#57 – Prof. Melanie Mitchell – Why AI is harder than we think

Since its beginning in the 1950s, the field of artificial intelligence has vacillated between periods of optimistic predictions and massive investment and periods of disappointment, loss of confidence, and reduced funding. Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected.

Professor Melanie Mitchell thinks one reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself.

Framing [00:00:00]
Dartmouth AI Summer Workshop [00:07:02]
Letitia Intro to Melanie [00:09:22]
The Googleplex situation with Melanie and Douglas Hofstadter [00:14:58]
Melanie paper [00:21:04]
Note on audio quality [00:25:45]
Main show kick off [00:26:51]
AI hype [00:29:57]
On GPT-3 [00:31:46]
Melanie’s “Why is AI harder than we think” paper [00:36:18]
The 3rd fallacy: Avoiding wishful mnemonics [00:42:23]
Concepts and primitives [00:47:56]
The 4th fallacy [00:51:19]
What can we learn from human intelligence? [00:53:00]
Pure intelligence [01:00:14]
Unrobust features [01:02:34]
The good things of the past in AI research [01:11:30]
Copycat [01:17:56]
Thoughts on the “neuro-symbolic camp” [01:26:49]
Type I or Type II [01:32:06]
Adversarial examples — a fun question. [01:35:55]
How much do we want human-like (human-interpretable) features? [01:43:44]
The difficulty of creating intelligence [01:47:49]
Show debrief [01:51:24]

Pod: https://anchor.fm/machinelearningstreettalk/episodes/57—Prof–Melanie-Mitchell—Why-AI-is-harder-than-we-think-e1502td

Dr. Tim Scarfe
Dr. Keith Duggar
Letitia Parcalabescu and and Ms. Coffee Bean (https://www.youtube.com/c/AICoffeeBreak/)

Why AI is Harder Than We Think – Melanie Mitchell





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