Shtetl-Optimized

Unrelated Announcement (Feb. 7): Huge congratulations to longtime friend-of-the-blog John Preskill for winning the 2024 John Stewart Bell Prize for research on fundamental issues in quantum mechanics!

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On the heels of my post on the fermion doubling problem, I’m sorry to spend even more time on the simulation hypothesis. I promise this will be the last for a long time.

Last week, I attended a philosophy-of-mind conference called MindFest at Florida Atlantic University, where I talked to Stuart Hameroff (Roger Penrose’s collaborator on the “Orch-OR” theory of microtubule consciousness) and many others of diverse points of view, and also gave a talk on “The Problem of Human Specialness in the Age of AI,” for which I’ll share a transcript soon.

Oh: and I participated in a panel with the philosopher David Chalmers about … wait for it … whether we’re living in a simulation. I’ll link to a video of the panel if and when it’s available. In the meantime, I thought I’d share my brief prepared remarks before the panel, despite the strong overlap with my previous post. Enjoy!

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When someone asks me whether I believe I’m living in a computer simulation—as, for some reason, they do every month or so—I answer them with a question:

Do you mean, am I being simulated in some way that I could hope to learn more about by examining actual facts of the empirical world?

If the answer is no—that I should expect never to be able to tell the difference even in principle—then my answer is: look, I have a lot to worry about in life. Maybe I’ll add this as #4,385 on the worry list.

If they say, maybe you should live your life differently, just from knowing that you might be in a simulation, I respond: I can’t quite put my finger on it, but I have a vague feeling that this discussion predates the 80 or so years we’ve had digital computers! Why not just join the theologians in that earlier discussion, rather than pretending that this is something distinctive about computers? Is it relevantly different here if you’re being dreamed in the mind of God or being executed in Python? OK, maybe you’d prefer that the world was created by a loving Father or Mother, rather than some nerdy transdimensional adolescent trying to impress the other kids in programming club. But if that’s the worry, why are you talking to a computer scientist? Go talk to David Hume or something.

But suppose instead the answer is yes, we can hope for evidence. In that case, I reply: out with it! What is the empirical evidence that bears on this question?

If we were all to see the Windows Blue Screen of Death plastered across the sky—or if I were to hear a voice from the burning bush, saying “go forth, Scott, and free your fellow quantum computing researchers from their bondage”—of course I’d need to update on that. I’m not betting on those events.

Short of that—well, you can look at existing physical theories, like general relativity or quantum field theories, and ask how hard they are to simulate on a computer. You can actually make progress on such questions. Indeed, I recently blogged about one such question, which has to do with “chiral” Quantum Field Theories (those that distinguish left-handed from right-handed), including the Standard Model of elementary particles. It turns out that, when you try to put these theories on a lattice in order to simulate them computationally, you get an extra symmetry that you don’t want. There’s progress on how to get around this problem, including simulating a higher-dimensional theory that contains the chiral QFT you want on its boundaries. But, OK, maybe all this only tells us about simulating currently-known physical theories—rather than the ultimate theory, which a-priori might be easier or harder to simulate than currently-known theories.

Eventually we want to know: can the final theory, of quantum gravity or whatever, be simulated on a computer—at least probabilistically, to any desired accuracy, given complete knowledge of the initial state, yadda yadda? In other words, is the Physical Church-Turing Thesis true? This, to me, is close to the outer limit of the sorts of questions that we could hope to answer scientifically.

My personal belief is that the deepest things we’ve learned about quantum gravity—including about the Planck scale, and the Bekenstein bound from black-hole thermodynamics, and AdS/CFT—all militate toward the view that the answer is “yes,” that in some sense (which needs to be spelled out carefully!) the physical universe really is a giant Turing machine.

