Yann LeCun
New Year

Yann LeCun: Learning From Observation

How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly we’re missing something big.
2 min read
Chelsea Finn
New Year

Chelsea Finn: Robots That Generalize

Many people in the AI community focus on achieving flashy results, like building an agent that can win at Go or Jeopardy. This kind of work is impressive in terms of complexity.
2 min read
Oren Etzioni
New Year

Oren Etzioni: Tools For Equality

In 2020, I hope the AI community will grapple with issues of fairness in ways that tangibly and directly benefit disadvantaged populations.
1 min read
Anima Anandkumar
New Year

Anima Anandkumar: The Power of Simulation

We’ve had great success with supervised deep learning on labeled data. Now it’s time to explore other ways to learn: training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world.
2 min read
Illustration of a reindeer with security cameras pointing at it
New Year

Face Recognition Meets Resistance

An international wave of anti-surveillance sentiment pushed back against the proliferation of face recognition systems.
2 min read
Illustration of a crystal snowball
New Year

Simulation Substitutes for Data

The future of machine learning may depend less on amassing ground-truth data than simulating the environment in which a model will operate. Deep learning works like magic with enough high-quality data. When examples are scarce, though, researchers are using simulation to fill the gap.
1 min read

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