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Comment by granzymes | Hacker News Reader

The “overlap” is that all machine learning is AI, but not all AI is machine learning.

There are both ML that is not AI, and AI that is not ML.

For example, if you pick them manually, decision trees can be AI but not ML. Video game character behavior is a trivial example.

Eliza for example is also not ML, but could be called AI.

Likewise, there is ML that is not AI. Such is debatable, because you could always argue that using machine-learning on anything results in intelligence. The way I see it, things like image enhancement or voice replacement are not artificial intelligence at all. I probably could not define a hard line where it becomes artificial intelligence though.

To me they're the same thing. If there's a bunch of training data that is fed into a system that creates a model, then it's not traditional programming, where someone laboriously writes out if statements by hand. AI and ML aren't, as far as I'm aware, rigorously specifically defined terms. They're words that marketing picked up and ran with it. To me, what matters is: is there a black box somewhere in the system that's a bag of numbers, or is it code that a human could dig in and read.

> The “overlap” is that all machine learning is AI ...

"All machine learning" is not AI, as k-means clustering and linear regression, amongst others, are very much ML without qualifying as AI algorithms.

https://en.wikipedia.org/wiki/Artificial_intelligence

As it is taught literally every single AI/machine learning course on the world, machine learning is very much part of AI completely since inception.

I don’t completely understand why it is this important for you to argue against this completely defined fact.

It is correct to argue about misleading terminology. "AI" contains the word "intelligence", and for instance logistic regression algorithm is not intelligent, while it is clearly ML, since machine learns something. As Machine learning is broader category, it should include Artificial Intelligence, not vice versa.

Also, 'every single course' is perhaps an overstatement - a course that I co-authored tries to get it right from the first principles.

You're just making up your own definitions. Have at it, but as you've been told: this stuff is not new.

It wasn't misleading for 70 years... How did it become misleading?

The machine is learning something so that it can produce outputs based on its learned knowledge. At a high level that seems to be very clearly AI. What am I missing here? You’re probably right, I’m asking genuinely.

It's a matter of definitions, but I can at least understand someone wanting to make a distinction between reactive and non-reactive 'AI' (such as data filters).

There's overlap and edge cases, though: Maybe you have a program that summarizes texts. One could argue that's no different from a passive filter. But can you then ask questions about the text? That's unquestionably AI.

Technically linear regression is statistics rather than ML, but I feel like the GNU/Linux people whenever I point that out.

Not even technically. I had to do linear regressions by hand, pencil and paper, in college stats course 20 years ago. No machine necessary

Bayes is turning in his grave fast enough to power Manhattan.

> but not all AI is machine learning

I will instead pick at this latter part of your claim. What is an example of something that is AI but that is not ML..?

A chess engine.

At this point AI is a marketing term not an actual category

See: Samsung selling "AI" vacuum cleaners and washing machines

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