Discover this podcast and so much more

Podcasts are free to enjoy without a subscription. We also offer ebooks, audiobooks, and so much more for just $11.99/month.

78. Melanie Mitchell - Existential risk from AI: A skeptical perspective

78. Melanie Mitchell - Existential risk from AI: A skeptical perspective

FromTowards Data Science


78. Melanie Mitchell - Existential risk from AI: A skeptical perspective

FromTowards Data Science

ratings:
Length:
45 minutes
Released:
Apr 7, 2021
Format:
Podcast episode

Description

As AI systems have become more powerful, an increasing number of people have been raising the alarm about its potential long-term risks. As we’ve covered on the podcast before, many now argue that those risks could even extend to the annihilation of our species by superhuman AI systems that are slightly misaligned with human values.
There’s no shortage of authors, researchers and technologists who take this risk seriously — and they include prominent figures like Eliezer Yudkowsky, Elon Musk, Bill Gates, Stuart Russell and Nick Bostrom. And while I think the arguments for existential risk from AI are sound, and aren’t widely enough understood, I also think that it’s important to explore more skeptical perspectives.
Melanie Mitchell is a prominent and important voice on the skeptical side of this argument, and she was kind enough to join me for this episode of the podcast. Melanie is the Davis Professor of complexity at the Santa Fe Institute, a Professor of computer science at Portland State University, and the author of Artificial Intelligence: a Guide for Thinking Humans — a book in which she explores arguments for AI existential risk through a critical lens. She’s an active player in the existential risk conversation, and recently participated in a high-profile debate with Stuart Russell, arguing against his AI risk position.
Released:
Apr 7, 2021
Format:
Podcast episode

Titles in the series (100)

Researchers and business leaders at the forefront of the field unpack the most pressing questions around data science and AI.