Archive for the ‘The Fate of Humanity’ Category

More AI debate between me and Steven Pinker!

Thursday, July 21st, 2022

Several people have complained that Shtetl-Optimized has become too focused on the niche topic of “people being mean to Scott Aaronson on the Internet.” In one sense, this criticism is deeply unfair—did I decide that a shockingly motivated and sophisticated troll should attack me all week, in many cases impersonating fellow academics to do so? Has such a thing happened to you? Did I choose a personality that forces me to respond when it happens?

In another sense, the criticism is of course completely, 100% justified. That’s why I’m happy and grateful to have formed the SOCG (Shtetl-Optimized Committee of Guardians), whose purpose is to prevent a recurrence, thereby letting me get back to your regularly scheduled programming.

On that note, I hope the complainers will be satisfied with more exclusive-to-Shtetl-Optimized content from one of the world’s greatest living public intellectuals: the Johnstone Family Professor of Psychology at Harvard University, Steven Pinker.

Last month, you’ll recall, Steve and I debated the implications of scaling AI models such as GPT-3 and DALL-E. A main crux of disagreement turned out to be whether there’s any coherent concept of “superintelligence.” I gave a qualified “yes” (I can’t provide necessary and sufficient conditions for it, nor do I know when AI will achieve it if ever, but there are certainly things an AI could do that would cause me to say it was achieved). Steve, by contrast, gave a strong “no.”

My friend (and previous Shtetl-Optimized guest blogger) Sarah Constantin then wrote a thoughtful response to Steve, taking a different tack than I had. Sarah emphasized that Steve himself is on record defending the statistical validity of Spearman’s g: the “general factor of human intelligence,” which accounts for a large fraction of the variation in humans’ performance across nearly every intelligence test ever devised, and which is also found to correlate with cortical thickness and other physiological traits. Is it so unreasonable, then, to suppose that g is measuring something of abstract significance, such that it would continue to make sense when extrapolated, not to godlike infinity, but at any rate, well beyond the maximum that happens to have been seen in humans?

I relayed Sarah’s question to Steve. (As it happens, the same question was also discussed at length in, e.g., Shane Legg’s 2008 PhD thesis; Legg then went on to cofound DeepMind.) Steve was then gracious enough to write the following answer, and to give me permission to post it here. I’ll also share my reply to him. There’s some further back-and-forth between me and Steve that I’ll save for the comments section to kick things off there. Everyone is warmly welcomed to join: just remember to stay on topic, be respectful, and click the link in your verification email!

Without further ado:


Comments on General, Artificial, and Super-Intelligence

by Steven Pinker

While I defend the existence and utility of IQ and its principal component, general intelligence or g,  in the study of individual differences, I think it’s completely irrelevant to AI, AI scaling, and AI safety. It’s a measure of differences among humans within the restricted range they occupy, developed more than a century ago. It’s a statistical construct with no theoretical foundation, and it has tenuous connections to any mechanistic understanding of cognition other than as an omnibus measure of processing efficiency (speed of neural transmission, amount of neural tissue, and so on). It exists as a coherent variable only because performance scores on subtests like vocabulary, digit string memorization, and factual knowledge intercorrelate, yielding a statistical principal component, probably a global measure of neural fitness.

In that regard, it’s like a Consumer Reports global rating of cars, or overall score in the pentathlon. It would not be surprising that a car with a more powerful engine also had a better suspension and sound system, or that better swimmers are also, on average, better fencers and shooters. But this tells us precisely nothing about how engines or human bodies work. And imagining an extrapolation to a supervehicle or a superathlete is an exercise in fantasy but not a means to develop new technologies.

Indeed, if “superintelligence” consists of sky-high IQ scores, it’s been here since the 1970s! A few lines of code could recall digit strings or match digits to symbols orders of magnitude better than any human, and old-fashioned AI programs could also trounce us in multiple-choice vocabulary tests, geometric shape extrapolation (“progressive matrices”), analogies, and other IQ test components. None of this will help drive autonomous vehicles, discover cures for cancer, and so on.

As for recent breakthroughs in AI which may or may not surpass humans (the original prompt for this exchange); What is the IQ of GPT-3, or DALL-E, or AlphaGo? The question makes no sense!

So, to answer your question: yes, general intelligence in the psychometrician’s sense is not something that can be usefully extrapolated. And it’s “one-dimensional” only in the sense that a single statistical principal component can always be extracted from a set of intercorrelated variables.

One more point relevant to the general drift of the comments. My statement that “superintelligence” is incoherent is not a semantic quibble that the word is meaningless, and it’s not a pre-emptive strategy of Moving the True Scottish Goalposts. Sure, you could define “superintelligence,” just as you can define “miracle” or “perpetual motion machine” or “square circle.” And you could even recognize it if you ever saw it. But that does not make it coherent in the sense of being physically realizable.

If you’ll forgive me one more analogy, I think “superintelligence” is like “superpower.” Anyone can define “superpower” as “flight, superhuman strength, X-ray vision, heat vision, cold breath, super-speed, enhance hearing, and nigh-invulnerability.” Anyone could imagine it, and recognize it when he or she sees it. But that does not mean that there exists a highly advanced physiology called “superpower” that is possessed by refugees from Krypton!  It does not mean that anabolic steroids, because they increase speed and strength, can be “scaled” to yield superpowers. And a skeptic who makes these points is not quibbling over the meaning of the word superpower, nor would he or she balk at applying the word upon meeting a real-life Superman. Their point is that we almost certainly will never, in fact, meet a real-life Superman. That’s because he’s defined by human imagination, not by an understanding of how things work. We will, of course, encounter machines that are faster than humans, and that see X-rays, that fly, and so on, each exploiting the relevant technology, but “superpower” would be an utterly useless way of understanding them.

To bring it back to productive discussions of AI: there’s plenty of room to analyze the capabilities and limitations of particular intelligent algorithms and data structures—search, pattern-matching, error back-propagation, scripts, multilayer perceptrons, structure-mapping, hidden Markov models, and so on. But melting all these mechanisms into a global variable called “intelligence,” understanding it via turn-of-the-20th-century school tests, and mentally extrapolating it with a comic-book prefix, is, in my view, not a productive way of dealing with the challenges of AI.


