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Inferential Distance part 2 of ?: Minsky's Marvelous Minutia, or why I'm bearish on GPT
This post is a continuation of / follow up to my post on Inferential distance from a month ago, inspired by the recent discussions of GPT-4 and @ymeskhout's comments on prosecutorial immunity. I also feel like this might end up turning into a series, hense the "part 2" and the question mark.
Two things that came up in that previous conversation were a) the apparent differences between thing-manipulators and symbol-manipulators. That is people's whose job, hobbies, day-to-day life revolve around manipulating objects and those whose lives revolve around manipulating symbols/feelings. And b) the question of what constitutes a "hard" social problem, and how/why thing-manipulators and symbol-manipulators seem to have such wildly diverging opinions on that question.
For a bit of context my degree is in math but entering the field as I did, later in life having already spent 12 years in another career, I tended towards the more applied/practical side of the discipline. This tendency seemed put me at odds with a lot of my instructors and fellow students, especially the "nerdier" sort. That is those who were "nerdy" even by the relative high standards of nerdiness expected from someone pursuing an advanced degree in mathematics. for whatever reason showing an interest in applications was kind of looked down upon. To be fair, I did understand where they were coming from. From a young age we're trained to admire the brilliance of guys like Pythagoras, Leibnitz, Newton, Euler, Keppler, Einstein, Et Al. Afterall, why does anyone even bother to study math if not to follow in those men's footsteps and unlock the grand fundamental truths of the universe? In contrast, while the principals of kinematics, control laws, and signal processing, may be mathematically intensive they also come across as very pedestrian. Pure math guys seem to regard them with a sort of casual disdain, the sort of thing you delegate to unpaid interns and teachers' assistants. Meanwhile truth is you can build yourself a pretty good career working on control laws and signal processing, just not in academia.
This brings us to the question of what constitutes a hard problem. If you spend enough time working in robotics or signal-processing, you'll eventually come across Moravec's Paradox. The paradox is best summed up by this xkcd comic from 2014, specifically the alt-text which reads...
...the "paradox" being that many functions that we consider baseline, and accordingly take for granted, are in fact extremely complex and computationally intensive. Whereas much of what we might label "higher reason" is actually quite simple and requires very little in terms of memory or processing power.
It turns out that it's relatively easy to teach a computer to play chess better than a human or to come up with mathematical proofs that are both novel and correct. And yet, after 60 years, despite the truly massive advances in both hardware and software represented by projects like stable diffusion Minsky's problem remains far from solved. In practice, you can pretty much graph straight line between the simpler a task seems/earlier a it appears in the evolutionary enviroment, to how hard it will be to replicate. Playing chess is easy, Bipedal locomotion is difficult. Bipedal locomotion only seems easy to creatures like you and me because we've been doing it since we were two-years-old, and our ancestors spent millions of years refining the techniques and bio-mechanics that were bequeathed to us as infants.
What does this have to do with anything? My answer is that I feel like a recognition/understanding of Moravec's Paradox is one of the major components of inferential distance between myself and most others both in the rationalist movement, and in academia. It is why I am reflexively skeptical of grand unified social/political theories. and It is also why I remain deeply skeptical of GPT and the oncoming AI apocalypse it allegedly represents.
One claim you'll see guys like Elizer Yudkowsky, Bryan Caplan, and posters here on TheMotte make on a semi-regular basis is that "GPT knows how to play Chess". But if you press them on the topic, or actually look at chess games that GPT has played it becomes readily apparent that GPT makes a lot of stupid and occasionally outright illegal moves (eg moving rooks diagonally, attacking it's own pieces, etc...). What this demonstrates is that GPT does not "know how to play chess" at all. At least not in the same sense that Deep Blue or my 9-year-old can be described as "knowing how to play chess", or AlphaGo can be described as "knowing how to play Go".
Furthermore, once you start digging into their inner workings this lack of "knowing" appears to be a fundamental weakness of the Large Language Model architecture. At the end of the day it's still just a regression calculating the next most plausible word (or in the case of GPT-4 string of words) based on the correlations found in it's training data. Granted GPT-4 is certainly a step up from GPT-3 in terms being able to pass as human. The shift towards correlating longer statements rather than individual words seems to have plastered over a lot of the jarring discontinuities that made GPT-3 generated posts so easy to pick out. In contrast GPT-4 can actually kind of pass for human from the proverbial 50 ft away. Unlike prior GPT iterations, identifying it actually requires a level of careful reading or some sort of interaction.
