Generally Intelligent
By Kanjun Qiu
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Image by Kanjun Qiu
Category: Technology
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Subscribers: 11
Reviews: 0
Episodes: 35
Description
Technical discussions with deep learning researchers who study how to build intelligence. Made for researchers, by researchers.
| Episode | Date |
|---|---|
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Episode 35: Percy Liang, Stanford: On the paradigm shift and societal effects of foundation models
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May 09, 2024 |
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Episode 34: Seth Lazar, Australian National University: On legitimate power, moral nuance, and the political philosophy of AI
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Mar 12, 2024 |
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Episode 33: Tri Dao, Stanford: On FlashAttention and sparsity, quantization, and efficient inference
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Aug 09, 2023 |
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Episode 32: Jamie Simon, UC Berkeley: On theoretical principles for how neural networks learn and generalize
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Jun 22, 2023 |
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Episode 31: Bill Thompson, UC Berkeley, on how cultural evolution shapes knowledge acquisition
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Mar 29, 2023 |
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Episode 30: Ben Eysenbach, CMU, on designing simpler and more principled RL algorithms
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Mar 23, 2023 |
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Episode 29: Jim Fan, NVIDIA, on foundation models for embodied agents, scaling data, and why prompt engineering will become irrelevant
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Mar 09, 2023 |
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Episode 28: Sergey Levine, UC Berkeley, on the bottlenecks to generalization in reinforcement learning, why simulation is doomed to succeed, and how to pick good research problems
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Mar 01, 2023 |
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Episode 27: Noam Brown, FAIR, on achieving human-level performance in poker and Diplomacy, and the power of spending compute at inference time
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Feb 09, 2023 |
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Episode 26: Sugandha Sharma, MIT, on biologically inspired neural architectures, how memories can be implemented, and control theory
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Jan 17, 2023 |
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Episode 25: Nicklas Hansen, UCSD, on long-horizon planning and why algorithms don't drive research progress
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Dec 16, 2022 |
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Episode 24: Jack Parker-Holder, DeepMind, on open-endedness, evolving agents and environments, online adaptation, and offline learning
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Dec 06, 2022 |
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Episode 23: Celeste Kidd, UC Berkeley, on attention and curiosity, how we form beliefs, and where certainty comes from
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Nov 22, 2022 |
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Episode 22: Archit Sharma, Stanford, on unsupervised and autonomous reinforcement learning
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Nov 17, 2022 |
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Episode 21: Chelsea Finn, Stanford, on the biggest bottlenecks in robotics and reinforcement learning
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Nov 03, 2022 |
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Episode 20: Hattie Zhou, Mila, on supermasks, iterative learning, and fortuitous forgetting
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Oct 14, 2022 |
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Episode 19: Minqi Jiang, UCL, on environment and curriculum design for general RL agents
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Jul 19, 2022 |
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Episode 18: Oleh Rybkin, UPenn, on exploration and planning with world models
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Jul 11, 2022 |
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Episode 17: Andrew Lampinen, DeepMind, on symbolic behavior, mental time travel, and insights from psychology
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Feb 28, 2022 |
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Episode 16: Yilun Du, MIT, on energy-based models, implicit functions, and modularity
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Dec 21, 2021 |
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Episode 15: Martín Arjovsky, INRIA, on benchmarks for robustness and geometric information theory
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Oct 15, 2021 |
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Episode 14: Yash Sharma, MPI-IS, on generalizability, causality, and disentanglement
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Sep 24, 2021 |
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Episode 13: Jonathan Frankle, MIT, on the lottery ticket hypothesis and the science of deep learning
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Sep 10, 2021 |
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Episode 12: Jacob Steinhardt, UC Berkeley, on machine learning safety, alignment and measurement
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Jun 18, 2021 |
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Episode 11: Vincent Sitzmann, MIT, on neural scene representations for computer vision and more general AI
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May 20, 2021 |
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Episode 10: Dylan Hadfield-Menell, UC Berkeley/MIT, on the value alignment problem in AI
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May 12, 2021 |
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Episode 09: Drew Linsley, Brown, on inductive biases for vision and generalization
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Apr 02, 2021 |
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Episode 08: Giancarlo Kerg, Mila, on approaching deep learning from mathematical foundations
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Mar 27, 2021 |
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Episode 07: Yujia Huang, Caltech, on neuro-inspired generative models
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Mar 18, 2021 |
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Episode 06: Julian Chibane, MPI-INF, on 3D reconstruction using implicit functions
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Mar 05, 2021 |
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Episode 05: Katja Schwarz, MPI-IS, on GANs, implicit functions, and 3D scene understanding
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Feb 24, 2021 |
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Episode 04: Joel Lehman, OpenAI, on evolution, open-endedness, and reinforcement learning
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Feb 17, 2021 |
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Episode 03: Cinjon Resnick, NYU, on activity and scene understanding
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Feb 01, 2021 |
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Episode 02: Sarah Jane Hong, Latent Space, on neural rendering & research process
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Jan 07, 2021 |
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Episode 01: Kelvin Guu, Google AI, on language models & overlooked research problems
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Dec 15, 2020 |