Profile 8 min read

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.
Profile 9 min read

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.
Profile 10 min read

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.
Profile 7 min read

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.
Profile 7 min read

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.
Profile 9 min read

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.
Profile 10 min read

Lessons from Pieter Abbeel

Pieter Abbeel is a UC Berkeley computer science professor and Covariant co-founder studying apprenticeship and deep reinforcement learning. He examines how machines acquire physical intelligence through human demonstration, trial and error, and embodied skill-building.
Profile 10 min read

Lessons from Sebastian Bubeck

Sébastien Bubeck is an OpenAI researcher in machine learning’s mathematical foundations. After leading the Phi small language models and co-authoring “Sparks of AGI,” he shifted from convex optimization toward empirical, physics-inspired study of deep neural networks.

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