58. David Duvenaud - Using generative models for explainable AI
In the early 1900s, all of our predictions were the direct product of human brains. Scientists, analysts, climatologists, mathematicians, bankers, lawyers and politicians did their best to anticipate ...
18 Nov 202036min
57. Dylan Hadfield-Menell - Humans in the loop
Human beings are collaborating with artificial intelligences on an increasing number of high-stakes tasks. I’m not just talking about robot-assisted surgery or self-driving cars here — every day, soci...
11 Nov 20201h 4min
56. Annette Zimmermann - The ethics of AI
As AI systems have become more powerful, they’ve been deployed to tackle an increasing number of problems. Take computer vision. Less than a decade ago, one of the most advanced applications of comput...
4 Nov 202056min
55. Rohin Shah - Effective altruism, AI safety, and learning human preferences from the state of the world
If you walked into a room filled with objects that were scattered around somewhat randomly, how important or expensive would you assume those objects were? What if you walked into the same room, and ...
28 Okt 202051min
54. Tim Rocktäschel - Deep reinforcement learning, symbolic learning and the road to AGI
Reinforcement learning can do some pretty impressive things. It can optimize ad targeting, help run self-driving cars, and even win StarCraft games. But current RL systems are still highly task-specif...
15 Okt 202053min
53. Edouard Harris - Emerging problems in machine learning: making AI "good"
Where do we want our technology to lead us? How are we falling short of that target? What risks might advanced AI systems pose to us in the future, and what potential do they hold? And what does it me...
8 Okt 20201h 6min
52. Sanyam Bhutani - Networking like a pro in data science
Networking is the most valuable career advancement skill in data science. And yet, almost paradoxically, most data scientists don’t spend any time on it at all. In some ways, that’s not terribly surpr...
23 Sep 202054min
51. Adrien Treuille and Tim Conkling - Streamlit Is All You Need
We’ve talked a lot about “full stack” data science on the podcast. To many, going full-stack is one of those long-term goals that we never get to. There are just too many algorithms and data structure...
16 Sep 202039min
