Henryk Michalewski (@hmichalewski) header
Henryk Michalewski (@hmichalewski) profile picture

Senior Staff Research Scientist @GoogleDeepMind. AlphaProof, Gemini, PaLM, Minerva. Maths → ML: reasoning, theorem proving, program synthesis.

Oxford, UK
Joined July 2009
Photo by Henryk Michalewski (@hmichalewski)
Henryk Michalewski retweeted
I’m excited to share the news of Gemini Deep Think’s gold-medal level performance 🥇 at the International Math Olympiad! It has been an absolute blast building Deep Think this year and then scaling it to the IMO.
An advanced version of Gemini with Deep Think has officially achieved gold medal-level performance at the International Mathematical Olympiad. 🥇 It solved 5️⃣ out of 6️⃣ exceptionally difficult problems, involving algebra, combinatorics, geometry and number theory. Here’s how 🧵
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Henryk Michalewski retweeted
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
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Henryk Michalewski retweeted
Extremely excited to announce Promptbreeder 🌱, a self-referential self-improving system that can automatically evolve effective domain-specific prompts in a given domain. Prompts evolved with Promptbreeder outperform Chain-of-Thought and Plan-and-Solve Prompting on a range of arithmetic and commonsense reasoning benchmarks. Creating self-referential self-improving systems is a holy grail of AI research, but prior self-referential approaches rely on costly model parameter updates—it's unclear how to scale them to the vast number of parameters in modern LLMs, let alone how to make them work with LLMs whose parameters are hidden behind APIs. Instead, in Promptbreeder the substrate for self-improvement is natural language, which allows us to modify mutation operators using an LLM in a self-referential way. Thus, Promptbreeder not only improves prompts, but over multiple evolutionary generations it also improves the way it is improving prompts. I keep on coming back to Figure 4 in the original Chain-of-Thought Prompting paper: as the LLM gets bigger and better, the gains from prompt strategies increase significantly. I believe the future is going to be wild as we will see increasingly open-ended self-referential self-improvement systems that continue to scale with ever larger and more capable LLMs. Promptbreeder is an important stepping stone in this direction. Really enjoyed this collaboration with amazing co-workers @chrisantha_f @2ne1 @hmichalewski @sindero arXiv: arxiv.org/abs/2309.16797
🌱 Introducing Promptbreeder: LLMs evolve their own prompts through self-referential self-improvement! Paper: arxiv.org/abs/2309.16797 #PromptEngineering #LLM #AI #ML #Promptbreeder
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🔬Found a brilliant #PhysicsCalculator on Google Search! Solves queries like energy calculation & estimates gravitational force, while explaining the science behind. Ideal for students, teachers, and curious minds. A gem for physics exploration! 💡🌌🔭 #GoogleSearch #ScienceTool
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Henryk Michalewski retweeted
Very excited to present Minerva🦉: a language model capable of solving mathematical questions using step-by-step natural language reasoning. Combining scale, data and others dramatically improves performance on the STEM benchmarks MATH and MMLU-STEM. goo.gle/3yGpTN7
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Henryk Michalewski retweeted
Super excited to share Minerva!! – a language model that is capable of solving MATH with 50% success rate, which was predicted to happen in 2025 by Steinhardt et. al. (bounded-regret.ghost.io/ai-f…)! #Minerva 1/
Very excited to present Minerva🦉: a language model capable of solving mathematical questions using step-by-step natural language reasoning. Combining scale, data and others dramatically improves performance on the STEM benchmarks MATH and MMLU-STEM. goo.gle/3yGpTN7
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Henryk Michalewski retweeted
Thrilled to announce🦉Minerva: a large language model capable of solving mathematical problems using step-by-step reasoning in natural language. See blog here: goo.gle/3yGpTN7 and samples here: minerva-demo.github.io (1/n)
Very excited to present Minerva🦉: a language model capable of solving mathematical questions using step-by-step natural language reasoning. Combining scale, data and others dramatically improves performance on the STEM benchmarks MATH and MMLU-STEM. goo.gle/3yGpTN7
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Henryk Michalewski retweeted
How can we effectively train generalist multi-environment agents? We trained a single Decision Transformer model to play many Atari games simultaneously and compared it to alternative approaches: sites.google.com/corp/view/m…
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Henryk Michalewski retweeted
I am looking for students who are interested in doing a Ph.D. in ML/NLP/DL/RL at @UCL @UCLCS starting in Fall 2019. See instructions below and get in touch via PM if you have questions. Application deadline is 5th of January 2019. cs.ucl.ac.uk/prospective_stu…
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