https://www.youtube.com/watch?v=ANRnuT9nLEE&list=PLrxfgDEc2N...
Transcript: https://www.preposterousuniverse.com/podcast/2022/02/14/184-...
"...And then there’s natural language understanding and reasoning, and I would say we have not really made progress at all. GPT-3, which we may wanna talk about, gives the illusion of having natural language of understanding, but I don’t really think that it does. And we are nowhere near, for example, an all-purpose general assistant. ..."
Either the quote is poorly chosen or reading this article is not worth the time.
The Mindscape Podcast is hosted by Sean Carroll. You have a very sharp quantum physicist interviewing an expert in the field of AI research.
The podcast is worth the time, and the quote is representative of an expert's take on the matter. He elaborates, but I don't need to write an essay just to argue on the internet.
Who was "clinging on to the idea that well, the machines can't really play chess" and when was that?
That’s the way it is with AI: first people say X is impossible for computers, then they say X is hard, then they say X doesn’t work for certain edge cases, then they say we’ve known all along that computers could do X.
>> Levy wrote, "I had proved that my 1968 assessment had been correct, but on the other hand my opponent in this match was very, very much stronger than I had thought possible when I started the bet."[37] He observed that, "Now nothing would surprise me (very much)."[38]
(Edit: the quote is from your WP link.)
So that at least really does not match the pattern you mention. Levy's evaluation of chess engines was pretty much in the water, all the way up to Kasparov's loss to Deep Blue. As far as I can tell he never said anything like "the machines can't really play chess" as the OP suggested. He just made a prediction about how much they could advance in ten years. And he was right.
I would therefore like more justification for your assertion that "that's the way it is in AI". I would also like a clarification: who are the "people" who say all those things? How representative are they of experts and researchers?
Who makes all those naive predictions about the impossibility of AI that are later proven wrong? For the time being, it seems that naive predictions can be traced more directly to luminaries of the field, like Alan Turing or Marvin Minsky [2], who predict that AI is "just around the corner", rather than skeptics and naysayers who say it wont' happen, as is usually suggested.
And then of course, there's Rodney Brooks' dated predictions, so far standing the test of time (although that's a short time!) [3].
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[1] Totally coincidentally, Donald Michie was my thesis advisor's thesis advisor, so I'm, like, his academic grandchild.
[2] See: https://web.eecs.umich.edu/~kuipers/opinions/AI-progress.htm...
"In 1958, Herbert Simon and Allen Newell wrote, “within ten years a digital computer will be the world’s chess champion”; note that this is 10 years before Levy and McCarthy's bet.
[3] https://rodneybrooks.com/predictions-scorecard-2022-january-...
https://news.ycombinator.com/item?id=11718300
If we count GitHub Copilot as the tip of the spear then I was too optimistic.
> GitHub Copilot ... was first announced by GitHub on 29 June 2021
https://en.wikipedia.org/wiki/Program_synthesis#Origin
Much work has been done since then and inductive program synthesis from incomplete specifications consisting of input/output examples (a form of machine learning) has been possible for quite some time. This is the second time this week I point to the report by Gulwani et al for a modern overview (of both kinds):
Program Synthesis
https://www.semanticscholar.org/paper/Program-Synthesis-Gulw...
Rather than Copilot being the "tip of the spear", it is really a step back, a system that can only generate code but not check its correctness, unlike pretty much every program synthesis system since forever. Although this may actually be an advantage for its commercial application (since it makes it easier to match expectations of the system's performance) it is not really any indication of progress, in any way, shape or form. In truth, Copilot is famous today because it is supported by a large company like Microsoft and because earlier work is not well-known by most people who get their AI knews from blogs and podcasts, who are also not very well aware of the history of the field. But, "tip of the spear"? Oh, no. Unless it's a very blunt, toy spear, more like a tech demo of a spear.
