53. Edouard Harris - Emerging problems in machine learning: making AI "good"
Towards Data Science
10/08/20
•66m
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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 mean to build ethical, safe, interpretable, and accountable AI that’s aligned with human values?
That’s what this year is going to be about for the Towards Data Science podcast. I hope you join us for that journey, which starts today with an interview with my brother Ed, who apart from being a colleague who’s worked with me as part of a small team to build the SharpestMinds data science mentorship program, is also collaborating with me on a number of AI safety, alignment and policy projects. I thought he’d be a perfect guest to kick off this new year for the podcast.
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52. Sanyam Bhutani - Networking like a pro in data science
September 23, 2020
•54m
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 surprising: data science is a pretty technical field, and technical people often prefer not to go out of their way to seek social interactions. We tend to think of networking with other “primates who code” as a distraction at best, and an anxiety-inducing nightmare at worst.
So how can data scientists overcome that anxiety, and tap into the value of network-building, and develop a brand for themselves in the data science community? That’s the question that brings us to this episode of the podcast. To answer it, I spoke with repeat guest Sanyam Bhutani — a top Kaggler, host of the Chai Time Data Science Show, Machine Learning Engineer and AI Content Creator at H2O.ai, about the unorthodox networking strategies that he’s leveraged to become a fixture in the machine learning community, and to land his current role.
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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-specific. Tesla’s self-driving car algorithm can’t win at StarCraft, and DeepMind’s AlphaZero algorithm can with Go matches against grandmasters, but can’t optimize your company’s ad spend.
So how do we make the leap from narrow AI systems that leverage reinforcement learning to solve specific problems, to more general systems that can orient themselves in the world? Enter Tim Rocktäschel, a Research Scientist at Facebook AI Research London and a Lecturer in the Department of Computer Science at University College London. Much of Tim’s work has been focused on ways to make RL agents learn with relatively little data, using strategies known as sample efficient learning, in the hopes of improving their ability to solve more general problems. Tim joined me for this episode of the podcast.
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