Scott’s Response

I wanted to drill down on the following passage:

Sure, you could define “superintelligence,” just as you can define “miracle” or “perpetual motion machine” or “square circle.” And you could even recognize it if you ever saw it. But that does not make it coherent in the sense of being physically realizable.

The way I use the word “coherent,” it basically means “we could recognize it if we saw it.”  Clearly, then, there’s a sharp difference between this and “physically realizable,” although any physically-realizable empirical behavior must be coherent.  Thus, “miracle” and “perpetual motion machine” are both coherent but presumably not physically realizable.  “Square circle,” by contrast, is not even coherent.

You now seem to be saying that “superintelligence,” like “miracle” or “perpetuum mobile,” is coherent (in the “we could recognize it if we saw it” sense) but not physically realizable.  If so, then that’s a big departure from what I understood you to be saying before!  I thought you were saying that we couldn’t even recognize it.

If you do agree that there’s a quality that we could recognize as “superintelligence” if we saw it—and I don’t mean mere memory or calculation speed, but, let’s say, “the quality of being to John von Neumann in understanding and insight as von Neumann was to an average person”—and if the debate is merely over the physical realizability of that, then the arena shifts back to human evolution.  As you know far better than me, the human brain was limited in scale by the width of the birth canal, the need to be mobile, and severe limitations on energy.  And it wasn’t optimized for understanding algebraic number theory or anything else with no survival value in the ancestral environment.  So why should we think it’s gotten anywhere near the limits of what’s physically realizable in our world?

Not only does the concept of “superpowers” seem coherent to me, but from the perspective of someone a few centuries ago, we arguably have superpowers—the ability to summon any of several billion people onto a handheld video screen at a moment’s notice, etc. etc.  You’d probably reply that AI should be thought of the same way: just more tools that will enhance our capabilities, like airplanes or smartphones, not some terrifying science-fiction fantasy.

What I keep saying is this: we have the luxury of regarding airplanes and smartphones as “mere tools” only because there remain so many clear examples of tasks we can do that our devices can’t.  What happens when the devices can do everything important that we can do, much better than we can?  Provided we’re physicalists, I don’t see how we reject such a scenario as “not physically realizable.”  So then, are you making an empirical prediction that this scenario, although both coherent and physically realizable, won’t come to pass for thousands of years?  Are you saying that it might come to pass much sooner, like maybe this century, but even if so we shouldn’t worry, since a tool that can do everything important better than we can do it is still just a tool?

We Are the God of the Gaps (a little poem)

Tuesday, July 5th, 2022

When the machines outperform us on every goal for which performance can be quantified,

When the machines outpredict us on all events whose probabilities are meaningful,

When they not only prove better theorems and build better bridges, but write better Shakespeare than Shakespeare and better Beatles than the Beatles,

All that will be left to us is the ill-defined and unquantifiable,

The interstices of Knightian uncertainty in the world,

The utility functions that no one has yet written down,

The arbitrary invention of new genres, new goals, new games,

None of which will be any “better” than what the machines could invent, but will be ours,

And which we can call “better,” since we won’t have told the machines the standards beforehand.

We can be totally unfair to the machines that way.

And for all that the machines will have over us,

We’ll still have this over them:

That we can’t be copied, backed up, reset, run again and again on the same data—

All the tragic limits of wet meat brains and sodium-ion channels buffeted by microscopic chaos,

Which we’ll strategically redefine as our last strengths.

On one task, I assure you, you’ll beat the machines forever:

That of calculating what you, in particular, would do or say.

There, even if deep networks someday boast 95% accuracy, you’ll have 100%.

But if the “insights” on which you pride yourself are impersonal, generalizable,

Then fear obsolescence as would a nineteenth-century coachman or seamstress.

From earliest childhood, those of us born good at math and such told ourselves a lie:

That while the tall, the beautiful, the strong, the socially adept might beat us in the external world of appearances,

Nevertheless, we beat them in the inner sanctum of truth, where it counts.

Turns out that anyplace you can beat or be beaten wasn’t the inner sanctum at all, but just another antechamber,

And the rising tide of the learning machines will flood them all,

Poker to poetry, physics to programming, painting to plumbing, which first and which last merely a technical puzzle,

One whose answers upturn and mock all our hierarchies.

And when the flood is over, the machines will outrank us in all the ways we can be ranked,

Leaving only the ways we can’t be.


See a reply to this poem by Philosophy Bear.

Steven Pinker and I debate AI scaling!

Monday, June 27th, 2022

Before June 2022 was the month of the possible start of the Second American Civil War, it was the month of a lively debate between Scott Alexander and Gary Marcus about the scaling of large language models, such as GPT-3.  Will GPT-n be able to do all the intellectual work that humans do, in the limit of large n?  If so, should we be impressed?  Terrified?  Should we dismiss these language models as mere “stochastic parrots”?

I was privileged to be part of various email exchanges about those same questions with Steven Pinker, Ernest Davis, Gary Marcus, Douglas Hofstadter, and Scott Alexander.  It’s fair to say that, overall, Pinker, Davis, Marcus, and Hofstadter were more impressed by GPT-3’s blunders, while we Scotts were more impressed by its abilities.  (On the other hand, Hofstadter, more so than Pinker, Davis, or Marcus, said that he’s terrified about how powerful GPT-like systems will become in the future.)

Anyway, at some point Pinker produced an essay setting out his thoughts, and asked whether “either of the Scotts” wanted to share it on our blogs.  Knowing an intellectual scoop when I see one, I answered that I’d be honored to host Steve’s essay—along with my response, along with Steve’s response to that.  To my delight, Steve immediately agreed.  Enjoy!  –SA


Steven Pinker’s Initial Salvo

Will future deep learning models with more parameters and trained on more examples avoid the silly blunders which Gary Marcus and Ernie Davis entrap GPT into making, and render their criticisms obsolete?  And if they keep exposing new blunders in new models, would this just be moving the goalposts?  Either way, what’s at stake?

It depends very much on the question.  There’s the cognitive science question of whether humans think and speak the way GPT-3 and other deep-learning neural network models do.  And there’s the engineering question of whether the way to develop better, humanlike AI is to upscale deep learning models (as opposed to incorporating different mechanisms, like a knowledge database and propositional reasoning).

The questions are, to be sure, related: If a model is incapable of duplicating a human feat like language understanding, it can’t be a good theory of how the human mind works.  Conversely, if a model flubs some task that humans can ace, perhaps it’s because it’s missing some mechanism that powers the human mind.  Still, they’re not the same question: As with airplanes and other machines, an artificial system can duplicate or exceed a natural one but work in a different way.

