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Comment by jmmcd | Hacker News Reader

> I especially like the early slides that help frame AI vs ML vs DL so that people can have a realistic understanding of what these technologies are for.

But they're wrong! I read "Deep learning drives machine learning which drives artificial intelligence." This is very wrong. I stopped reading.

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How is it wrong? What's the correct hierarchy ?

My understanding is that Deep Learning is a type of Machine Learning. Artificial Intelligence is the idea that a machine performs similarly or better than a human for a specific task. Artificial General Intelligence is when a machine performs similarly to a human in many different kinds of tasks.

AI is the overall field and the superset of all of the various approaches.

ML is one family of approaches for knowledge acquisition in AI, but far from the only one (eg. logic based inference is another big one).

DL is a family of approaches in supervised ML. As the author points out, it's a subset of a subset.

But saying that this sub-subset "drives" AI is like saying endocrinology "drives" medicine: not the right mental model at all.

If endocrinology was the most talked-about, hyped, and invested-in form of medicine I think it would be fair. DL is where an enormous amount of AI growth and progress is.

hyped things are made hype from a belief that it can be 'done' therefore invested in, but that can be done in one part of ML, which doesn't give any reason to say it generates all intelligence for a machine for human tasks.

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