Generally Intelligent

36 Episodes

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

   Published: 12/21/2021
2. Episode 15: Martín Arjovsky, INRIA, on benchmarks for robustness and geometric information theory

   Published: 10/15/2021
3. Episode 14: Yash Sharma, MPI-IS, on generalizability, causality, and disentanglement

   Published: 9/24/2021
4. Episode 13: Jonathan Frankle, MIT, on the lottery ticket hypothesis and the science of deep learning

   Published: 9/10/2021
5. Episode 12: Jacob Steinhardt, UC Berkeley, on machine learning safety, alignment and measurement

   Published: 6/18/2021
6. Episode 11: Vincent Sitzmann, MIT, on neural scene representations for computer vision and more general AI

   Published: 5/20/2021
7. Episode 10: Dylan Hadfield-Menell, UC Berkeley/MIT, on the value alignment problem in AI

   Published: 5/12/2021
8. Episode 09: Drew Linsley, Brown, on inductive biases for vision and generalization

   Published: 4/2/2021
9. Episode 08: Giancarlo Kerg, Mila, on approaching deep learning from mathematical foundations

   Published: 3/27/2021
10. Episode 07: Yujia Huang, Caltech, on neuro-inspired generative models

    Published: 3/18/2021
11. Episode 06: Julian Chibane, MPI-INF, on 3D reconstruction using implicit functions

    Published: 3/5/2021
12. Episode 05: Katja Schwarz, MPI-IS, on GANs, implicit functions, and 3D scene understanding

    Published: 2/24/2021
13. Episode 04: Joel Lehman, OpenAI, on evolution, open-endedness, and reinforcement learning

    Published: 2/17/2021
14. Episode 03: Cinjon Resnick, NYU, on activity and scene understanding

    Published: 2/1/2021
15. Episode 02: Sarah Jane Hong, Latent Space, on neural rendering & research process

    Published: 1/7/2021
16. Episode 01: Kelvin Guu, Google AI, on language models & overlooked research problems

    Published: 12/15/2020