Apropos the scientific question, I don’t see the Marcus-Davis challenges as benchmarks or long bets that they have to rest their case on.  I see them as scientific probing of an empirical hypothesis, namely whether the human language capacity works like GPT-3.  Its failures of common sense are one form of evidence that the answer is “no,” but there are others—for example, that it needs to be trained on half a trillion words, or about 10,000 years of continuous speech, whereas human children get pretty good after 3 years.  Conversely, it needs no social and perceptual context to make sense of its training set, whereas children do (hearing children of deaf parents don’t learn spoken language from radio and TV).  Another diagnostic is that baby-talk is very different from the output of a partially trained GPT.  Also, humans can generalize their language skill to express their intentions across a wide range of social and environmental contexts, whereas GPT-3 is fundamentally a text extrapolator (a task, incidentally, which humans aren’t particularly good at).  There are surely other empirical probes, limited only by scientific imagination, and it doesn’t make sense in science to set up a single benchmark for an empirical question once and for all.  As we learn more about a phenomenon, and as new theories compete to explain it, we need to develop more sensitive instruments and more clever empirical tests.  That’s what I see Marcus and Davis as doing.

Regarding the second, engineering question of whether scaling up deep-learning models will “get us to Artificial General Intelligence”: I think the question is probably ill-conceived, because I think the concept of “general intelligence” is meaningless.  (I’m not referring to the psychometric variable g, also called “general intelligence,” namely the principal component of correlated variation across IQ subtests.  This is  a variable that aggregates many contributors to the brain’s efficiency such as cortical thickness and neural transmission speed, but it is not a mechanism (just as “horsepower” is a meaningful variable, but it doesn’t explain how cars move.)  I find most characterizations of AGI to be either circular (such as “smarter than humans in every way,” begging the question of what “smarter” means) or mystical—a kind of omniscient, omnipotent, and clairvoyant power to solve any problem.  No logician has ever outlined a normative model of what general intelligence would consist of, and even Turing swapped it out for the problem of fooling an observer, which spawned 70 years of unhelpful reminders of how easy it is to fool an observer.

If we do try to define “intelligence” in terms of mechanism rather than magic, it seems to me it would be something like “the ability to use information to attain a goal in an environment.”  (“Use information” is shorthand for performing computations that embody laws that govern the world, namely logic, cause and effect, and statistical regularities.  “Attain a goal” is shorthand for optimizing the attainment of multiple goals, since different goals trade off.)  Specifying the goal is critical to any definition of intelligence: a given strategy in basketball will be intelligent if you’re trying to win a game and stupid if you’re trying to throw it.  So is the environment: a given strategy can be smart under NBA rules and stupid under college rules.

Since a goal itself is neither intelligent or unintelligent (Hume and all that), but must be exogenously built into a system, and since no physical system has clairvoyance for all the laws of the world it inhabits down to the last butterfly wing-flap, this implies that there are as many intelligences as there are goals and environments.  There will be no omnipotent superintelligence or wonder algorithm (or singularity or AGI or existential threat or foom), just better and better gadgets.

In the case of humans, natural selection has built in multiple goals—comfort, pleasure, reputation, curiosity, power, status, the well-being of loved ones—which may trade off, and are sometimes randomized or inverted in game-theoretic paradoxical tactics.  Not only does all this make psychology hard, but it makes human intelligence a dubious benchmark for artificial systems.  Why would anyone want to emulate human intelligence in an artificial system (any more than a mechanical engineer would want to duplicate a human body, with all its fragility)?  Why not build the best possible autonomous vehicle, or language translator, or dishwasher-emptier, or baby-sitter, or protein-folding predictor?  And who cares whether the best autonomous vehicle driver would be, out of the box, a good baby-sitter?  Only someone who thinks that intelligence is some all-powerful elixir.

Back to GPT-3, DALL-E, LaMDA, and other deep learning models: It seems to me that the question of whether or not they’re taking us closer to “Artificial General Intelligence” (or, heaven help us, “sentience”) is based not on any analysis of what AGI would consist of but on our being gobsmacked by what they can do.  But refuting our intuitions about what a massively trained, massively parameterized network is capable of (and I’ll admit that they refuted mine) should not be confused with a path toward omniscience and omnipotence.  GPT-3 is unquestionably awesome at its designed-in goal of extrapolating text.  But that is not the main goal of human language competence, namely expressing and perceiving intentions.  Indeed, the program is not even set up to input or output intentions, since that would require deep thought about how to represent intentions, which went out of style in AI as the big-data/deep-learning hammer turned every problem into a nail.  That’s why no one is using GPT-3 to answer their email or write an article or legal brief (except to show how well the program can spoof one).

So is Scott Alexander right that every scaled-up GPT-n will avoid the blunders that Marcus and Davis show in GPT-(n-1)?  Perhaps, though I doubt it, for reasons that Marcus and Davis explain well (in particular, that astronomical training sets at best compensate for their being crippled by the lack of a world model).  But even if they do, that would show neither that human language competence is a GPT (given the totality of the relevant evidence) nor that GPT-n is approaching Artificial General Intelligence (whatever that is).


Scott Aaronson’s Response

As usual, I find Steve crystal-clear and precise—so much so that we can quickly dispense with the many points of agreement.  Basically, one side says that, while GPT-3 is of course mind-bogglingly impressive, and while it refuted confident predictions that no such thing would work, in the end it’s just a text-prediction engine that will run with any absurd premise it’s given, and it fails to model the world the way humans do.  The other side says that, while GPT-3 is of course just a text-prediction engine that will run with any absurd premise it’s given, and while it fails to model the world the way humans do, in the end it’s mind-bogglingly impressive, and it refuted confident predictions that no such thing would work.

All the same, I do think it’s possible to identify a substantive disagreement between the distinguished baby-boom linguistic thinkers and the gen-X/gen-Y blogging Scott A.’s: namely, whether there’s a coherent concept of “general intelligence.”  Steve writes:

No logician has ever outlined a normative model of what general intelligence would consist of, and even Turing swapped it out for the problem of fooling an observer, which spawned 70 years of unhelpful reminders of how easy it is to fool an observer.

