/ Modified feb 20, 2020 4:03 p.m.

Episode 220: Understanding the positives and negatives of machine learning

The AI revolution comes with great promise, but also peril.

AZSCI 220 Machine Learning The use of large computer data sets is a fundamental part of modern research.
Pixabay

The Research Corporation for Science Advancement
Arizona Science

Understanding the positives and negatives of machine learning

This episode is supported by the The Research Corporation for Science Advancement.
NPR
Episode 220: University of Arizona computer scientist Carlos Scheidegger

University of Arizona computer scientist Carlos Scheidegger is studying how we interpret huge data sets that allow computers to improve our lives. But he warns there are perils along with the promise that machine learning brings to society.

Scheidegger will be a featured speaker at the College of Science's annual public lecture series Tuesday, Feb. 25.

Arizona Science
Catch Arizona Science each Friday during Science Friday on NPR 89.1. You can subscribe to our podcast on Apple Music, Spotify, Amazon Music, or the NPR App.. See more from Arizona Science.
By posting comments, you agree to our
AZPM encourages comments, but comments that contain profanity, unrelated information, threats, libel, defamatory statements, obscenities, pornography or that violate the law are not allowed. Comments that promote commercial products or services are not allowed. Comments in violation of this policy will be removed. Continued posting of comments that violate this policy will result in the commenter being banned from the site.

By submitting your comments, you hereby give AZPM the right to post your comments and potentially use them in any other form of media operated by this institution.
AZPM is a service of the University of Arizona and our broadcast stations are licensed to the Arizona Board of Regents who hold the trademarks for Arizona Public Media and AZPM. We respectfully acknowledge the University of Arizona is on the land and territories of Indigenous peoples.
The University of Arizona