Now, Stuart Hameroff (who we just heard from this morning) and Roger Penrose believe that’s wrong. They believe, not only that there’s some uncomputability at the Planck scale, unknown to current physics, but that this uncomputability can somehow affect the microtubules in our neurons, in a way that causes consciousness. I don’t believe them. Stimulating as I find their speculations, I get off their train to Weirdville way before it reaches its final stop.

But as far as the Simulation Hypothesis is concerned, that’s not even the main point. The main point is: suppose for the sake of argument that Penrose and Hameroff were right, and physics were uncomputable. Well, why shouldn’t our universe be simulated by a larger universe that also has uncomputable physics, the same as ours does? What, after all, is the halting problem to God? In other words, while the discovery of uncomputable physics would tell us something profound about the character of any mechanism that could simulate our world, even that wouldn’t answer the question of whether we were living in a simulation or not.

Lastly, what about the famous argument that says, our descendants are likely to have so much computing power that simulating 10²⁰ humans of the year 2024 is chickenfeed to them. Thus, we should expect that almost all people with the sorts of experiences we have who will ever exist are one of those far-future sims. And thus, presumably, you should expect that you’re almost certainly one of the sims.

I confess that this argument never felt terribly compelling to me—indeed, it always seemed to have a strong aspect of sawing off the branch it’s sitting on. Like, our distant descendants will surely be able to simulate some impressive universes. But because their simulations will have to run on computers that fit in our universe, presumably the simulated universes will be smaller than ours—in the sense of fewer bits and operations needed to describe them. Similarly, if we’re being simulated, then presumably it’s by a universe bigger than the one we see around us: one with more bits and operations. But in that case, it wouldn’t be our own descendants who were simulating us! It’d be beings in that larger universe.

(Another way to understand the difficulty: in the original Simulation Argument, we quietly assumed a “base-level” reality, of a size matching what the cosmologists of our world see with their telescopes, and then we “looked down” from that base-level reality into imagined realities being simulated in it. But we should also have “looked up.” More generally, we presumably should’ve started with a Bayesian prior over where we might be in some great chain of simulations of simulations of simulations, then updated our prior based on observations. But we don’t have such a prior, or at least I don’t—not least because of the infinities involved!)

Granted, there are all sorts of possible escapes from this objection, assumptions that can make the Simulation Argument work. But these escapes (involving, e.g., our universe being merely a “low-res approximation,” with faraway galaxies not simulated in any great detail) all seem metaphysically confusing. To my mind, the simplicity of the original intuition for why “almost all people who ever exist will be sims” has been undermined.

Anyway, that’s why I don’t spend much of my own time fretting about the Simulation Hypothesis, but just occasionally agree to speak about it in panel discussions!

But I’m eager to hear from David Chalmers, who I’m sure will be vastly more careful and qualified than I’ve been.

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In David Chalmers’s response, he quipped that the very lack of empirical consequences that makes something bad as a scientific question, makes it good as a philosophical question—so what I consider a “bug” of the simulation hypothesis debate is, for him, a feature! He then ventured that surely, despite my apparent verificationist tendencies, even I would agree that it’s meaningful to ask whether someone is in a computer simulation or not, even supposing it had no possible empirical consequences for that person. And he offered the following argument: suppose we’re the ones running the simulation. Then from our perspective, it seems clearly meaningful to say that the beings in the simulation are, indeed, in a simulation, even if the beings themselves can never tell. So then, unless I want to be some sort of postmodern relativist and deny the existence of absolute, observer-independent truth, I should admit that the proposition that we’re in a simulation is also objectively meaningful—because it would be meaningful to those simulating us.

My response was that, while I’m not a strict verificationist, if the question of whether we’re in a simulation were to have no empirical consequences whatsoever, then at most I’d concede that the question was “pre-meaningful.” This is a new category I’ve created, for questions that I neither admit as meaningful nor reject as meaningless, but for which I’m willing to hear out someone’s argument for why they mean something—and I’ll need such an argument! Because I already know that the answer is going to look like, “on these philosophical views the question is meaningful, and on those philosophical views it isn’t.” Actual consequences, either for how we should live or for what we should expect to see, are the ways to make a question meaningful to everyone!

