Yes I agree, but OP's link is not aimed at academics, it's aimed at the general public. And you can't expect the general public to know that distinction: they see 'AI', they think 'ChatGPT', not Random Forests

AI has been discussed in public for years and was attached to huge numbers of things well before chatgpt. Chatgpt only came out less than a year ago.

We've also been using the term Machine Learning for a long time to try to avoid this. Using "AI" where "ML" would suffice is just trying to cash in on recent hype for clicks.

Still an interesting result of course.

My understanding is "ML" came into existence after the term "AI" became an untouchable and disrespectful dirty word in academia after a previous hype cycle went bust. Statisticians wouldn't be caught dead working on "AI". See also xkcd purity scale (435).

It looks like machine learning took over in the 2010s after a long convergence starting in the 2000s.

https://books.google.com/ngrams/graph?content=artificial+int...

This is from published books, so probably less affected by popular narratives. For that, we have Google Trends!

It looks like they trade places repeatedly when constrained to news searches: https://trends.google.com/trends/explore?cat=5&date=all_2008...

Right. ML is less likely to arouse emotions in either direction.

It's also a refinement on the term. AI can include a lot while ML is much more specific.

Not really though it may seem that way. Not all AI is ML. There are a ton of things within AI such as planning algorithms or Expert Systems. Again those are under the academic discipline of AI but not in the subfield of ML.