Computer programs that purport to help humans learn have been around almost as long as there have been computer programs, but their track record for success has been less than impressive. Emma Brunskill, an expert on artificial intelligence and machine learning, thinks that less-than-stellar record is about to change and has dedicated her career to finding new and better ways to teach computers to teach humans. Her research creates innovative "reinforcement learning" algorithms in which computers learn through experience to get better at teaching humans. In the process, the algorithms lead people to make better, more-informed decisions that produce better outcomes in the long run. To Brunskill this is no schoolroom affair, but an endeavor where the stakes are high. She says that better education is key to big societal challenges, like alleviating poverty. She believes that better training of new workers — or retraining of older ones — can yield better paying jobs for more people. What’s more, she’s turning her attention to other fields, namely healthcare, where better decisions can have life-or-death implications. Join host Russ Altman and Stanford computer scientist Emma Brunskill for a deep exploration of the new age of computer-assisted learning and decision-making. You can listen to The Future of Everything on Sirius XM Insight Channel 121, iTunes, Google Play, SoundCloud, Spotify, Stitcher or via Stanford Engineering Magazine. Connect With Us: Episode Transcripts The Future of Everything Website Connect with Russ Threads / Bluesky / Mastodon Connect with School of Engineering Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

Ask this episode

“What did the council decide on item 42?” — answered with the speaker and the moment.

Transcript

Related