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Generally Intelligent

Technical discussions with deep learning researchers who study how to build intelligence. Made for researchers, by researchers.

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Ranked #1

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Episode 18: Oleh Rybkin, UPenn, on exploration and planning with world models

Episode 18: Oleh Rybkin, UPenn, on exploration and planning with world models

Oleh Rybkin is a Ph.D. student at the University of Pennsylvania and a student researcher at Google. He is advised by Ko... Read more

11 Jul 2022

2hr 1min

Ranked #2

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Episode 17: Andrew Lampinen, DeepMind, on symbolic behavior, mental time travel, and insights from psychology

Episode 17: Andrew Lampinen, DeepMind, on symbolic behavior, mental time travel, and insights from psychology

Andrew Lampinen is a Research Scientist at DeepMind. He previously completed his Ph.D. in cognitive psychology at Stanfo... Read more

28 Feb 2022

1hr 59mins

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Ranked #3

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Episode 16: Yilun Du, MIT, on energy-based models, implicit functions, and modularity

Episode 16: Yilun Du, MIT, on energy-based models, implicit functions, and modularity

Yilun Du is a graduate student at MIT advised by Professors Leslie Kaelbling, Tomas Lozano-Perez, and Josh Tenenbaum. He... Read more

21 Dec 2021

1hr 25mins

Ranked #4

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Episode 15: Martín Arjovsky, INRIA, on benchmarks for robustness and geometric information theory

Episode 15: Martín Arjovsky, INRIA, on benchmarks for robustness and geometric information theory

Martín Arjovsky did his Ph.D. at NYU with Leon Bottou. Some of his well-known works include the Wasserstein GAN and a pa... Read more

15 Oct 2021

1hr 26mins

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Ranked #5

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Episode 14: Yash Sharma, MPI-IS, on generalizability, causality, and disentanglement

Episode 14: Yash Sharma, MPI-IS, on generalizability, causality, and disentanglement

Yash Sharma is a Ph.D. student at the International Max Planck Research School for Intelligent Systems. He previously st... Read more

24 Sep 2021

1hr 27mins

Ranked #6

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Episode 13: Jonathan Frankle, MIT, on the lottery ticket hypothesis and the science of deep learning

Episode 13: Jonathan Frankle, MIT, on the lottery ticket hypothesis and the science of deep learning

Jonathan Frankle (Google Scholar) (Website) is finishing his PhD at MIT, advised by Michael Carbin. His main research in... Read more

10 Sep 2021

1hr 21mins

Ranked #7

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Episode 12: Jacob Steinhardt, UC Berkeley, on machine learning safety, alignment and measurement

Episode 12: Jacob Steinhardt, UC Berkeley, on machine learning safety, alignment and measurement

Jacob Steinhardt (Google Scholar) (Website) is an assistant professor at UC Berkeley.  His main research interest is in ... Read more

18 Jun 2021

1hr

Ranked #8

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Episode 11: Vincent Sitzmann, MIT, on neural scene representations for computer vision and more general AI

Episode 11: Vincent Sitzmann, MIT, on neural scene representations for computer vision and more general AI

Vincent Sitzmann (Google Scholar) (Website) is a postdoc at MIT. His work is on neural scene representations in computer... Read more

20 May 2021

1hr 10mins

Ranked #9

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Episode 10: Dylan Hadfield-Menell, UC Berkeley/MIT, on the value alignment problem in AI

Episode 10: Dylan Hadfield-Menell, UC Berkeley/MIT, on the value alignment problem in AI

Dylan Hadfield-Menell (Google Scholar) (Website) recently finished his PhD at UC Berkeley and is starting as an assistan... Read more

12 May 2021

1hr 32mins

Ranked #10

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Episode 09: Drew Linsley, Brown, on inductive biases for vision and generalization

Episode 09: Drew Linsley, Brown, on inductive biases for vision and generalization

Drew Linsley (Google Scholar) is a Paul J. Salem senior research associate at Brown, advised by Thomas Serre. He is work... Read more

2 Apr 2021

1hr 12mins

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