Why AI is Harder Than We Think

TL;DR

This paper discusses the historical cycles of AI optimism and disappointment, highlighting four common fallacies that lead to overconfidence and emphasizing the complexity of achieving humanlike intelligence.

Contribution

It identifies four fallacies in AI research assumptions that contribute to overconfidence and discusses open questions like instilling common sense in machines.

Findings

Four fallacies in AI assumptions are identified.

AI development is more complex than commonly believed.

Open questions include achieving humanlike common sense.

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.