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Comment by halomru | Hacker News Reader

It's not just his definition. Many AI courses will teach you pathfinding algorithms as examples of AI, along with optimisation algorithms, genetic algorithms and neutral nets.

There is a valuable distinction in hard AI vs soft AI (hard AI being that whole thinking, emotional maschines thing that we are not really getting closer to, and soft AI being the things that actually generate money because we know how to do them)

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I think what he's doing would be more acurately described as Machine Learning, rather than AI. His algorithms are producing better logo proposals based on the data it is collecting from other users, which fits a common definition of ML [1]

[1] A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E (Tom M. Mitchell, 1997)

Machine learning is a better term because it's more specific (and less controversial). But under most definitions of AI, ML is a subfield of AI, so calling it AI may be suboptimal, but not wrong.

Personally I'm a fan of the AI definition "things humans can do and computers can't do yet", but I recognize that that definition isn't terribly useful.

The distinction you are drawing is between Strong and Weak AI. Weak AI is any sort of useful application of computation. Strong AI is solving the AGI (Artificial General Intelligence) problem.

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