And this is by no means restricted to program synthesis, and Copilot. For example, the first self-driving car to drive on a stretch of real road, with real traffic, without human intervention, was Ernst Dickmann's 1995 robot car:
https://people.idsia.ch/~juergen/robotcars.html
Again: 1995. And yet, fully autonomous self-driving cars are still not here. The revolution hasn't happened and progress has only crawled marginally forward.
Waymo has taxi service. It's losing money doing that, and it's only in a few select places, but it's doing autonomous journeys, with no "safety driver" on city streets. It understands junctions, and traffic signals, pedestrians and cyclists, because on the city streets those things are commonplace. It's doing the hard part.
I don't think that constitutes "crawled marginally forward", it's a considerable advance.
Gosh, the robot car work was incredible, I can't believe I've never heard of it before!
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Copilot isn't cutting edge but it's significance is just that it's a mass market tool. It will be interesting to see how well it does, and whether users find it worthwhile overall after a couple of years. Will it be improved to the point where it starts to compete with its users?
I've asked other programmers before, if you could write a program that replaced you would you do it?
(One of the reasons I like Schmidhuber is that his goal, since early on, is to "Create an automatic scientist, and then retire.")
To me the ultimate effect of programming is to obviate the programmer.
I think Copilot can be used very effectively, as long as its capabilities and their limitations are communicated clearly. For instance, I think it can make a great boilerplate generator, as long as users stick to short code snippets.
Well, I don't know about replacing programmers. I think that's Sci-Fi, for the time being, and for a while longer still. What I'm more interested in is creating tools to help programmers do their job. Copilot does that already, btw, I'm not dissing it. I'm just pointing out it doesn't represent a sudden shift in capabilities, to be clear.
>> (One of the reasons I like Schmidhuber is that his goal, since early on, is to "Create an automatic scientist, and then retire.")
I didn't know Schmidhuber had said that. My thesis advisor, Stephen Muggleton, was part of an interdisciplinary team who created a robot scientist that can develop its own theories and then choose, and run, the experiments to prove them:
https://en.wikipedia.org/wiki/Robot_Scientist
Another one of those things that are not well-known, I guess. I wasn't involved with that, btw, but I think recent advances could make for a much more powerful system. I am considering something similar as a research project, post-doc.
Cheers!
Text conditional image generation is still a fairly new thing, we don’t know what its upper limits will prove to be. Also, it seems likely that, whatever level of capability they reach, humans will learn how to use them effectively as tools for their own purposes. Some people might use them for quickly preflighting ideas, others to create boilerplate, some people may use them to speed up their workflow or scale up productivity. People didn’t know what all the use cases for personal computers would be when they were introduced in the mid-70’s, or what capabilities they would possess decades later.
I'm going to try to work up a decent reply to your article, but it might take me a day or two.
Have you read Wendell Berry's "What are People For?" (that essay not the whole eponymous book)?
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The (open) secret at the heart of AI is that no AI can answer: "What is good?" ( https://news.ycombinator.com/item?id=31720621 )
AKA "Lucky for whom?" Teela Brown, fictional character in Larry Niven novels
Spoiler alert!
This link gives details of the character that are spoilers: https://larryniven.fandom.com/wiki/Teela_Brown
Unfortunately those details are the point I'm trying to make, and I'm a huge Niven fan so I'm not going to spoil Ringworld, but it ties in with the (ultimately metaphysical) question, "What is good?"
If that's the actual meat of your claim, it's weird to just put it unsupported at the end. If you think it's an aside you've got a big problem, because without it the rest is pretty weak.
We saw this with chess. Anybody who was clinging on to the idea that well, the machines can't really play chess, because technically there do seem to be a few humans who are better, was screwed the moment the machines begin routinely beating grand masters. Either this categorically isn't something that the machines can do, or, it is and so it's important that they can do it at all. We shouldn't expect any half measures on this.
Do you consider Rob Liefeld an artist? What do you reckon the chances are that the machine can get human anatomy right more often than Rob Liefeld? Rob is a human, so it seems like that should give him an advantage. However unlike Rob DALL-E has learned by seeing lots of existing pictures of humans that they don't look the way Rob draws them...