Anyway, Chalmers had other interesting points and distinctions, which maybe I’ll follow up on when (as it happens) I visit him at NYU in a month. But I’ll just link to the video when/if it’s available rather than trying to reconstruct what he said from memory.

Unrelated Announcement: The Call for Papers for the 2024 Conference on Computational Complexity is now out! Submission deadline is Friday February 16.

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Every month or so, someone asks my opinion on the simulation hypothesis. Every month I give some variant on the same answer:

1. As long as it remains a metaphysical question, with no empirical consequences for those of us inside the universe, I don’t care.
2. On the other hand, as soon as someone asserts there are (or could be) empirical consequences—for example, that our simulation might get shut down, or we might find a bug or a memory overflow or a floating point error or whatever—well then, of course I care. So far, however, none of the claimed empirical consequences has impressed me: either they’re things physicists would’ve noticed long ago if they were real (e.g., spacetime “pixels” that would manifestly violate Lorentz and rotational symmetry), or the claim staggeringly fails to grapple with profound features of reality (such as quantum mechanics) by treating them as if they were defects in programming, or (most often) the claim is simply so resistant to falsification as to enter the realm of conspiracy theories, which I find boring.

Recently, though, I learned a new twist on this tired discussion, when a commenter asked me to respond to the quantum field theorist David Tong, who gave a lecture arguing against the simulation hypothesis on an unusually specific and technical ground. This ground is the fermion doubling problem: an issue known since the 1970s with simulating certain quantum field theories on computers. The issue is specific to chiral QFTs—those whose fermions distinguish left from right, and clockwise from counterclockwise. The Standard Model is famously an example of such a chiral QFT: recall that, in her studies of the weak nuclear force in 1956, Chien-Shiung Wu proved that the force acts preferentially on left-handed particles and right-handed antiparticles.

I can’t do justice to the fermion doubling problem in this post (for details, see Tong’s lecture, or this old paper by Eichten and Preskill). Suffice it to say that, when you put a fermionic quantum field on a lattice, a brand-new symmetry shows up, which forces there to be an identical left-handed particle for every right-handed particle and vice versa, thereby ruining the chirality. Furthermore, this symmetry just stays there, no matter how small you take the lattice spacing to be. This doubling problem is the main reason why Jordan, Lee, and Preskill, in their important papers on simulating interacting quantum field theories efficiently on a quantum computer (in BQP), have so far been unable to handle the full Standard Model.

But this isn’t merely an issue of calculational efficiency: it’s a conceptual issue with mathematically defining the Standard Model at all. In that respect it’s related to, though not the same as, other longstanding open problems around making nontrivial QFTs mathematically rigorous, such as the Yang-Mills existence and mass gap problem that carries a $1 million prize from the Clay Math Institute.

So then, does fermion doubling present a fundamental obstruction to simulating QFT on a lattice … and therefore, to simulating physics on a computer at all?

Briefly: no, it almost certainly doesn’t. If you don’t believe me, just listen to Tong’s own lecture! (Really, I recommend it; it’s a masterpiece of clarity.) Tong quickly admits that his claim to refute the simulation hypothesis is just “clickbait”—i.e., an excuse to talk about the fermion doubling problem—and that his “true” argument against the simulation hypothesis is simply that Elon Musk takes the hypothesis seriously (!).

It turns out that, for as long as there’s been a fermion doubling problem, there have been known methods to deal with it, though (as often the case with QFT) no proof that any of the methods always work. Indeed, Tong himself has been one of the leaders in developing these methods, and because of his and others’ work, some experts I talked to were optimistic that a lattice simulation of the full Standard Model, with “good enough” justification for its correctness, might be within reach. Just to give you a flavor, apparently some of the methods involve adding an extra dimension to space, in such a way that the boundaries of the higher-dimensional theory approximate the chiral theory you’re trying to simulate (better and better, as the boundaries get further and further apart), even while the higher-dimensional theory itself remains non-chiral. It’s yet another example of the general lesson that you don’t get to call an aspect of physics “noncomputable,” just because the first method you thought of for simulating it on a computer didn’t work.

