Gradient Dissent: Conversations on AI

107 Episodes

1. Peter Wang — Anaconda, Python, and Scientific Computing

   Published: 21/01/2021
2. Chris Anderson — Robocars, Drones, and WIRED Magazine

   Published: 14/01/2021
3. Adrien Treuille — Building Blazingly Fast Tools That People Love

   Published: 4/12/2020
4. Peter Norvig – Singularity Is in the Eye of the Beholder

   Published: 20/11/2020
5. Robert Nishihara — The State of Distributed Computing in ML

   Published: 13/11/2020
6. Ines & Sofie — Building Industrial-Strength NLP Pipelines

   Published: 29/10/2020
7. Daeil Kim — The Unreasonable Effectiveness of Synthetic Data

   Published: 15/10/2020
8. Joaquin Candela — Definitions of Fairness

   Published: 1/10/2020
9. Richard Socher — The Challenges of Making ML Work in the Real World

   Published: 29/09/2020
10. Zack Chase Lipton — The Medical Machine Learning Landscape

    Published: 17/09/2020
11. Anthony Goldbloom — How to Win Kaggle Competitions

    Published: 9/09/2020
12. Suzana Ilić — Cultivating Machine Learning Communities

    Published: 2/09/2020
13. Jeremy Howard — The Story of fast.ai and Why Python Is Not the Future of ML

    Published: 25/08/2020
14. Anantha Kancherla — Building Level 5 Autonomous Vehicles

    Published: 12/08/2020
15. Bharath Ramsundar — Deep Learning for Molecules and Medicine Discovery

    Published: 5/08/2020
16. Chip Huyen — ML Research and Production Pipelines

    Published: 29/07/2020
17. Peter Skomoroch — Product Management for AI

    Published: 21/07/2020
18. Josh Tobin — Productionizing ML Models

    Published: 8/07/2020
19. Miles Brundage — Societal Impacts of Artificial Intelligence

    Published: 1/07/2020
20. Hamel Husain — Building Machine Learning Tools

    Published: 24/06/2020