4 sources
One of the better people to track for the sequence from Gopher to retrieval-augmented language models and then into Gemini, especially if you care about how DeepMind actually iterated on the frontier-model recipe.
Researcher Profile
Editor reviewedLisa Anne Hendricks
Compute-optimal scaling for LLM training
Contributor to Google DeepMind's Gemini and large-language-model program
A useful page for the DeepMind work that connected large-language-model scaling to the multimodal Gemini push, with a clearer safety-and-evaluation flavor than many purely scaling-focused pages.
Organizations
Google DeepMind
Labs
About This Page
This profile is meant to help you get oriented quickly: why this researcher matters, what to read first, and where to explore next.
Known For
The ideas, systems, and research directions that make this person worth knowing.
01
Gemini
02
Chinchilla and compute-optimal scaling
03
Gopher-era large-language-model work
04
Compute-optimal scaling for LLM training
05
Training Compute-Optimal Large Language Models
06
DeepMind
Start Here
Canonical papers, project pages, or repositories that anchor this profile.
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3 sources
Worth tracking for the DeepMind thread that links large-model scaling research to the multimodal Gemini stack, rather than treating those as separate eras.
3 sources
A useful profile for the core DeepMind contributor layer behind Chinchilla, Gopher, and Gemini rather than only the more public faces of those systems.
3 sources
A useful profile for the DeepMind researchers who helped carry the lab’s language-model program from scaling-law work into Gemini rather than appearing only on the final product layer.
3 sources
A useful profile for the DeepMind researchers who sat inside the core language-model program as it moved from scaling-law analysis into the Gemini family.
3 sources
A useful page for the less visible engineering and research work behind DeepMind’s modern language-model stack, especially across the jump from Gopher and Chinchilla into Gemini.