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Culture War Roundup for the week of April 10, 2023

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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...

In the 60s, Marvin Minsky assigned a couple of undergrads to spend the summer programming a computer to use a camera to identify objects in a scene. He figured they'd have the problem solved by the end of the summer. Half a century later, we're still working on it.

...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

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.

At the end of the day human brain is still just a bunch of biochemical reactions, how can biochemical reactions "know" anything? Does Stockfish "know" how to play chess?

In 2014, there was this xkcd comic, claiming that it would require a team of researchers and five years to automatically tag images of birds. A month later, Flickr showed a working prototype. In 2023 I can train a model that recognizes birds by putting a bunch of images in two folders and hitting "Run". The resulting model will have different failure modes than human pattern recognition: it will ignore some obviously birdlike images and claim that what most humans will agree is a kettle is obviously a bird. But does that mean it doesn't understand what a bird is? A model can predict you sex from your retinal fundus photo, something no human can do, does it matter if it doesn't "understand" what it's doing?

At the end of the day human brain is still just a bunch of biochemical reactions

I will never not point out that this is materialist mythology supported by nothing. And that nobody who makes this claim, not to mention nobody at all, can explain how and why the unspecified biochemical reactions produce consciousness, agency, though or qualia.

The brain is not a computer. And the only reason people believe it is is based on metaphysical assumption rather than logic or evidence.

It is not a computer for the same reason it isn't a clock, or a ship, or a river. These are metaphors. The map is not the territory.

I see no reason why biochemistry should not be able to produce consciousness, agency, thought and qualia. In the modus-ponens-modus tollens sense: "clearly they can, because they do." Where is the actual contradiction?

Don't multiply entities beyond necessity. Clearly brains have something to do with qualia. Why not "A causes B"? Why should I look beyond this intuitively obvious structure?

I mean it could.

But if you want to argue that this is the most parcimonious theory, you have a lot more legwork to do.

A lot of other things in your body also have similar effects. There has been a lot of hay recently made about other parts of your nervous system being more influential in your experience than previously thought, for instance.

But let's just leave the exact seat of consciousness problem aside since it's still ultimately within the body in this conception.

A harder problem is that none of the chemical processes as we currently understand them should generate this behavior.

Now they do of course, but in no ways that are predicted by the laws we understand. The fact that death is permanent is very weird for instance and it seems much more parsimonious to say the link between the body and the soul has been severed than that the extremely complex computer has been broken in a subtle way that can't be repaired.

If consciousness was simply a property of certain arrangements of matter, you wouldn't really expect nature to select the ones that can be bricked. But of course both theories are equivalent in practice.

All this really is just pointless arguing about which theory of a mysterious phenomenon is the most elegant. It's not inquiry. It's the same sort of rotten masturbatory behavior physics has fallen pray to in its absence of new discoveries.

I believe the most honest thing to do here is to be humble and admit that we don't know how consciousness works and stop ourselves from making assumptions on top of theories that haven't been tested by experience.

The fact that death is permanent is very weird for instance and it seems much more parsimonious to say the link between the body and the soul has been severed than that the extremely complex computer has been broken in a subtle way that can't be repaired.

On the other hand, obviously material things like strokes, lobotomies, head injuries, and drugs appear to have an obvious effect on the qualia of an individual. Why does death - which materially seems to just be an extreme extension of brain injury - suddenly now need an ineffable soul? Or do clots in your brain, or a metal rod through your head, claw at your spirit as well?

Put it another way, we can’t fix the brain once it’s dead right now, as you say, because it is Too Complex and Can’t Be Repaired. Would being able to fix death, in your eyes, be good evidence for the material basis of “consciousness”?

We also often can’t fix computers (or other complex machines) without replacing parts once some parts have degraded enough. Is that not dissimilar to how we cannot fix the brain (except that we can replace parts for other things and less so for a brain)?