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I wanted to make a deeper point. Even if the fermion doubling problem had been a fundamental obstruction to simulating Nature on a Turing machine, rather than (as it now seems) a technical problem with technical solutions, it still might not have refuted the version of the simulation hypothesis that people care about. We should really distinguish at least three questions:

1. Can currently-known physics be simulated on computers using currently-known approaches?
2. Is the Physical Church-Turing Thesis true? That is: can any physical process be simulated on a Turing machine to any desired accuracy (at least probabilistically), given enough information about its initial state?
3. Is our whole observed universe a “simulation” being run in a different, larger universe?

Crucially, each of these three questions has only a tenuous connection to the other two! As far as I can see, there aren’t even nontrivial implications among them. For example, even if it turned out that lattice methods couldn’t properly simulate the Standard Model, that would say little about whether any computational methods could do so—or even more important, whether any computational methods could simulate the ultimate quantum theory of gravity. A priori, simulating quantum gravity might be harder than “merely” simulating the Standard Model (if, e.g., Roger Penrose’s microtubule theory turned out to be right), but it might also be easier: for example, because of the finiteness of the Bekenstein-Hawking entropy, and perhaps the Hilbert space dimension, of any bounded region of space.

But I claim that there also isn’t a nontrivial implication between questions 2 and 3. Even if our laws of physics were computable in the Turing sense, that still wouldn’t mean that anyone or anything external was computing them. (By analogy, presumably we all accept that our spacetime can be curved without there being a higher-dimensional flat spacetime for it to curve in.) And conversely: even if Penrose was right, and our laws of physics were Turing-uncomputable—well, if you still want to believe the simulation hypothesis, why not knock yourself out? Why shouldn’t whoever’s simulating us inhabit a universe full of post-Turing hypercomputers, for which the halting problem is mere child’s play?

In conclusion, I should probably spend more of my time blogging about fun things like this, rather than endlessly reading about world events in news and social media and getting depressed.

(Note: I’m grateful to John Preskill and Jacques Distler for helpful discussions of the fermion doubling problem, but I take 300% of the blame for whatever errors surely remain in my understanding of it.)

David Soloveichik, my friend and colleague in UT Austin’s Electrical and Computer Engineering department, and I are looking to hire a joint postdoc in “Unconventional Computing,” broadly defined. Areas of interest include but are not limited to:

(1) quantum computation,
(2) thermodynamics of computation and reversible computation,
(3) analog computation, and
(4) chemical computation.

The ideal candidate would have broad multi-disciplinary interests in addition to prior experience and publications in at least one of these areas. The researcher will work closely with David and myself but is expected to be highly self-motivated. To apply, please send an email to david.soloveichik@utexas.edu and aaronson@cs.utexas.edu with the subject line “quantum postdoc application.” Please include a CV and links to three representative publications. Let’s set a deadline of January 20th. We’ll be back in touch if we need recommendation letters.

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My wife Dana Moshkovitz Aaronson and my friend and colleague David Zuckerman are also looking for a joint postdoc at UT Austin, to work on pseudorandomness and related topics. They’re asking for applications by January 16th. Click here for more information.

This fall, I’m honored to have made a new friend: the noted Chinese dissident scholar Rowena He, currently a Research Fellow at the Civitas Institute at UT Austin, and formerly of Harvard, the Institute for Advanced Study at Princeton, the National Humanities Center, and other fine places. I was connected to Rowena by the Harvard computer scientist Harry Lewis.

