Summary: The Fallacies of AI: Why Artificial Intelligence is Harder Than We Think

Melanie Mitchell, a computer scientist and author at the Santa Fe Institute in New Mexico, believes that artificial intelligence is harder than we think because of our limited understanding of the complexity that underlies it. Mitchell believes that there are four main fallacies that explain our inability to accurately predict AI’s trajectory.

The first of these fallacies is the idea that narrow intelligence is part of a continuum that leads to general intelligence. The second fallacy is the belief that difficult tasks for humans are relatively easy for computers. The third fallacy is the idea that intelligence resides entirely in the brain. The fourth and final fallacy is the idea that a superhuman intelligence could be entirely disembodied.

Together, these fallacies have given many AI researchers a false sense of the progress made in the past and what is likely in future. Indeed, an important open question is what it means to be intelligent. Without a clear understanding of the very thing researchers are hoping to emulate, the possibility of progress seems

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