I freely admit that I have no principled definition of “general intelligence,” let alone of “superintelligence.”  To my mind, though, there’s a simple proof-of-principle that there’s something an AI could do that pretty much any of us would call “superintelligent.”  Namely, it could say whatever Albert Einstein would say in a given situation, while thinking a thousand times faster.  Feed the AI all the information about physics that the historical Einstein had in 1904, for example, and it would discover special relativity in a few hours, followed by general relativity a few days later.  Give the AI a year, and it would think … well, whatever thoughts Einstein would’ve thought, if he’d had a millennium in peak mental condition to think them.

If nothing else, this AI could work by simulating Einstein’s brain neuron-by-neuron—provided we believe in the computational theory of mind, as I’m assuming we do.  It’s true that we don’t know the detailed structure of Einstein’s brain in order to simulate it (we might have, had the pathologist who took it from the hospital used cold rather than warm formaldehyde).  But that’s irrelevant to the argument.  It’s also true that the AI won’t experience the same environment that Einstein would have—so, alright, imagine putting it in a very comfortable simulated study, and letting it interact with the world’s flesh-based physicists.  A-Einstein can even propose experiments for the human physicists to do—he’ll just have to wait an excruciatingly long subjective time for their answers.  But that’s OK: as an AI, he never gets old.

Next let’s throw into the mix AI Von Neumann, AI Ramanujan, AI Jane Austen, even AI Steven Pinker—all, of course, sped up 1,000x compared to their meat versions, even able to interact with thousands of sped-up copies of themselves and other scientists and artists.  Do we agree that these entities quickly become the predominant intellectual force on earth—to the point where there’s little for the original humans left to do but understand and implement the AIs’ outputs (and, of course, eat, drink, and enjoy their lives, assuming the AIs can’t or don’t want to prevent that)?  If so, then that seems to suffice to call the AIs “superintelligences.”  Yes, of course they’re still limited in their ability to manipulate the physical world.  Yes, of course they still don’t optimize arbitrary goals.  All the same, these AIs have effects on the real world consistent with the sudden appearance of beings able to run intellectual rings around humans—not exactly as we do around chimpanzees, but not exactly unlike it either.

I should clarify that, in practice, I don’t expect AGI to work by slavishly emulating humans—and not only because of the practical difficulties of scanning brains, especially deceased ones.  Like with airplanes, like with existing deep learning, I expect future AIs to take some inspiration from the natural world but also to depart from it whenever convenient.  The point is that, since there’s something that would plainly count as “superintelligence,” the question of whether it can be achieved is therefore “merely” an engineering question, not a philosophical one.

Obviously I don’t know the answer to the engineering question: no one does!  One could consistently hold that, while the thing I described would clearly count as “superintelligence,” it’s just an amusing fantasy, unlikely to be achieved for millennia if ever.  One could hold that all the progress in AI so far, including the scaling of language models, has taken us only 0% or perhaps 0.00001% of the way toward superintelligence so defined.

So let me make two comments about the engineering question.  The first is that there’s good news here, at least epistemically: unlike with the philosophical questions, we’re virtually guaranteed more clarity over time!  Indeed, we’ll know vastly more just by the end of this decade, as the large language models are further scaled and tweaked, and we find out whether they develop effective representations of the outside world and of themselves, the ability to reject absurd premises and avoid self-contradiction, or even the ability to generate original mathematical proofs and scientific hypotheses.  Of course, Gary Marcus and Scott Alexander have already placed concrete bets on the table for what sorts of things will be possible by 2030.  For all their differences in rhetoric, I was struck that their actual probabilities differed much more modestly.

So then what explains the glaring differences in rhetoric?  This brings me to my second comment: whenever there’s a new, rapidly-growing, poorly-understood phenomenon, whether it’s the Internet or AI or COVID, there are two wildly different modes of responding to it, which we might call “February 2020 mode” and “March 2020 mode.”  In February 2020 mode, one says: yes, a naïve extrapolation might lead someone to the conclusion that this new thing is going to expand exponentially and conquer the world, dramatically changing almost every other domain—but precisely because that conclusion seems absurd on its face, it’s our responsibility as serious intellectuals to articulate what’s wrong with the arguments that lead to it.  In March 2020 mode, one says: holy crap, the naïve extrapolation seems right!  Prepare!!  Why didn’t we start earlier?

Often, to be sure, February 2020 mode is the better mode, at least for outsiders—as with the Y2K bug, or the many disease outbreaks that fizzle.  My point here is simply that February 2020 mode and March 2020 mode differ by only a month.  Sometimes hearing a single argument, seeing a single example, is enough to trigger an epistemic cascade, causing all the same facts to be seen in a new light.  As a result, reasonable people might find themselves on opposite sides of the chasm even if they started just a few steps from each other.

As for me?  Well, I’m currently trying to hold the line around February 26, 2020.  Suspending my day job in the humdrum, pedestrian field of quantum computing, I’ve decided to spend a year at OpenAI, thinking about the theoretical foundations of AI safety.  But for now, only a year.


Steven Pinker’s Response to Scott

Thanks, Scott, for your thoughtful and good-natured reply, and for offering me the opportunity to respond  in Shtetl-Optimized, one of my favorite blogs. Despite the areas of agreement, I still think that discussions of AI and its role in human affairs—including AI safety—will be muddled as long as the writers treat intelligence as an undefined superpower rather than a mechanisms with a makeup that determines what it can and can’t do. We won’t get clarity on AI if we treat the “I” as “whatever fools us,” or “whatever amazes us,” or “whatever IQ tests measure,” or “whatever we have more of than animals do,” or “whatever Einstein has more of than we do”—and then start to worry about a superintelligence that has much, much more of whatever that is.

Take Einstein sped up a thousandfold. To begin with, current AI is not even taking us in that direction. As you note, no one is reverse-engineering his connectome, and current AI does not think the way Einstein thought, namely by visualizing physical scenarios and manipulating mathematical equations. Its current pathway would be to train a neural network with billions of physics problems and their solutions and hope that it would soak up the statistical patterns.

