynamic programming algorithms as contraction mappings, looking at when and how they converge to the right solutions: dpmd.ai/RLseries4

#DeepMindxUCL @ai_ucl Image
In this lecture, Hado explores model-free prediction and its relation to Monte Carlo and temporal difference algorithms: dpmd.ai/RLseries5

#DeepMindxUCL @ai_ucl Image
In part two of the model-free lecture, Hado explains how to use prediction algorithms for policy improvement, leading to algorithms - like Q-learning - that can learn good behaviour policies from sampled experience: dpmd.ai/RLseries6

#DeepMindxUCL @ai_ucl Image
In this lecture, Hado explains how to combine deep learning with reinforcement learning for deep reinforcement learning. He looks at the properties and difficulties that arise when combining function approximation with RL algorithms: dpmd.ai/RLseries7

#DeepMindxUCL @ai_ucl Image
In this lecture, Research Engineer Matteo explains how to learn and use models, including algorithms like Dyna and Monte-Carlo tree search (MCTS): dpmd.ai/RLseries8

#DeepMindxUCL @ai_ucl Image

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More from @GoogleDeepMind

Sep 4
We’re helping to unlock the mysteries of the universe with AI. 🌌

Our novel Deep Loop Shaping method
published in @ScienceMagazine could help astronomers observe more events like collisions and mergers of black holes in greater detail, and gather more data about rare space phenomena. 🧵
Astronomers already know a lot about the smallest and largest black holes. ⚫

But we have limited data on intermediate-mass black holes, and the observatories we use to measure their gravitational waves need improved control, and expanded reach. ↓ goo.gle/47oalza
⚡This is where Deep Loop Shaping comes in.

Developed in collaboration with @LIGO Laser Interferometer Gravitational-Wave Observatory, @CalTech and the Gran Sasso Science Institute, it reduces noise and improves control in an observatory’s feedback system - helping stabilize components used for measuring gravitational waves.📸 Caltech/MIT/LIGO Lab
Read 8 tweets
Aug 26
Image generation with Gemini just got a bananas upgrade and is the new state-of-the-art image generation and editing model. 🤯

From photorealistic masterpieces to mind-bending fantasy worlds, you can now natively produce, edit and refine visuals with new levels of reasoning, control and creativity.

A quick dive into Gemini 2.5 Flash’s capabilities 🧵
🎯 Character consistency

Give the model reference images and it can produce new visuals that maintain a character, subject or object’s likeness across different poses, lighting, environments or styles - helping you create more compelling, narrative-driven work.Image
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🔄 Design application

Looking to apply a specific artistic style, design, or texture? 2.5 Flash can now easily transfer this from one image to another while preserving the previous subject's form and details.Image
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Read 6 tweets
Jul 23
Our new state-of-the-art AI model Aeneas transforms how historians connect the past. 📜

Ancient inscriptions often lack context – it's like solving a puzzle with 90% of the pieces lost to time. It helps researchers interpret and situate inscriptions in their past context. 🧵
By transforming each ancient text into a unique historical fingerprint, Aeneas can identify similarities across 176,000 Latin inscriptions.

In our study, historians found these ‘parallels’ to be helpful research starting points 9 out of 10 times - improving their confidence by 44%.Image
We tested Aeneas on the Res Gestae Divi Augusti – one of the most debated inscriptions.

Without prior knowledge, it successfully mapped out the leading scholarly theories on its dating, showing how AI can help model history in a quantitative way. 📊 Image
Read 4 tweets
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 demonstrationsImage
Image
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
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.Image
Read 5 tweets
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.Image
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

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