
92. Daniel Filan - Peering into neural nets for AI safety
Towards Data Science
07/14/21
•66m
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Many AI researchers think it’s going to be hard to design AI systems that continue to remain safe as AI capabilities increase. We’ve seen already on the podcast that the field of AI alignment has emerged to tackle this problem, but a related effort is also being directed at a separate dimension of the safety problem: AI interpretability.
Our ability to interpret how AI systems process information and make decisions will likely become an important factor in assuring the reliability of AIs in the future. And my guest for this episode of the podcast has focused his research on exactly that topic. Daniel Filan is an AI safety researcher at Berkeley, where he’s supervised by AI pioneer Stuart Russell. Daniel also runs AXRP, a podcast dedicated to technical AI alignment research.
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91. Peter Gao - Self-driving cars: Past, present and future
July 7, 2021
•61m
Cruise is a self-driving car startup founded in 2013 — at a time when most people thought of self-driving cars as the stuff of science fiction. And yet, just three years later, the company was acquired by GM for over a billion dollars, having shown itself to be a genuine player in the race to make autonomous driving a reality. Along the way, the company has had to navigate and adapt to a rapidly changing technological landscape, mixing and matching old ideas from robotics and software engineering with cutting edge techniques like deep learning.
My guest for this episode of the podcast was one of Cruise’s earliest employees. Peter Gao is a machine learning specialist with deep experience in the self-driving car industry, and is also the co-founder of Aquarium Learning, a Y Combinator-backed startup that specializes in improving the performance of machine learning models by fixing problems with the data they’re trained on. We discussed Peter’s experiences in the self-driving car industry, including the innovations that have spun out of self-driving car tech, as well as some of the technical and ethical challenges that need to be overcome to make self-driving cars hit mainstream use around the world.
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2020 was an incredible year for AI. We saw powerful hints of the potential of large language models for the first time thanks to OpenAI’s GPT-3, DeepMind used AI to solve one of the greatest open problems in molecular biology, and Boston Dynamics demonstrated their ability to blend AI and robotics in dramatic fashion.
Progress in AI is accelerating exponentially, and though we’re just over halfway through 2021, this year is already turning into another one for the books. So we decided to partner with our friends over at Let’s Talk AI, a podcast co-hosted by Stanford PhD and former Googler Sharon Zhou, and Stanford PhD student Andrey Kurenkov, that covers current events in AI.
This was a fun chat, and a format we’ll definitely be playing with more in the future :)
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