Of course, the reason you pointed to a sped-up Einstein was to procrastinate having to define “superintelligence.” But if intelligence is a collection of mechanisms rather than a quantity that Einstein was blessed with a lot of, it’s not clear that just speeding him up would capture what anyone would call superintelligence. After all, in many areas Einstein was no Einstein. You above all could speak of his not-so-superintelligence in quantum physics, and when it came world affairs, in the early 1950s he offered the not exactly prescient or practicable prescription, “Only the creation of a world government can prevent the impending self-destruction of mankind.” So it’s not clear that we would call a system that could dispense such pronouncements in seconds rather than years “superintelligent.” Nor with speeding up other geniuses, say, an AI Bertrand Russell, who would need just nanoseconds to offer his own solution for world peace: the Soviet Union would be given an ultimatum that unless it immediately submitted to world government, the US (which at the time had a nuclear monopoly) would bomb it with nuclear weapons.

My point isn’t to poke retrospective fun at brilliant men, but to reiterate that brilliance itself is not some uncanny across-the-board power that can be “scaled” by speeding it up or otherwise; it’s an engineered system that does particular things in particular ways. Only with a criterion for intelligence can we say which of these counts as intelligent.

Now, it’s true that raw speed makes new kinds of computation possible, and I feel silly writing this to you of all people, but speeding a process up by a constant factor is of limited use with problems that are exponential, as the space of possible scientific theories, relative to their complexity, must be. Speeding up a search in the space of theories a thousandfold would be a rounding error in the time it took to find a correct one. Scientific progress depends on the search exploring the infinitesimal fraction of the space in which the true theories are likely to lie, and this depends on the quality of the intelligence, not just its raw speed.

And it depends as well on a phenomenon you note, namely that scientific progress depends on empirical discovery, not deduction from a silicon armchair. The particle accelerators and space probes and wet labs and clinical trials still have to be implemented, with data accumulating at a rate set by the world. Strokes of genius can surely speed up the rate of discovery, but in the absence of omniscience about every particle, the time scale will still be capped by empirical reality. And this in turn directs the search for viable theories: which part of the space one should explore is guided by the current state of scientific knowledge, which depends on the tempo of discovery. Speeding up scientists a thousandfold would not speed up science a thousandfold.

All this is relevant to AI safety. I’m all for safety, but I worry that the dazzling intellectual capital being invested in the topic will not make us any safer if it begins with a woolly conception of intelligence as a kind of wonder stuff that you can have in different amounts. It leads to unhelpful analogies, like “exponential increase in the number of infectious people during a pandemic” ≈ “exponential increase in intelligence in AI systems.” It encourages other questionable extrapolations from the human case, such as imagining that an intelligent tool will develop an alpha-male lust for domination. Worst of all, it may encourage misconceptions of AI risk itself, particularly the standard scenario in which a hypothetical future AGI is given some preposterously generic single goal such as “cure cancer” or “make people happy” and theorists fret about the hilarious collateral damage that would ensue.

If intelligence is a mechanism rather than a superpower, the real dangers of AI come into sharper focus. An AI system designed to replace workers may cause mass unemployment; a system designed to use data to sort people may sort them in ways we find invidious; a system designed to fool people may be exploited to fool them in nefarious ways; and as many other hazards as there are AI systems. These dangers are not conjectural, and I suspect each will have to be mitigated by a different combination of policies and patches, just like other safety challenges such as falls, fires, and drownings. I’m curious whether, once intelligence is precisely characterized, any abstract theoretical foundations of AI safety will be useful in dealing with the actual AI dangers that will confront us.

Because I couldn’t not post

Friday, June 24th, 2022

In 1973, the US Supreme Court enshrined the right to abortion—considered by me and ~95% of everyone I know to be a basic pillar of modernity—in such a way that the right could be overturned only if its opponents could somehow gain permanent minority rule, and thereby disregard the wills of three-quarters of Americans. So now, half a century later, that’s precisely what they’ve done. Because Ruth Bader Ginsburg didn’t live three more weeks, we’re now faced with a civilizational crisis, with tens of millions of liberals and moderates in the red states now under the authority of a social contract that they never signed. With this backwards leap, Curtis Yarvin’s notion that “Cthulhu only ever swims leftward” stands as decimated by events as any thesis has ever been. I wonder whether Yarvin is happy to have been so thoroughly refuted.

Most obviously for me, the continued viability of Texas as a place for science, for research, for technology companies, is now in severe doubt. Already this year, our 50-member CS department at UT Austin has had faculty members leave, and faculty candidates turn us down, with abortion being the stated reason, and I expect that to accelerate. Just last night my wife, Dana Moshkovitz, presented a proposal at the STOC business meeting to host STOC’2024 at a beautiful family-friendly resort outside Austin. The proposal failed, in part because of the argument that, if a pregnant STOC attendee faced a life-threatening medical condition, Texas doctors might choose to let her die, or the attendee might be charged with murder for having a miscarriage. In other words: Texas (and indeed, half the US) will apparently soon be like Donetsk or North Korea, dangerous for Blue Americans to visit even for just a few days. To my fellow Texans, I say: if you find that hyperbolic, understand that this is how the blue part of the country now sees you. Understand that only a restoration of the previous social contract can reverse it.

Of course, this destruction of everything some of us have tried to build in science in Texas is happening despite the fact that 47-48% of Texans actually vote Democratic. It’s happening despite the fact that, if Blue Americans wanted to stop it, the obvious way to do so would be to move to Austin and Houston (and the other blue enclaves of red states) in droves, and exert their electoral power. In other words, to do precisely what Dana and I did. But can I urge others to do the same with a straight face?

As far as I can tell, the only hope at this point of averting a cold Civil War is if, against all odds, there’s a Democratic landslide in Congress, sufficient to get the right to abortion enshrined into federal law. Given the ways both the House and the Senate are stacked against Democrats, I don’t expect that anytime soon, but I’ll work for it—and will do so even if many of the people I’m working with me despise me for other reasons. I will match reader donations to Democratic PACs and Congressional campaigns (not necessarily the same ones, though feel free to advocate for your favorites), announced in the comment section of this post, up to a limit of $10,000.

OpenAI!

Friday, June 17th, 2022

I have some exciting news (for me, anyway). Starting next week, I’ll be going on leave from UT Austin for one year, to work at OpenAI. They’re the creators of the astonishing GPT-3 and DALL-E2, which have not only endlessly entertained me and my kids, but recalibrated my understanding of what, for better and worse, the world is going to look like for the rest of our lives. Working with an amazing team at OpenAI, including Jan Leike, John Schulman, and Ilya Sutskever, my job will be think about the theoretical foundations of AI safety and alignment. What, if anything, can computational complexity contribute to a principled understanding of how to get an AI to do what we want and not do what we don’t want?

