Education
Andrew Ng
Machine-learning education and practical approaches to building data-driven systems.
Ask yourself: What simple baseline would you build before using a large model?
Read the sourceHome / People in AI
People in AI
Follow the people.
Understand the ideas.
Discover educators, researchers, and builders through the ideas they helped develop. These are reading paths, not a ranking.
Scientific AI
Demis Hassabis
The intersection of general-purpose AI and scientific discovery, including AlphaFold.
Ask yourself: How does a scientific prediction become experimentally testable?
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Representation learning
Yann LeCun
Foundational work in convolutional networks and research on representation learning.
Ask yourself: What can a model learn from unlabeled observations?
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Generative models
Ian Goodfellow
The generative adversarial network framework: learning through a generator and discriminator.
Ask yourself: When does competition between models help or destabilize training?
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Deep learning
Ilya Sutskever
Work on deep neural networks and sequence-to-sequence learning.
Ask yourself: Why did learning a sequence representation matter for translation?
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AI infrastructure
Jensen Huang
NVIDIA’s role in accelerated computing and the hardware ecosystem used by AI researchers.
Ask yourself: How do memory and energy limit a model you can actually deploy?
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Open ecosystem
Clément Delangue
Hugging Face and a shared ecosystem of models, datasets, and documentation.
Ask yourself: What information should every useful model card contain?
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AI deployment
Sam Altman
The product and deployment questions around widely used general-purpose AI systems.
Ask yourself: Which capability claims require independent evaluation?
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Data & evaluation
Alexandr Wang
The importance of data infrastructure, annotation, and evaluation in applied AI.
Ask yourself: How would you audit label quality before trusting a benchmark?
Read the sourceMake your next step a small experiment.
Read the original work before repeating a claim. People’s roles change; this guide focuses on contributions.