Paper Explained - Why AI is Harder Than We Think (Machine Learning Full Video Analysis)

The AI community has gone through regular cycles of AI Springs, where rapid progress gave rise to massive overconfidence, high funding, and overpromise, followed by these promises being unfulfilled, subsequently diving into periods of disenfranchisement and underfunding, called AI Winters. This paper examines the reasons for the repeated periods of overconfidence and identifies four fallacies that people make when they see rapid progress in AI.

OUTLINE:
0:00​ - Intro & Overview
2:10​ - AI Springs & AI Winters
5:40​ - Is the current AI boom overhyped?
15:35​ - Fallacy 1: Narrow Intelligence vs General Intelligence
19:40​ - Fallacy 2: Hard for humans doesn’t mean hard for computers
21:45​ - Fallacy 3: How we call things matters
28:15​ - Fallacy 4: Embodied Cognition
35:30​ - Conclusion & Comments

Paper: [2104.12871] Why AI is Harder Than We Think

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