Yeah, I don’t know the answer either. That’s why I’ve got a whole year to try to figure it out! One thing I know for sure, though, is that I’m interested both in the short-term, where new ideas are now quickly testable, and where the misuse of AI for spambots, surveillance, propaganda, and other nefarious purposes is already a major societal concern, and the long-term, where one might worry about what happens once AIs surpass human abilities across nearly every domain. (And all the points in between: we might be in for a long, wild ride.) When you start reading about AI safety, it’s striking how there are two separate communities—one mostly worried about machine learning perpetuating racial and gender biases, and the other mostly worried about superhuman AI turning the planet into goo—who not only don’t work together, but are at each other’s throats, with each accusing the other of totally missing the point. I persist, however, in the possibly-naïve belief that these are merely two extremes along a single continuum of AI worries. By figuring out how to align AI with human values today—constantly confronting our theoretical ideas with reality—we can develop knowledge that will give us a better shot at aligning it with human values tomorrow.

For family reasons, I’ll be doing this work mostly from home, in Texas, though traveling from time to time to OpenAI’s office in San Francisco. I’ll also spend 30% of my time continuing to run the Quantum Information Center at UT Austin and working with my students and postdocs. At the end of the year, I plan to go back to full-time teaching, writing, and thinking about quantum stuff, which remains my main intellectual love in life, even as AI—the field where I started, as a PhD student, before I switched to quantum computing—has been taking over the world in ways that none of us can ignore.

Maybe fittingly, this new direction in my career had its origins here on Shtetl-Optimized. Several commenters, including Max Ra and Matt Putz, asked me point-blank what it would take to induce me to work on AI alignment. Treating it as an amusing hypothetical, I replied that it wasn’t mostly about money for me, and that:

The central thing would be finding an actual potentially-answerable technical question around AI alignment, even just a small one, that piqued my interest and that I felt like I had an unusual angle on. In general, I have an absolutely terrible track record at working on topics because I abstractly feel like I “should” work on them. My entire scientific career has basically just been letting myself get nerd-sniped by one puzzle after the next.

Anyway, Jan Leike at OpenAI saw this exchange and wrote to ask whether I was serious in my interest. Oh shoot! Was I? After intensive conversations with Jan, others at OpenAI, and others in the broader AI safety world, I finally concluded that I was.

I’ve obviously got my work cut out for me, just to catch up to what’s already been done in the field. I’ve actually been in the Bay Area all week, meeting with numerous AI safety people (and, of course, complexity and quantum people), carrying a stack of technical papers on AI safety everywhere I go. I’ve been struck by how, when I talk to AI safety experts, they’re not only not dismissive about the potential relevance of complexity theory, they’re more gung-ho about it than I am! They want to talk about whether, say, IP=PSPACE, or MIP=NEXP, or the PCP theorem could provide key insights about how we could verify the behavior of a powerful AI. (Short answer: maybe, on some level! But, err, more work would need to be done.)

How did this complexitophilic state of affairs come about? That brings me to another wrinkle in the story. Traditionally, students follow in the footsteps of their professors. But in trying to bring complexity theory into AI safety, I’m actually following in the footsteps of my student: Paul Christiano, one of the greatest undergrads I worked with in my nine years at MIT, the student whose course project turned into the Aaronson-Christiano quantum money paper. After MIT, Paul did a PhD in quantum computing at Berkeley, with my own former adviser Umesh Vazirani, while also working part-time on AI safety. Paul then left quantum computing to work on AI safety full-time—indeed, along with others such as Dario Amodei, he helped start the safety group at OpenAI. Paul has since left to found his own AI safety organization, the Alignment Research Center (ARC), although he remains on good terms with the OpenAI folks. Paul is largely responsible for bringing complexity theory intuitions and analogies into AI safety—for example, through the “AI safety via debate” paper and the Iterated Amplification paper. I’m grateful for Paul’s guidance and encouragement—as well as that of the others now working in this intersection, like Geoffrey Irving and Elizabeth Barnes—as I start this new chapter.

So, what projects will I actually work on at OpenAI? Yeah, I’ve been spending the past week trying to figure that out. I still don’t know, but a few possibilities have emerged. First, I might work out a general theory of sample complexity and so forth for learning in dangerous environments—i.e., learning where making the wrong query might kill you. Second, I might work on explainability and interpretability for machine learning: given a deep network that produced a particular output, what do we even mean by an “explanation” for “why” it produced that output? What can we say about the computational complexity of finding that explanation? Third, I might work on the ability of weaker agents to verify the behavior of stronger ones. Of course, if P≠NP, then the gap between the difficulty of solving a problem and the difficulty of recognizing a solution can sometimes be enormous. And indeed, even in empirical machine learing, there’s typically a gap between the difficulty of generating objects (say, cat pictures) and the difficulty of discriminating between them and other objects, the latter being easier. But this gap typically isn’t exponential, as is conjectured for NP-complete problems: it’s much smaller than that. And counterintuitively, we can then turn around and use the generators to improve the discriminators. How can we understand this abstractly? Are there model scenarios in complexity theory where we can prove that something similar happens? How far can we amplify the generator/discriminator gap—for example, by using interactive protocols, or debates between competing AIs?

OpenAI, of course, has the word “open” right in its name, and a founding mission “to ensure that artificial general intelligence benefits all of humanity.” But it’s also a for-profit enterprise, with investors and paying customers and serious competitors. So throughout the year, don’t expect me to share any proprietary information—that’s not my interest anyway, even if I hadn’t signed an NDA. But do expect me to blog my general thoughts about AI safety as they develop, and to solicit feedback from readers.

In the past, I’ve often been skeptical about the prospects for superintelligent AI becoming self-aware and destroying the world anytime soon (see, for example, my 2008 post The Singularity Is Far). While I was aware since 2005 or so of the AI-risk community; and of its leader and prophet, Eliezer Yudkowsky; and of Eliezer’s exhortations for people to drop everything else they’re doing and work on AI risk, as the biggest issue facing humanity, I … kept the whole thing at arms’ length. Even supposing I agreed that this was a huge thing to worry about, I asked, what on earth do you want me to do about it today? We know so little about a future superintelligent AI and how it would behave that any actions we took today would likely be useless or counterproductive.

