Antoine Buteau

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Lessons from Lukasz Kaiser

Lukasz Kaiser co-authored “Attention Is All You Need,” introducing the Transformer, before focusing at OpenAI on process-based supervision. His work asks how intermediate reasoning steps can improve model performance on mathematical and logical problems.

Lessons from Illia Polosukhin

Illia Polosukhin, co-creator of the NEAR Protocol and co-author of “Attention Is All You Need,” spans generative AI and decentralized payments. His ideas connect system design and cryptographic infrastructure to an internet run by user-controlled software agents.

Lessons from Niki Parmar

Niki Parmar, self-taught engineer and co-author of “Attention Is All You Need,” joined Google Brain without a PhD and co-founded Adept and Essential AI. Her experience connects model design, enterprise automation, and the realities of building within technology.

Lessons from Jakob Uszkoreit

Jakob Uszkoreit, co-author of “Attention Is All You Need,” helped introduce the Transformer before co-founding Inceptive to use deep learning in RNA medicine design. His trajectory connects language models with the challenge of engineering biological software.

Lessons from Ashish Vaswani

Ashish Vaswani, lead author of “Attention Is All You Need,” helped introduce the Transformer and lay the groundwork for modern language models. His startups, Adept AI and Essential AI, frame his interest in enterprise AI and human-computer collaboration.

Lessons from Llion Jones

Llion Jones co-authored “Attention Is All You Need,” introducing the Transformer used in large language models, then co-founded Sakana AI in Tokyo. His work explores criticisms of the AI industry, evolutionary alternatives, and dynamic computation.

Lessons from Chelsea Finn

Chelsea Finn, a Stanford professor and Physical Intelligence co-founder, developed MAML to train machines to learn new skills rather than master single tasks. Her work examines reinforcement learning and why adaptable machines struggle beyond the lab.

Lessons from Jeff Clune

Jeff Clune is a UBC computer science professor and AI researcher studying open-endedness: algorithms that create their own training environments and keep learning. His work asks whether evolutionary robotics and machine learning can develop general capabilities.

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