In general, yes, that's it. But to be honest I'm actually not that interested in making "machines that think like humans". I say that's the "promise of AI" because it was certainly the goal at the beginning of the field, specifically at the Dartmouth workshop where John McCarthy coined the term "Artificial Intelligence" [1]. Researchers in the field have varying degrees of interest in that lofty goal but the public certainly has great expectations, as seen everytime OpenAI releases a language model and people start writing or tweeting stuff about how AGI is right around the corner etc.

Personally, I came into AI (I'm a PhD research student) because I got (really, really) interested in logic programming languages and well, to be frank, there's no other place than in academia that I can work on them. On the other hand, my interest in logic programming is very much an interest in ways to make computers not be so infuriatingly dumb as they are right now.

This explains why I dislike neural machine translation and similar statistical NLP approaches: because while they can model the structure of language well, they do nothing for the meaning carried by those structures which they completely ignore by design. My favourite example is treating sentences as a "bag of words", as if order doesn't make a difference- and yet this is a popular technique... because it improves performance on benchmarks (by approximately 1.5 fired linguists).

The same goes with google translate. While I'd have to be more stubborn than I am to realise that people use it and like it, I find it depends on the use case -and on the willingness of users to accept its dumbness. For me, it's good where I don't need it and bad where I do. For example, it translates well enough between languages I know and can translate to some extent myself, say English and French. But if I want to translate between a language that is very far from the ones I know - say I want to translate from Hungarian to my native Greek- that's just not going to work, not least because the translation goes through English (because of a dearth of parallel texts and despite the fact that Google laughably claims its model actually has an automatically learned "interlingua") and the result is mangled twice and I get gibberish on the other end.

I could talk at length on why and how this happens, but the gist of it is that Google translate stubbornly refuses to use any information to decide what translation to choose, among many possible translations of an expression, by looking at the frequencies of token collocations- and nothing else. So for example, if I ask it to translate a single word, "χελιδόνι", meaning the bird swallow, from Greek to French, I get back "avaler" which is the word for the verb to swallow- because translation goes through English where "swallow" has two meanings and the verb happens to be more common than the bird. The information that "χελιδόνι" is a noun and "avaler" is a verb exists, but Google translate will just not use it. Why? Well, because the current trend in AI is to learn everything end-to-end from raw data and without prior knowledge. And that's because prior knowledge doesn't help to beat benchmarks, which are not designed to test world-knowledge in the first place. It's a vicious circle.

So, yes, to some extent it's what you say- I don't quite expect "machines that think like humans", but I do want machines that can interact with a human user in a slightly more intelligent manner than now. I gave the example of SHRDLU above because it was such a system. I'm sad the effort to reproduce such results, even in a very limited domain, has been abandoned.

P.S. Sorry, this got a bit too long, especially for the late stages of an HN conversation :)

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[1] That was in 1956: https://en.wikipedia.org/wiki/Dartmouth_workshop