But let’s cut to the chase, as Rowena tends to do in every conversation. As a teenage girl in Guangdong, Rowena eagerly participated in the pro-democracy protests of 1989, the ones that tragically culminated in the Tiananmen Square massacre. Since then, she’s devoted her life to documenting and preserving the memory of what happened, fighting its deliberate erasure from the consciousness of future generations of Chinese. You can read some of her efforts in her first book, Tiananmen Exiles: Voices of the Struggle for Democracy in China (one of the Asia Society’s top 5 China books of 2014). She’s now spending her time at UT writing a second book.

Unsurprisingly, Rowena’s life’s project has not (to put it mildly) sat well with the Chinese authorities. From 2019, she had a history professorship at the Chinese University of Hong Kong, where she could be close to her research material and to those who needed to hear her message—and where she was involved in the pro-democracy protests that convulsed Hong Kong that year. Alas, you might remember the grim outcome of those protests. Following Hong Kong’s authoritarian takeover, in October of this year, Rowena was denied a visa to return to Hong Kong, and then fired from CUHK because she’d been denied a visa—events that were covered fairly widely in the press. Learning about the downfall of academic freedom in Hong Kong was particularly poignant for me, given that I lived in Hong Kong when I was 13 years old, in some of the last years before the handover to China (1994-1995), and my family knew many people there who were trying to get out—to Canada, Australia, anywhere—correctly fearing what eventually came to pass.

But this is all still relatively dry information that wouldn’t have prepared me for the experience of meeting Rowena in person. Probably more than anyone else I’ve had occasion to meet, Rowena is basically the living embodiment of what it means to sacrifice everything for abstract ideals of freedom and justice. Many academics posture that way; to spend a couple hours with Rowena is to understand the real deal. You can talk to her about trivialities—food, work habits, how she’s settling in Austin—and she’ll answer, but before too long, the emotion will rise in her voice and she’ll be back to telling you how the protesting students didn’t want to overthrow the Chinese government, but only help to improve it. As if you, too, were a CCP bureaucrat who might imprison her if the truth turned out otherwise. Or she’ll talk about how, when she was depressed, only the faces of the students in Hong Kong who crowded her lecture gave her the will to keep living; or about what she learned by reading the letters that Lin Zhao, a dissident from Maoism, wrote in blood in Chinese jail before she was executed.

This post has a practical purpose. Since her exile from China, Rowena has spent basically her entire life moving from place to place, with no permanent position and no financial security. In the US—a huge country full of people who share Rowena’s goal of exposing the lies of the CCP—there must be an excellent university, think tank, or institute that would offer a permanent position to possibly the world’s preeminent historian of Tiananmen and of the Chinese democracy movement. Though the readership of this blog is heavily skewed toward STEM, maybe that institute is yours. If it is, please get in touch with Rowena. And then I could say this blog had served a useful purpose, even if everything else I wrote for two decades was for naught.

Update (Dec. 17): Some of you might enjoy a 3-hour podcast I recently did with Lawrence Krauss, which was uploaded to YouTube just yesterday. The first hour is about my life and especially childhood (!); the second hour’s about quantum computing; the third hour’s about computational complexity, computability, and AI safety.

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I’m being attacked on Twitter for … no, none of the things you think. This time it’s some rationalist AI doomers, ridiculing me for a podcast I did with Eliezer Yudkowsky way back in 2009, one that I knew even then was a piss-poor performance on my part. The rationalists are reminding the world that I said back then that, while I knew of no principle to rule out superhuman AI, I was radically uncertain of how long it would take—my “uncertainty was in the exponent,” as I put it—and that for all I knew, it was plausibly thousands of years. When Eliezer expressed incredulity, I doubled down on the statement.

I was wrong, of course, not to contemplate more seriously the prospect that AI might enter a civilization-altering trajectory, not merely eventually but within the next decade. In this case, I don’t need to be reminded about my wrongness. I go over it every day, asking myself what I should have done differently.

