site banner

Culture War Roundup for the week of April 10, 2023

This weekly roundup thread is intended for all culture war posts. 'Culture war' is vaguely defined, but it basically means controversial issues that fall along set tribal lines. Arguments over culture war issues generate a lot of heat and little light, and few deeply entrenched people ever change their minds. This thread is for voicing opinions and analyzing the state of the discussion while trying to optimize for light over heat.

Optimistically, we think that engaging with people you disagree with is worth your time, and so is being nice! Pessimistically, there are many dynamics that can lead discussions on Culture War topics to become unproductive. There's a human tendency to divide along tribal lines, praising your ingroup and vilifying your outgroup - and if you think you find it easy to criticize your ingroup, then it may be that your outgroup is not who you think it is. Extremists with opposing positions can feed off each other, highlighting each other's worst points to justify their own angry rhetoric, which becomes in turn a new example of bad behavior for the other side to highlight.

We would like to avoid these negative dynamics. Accordingly, we ask that you do not use this thread for waging the Culture War. Examples of waging the Culture War:

  • Shaming.

  • Attempting to 'build consensus' or enforce ideological conformity.

  • Making sweeping generalizations to vilify a group you dislike.

  • Recruiting for a cause.

  • Posting links that could be summarized as 'Boo outgroup!' Basically, if your content is 'Can you believe what Those People did this week?' then you should either refrain from posting, or do some very patient work to contextualize and/or steel-man the relevant viewpoint.

In general, you should argue to understand, not to win. This thread is not territory to be claimed by one group or another; indeed, the aim is to have many different viewpoints represented here. Thus, we also ask that you follow some guidelines:

  • Speak plainly. Avoid sarcasm and mockery. When disagreeing with someone, state your objections explicitly.

  • Be as precise and charitable as you can. Don't paraphrase unflatteringly.

  • Don't imply that someone said something they did not say, even if you think it follows from what they said.

  • Write like everyone is reading and you want them to be included in the discussion.

On an ad hoc basis, the mods will try to compile a list of the best posts/comments from the previous week, posted in Quality Contribution threads and archived at /r/TheThread. You may nominate a comment for this list by clicking on 'report' at the bottom of the post and typing 'Actually a quality contribution' as the report reason.

Jump in the discussion.

No email address required.

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

...and now the conclusion

From the outside it might seem like a straight-forward fix to just add a line to the prompt that says "only provide real quotes / true statements". but to implement such a function requires imbuing GPT with an understanding of the difference between "true" and "false" and between "real" and "imagined". That is a real hum-dinger of a problem. It is such a hum-dinger of a problem that there is an entire branch of philosophy devoted to discussing it, that being Epistemology. As simple and foundational to computer science as the concept of a boolean might be, this is not a problem I see getting solved anytime soon.

Accordingly, when i see some AI-doomer post about how GPT-4 has passed the BAR exam in some state or gotten an A on Bryan Caplan's mid-term economics exam, my first thought is in not "oh shit here comes the fast take-off". It's more "and just how diligent were people grading the papers being?". In one of those threads the topic of grading on a curve came up and the question was asked why should we ask professors to go through the effort of calibrating tests to the material when it is so much simpler/easier/more efficient to ask a spread of arbitrarily difficult questions and award the top x% of answers 'A's. I ended up biting my tongue at the time because my knee-jerk response was something to the effect of "because that's fucking retarded and ultimately defeats the purpose of even administering a test in the first place" But upon a moment's reflection I realized that was a very "thing-manipulator" thought to have.

Thus we come back to the issue of inferential distance. I struggle to articulate just how brain-meltingly stupid and arbitrary the whole concept of "grading on a curve" seems to me. But I also recognize that grading on a curve is a widely accepted practice. From this I infer that my concept of a test and it's purpose is wildly different from that of Bryan Caplan and a lot of other users here on theMotte.

Perhaps this is my "thing-manipulator"-ness talking, but it seems intuitively obvious to me that if a teacher or professor is grading on a curve, they are not grading you on your capability or knowledge of the subject. and if they are not grading you on your capability or knowledge of the subject what re they grading you on? It seems to me that if a teacher and their students are on their game it should be possible for 100% of a class to earn a 100% grade. Just as if manufacturing is truly on the ball it should be possible to achieve a 100% pass rate from the QA department. Granted this never actually happens in the real world because life is imperfect but it's something to strive for isn't it? A man might just find himself a member of the '72 Dolphins.

What is the purpose of a test or inspection in the first place if not to verify capability?

Ironically, I think the real existential threat posed by GPT is not to humanity but to humanities professors. I would argue that if Caplan had been grading his students on their knowledge and understanding of the material (as he ought to have been from the outset) he wouldn't have found himself in this pickle. That GPT-4 got an A on Caplan's mid-term is not evidence that GPT-4 understands economics or history, it's evidence that Caplan does not understand his role as a educator. GPT is essentially the prefect Post-Modernist, and in so being it is exposing post-modernism and the entire edifice of contemporary academia as the farce it's always been.

The cynical bastard in me suspects that the reason "Symbol-Manipulators" seem to be so freaked out about GPT is that it represents a fully-automated-luxury-gay-reductio-ad-absurdum of everything that they think they know.

