I prefer GenAI except the “Gen” stands for “general” because, really, the other kinds of ML/AI being contemplated in this thread are not at all general.

Like, before this you had extremely specialized models being painstakingly trained for an extremely specialized task using mountains of specialized data, painstakingly collected and labeled for correspondingly specific features, typically done by ML engineers (a specialized discipline) further specialized in that problem domain. And most users only have ever experienced all this abstractly through extremely narrow and specific functionalities like recommendations or ads.

On the other hand, any random person can hand these GenAI models any task that can be expressed as words or images or sound or some combination thereof and have a shockingly good chance of getting useful results. Yes they sometimes contain mistakes, but if you simply point them out, the models can often fix them!

I don’t know how much more general things can get than that.

It does get a bit uncomfortably close to “AGI”, which is a vague, heavily contentious term. But this sidesteps all that by not calling it AGI. The conventional “Generative AI” interpretation of GenAI thus becomes a convenient decoy for “General AI” ;-)

CI. Conversational interface. That's the difference. Simply a way to interact with a computer through the same mechanisms that you would use to interact with a person, through that computer's calculating a probable human reaction to a stimulus, after looking at millions of them and counting the likelihood of all possible reactions.

The real problem is pretending like there's any thought there at all. It's not programming because it knows how a computer works, it's programming because it can calculate what a person could likely write - which is why it writes source code rather than machine language. The value comes from the fact that it can chain a bunch of these reactions together, and react to things that don't originate from a human - if set to do so by a human, and they are things that a human could also predictably react to.

I'm not 100% sure you should be able to separate it from spam detection, though. It's learning from reading emails, and later whether that email is later marked as spam or saved from the spam pile. The critical part for me is whether that information is just going to be used to detect spam, or whether it's going to be used for something else.

If you're telling me you're not going to offer me spam detection, unless I not only agree to train your spam classifier, but some other model(s) too, just say that. It makes it clear that you're taking ownership of my data as payment.

As for children being able to sell their data as payment for the use of a search engine, the government pretends to be concerned about that - why doesn't it act here?

I am actually of the opinion that the problematic term is intelligence and that “super intelligence” won‘t actually help. This term has caused tons of issues in psychology and is now causing other issues in technology and journalism.

However I think it is a lost battle and I am willing to concede the term over to its current usage. I am however glad that traditional statistical models are no longer referred to by this trouble term. I think machine learning also has issues, and wish people would just be specific with their description by stating the particular algorithm they use (e.g. Markov Chains; ANN; Kalman Filter; etc.) but still accept “machine learning” this as a massive improvement over “artificial intelligence”.