[Ml-stat-talks] Fwd: Yann Le Cun lecture at IAS

Barbara Engelhardt bee at princeton.edu
Wed Dec 6 11:14:52 EST 2017

Talk of interest.

Please join us for:

*Theoretical Machine Learning Lecture Series: How Could Machines Learn as
Efficiently as Animals and Humans?*

Yann LeCun, Director of Facebook AI Research & Silver Professor of Computer
Science, New York University

December 12, 2017, 5:30 p.m.

Wolfensohn Hall

The Institute of Advanced Study (IAS)


Follow link to register

Deep learning has caused revolutions in computer perception and natural
language understanding, but almost all of these successes largely rely on
supervised learning, where the machine is required to predict
human-provided annotations. For game AI, most systems use model-free
reinforcement learning, which requires too many trials to be practical in
the real world. However, animals and humans seem to learn vast amounts of
knowledge about how the world works through mere observation and occasional
actions. Good predictive world models are an essential component of
intelligent behavior and with them, one can predict outcomes and plan
courses of actions. One could argue that prediction is the essence of
intelligence in everyday life and science. These models may be the basis of
common sense reasoning and intuition, allowing us to fill in missing
information such as predicting the future from the past and present or the
state of the world from noisy percepts.

In this public lecture, Yann LeCun will discuss the state of deep learning
and promising principles and methods for predictive learning.

This lecture is part of the Theoretical Machine Learning Lecture Series, a
new series curated by Sanjeev Arora, Visiting Professor in the School of
Mathematics, and is made possible by a gift from Eric and Wendy Schmidt.
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