The Gradient: Perspectives on AI

143 Episodes

1. Joel Lehman: Open-Endedness and Evolution through Large Models

   Published: 9/22/2022
2. Andrew Feldman: Cerebras and AI Hardware

   Published: 9/15/2022
3. Christopher Manning: Linguistics and the Development of NLP

   Published: 9/8/2022
4. Jeff Clune: Genetic Algorithms, Quality-Diversity, Curiosity

   Published: 9/1/2022
5. Catherine Olsson and Nelson Elhage: Anthropic, Understanding Transformers

   Published: 8/26/2022
6. Been Kim: Interpretable Machine Learning

   Published: 8/18/2022
7. Laura Weidinger: Ethical Risks, Harms, and Alignment of Large Language Models

   Published: 8/5/2022
8. Sebastian Raschka: AI Education and Research

   Published: 7/29/2022
9. Lt. General Jack Shanahan: AI in the DoD, Project Maven, and Bridging the Tech-DoD Gap

   Published: 7/22/2022
10. Sara Hooker: Cohere For AI, the Hardware Lottery, and DL Tradeoffs

    Published: 7/14/2022
11. Lukas Biewald: Crowdsourcing at CrowdFlower and ML Tooling at Weights & Biases

    Published: 7/7/2022
12. Chip Huyen: Machine Learning Tools and Systems

    Published: 6/30/2022
13. Preetum Nakkiran: An Empirical Theory of Deep Learning

    Published: 6/24/2022
14. Max Woolf: Data Science at BuzzFeed and AI Content Generation

    Published: 6/16/2022
15. Rosanne Liu: Paths in AI Research and ML Collective

    Published: 6/10/2022
16. Ben Green: "Tech for Social Good" Needs to Do More

    Published: 6/2/2022
17. Max Braun: Teaching Robots to Help People in their Everyday Lives

    Published: 5/26/2022
18. Yejin Choi: Teaching Machines Common Sense and Morality

    Published: 5/19/2022
19. David Chalmers on AI and Consciousness

    Published: 5/12/2022
20. Greg Yang on Communicating Research, Tensor Programs, and µTransfer

    Published: 4/28/2022