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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.

Except from your own link the author himself goes well beyond the evidence he has:

"Misleading headlines notwithstanding, no one really has the slightest idea how the brain changes after we have learned to sing a song or recite a poem. But neither the song nor the poem has been ‘stored’ in it. The brain has simply changed in an orderly way that now allows us to sing the song or recite the poem under certain conditions. When called on to perform, neither the song nor the poem is in any sense ‘retrieved’ from anywhere in the brain, any more than my finger movements are ‘retrieved’ when I tap my finger on my desk. We simply sing or recite – no retrieval necessary."

If your brain is changed in an orderly way so that you can now sing a song or recite a poem after reading/hearing them, in what way is that different than it being stored? Isn't that the definition of information storage? Even for a computer: The hard drive is changed in an orderly way so that it can recreate a song or poem (with the appropriate software in this case). If the song is not stored and retrieved from anywhere how can you recreate it, even badly? It may not be in the same way as a computer. And it may be vastly complex, but information is stored and is retrieved. I can think about my social security number and think about the numbers. My brain was (as the author states) changed in some orderly way when I first read those numbers and was changed in some orderly way to associate those numbers with "My social security number" such that when I think, "what is my SSN?" that orderly change is accessible in some way to my conscious thoughts.

It keeps saying the information is not retrieved, but then keeps saying "the brain is changed in an orderly way so that it you are able to then replicate experience X at a later point" That is a good definition of what being stored and retrieved means! The standard model may be wrong about how, but this article doesn't actually refute that it is indeed stored somehow, no matter how many times they say just that.

"they can re-experience hearing the story to some extent, although not very well (see the first drawing of the dollar bill, above)."

"For any given experience, orderly change could involve a thousand neurons, a million neurons or even the entire brain, with the pattern of change different in every brain."

His actual argument appears to be that the orderly change is large in scope and different for each person. Which may be true. And that it isn't stored in the same way as in a computer. Which also may be entirely true. But that doesn't mean that change is not storage and retrieval of information/data at all which is what he claims. It must be or you could not re-experience the story. That change must encode some amount of data about the experience. When you re-experience it (or remember it) you must be somehow accessing that stored information. It might certainly be more complex than the standard model suggests which is what his latter portions indicate:

"Worse still, even if we had the ability to take a snapshot of all of the brain’s 86 billion neurons and then to simulate the state of those neurons in a computer, that vast pattern would mean nothing outside the body of the brain that produced it."

"Think how difficult this problem is. To understand even the basics of how the brain maintains the human intellect, we might need to know not just the current state of all 86 billion neurons and their 100 trillion interconnections, not just the varying strengths with which they are connected, and not just the states of more than 1,000 proteins that exist at each connection point, but how the moment-to-moment activity of the brain contributes to the integrity of the system. "

This argument is not saying that the brain is not a computer. This argument is saying the brain is a hugely complicated and unique computer that is only understandable within the confines of the whole brain itself. Which may well be true (and may well be an argument that the most amazing advance in Star Trek is a transporter that can read and replicate your entire mind). But it doesn't prove his closing line:

"We are organisms, not computers. Get over it."

Those are not mutually exclusive categories even if materialism is incorrect. He takes a valid criticism of the standard model but then runs way too far than that criticism and his own evidence actually points towards. That the human brain does not store and retrieve information/memories in the same way a computer does is probably true. That thinking of it that way, might push people into misunderstanding is also probably true. That "no image of the dollar bill has in any sense been ‘stored’ in Jinny’s brain. She has simply become better prepared to draw it accurately, just as, through practice, a pianist becomes more skilled in playing a concerto without somehow inhaling a copy of the sheet music." is not actually supported however by evidence the author provides. If some information about what a dollar bill looks like has not been in some sense stored somewhere then Jinny would not be able to be better prepared to draw it again. He even states that you can detect activity in the brain when people are recalling memories. He says that isn't information storage and retrieval but he doesn't actually provide any proof. The fact we draw things badly from memory is not evidence that we're not storing and retrieving information, it's evidence we are storing and retrieving information badly. The fact we can detect brain activity when doing so indicates the brain is involved somehow in this storage and retrieval.

Now perhaps it is only as a conduit to the Platonic plane of metaphysical thought or as a translation device from our soul where consciousness and memory actually rests but the author doesn't provide any evidence for any alternatives.

Hilariously, his argument applies rather well to artificial neural networks. There, learning updates are also system-wide (unless you deliberately constrain them to a subset of weights) and we also can't always point to parameters that «store a fact», despite knowing perfectly that neural networks memorize, and even understanding how they do it. And if it's something less legible than a fact, such as a reasoning heuristic…