Pagish

People

Public professional AI profiles appear here when source-backed entity extraction finds researchers, founders, executives, or policy actors.

Deep learning pioneer

Geoffrey Hinton

neural networks, representation learning, AI risk

Signals when foundational researchers are reframing the public safety debate.

AI scientist and safety researcher

Yoshua Bengio

deep learning, scientific AI, AI safety

Useful for tracking technical safety arguments and public-interest AI governance.

AI researcher

Yann LeCun

self-supervised learning, world models, open science

Often posts contrarian technical arguments that shape model architecture debates.

AI educator and builder

Andrew Ng

applied AI, education, startup adoption

Good signal for what practical AI teams and learners are about to care about.

AI educator and systems builder

Andrej Karpathy

language models, autonomy, software and model interfaces

High-signal explanations often turn technical model shifts into mainstream developer narratives.

Frontier AI researcher

Ilya Sutskever

deep learning, frontier model scaling, safety

Sparse posts, but major lab and scaling signals can move the entire AI conversation.

Machine learning researcher

Ian Goodfellow

generative models, security, AI research systems

Relevant for GAN history, generative media, and applied research commentary.

Robot learning researcher

Pieter Abbeel

robotics, reinforcement learning, embodied AI

Helpful for robotics, agent training, and physical-world AI adoption signals.

Robot learning researcher

Chelsea Finn

meta-learning, robotics, continual learning

Good for early research signals around agents that learn from experience.

NLP researcher

Christopher Manning

language understanding, Stanford NLP, evaluation

Tracks NLP research, conference themes, and evaluation debates before they go broad.

AI researcher and engineer

Francois Chollet

abstraction, reasoning benchmarks, Keras

Often surfaces sharp arguments about intelligence, benchmarks, and model limits.

AI systems researcher

Jeff Dean

large-scale ML systems, infrastructure, Google research

Useful for infrastructure, scaling, and production research signals.