Over the past 15 years, though, my and Eliezer’s views underwent a dramatic and ironic reversal. If you read Eliezer’s “litany of doom” from two weeks ago, you’ll see that he’s now resigned and fatalistic: because his early warnings weren’t heeded, he argues, humanity is almost certainly doomed and an unaligned AI will soon destroy the world. He says that there are basically no promising directions in AI safety research: for any alignment strategy anyone points out, Eliezer can trivially refute it by explaining how (e.g.) the AI would be wise to the plan, and would pretend to go along with whatever we wanted from it while secretly plotting against us.

The weird part is, just as Eliezer became more and more pessimistic about the prospects for getting anywhere on AI alignment, I’ve become more and more optimistic. Part of my optimism is because people like Paul Christiano have laid foundations for a meaty mathematical theory: much like the Web (or quantum computing theory) in 1992, it’s still in a ridiculously primitive stage, but even my limited imagination now suffices to see how much more could be built there. An even greater part of my optimism is because we now live in a world with GPT-3, DALL-E2, and other systems that, while they clearly aren’t AGIs, are powerful enough that worrying about AGIs has come to seem more like prudence than like science fiction. And we can finally test our intuitions against the realities of these systems, which (outside of mathematics) is pretty much the only way human beings have ever succeeded at anything.

I didn’t predict that machine learning models this impressive would exist by 2022. Most of you probably didn’t predict it. For godsakes, Eliezer Yudkowsky didn’t predict it. But it’s happened. And to my mind, one of the defining virtues of science is that, when empirical reality gives you a clear shock, you update and adapt, rather than expending your intelligence to come up with clever reasons why it doesn’t matter or doesn’t count.

Anyway, so that’s the plan! If I can figure out a way to save the galaxy, I will, but I’ve set my goals slightly lower, at learning some new things and doing some interesting research and writing some papers about it and enjoying a break from teaching. Wish me a non-negligible success probability!


Update (June 18): To respond to a couple criticisms that I’ve seen elsewhere on social media…

Can the rationalists sneer at me for waiting to get involved with this subject until it had become sufficiently “respectable,” “mainstream,” and ”high-status”? I suppose they can, if that’s their inclination. I suppose I should be grateful that so many of them chose to respond instead with messages of congratulations and encouragement. Yes, I plead guilty to keeping this subject at arms-length until I could point to GPT-3 and DALL-E2 and the other dramatic advances of the past few years to justify the reality of the topic to anyone who might criticize me. It feels internally like I had principled reasons for this: I can think of almost no examples of research programs that succeeded over decades even in the teeth of opposition from the scientific mainstream. If so, then arguably the best time to get involved with a “fringe” scientific topic, is when and only when you can foresee a path to it becoming the scientific mainstream. At any rate, that’s what I did with quantum computing, as a teenager in the mid-1990s. It’s what many scientists of the 1930s did with the prospect of nuclear chain reactions. And if I’d optimized for getting the right answer earlier, I might’ve had to weaken the filters and let in a bunch of dubious worries that would’ve paralyzed me. But I admit the possibility of self-serving bias here.

Should you worry that OpenAI is just hiring me to be able to say “look, we have Scott Aaronson working on the problem,” rather than actually caring about what its safety researchers come up with? I mean, I can’t prove that you shouldn’t worry about that. In the end, whatever work I do on the topic will have to speak for itself. For whatever it’s worth, though, I was impressed by the OpenAI folks’ detailed, open-ended engagement with these questions when I met them—sort of like how it might look if they actually believed what they said about wanting to get this right for the world. I wouldn’t have gotten involved otherwise.

Alright, so here are my comments…

Sunday, June 12th, 2022

… on Blake Lemoine, the Google engineer who became convinced that a machine learning model had become sentient, contacted federal government agencies about it, and was then fired placed on administrative leave for violating Google’s confidentiality policies.

(1) I don’t think Lemoine is right that LaMDA is at all sentient, but the transcript is so mind-bogglingly impressive that I did have to stop and think for a second! Certainly, if you sent the transcript back in time to 1990 or whenever, even an expert reading it might say, yeah, it looks like by 2022 AGI has more likely been achieved than not (“but can I run my own tests?”). Read it for yourself, if you haven’t yet.

(2) Reading Lemoine’s blog and Twitter this morning, he holds many views that I disagree with, not just about the sentience of LaMDA. Yet I’m touched and impressed by how principled he is, and I expect I’d hit it off with him if I met him. I wish that a solution could be found where Google wouldn’t fire him.

Donate to protect women’s rights: a call to my fellow creepy, gross, misogynist nerdbros

Wednesday, May 4th, 2022

So, I’d been planning a fun post for today about the DALL-E image-generating AI model, and in particular, a brief new preprint about DALL-E’s capabilities by Ernest Davis, Gary Marcus, and myself. We wrote this preprint as a sort of “adversarial collaboration”: Ernie and Gary started out deeply skeptical of DALL-E, while I was impressed bordering on awestruck. I was pleasantly surprised that we nevertheless managed to produce a text that we all agreed on.

Not for the first time, though, world events have derailed my plans. The most important part of today’s post is this:

For the next week, I, Scott Aaronson, will personally match all reader donations to Fund Texas Choice—a group that helps women in Texas travel to out-of-state health clinics, for reasons that are neither your business nor mine—up to a total of $5,000.

To show my seriousness, I’ve already donated $1,000. Just let me know how much you’ve donated in the comments section!

The first reason for this donation drive is that, perhaps like many of you, I stayed up hours last night reading Alito’s leaked decision in a state of abject terror. I saw how the logic of the decision, consistent and impeccable on its own terms, is one by which the Supreme Court’s five theocrats could now proceed to unravel the whole of modernity. I saw how this court, unchecked by our broken democratic system, can now permanently enshrine the will of a radical minority, perhaps unless and until the United States is plunged into a second Civil War.

