This started out as "one more point before I go" in the previous post, but it grew enough while I was getting that one ready to publish that it seemed like it should have its own post.
Where machine learning systems like LLMs do unexpectedly well, like in mimicking our use of language, it might not be because they've developed unanticipated special abilities. Maybe ML being good at generating convincing text says as much about the problem of generating convincing text as it does about the ML doing it.
The current generation of chatbots makes it pretty clear that producing language that's hard to distinguish from what a person would produce isn't actually that hard a problem, if you have a general pattern-matcher (and a lot of training text and computing power). In that case, the hard part, that people have spent decades trying to perfect and staggering amounts of compute power implementing, is the general pattern-matcher itself.
We tend to look at ML systems as problem solvers, and fair enough, but we can also look at current ML technology as a problem classifier. That is, you can sort problems according to whether ML is good at them. From that point of view, producing convincing text, recognizing faces, spotting tumors in radiological images, producing realistic (though still somewhat funny-looking) images and videos, spotting supernovas in astronomical images, predicting how proteins will fold and many other problems are all examples of pattern-matching that a general ML-driven pattern-matcher can solve as well as, or even better than, our own naturally evolved neural networks can.
Not knowing a better term, I'll call these ML-friendly problems. In the previous post, I argued that understanding the structure of natural languages is a separate problem from understanding what meaning natural language is conveying. Pretty clearly, understanding the structure of natural languages is an ML-friendly problem. If you buy that understanding meaning is a distinct problem, I would argue that we don't know one way or another whether it's ML-friendly, partly, I would further argue, because we don't know nearly as much about what that problem involves.
From about 150 years ago into the early 20th century, logicians made a series of discoveries about what we call reasoning and developed formal systems to describe it. This came out of a school of thought, dating back to Leibniz (and as usual, much farther and wider if you look for it), holding that if we could capture rules describing how reasoning worked, we could use those rules to remove all uncertainty from any kind of thought.
Leibniz envisioned a world where, "when there are disputes among persons, we can simply say: Let us calculate, without further ado, to see who is right". That grand vision failed, of course, both because, as Gödel and others discovered, formal logic has inescapable limitations, but also because formal reasoning captures only a small portion of what our minds actually do and how we reason about the world.
Nonetheless, it succeeded in a different sense. The work of early 20th-century logicians was essential to the development of computing in the mid-20th century. For example, LISP -- for my money one of the two most influential programming languages ever, along with ALGOL -- was based directly Church's lambda calculus. I run across and/or use Java lambda expressions on a near-daily basis. For another example, Turing's paper on the halting problem used the same proof technique of diagonalization that Gödel borrowed from Cantor to prove incompleteness, and not by accident.
Current ML technology captures another, probably larger, chunk of what naturally-evolved minds do. Just as formal logic broke open a set of problems in mathematics, ML has broken open a set of problems in computing. Just as formal logic didn't solve quite as wide a range of problems as people thought it might, ML might not solve quite the range of problems people today think it might, but just as formal logic also led to significant advances in other ways, so might ML.
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