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We also applied AlphaEvolve to over 50 open problems in analysis ✍️, geometry 📐, combinatorics ➕ and number theory 🔂, including the kissing number problem. 🔵 In 75% of cases, it rediscovered the best solution known so far. 🔵 In 20% of cases, it improved upon the previously best... show more

Google DeepMind

1,487,218 subscribers

95,738 次观看 • 1 年前 •via X (Twitter)

科学技术新闻政治教育

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Google DeepMind 的头像

Google DeepMind1 年前

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. 🧵

Google DeepMind 的头像

Google DeepMind1 年前

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.

Google DeepMind 的头像

Google DeepMind1 年前

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

Google DeepMind 的头像

Google DeepMind1 年前

We applied AlphaEvolve to a fundamental problem in computer science: discovering algorithms for matrix multiplication. It managed to identify multiple new algorithms. This significantly advances our previous model AlphaTensor, which AlphaEvolve outperforms using its better and more generalist approach. ↓

Google DeepMind 的头像

Google DeepMind1 年前

We’re excited to keep developing AlphaEvolve. This system and its general approach has potential to impact material sciences, drug discovery, sustainability and wider technological and business applications. Find out more ↓

GC 的头像

GC1 年前

@kevinsekniqi does this count

Hashir Omer Farooqi 的头像

Hashir Omer Farooqi1 年前

*In 75% of cases, it rediscovered the best solution known so far.* If we don't have a proof that our best solutions are optimal, this 75% is a good evidence of memorization or training bias.

tumin~ 的头像

tumin~1 年前

I'm feeling insecure

Roman Leventov 的头像

Roman Leventov1 年前

This NotebookLM style is unbearable. Please don't use it for public posts like this

3THER_ 的头像

3THER_1 年前

Nice touch using notebookLM podcast for the explanation - I see what you did there ;D

Ciprianii 的头像

Ciprianii1 年前

applause

Achesui 的头像

Achesui1 年前

@grok explain this for a simple human being with low self-steem and with around 70 IQ, why is this important?

Oded Ben Dov 🧬 的头像

Oded Ben Dov 🧬1 年前

I need this for a missing piece of an P=NP proof. PLEASE

bry257 的头像

bry2571 年前

@gork holy shit look at this

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