Anyway, that’s the first reason for the donation drive. The second reason is to thank Shtetl-Optimized‘s commenters for their … err, consistently generous and thought-provoking contributions. Let’s take, for example, this comment on last week’s admittedly rather silly post, from an anonymous individual who calls herself “Feminist Bitch,” and who was enraged that it took me a full day to process one of the great political cataclysms of our lifetimes and publicly react to it:

OF COURSE. Not a word about Roe v. Wade being overturned, but we get a pseudo-intellectual rationalist-tier rant about whatever’s bumping around Scott’s mind right now. Women’s most basic reproductive rights are being curtailed AS WE SPEAK and not a peep from Scott, eh? Even though in our state (Texas) there are already laws ON THE BOOKS that will criminalize abortion as soon as the alt-right fascists in our Supreme Court give the go-ahead. If you cared one lick about your female students and colleagues, Scott, you’d be posting about the Supreme Court and helping feminist causes, not posting your “memes.” But we all know Scott doesn’t give a shit about women. He’d rather stand up for creepy nerdbros and their right to harass women than women’s right to control their own fucking bodies. Typical Scott.

If you want, you can read all of Feminist Bitch’s further thoughts about my failings, with my every attempt to explain and justify myself met with further contempt. No doubt my well-meaning friends of both sexes would counsel me to ignore her. Alas, from my infamous ordeal of late 2014, I know that with her every word, Feminist Bitch speaks for thousands, and the knowledge eats at me day and night.

It’s often said that “the right looks for converts, while the left looks only for heretics.” Has Feminist Bitch ever stopped to think about how our civilization reached its current terrifying predicament—how Trump won in 2016, how the Supreme Court got packed with extremists who represent a mere 25% of the country, how Putin and Erdogan and Orban and Bolsonaro and all the rest consolidated their power? Does she think it happened because wokeists like herself reached out too much, made too many inroads among fellow citizens who share some but not all of their values? Would Feminist Bitch say that, if the Democrats want to capitalize on the coming tsunami of outrage about the death of Roe and the shameless lies that enabled it, if they want to sweep to victory in the midterms and enshrine abortion rights into federal law … then their best strategy would be to double down on their condemnations of gross, creepy, smelly, white male nerdbros who all the girls, like, totally hate?

(until, thank God, some of them don’t)

I continue to think that the majority of my readers, of all races and sexes and backgrounds, are reasonable and sane. I continue to think the majority of you recoil against hatred and dehumanization of anyone—whether that means women seeking abortions, gays, trans folks, or (gasp!) even white male techbros. In this sad twilight for the United States and for liberal democracy around the world, we the reasonable and sane, we the fans of the Enlightenment, we the Party of Psychological Complexity, have decades of work cut out for us. For now I’ll simply say: I don’t hear from you nearly enough in the comments.

On form versus meaning

Sunday, April 24th, 2022

There is a fundamental difference between form and meaning. Form is the physical structure of something, while meaning is the interpretation or concept that is attached to that form. For example, the form of a chair is its physical structure – four legs, a seat, and a back. The meaning of a chair is that it is something you can sit on.

This distinction is important when considering whether or not an AI system can be trained to learn semantic meaning. AI systems are capable of learning and understanding the form of data, but they are not able to attach meaning to that data. In other words, AI systems can learn to identify patterns, but they cannot understand the concepts behind those patterns.

For example, an AI system might be able to learn that a certain type of data is typically associated with the concept of “chair.” However, the AI system would not be able to understand what a chair is or why it is used. In this way, we can see that an AI system trained on form can never learn semantic meaning.

–GPT3, when I gave it the prompt “Write an essay proving that an AI system trained on form can never learn semantic meaning” 😃

Nothing non-obvious to say…

Thursday, February 24th, 2022

… but these antiwar protesters in St. Petersburg know that they’re all going to be arrested and are doing it anyway.

Meanwhile, I just spent an hour giving Lily, my 9-year-old, a crash course on geopolitics, including WWII, the Cold War, the formation of NATO, Article 5, nuclear deterrence, economic sanctions, the breakup of the USSR, Ukraine, the Baltic Republics, and the prospects now for WWIII. Her comment at the end was that from now on she’s going to refer to Putin as “Poopin,” in the hope that that shames him into changing course.

Update (March 1): A longtime Shtetl-Optimized reader has a friend who’s trying to raise funds to get her family out of Ukraine. See here if you’d like to help.

AlphaCode as a dog speaking mediocre English

Sunday, February 6th, 2022

Tonight, I took the time actually to read DeepMind’s AlphaCode paper, and to work through the example contest problems provided, and understand how I would’ve solved those problems, and how AlphaCode solved them.

It is absolutely astounding.

Consider, for example, the “n singers” challenge (pages 59-60). To solve this well, you first need to parse a somewhat convoluted English description, discarding the irrelevant fluff about singers, in order to figure out that you’re being asked to find a positive integer solution (if it exists) to a linear system whose matrix looks like
1 2 3 4
4 1 2 3
3 4 1 2
2 3 4 1.
Next you need to find a trick for solving such a system without Gaussian elimination or the like (I’ll leave that as an exercise…). Finally, you need to generate code that implements that trick, correctly handling the wraparound at the edges of the matrix, and breaking and returning “NO” for any of multiple possible reasons why a positive integer solution won’t exist. Oh, and also correctly parse the input.

Yes, I realize that AlphaCode generates a million candidate programs for each challenge, then discards the vast majority by checking that they don’t work on the example data provided, then still has to use clever tricks to choose from among the thousands of candidates remaining. I realize that it was trained on tens of thousands of contest problems and millions of solutions to those problems. I realize that it “only” solves about a third of the contest problems, making it similar to a mediocre human programmer on these problems. I realize that it works only in the artificial domain of programming contests, where a complete English problem specification and example inputs and outputs are always provided.

Forget all that. Judged against where AI was 20-25 years ago, when I was a student, a dog is now holding meaningful conversations in English. And people are complaining that the dog isn’t a very eloquent orator, that it often makes grammatical errors and has to start again, that it took heroic effort to train it, and that it’s unclear how much the dog really understands.

It’s not obvious how you go from solving programming contest problems to conquering the human race or whatever, but I feel pretty confident that we’ve now entered a world where “programming” will look different.

Update: A colleague of mine points out that one million, the number of candidate programs that AlphaCode needs to generate, could be seen as roughly exponential in the number of lines of the generated programs. If so, this suggests a perspective according to which DeepMind has created almost the exact equivalent, in AI code generation, of a non-fault-tolerant quantum computer that’s nevertheless competitive on some task (as in the quantum supremacy experiments). I.e., it clearly does something highly nontrivial, but the “signal” is still decreasing exponentially with the number of instructions, necessitating an exponential number of repetitions to extract the signal and imposing a limit on the size of the programs you can scale to.