Logic programming? AI until SQL came out. Now it's not AI.
OCR, computer algebra systems, voice recognition, checkers, machine translation, go, natural language search.
All solved, all not AI any more yet all were AI before they got solved by AI researchers.
There's even a name for it: https://en.m.wikipedia.org/wiki/AI_effect?utm_source=perplex...
> As soon as definite knowledge concerning any subject becomes possible, this subject ceases to be called philosophy, and becomes a separate science.
I'm not actually sure I agree with it, especially in light of less provable schools of science like string theory or some branches of economics, but it's a great idea.
Doctor is similar - in the US, when someone says "Doctor" they usually mean "Medical Doctor" but "Doctor" just comes from the Greek "teacher" / "scholar" which is more broad and the title can still be used officially and correctly for PhDs. https://en.wikipedia.org/wiki/Doctor_(title)
Science also originally referred to knowledge. What we think of as "science" used to be called the natural sciences. Sometimes people get confused because I have a B.S. in Classics because science has lost that broader meaning.
And it is interesting, as you say, that when it comes to Bachelor/Master/Doctor of Science/Art/Philosophy (even professor), these are all titles formed from arbitrary terms that have been enshrined by the institutions that give people these titles.
I think the reasoning behind "doctor of philosophy" may be lost to history. All knowing Wikipedia suggests that it didn't happen at once. My take was that the requirements for a modern PhD were added long after the title was adopted.
https://en.wikipedia.org/wiki/Doctor_of_Philosophy
I suspect there was a time when a person could be well versed in multiple of what are now separated fields, and that you had to be a philosopher to make sense of science and math. Also, as science was flexing its own wings, claiming to be a philosopher might have been a way to gain an air of respectability, just like calling a physician "doctor" when the main impact of medicine was to kill rich people.
Disclosure: PhD, but ambivalent about titles.
So, what are good examples of some things that we used to call AI, which we don't call AI anymore because they work? All the examples that come to my mind (recommendation engines, etc.) do not have any real societal benefits.
It's only recently with generative AI that you see any examples of the opposite, people outside the field calling LLMs or image generation "AI".
It’s the cognitive equivalent of the Indiana Jones sword vs gun scene.
A hundred years ago computers were 'electronic brains'.
It goes dormant after loosing its edge and then re-emerges when some research project gains traction again.
But more seriously, I wonder what the etymology of 'völva' is...
logic programming is not directly linked to SQL, and has its own AI term now: https://en.wikipedia.org/wiki/GOFAI.
Humans are intelligent + humans play go => playing go is intelligent
Humans are intelligent + humans do algebra => doing algebra is intelligent
Meanwhile, humans in general are pretty terrible at exact, instantaneous arithmetic. But we aren’t claiming that computers are intelligent because they’re great at it.
Building a machine that does a narrowly defined task better than a human is an achievement, but it’s not intelligence.
Although, in the case of LLMs, in context learning is the closest thing I’ve seen to breaking free from the single-purpose nature of traditional ML/AI systems. It’s been interesting to watch for the past couple years because I still don’t think they’re “intelligent”, but it’s not just because they’re one trick ponies anymore. (So maybe the goalposts really are shifting?) I can’t quite articulate yet what I think is missing from current AI to bridge the gap.
"The question of whether a computer can think is no more interesting than the question of whether a submarine can swim." - Edsger Dijkstra
We are already playing with some incredible ingredients. Machines that can instantly recall information, (in principle) connect to any electronic device, and calculate millions of times faster than brains, and perfectly self-replicate. Just using these abilities in a simple way is already pretty darn powerful.
it is really breaking free? so far LLMs in action seem to have a fairly limited scope -- there are a variety of purposes to which they can be applied but it's all essentially the same underlying task
Indeed, if AI was an algorithm, imagine what would it feel like to be like one: at every step of your thinking process you are dragged by the iron hand of the algorithm, you have no agency in decision making, for every step is pre-determined already, and you're left the role of an observer. The algorithm leaves no room for intelligence.
our brain is mostly scatter-gather with fuzzy pattern matching that loops back on itself. which is a nice loop, inputs feeding in, found patterns producing outputs and then it echoes back for some learning.
but of course most of it is noise, filtered out, most of the output is also just routine, most of the learning happens early when there's a big difference between the "echo" and the following inputs.
it's a huge self-referential state-machine. of course running it feels normal, because we have an internal model of ourselves, we ran it too, and if things are going as usual, it's giving the usual output. (and when the "baseline" is out of whack then even we have the psychopathologies.)
But that is an uncomfortable idea for most people.
There is not much of a difference in considering it deterministic or not, because we can't determine it.
Has there been a serious effort to, using something that might be capable of doing so, like neural nets?
This "I'm just an observer" idea may be true at some higher level, if you're a monk on the threshold of nirvana, but for common folk this mindset leads nowhere.
I don't agree with this framing. A person's opinion of what a "good" or "right" outcome is is one of the inputs to the algorithm.
When you decide to put in less effort it's affecting the algorithm because the weight of the high-effort outcome is reduced.
The trouble is, you see, that if you do know Right from Wrong you can't choose Wrong. You just can't do it and live.
It is quite a convincing book and maybe even a dangerous book in a sense.
I agree with you that people don't consider intelligence as fundamentally algorithmic. But I think the appeal of algorithmic intelligence comes from the fact that a lot of intelligent behaviours (strategic thinking, decomposing a problem into subproblems, planning) are (or at least feel) algorithmic.
There are really very few situations where people really want voice recognition. The main one is hands free controls when driving or for as a remote control for TV or music.
Touchscreens are great for phones, but I'm really not a fan of them in cars where I prefer the tactile feedback of knowing what button is under my finger.
A lot of it just comes down to having the right tool for the right usecase, really.
But it may be a while.
Just don't use the word Champaign anywhere near a Miller Highlife in France. I think they're digging up old guillotines for that.
Edit: Your Dijkstra quote may be missing an "is":
..the question whether machines can think is as relevant
I don't doubt that there were marketing people wanting to attach an AI label to them, but that's just marketing BS.
You can thank social media for dumbing down a human technological milestone in artificial intelligence. I bet if there was social media around when we landed on the moon you’d get a lot of self important people rolling their eyes at the whole thing too.
AI = machine learning
Yes, that makes a convolutional net trained on recognizing digits an AI.
I'm not sure, but I think statistical regression already specifies a simple model (e.g. a linear one), while the approach with neural networks doesn't make such strong assumptions about the model.
I actually think linear regression isn't usually called ML in practice. Anyway, I meant something like neural networks.