Eugene Volokh's posts on Large Libel Models probably deserves a discussion of their own but INAL and not really interested in questions of liability. In any case he ends up running into the same issue with GPT that I did. Users here talk about instances of GPT "lying" or "hallucinating" and how to reduce the frequency of such instances, but the conversations inevitably devolve into self-referential nonsense because neither of these terms really describe what is actually happening. In order to "hallucinate" one must first be able to perceive. In order to "lie" one must first understand the difference between true and false. and GPT possesses neither. Simple fact is ask GPT for five examples of prosecutorial misconduct complete with citations and newspaper quotes and it will provide the names of five prosecutors, their alleged crimes, some juicy quotes, and supposed case numbers. However while the names provided might actually be real prosecutors, and the media outlet quoted might be a real outlet, if you actually look up the court records or try to find the quotes you're going to come up short because the example was not something that was pulled out of memory and provided, it was "generated" form the prompt in exactly the manner that a Large Language Model is designed to do.
to be continued...
edit: fixed link
(As an aside: this text is confusing, you jump from psychologizing to chess to Moravec, and it's hard to keep track of the core assertion).
Hlynka do you really want to go another round. Because I don't particularly care for it, especially not enough to respond with pure object-level to your Bulverism – even though this is what we do here all the time, to the point of obsessiveness. How boring is your life exactly? Maybe go on a trip to ol' Europe (or Asia), come here and we talk it over. Bring some good liquor if you can, Turks are lousy in this regard.
The real inferential distance here seems to come from inferences made on the basis of evidence, versus whatever you're trusting. Say, Caplan isn't known for being impressed by GPT's chess skills – he tests it on exam questions he believes are tricky. You tried that too: last time you've been claiming that autoregressive LLMs cannot not hallucinate your daughter's name, due to how they're trained. I've shown that ChatGPT replies as well as you said a human would, admitting it doesn't know. Since then, it's become possible to have coherent dialogues with files below 4 Gb and very sensible ones with stuff like Vicuna-13B. I assume you haven't tested that yet, despite insistence of multiple people here, because I haven't seen you concede the issue. Now you're simply dropping that and pivoting to saying they can't play chess, again due to fundamentals of their training. It's «just» regression, «just» predicting words, see. And words aren't chess moves or facts, so of coursh' flawlessly modeling grammar ought to be unsurprising, trivially feasible for an LLM – unlike modeling the logic of the game board. Or something. Although Chomsky, another guy who does not check whether his punches land, still seems to think that grammar cannot be solved with «just» statistics either. And while we're at it, Minsky's arguments were also obsolete at release. Now Dreyfus, this Heidegger scholar, was largely correct about Minsky's Talmudic-symbolic approach, but of him, Minsky only had to say that he doesn't understand and should be ignored.
On a meta-level, your rhetorical similarity to all those eggheaded paper-pushers is a rather bigger indictment of your position than whatever you say specifically about the tech. You scoff at pure math guys, at ivory tower symbol manipulators, but your arguments here are: brandishing your degree, discussing the history of academic debate, a bit of homegrown epistemology, dismissive blogposts and nerdy web comics, throwing around applause lights and rat lingo. You do not apply the pragmatic and entrepreneurial American lens, the «does it work tho» ethos. You treat LLM enthusiasts (and by proxy, developers who say the same) with the sort of casual disdain you believe pure math bros have for signal-processing researchers; where do you think notions like gradient, dropout and channel came from to LLMs? Read about Hyena Filters some time to stay ahead of the curve.
As a man involved with engineering, you ought to know that a skilled engineer can make bits and bytes perform bizarre magical circus tricks a limp-wristed intellectual would not see coming, and on the other hand that some problems are vastly harder than a sedentary utopian imagines, for not everything is deducible from first principles; that processes can be very deep and counterintuitive, that it can take a lifetime to figure out the nitty-gritty of how something actually works, so it is sensible to defer to reality over theory and assumption; worse, you preach this attitude. But you do not practice what you preach. Are you really a thing-manipulator or a symbol-manipulator? Or maybe more of a people-manipulator, at this stage of your career?
You are wrestling with your own shadow.
Congrats on your nine-year old never making illegal moves, by the way. You teach them well. Recently I've learned that my gainfully employed backend dev friend, 32, doesn't know how castling works, and is uncertain about pawn's inability to attack straight. I'd say he should be able to get to 1600 ELO, at least, with a little bit of finetuning. It's an issue of knowledge and experience, not just ineffable innate properties of the mind.
Do you have enough experience with LLMs to justify your conclusions?
I'll cite again Piantadosi again.
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