Carnegie Mellon University: Leading AI Scholars Featured in New Oral Archive

Carnegie Mellon University: Leading AI Scholars Featured in New Oral Archive. “[Illah] Nourbakhsh and [Jennifer] Keating have captured the thoughts of some leading AI scholars in a new oral archive that became available online this year. It includes video and transcripts from 22 people, including MIT’s Daniela Rus, Harvard University’s Barbara Grosz and Microsoft’s Eric Horvitz, as well as a number of CMU faculty members such as Martial Hebert, David Danks, Mark Kamlet, Tuomas Sandholm and Jim Herbsleb.”

Ars Technica: How neural networks work—and why they’ve become a big business

Ars Technica: How neural networks work—and why they’ve become a big business. “Computer scientists have been experimenting with neural networks since the 1950s. But two big breakthroughs—one in 1986, the other in 2012—laid the foundation for today’s vast deep learning industry. The 2012 breakthrough—the deep learning revolution—was the discovery that we can get dramatically better performance out of neural networks with not just a few layers but with many. That discovery was made possible thanks to the growing amount of both data and computing power that had become available by 2012. This feature offers a primer on neural networks. We’ll explain what neural networks are, how they work, and where they came from. And we’ll explore why—despite many decades of previous research—neural networks have only really come into their own since 2012.”