Back in college, I took an AI / Cognitive Science class that was enormously formative in my thinking. And the most formative session was the one where the professor sat us all down and said that today's challenge was simple: define "chair". The entire class spent the better part of an hour at it, and could not come up with a rule set that he couldn't provide counter-examples for.
Human cognition is all about pattern-matching, and those patterns are vague. We tend to agree on them, and they often aren't even controversial, but as soon as you try to describe them in terms of rules, you're probably in trouble.
The outcome of that class was me deciding that most of AI as it was defined back then -- very rule-and-category-centric -- was clearly nonsense, and that multi-layered neural networks with vast amounts of data were the only way to go if you wanted "AI" worthy of the name. The thirty years since have been pretty gratifying, although we still have a fair ways to go.
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Back in college, I took an AI / Cognitive Science class that was enormously formative in my thinking. And the most formative session was the one where the professor sat us all down and said that today's challenge was simple: define "chair". The entire class spent the better part of an hour at it, and could not come up with a rule set that he couldn't provide counter-examples for.
Human cognition is all about pattern-matching, and those patterns are vague. We tend to agree on them, and they often aren't even controversial, but as soon as you try to describe them in terms of rules, you're probably in trouble.
The outcome of that class was me deciding that most of AI as it was defined back then -- very rule-and-category-centric -- was clearly nonsense, and that multi-layered neural networks with vast amounts of data were the only way to go if you wanted "AI" worthy of the name. The thirty years since have been pretty gratifying, although we still have a fair ways to go.