Why AI Is Harder Than We Think

Explore a comprehensive analysis of the cyclical nature of AI development in this 37-minute video. Delve into the concept of AI Springs and Winters, examining the reasons behind repeated periods of overconfidence in the field. Discover four common fallacies made by AI researchers that lead to unrealistic predictions. Learn about the distinctions between narrow and general intelligence, the misconceptions about task difficulty for humans versus computers, the impact of terminology on perceptions, and the role of embodied cognition in AI development. Gain insights into the challenges of creating truly intelligent machines and the open questions that remain in the field of artificial intelligence.

- Intro & Overview
- AI Springs & AI Winters
- Is the current AI boom overhyped?
- Fallacy 1: Narrow Intelligence vs General Intelligence
- Fallacy 2: Hard for humans doesn't mean hard for computers
- Fallacy 3: How we call things matters
- Fallacy 4: Embodied Cognition
- Conclusion & Comments