Another one:
https://pbs.twimg.com/media/FnAWOGLWIAAg4sA?format=jpg&name=large

Love the topic of the paper.Absolutely HATE the figures showing taxonomies / example AI tools. These visualisations with boxes and arrows are really awful. These arrows are all over the place and meaningless. And the category boxes look the same as the application boxes.

It could have looked more like this:https://the-decoder.com/wp-content/uploads/2022/10/market_map_generative_AI-770x1027.png.webp

Or like this:https://www.sequoiacap.com/wp-content/uploads/sites/6/2022/09/genai-landscape-8.png

I don't even particularly like my examples. But there is no need for all these arrows and category boxes looking like the examples.

Why are you both condescending and factually wrong?

(a) They ARE learning.
(b) The learning definition I used is the one that defines ML according to Tom Mitchell. You thinking it was some kind of useless definition tells more about you.

Yes. My PhD was on applied ML. My current day job is at a center for AI. There are many people dimensions smarter than me -- but AI is all I deal with every day.

The reason for the nebulous concept is that intelligence is hard to define. Thus, in the past it was often defined by relating it back to human intelligence, e.g. "automating tasks that would require human intelligence to solve" and even the Turing Test.
But there are harder definitions of intelligence, such as Francois Chollet's paper.

It is definitely NOT correct to say << [AI] is just using some search algorithm with heuristics to make the search more "intelligent">>.
AI covers way more and goes far beyond search.

>AI is a bit more of a nebulous concept, but generally speaking it's just using some search algorithm with heuristics to make the search more "intelligent".

No. Just no.

AI is the vision / effort / field of study that deals with replicating (human) intelligence.

I agree.

I still resist though and do not say that "I have developed an AI that does xyz" but say that "I developed a predictive ML model that does xyz".

Very easy-to-understand, well-written summary. Many thanks!

To me, a linear regression is part of Machine Learning, and, thus, part of the broader vision of AI. Even though linear regressions are old statistical models and probably existed long before the term ML.The linear regression algorithm is learning from data (i.e. improves the line fitting after observing more data. hence, it is ML in my book) -- it just has a very limited hypothesis space. It will only ever fit a straight line (or hyperplane, in the general case). It is not a general learner like a Deep Neural Network which can approximate any function.

This is not correct.
These algorithms are definitely learning (i.e. improving performance at a task through experience, i.e. by observing more data).

Intelligence is hard to define. Something like 'efficiency at acquiring skills at a broad range of tasks' would be one definition. We're getting there. This is the weak vs strong AI hypothesis: can we merely simulate intelligence or are we creating actual intelligence.

They really do. Just take any (somewhat reputable) book on ML.

Your statement is incorrect. When people (in the field) say ML they mean the whole toolbox of learning algorithms, incl. Deep Learning, trees & forests, kernel methods, etc.

(Which is OK. Sebastian Raschka, for example, has this view that AI and ML are not concentric circles but merely overlapping. He thinks a cat vs. dog classifier is so narrow, it is not aiming for the larger vision of AI. Fair.)

Sorry, no.

There are many valid and different views on AI, ML, Deep Learning.

This is not one of them. It's plain wrong.

Your statement only makes sense if one's mental model is not a Venn diagram of concentric circles: AI > ML > Deep Learning.

I should explain to be useful:AI is the vision and effort to replicate human intelligence. Human intelligence includes learning from data (--> ML). But one could argue there is more to it, e.g. knowledge bases etc. This is not something typical ML algorithms consider (LLMs do that indirectly). Also, our current ML models are still super narrow. The idea of AI is general intelligence.

This is not true. It sounds like an educated statement - but it isn't true.