Why AI is Harder Than We Think (Machine Learning Research Paper Explained)

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Switzerland Yannic Kilcher
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#aiwinter #agi #embodiedcognition

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: https://arxiv.org/abs/2104.12871

My Video on Shortcut Learning: https://youtu.be/D-eg7k8YSfs

Abstract:
Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment ("AI spring") and periods of disappointment, loss of confidence, and reduced funding ("AI winter"). 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. One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself. In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I conclude by discussing the open questions spurred by these fallacies, including the age-old challenge of imbuing machines with humanlike common sense.

Authors: Melanie Mitchell

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