Ironically, I think the real existential threat posed by GPT is not to humanity but to humanities professors.

Why can the humanities never catch a break?

TheMotte is a humanities discussion forum. We take questions from the humanities (mainly politics and philosophy) and discuss them using methods of analysis that are typical of the humanities. Anyone who posts here shows a revealed preference for humanistic thinking and, plainly, symbol manipulation as well.

What you have written here, in this very post, is a humanities essay. You have engaged in armchair psychological speculation about the motivations and thought processes of a vast and heterogeneous group of people while adducing very little empirical evidence to support your claims. You haven't defined a precise hypothesis (how could we turn "GPT-4 is exposing post-modernism and the entire edifice of contemporary academia as the farce it's always been" into an empirically falsifiable hypothesis?), collected a sufficiently large sample, done a rigorous statistical analysis, or anything else that might move your speculations out of the realm of "mere humanities" and into the vaunted halls of science. This isn't meant to be a criticism of your post, of course. I too prefer to do all my thinking from the armchair - it's a very comfy place to be. We should just recognize it for what it is, is all. You should reflect on whether what you're doing here - what any of us are doing here - is really any different from what the "postmodernist symbol manipulators" are doing.

I think he would not disagree. I ran Hlynka's text through Bing Chat and this was its summary regarding the humanities professor part:

The author is suggesting that GPT-4 and humanities professors have similar goals and methods, and that they are both inferior to thing-manipulators who can challenge their claims with facts and data. He is implying that GPT-4 could replace humanities professors because they are both producing meaningless or misleading content.

I asked about some counterarguments and here is what it came up with:

The author’s distinction between thing-manipulators and symbol-manipulators is too simplistic and ignores the diversity and complexity of both types of activities. Symbols are not just arbitrary or meaningless signs, but tools for communication, reasoning, and representation that can have various interpretations and functions. Symbols can also be manipulated in ways that reflect the properties and relationships of the things they represent, such as in mathematics or computer science. Thing-manipulators are not just concerned with facts and data, but also with creativity, intuition, and problem-solving. Thing-manipulation can also involve symbolic aspects, such as using models, diagrams, or equations to describe or analyze physical phenomena. Both thing-manipulators and symbol-manipulators can benefit from each other’s perspectives and methods, and collaborate to solve hard social problems that require both empirical and conceptual approaches123

I then asked it to tailor an argument in favor of humanities professors. It searched for "value of humanities professors" on google and incorporated it into the context:

Humanities professors are not just producing symbols and feelings that appeal to a certain audience, but engaging with the world in important ways. They are exploring the diversity and complexity of human cultures, values, and experiences, and contributing to the advancement of knowledge, creativity, and critical thinking. They are also addressing the ethical, social, and political challenges that face humanity in the present and the future, and fostering dialogue and understanding among different perspectives and communities. Humanities professors are not disconnected from reality, but rather enriching it with their insights and interpretations12

I then asked it if all those things cannot be also done by GPT-4. It then searched for "GPT-4 limitations and capabilities" and it came up with the following:

It is possible that GPT-4 can perform some of the tasks that humanities professors do, such as writing essays, generating quotes, or passing exams. However, GPT-4 is not a substitute for humanities professors, because it still has many limitations and challenges that prevent it from achieving the same level of understanding, creativity, and ethics as human scholars. For example, GPT-4 has difficulty with:

  • Distinguishing between true and false statements, and between real and imagined scenarios1
  • Reasoning, planning ahead, working memory, and backtracking, due to its autoregressive architecture2
  • Avoiding social biases, hallucinations, and adversarial prompts that can affect its outputs3
  • Handling visual input, which is not yet widely available or reliable4

Therefore, GPT-4 is not likely to replace humanities professors anytime soon, but rather complement their work or provide new opportunities for collaboration and innovation.

So in a way we came full circle, the last part is I think a version of original argument Hlynka made.

Ah man it really is like talking to an academic bullshitter. Continuum fallacies and all.

It even brings in random definitions of things that have no connection to the underlying argument just to make the aggrieved party sound more important.

All the tactics, none of the substance.

The hopelessly ironic part is that it seems to be arguing that humanities professors can distinguish between true and false and avoid social biases, having been trained on their writings.

One has seldom produced such a clear example of self refuting nature of the post modern condition.

The hopelessly ironic part is that it seems to be arguing that humanities professors can distinguish between true and false and avoid social biases, having been trained on their writings.

It is arguing in favor of humanities professors because I told it to argue that position. It researched that GPT may have trouble discerning true and false statement, and it argued that humanities professors have that capacity. It implicitly asserted that argument, but Hlynka asserts without proof that humanities professors are pomo text generators. But unlike Hlynka GPT it at least provided links to its statements, it used some jargon like autoregressive architecture and in general repeated original Hlynka's argument about deficiencies of GPT better. I think that it also correctly pointed out that this whole thing vs symbol manipulator distinction is a lot more complicated.

I think that it also correctly pointed out that this whole thing vs symbol manipulator distinction is a lot more complicated.

While I instinctively believe things are more complicated than Hlynka's distinction, I became less and less convinced of this the more I waded through Bing's verbiage on the matter.