[janalsncm]

Probably the main reason for this confusion is the lack of a good definition of “intelligence”. Basically no consensus if you browse the associated Wikipedia page. Of course the definition is also bogged down in human-centered history, assuming that humans are at the pinnacle of some contrived intelligence scale and placing other animals below us. I’m sure the genius philosophers who came up with this scale would’ve placed themselves at the pinnacle within humans as well.

Tacking “artificial” on top of that doesn’t make it any clearer.

My understanding of the taxonomy of these things within CS is that AI is a broad class of techniques for problem solving. Machine learning is a subset of those techniques which uses data and statistical methods. Non-ML AI is sometimes called GOFAI (“good old-fashioned AI”).

[HarHarVeryFunny]

AI and ML are two different things. AI can either be considered as a research area or simply end goal/result. AI doesn't itself refer to any set of techniques - it's the goal, not the means to the goal. You can consider GOFAI as non-ML, although perhaps it's more correct to regard it as older symbolic approaches (rule-based systems, etc) vs modern connectionist/ANN ones.

Machine Learning (ML) is a catch-all term (not in of itself a technology) for any approach where systems are designed to learn from data. This includes techniques such as random forests and SVMs, as well as neural nets.

There's really no fundamental relationship between AI and ML. Neither one is a subset of the other as this is an apples and oranges comparison - one is a goal, and the other is an approach). That said, all recent progress towards AI has been achieved by using ML, although not all uses of ML can really be regarded as AI.

Hope that helps define terms.

[janalsncm]

You may not see one as a subset of the other but there is no shortage of nested diagrams like [1], showing machine learning as a sub-field of artificial intelligence.

Further, the canonical Russell and Norvig AI textbook [2] only mentions machine learning briefly, as one of several skills that a computer would need in order to pass the Turing Test:

> machine learning to adapt to new circumstances and to draw new conclusions

And the Wikipedia page (which also contains a similar nested diagram) describes ML as a “part of” artificial intelligence.

So it is pretty clear to me that ML is treated as a sub-field of AI. However I still believe the AI field dances around the definition of Intelligence, preferring a practical task-oriented definition instead.

[1] https://www.edureka.co/blog/ai-vs-machine-learning-vs-deep-l...

[2] https://www.amazon.com/Artificial-Intelligence-Approach-Stua...

[3] https://en.wikipedia.org/wiki/Machine_learning

[HarHarVeryFunny]

Actually the WikiPedia page you linked to there shows two diagrams, one with ML as a subfield of AI, and another with them as separate overlapping fields (I guess the overlap is meant to represent where ML is being used to pursue AI).

> As of 2020, many sources continue to assert that ML remains a subfield of AI.[29][30][27] Others have the view that not all ML is part of AI, but only an 'intelligent subset' of ML should be considered AI.[5][31][32]

I'm well aware that there are some people who regard anything ML-related to be AI, which is the sensationalist view taken by the popular press who want to write stories about AI, but that doesn't mean it's true. What does something like a SVM classifier have to do with AI - it's more like a line fitting technique. Or what about a neural net that classifies photos as cat vs dog - seems better described as image recognition than AI (and the whole post-AlexNet modern neural net revolution was born out of the 2012 ImageNet image recognition competition). Or how about an echo-cancelling circuit in a telephone - is that AI to you? Why not - it's based on machine learning ...

As for ChatGPT, technically it's a language model, so really part of NLP - linguistics research. All it does it try to predict the next word in a word sequence. Of course it therefore ends up regurgitating lots of intelligent stuff it was trained on (as well as dumb and false stuff it was trained on), but that doesn't make it intelligent. Similarly, we don't say Google is AI just because it responds to search queries by finding web pages with intelligent content.