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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
This is something that I find very unconvincing on the anti-AI side of the debate. First one is what I will call "just" argument. GPT is just next word prediction machine, it is just stochastic parrot and so forth. This type of arguments seem to argue that certain method such as training LLMs on predicting text will obviously result just in text predicting system. Which I think is red herring - training on text is obviously sufficient for LLMs to develop qualitatively different capacities such as multimodality. As the old saying goes - quantity has quality of it's own. It seems to me that it should be on proponents of just argument - who pretend to have deep knowledge of these models - to explain and possibly predict these things before saying stochastic parrot .... and therefore multimodality. Plus of course these types of cheap arguments can be used against humans - human brain is just a product of blind evolution. Or as in this book review, human brain is just a multi-layer prediction machine.
It seems to me that for AI the focus is a lot on hardware, the training process or on the output. But for humans it is always highly spiritual focus on qualia, feeling of understanding and other subjective things - it is not about brain structure, or pointing out how humans produce stupid output and therefore brains cannot truly understand, they do not know, they do not have representation of the universe or that they cannot plan. There are more obnoxious types like this episode of Adam Ruins Everything but there are also other and more sophisticated critics - the common denominator of all of these is that they are awfully certain to know what is [not]happening inside LLM models. I do not see many legibility experts who would really claim to know for certain that LLMs do not understand. Because who knows what is happening in this whole mess of inscrutable matrices of parameters, maybe somewhere in there is some kind of representation of the universe. We certainly do not know what is happening inside human brain when we scan it - unless of course we use Machine Learning for that But more importantly, LLMs can predict text on par of some of the top percentiles of people who understand, know or plan. So yes, LLMs can pass test specifically designed to test for understanding, they can produce plans on par with human planners and so forth, but for some reason despite all that one can simply claim is that they do not truly know or plan because of stochastic parrot or some such.
More convincing argument - or The Motte if you wish - is that LLMs do not understand, plan etc. like humans. Which is perfectly reasonable argument, except that they do kind of develop certain things that humans and also some animals also develop. So they are like humans in certain way but completely alien in other ways. However even this is loaded questions as LLMs can produce some output equivalent to humans but they may still not do it like humans. But each new implementation of these models are improving in certain tasks that were still outsourced to Mechanical Turks, the space for unique human application in this space is narrowing.
Now I have to say that I do not know where this all will lead. It may very well be so that current Transformer approach will reach certain plateau and then stops. There may be significant areas where humans will remain superior, and it may even have something to do with the fact that "Auto-Regressive LLMs are exponentially diverging diffusion processes" as LeCunn says. I do not know, but neither do these people. What I see is quite a rapid growth in capabilities of these models just with more compute.
We had some fun with this over the holidays. My family has a know-it-all uncle who's fairly smart but even more so confident.
He holds to some theory courtesy of supposedly Penrose that proclaims we humans are capable of conceptual leaps - insight, I guess, because neurons somehow exist or get information from adjacent worlds of the Many World interpretation theory.
Therefore, LLMs, being just run on chips will never be able to do truly useful intellectual work.
Meanwhile, if you ask him about something he doesn't know much about (so not politics, math or economics) he will, with perfect confidence say that e.g. plutonium in nuclear warheads is in the form of dust, as why else would they need to compute the implosion geometry. Etc.
So, ironically, like LLMs, he's prone to hallucinating if you ask him about things he doesn't know much about. Getting to admit him he doesn't know something is next to impossible.
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