Building Systems from Scratch and the Possibilities of Explainable Knowledge

Few people are better at explaining the science of artificial intelligence than Yejin Choi. She’s a computer science professor at the University of Washington, senior resource manager at the Allen Institute for AI, and the recipient of a MacArthur Fellowship. I thought her recent TED talk was terrific, and I was thrilled to talk to her about how you train a large language model, why it’s so hard for robots to pick tools out of a box, and why universities must play a key role in the future of AI research.

Show notes:

• Yejin’s TED talk: https://www.ted.com/talks/yejin_choi_why_ai_is_incredibly_smart_and_shockingly_stupid?language=en

• Moravec’s paradox: https://en.wikipedia.org/wiki/Moravec%27s_paradox

• Virtual Insanity by Jamiroquai: https://open.spotify.com/track/24SUWisv2lYQiB3bVpE1sn?si=b290674d2791460d