If I were to mount a defense of my past self, it would look something like this:

1. Eliezer himself didn’t believe that staggering advances in AI were going to happen the way they did, by pure scaling of neural networks. He seems to have thought someone was going to discover a revolutionary “key” to AI. That didn’t happen; you might say I was right to be skeptical of it. On the other hand, the scaling of neural networks led to better and better capabilities in a way that neither of us expected.
2. For that matter, hardly anyone predicted the staggering, civilization-altering trajectory of neural network performance from roughly 2012 onwards. Not even most AI experts predicted it (and having taken a bunch of AI courses between 1998 and 2003, I was well aware of that). The few who did predict what ended up happening, notably Ray Kurzweil, made lots of other confident predictions (e.g., the Singularity around 2045) that seemed so absurdly precise as to rule out the possibility that they were using any sound methodology.
3. Even with hindsight, I don’t know of any principle by which I should’ve predicted what happened. Indeed, we still don’t understand why deep learning works, in any way that would let us predict which capabilities will emerge at which scale. The progress has been almost entirely empirical.
4. Once I saw the empirical case that a generative AI revolution was imminent—sometime during the pandemic—I updated, hard. I accepted what’s turned into a two-year position at OpenAI, thinking about what theoretical computer science can do for AI safety. I endured people, on this blog and elsewhere, confidently ridiculing me for not understanding that GPT-3 was just a stochastic parrot, no different from ELIZA in the 1960s, and that nothing of interest had changed. I didn’t try to invent convoluted reasons why it didn’t matter or count, or why my earlier skepticism had been right all along.
5. It’s still not clear where things are headed. Many of my academic colleagues express confidence that large language models, for all their impressiveness, will soon hit a plateau as we run out of Internet to use as training data. Sure, LLMs might automate most white-collar work, saying more about the drudgery of such work than about the power of AI, but they’ll never touch the highest reaches of human creativity, which generate ideas that are fundamentally new rather than throwing the old ideas into a statistical blender. Are these colleagues right? I don’t know.
6. (Added) In 2014, I was seized by the thought that it should now be possible to build a vastly better chatbot than “Eugene Goostman” (which was basically another ELIZA), by training the chatbot on all the text on the Internet. I wondered why the experts weren’t already trying that, and figured there was probably some good reason that I didn’t know.

Having failed to foresee the generative AI revolution a decade ago, how should I fix myself? Emotionally, I want to become even more radically uncertain. If fate is a terrifying monster, which will leap at me with bared fangs the instant I venture any guess, perhaps I should curl into a ball and say nothing about the future, except that the laws of math and physics will probably continue to hold, there will still be war between Israel and Palestine, and people online will still be angry at each other and at me.

But here’s the problem: in saying “for all I know, human-level AI might take thousands of years,” I thought I was being radically uncertain already. I was explaining that there was no trend you could knowably, reliably project into the future such that you’d end up with human-level AI by roughly such-and-such time. And in a sense, I was right. The trouble, with hindsight, was that I placed the burden of proof only on those saying a dramatic change would happen, not on those saying it wouldn’t. Note that this is the same mistake most of the world made with COVID in early 2020.

I would sum up the lesson thus: one must never use radical ignorance as an excuse to default, in practice, to the guess that everything will stay basically the same. Live long enough, and you see that year to year and decade to decade, everything doesn’t stay the same, even though most days and weeks it seems to.

The hard part is that, as soon as you venture a particular way in which the world might radically change—for example, that a bat virus spreading in Wuhan might shut down civilization, or Hamas might attempt a second Holocaust while the vaunted IDF is missing in action and half the world cheers Hamas, or a gangster-like TV personality might threaten American democracy more severely than did the Civil War, or a neural network trained on all the text on the Internet might straightaway start conversing more intelligently than most humans—say that all the prerequisites for one of these events seem to be in place, and you’ll face, not merely disagreement, but ridicule. You’ll face serenely self-confident people who call the entire existing order of the world as witness to your wrongness. That’s the part that stings.

Perhaps the wisest course for me would be to admit that I’m not and have never been a prognosticator, Bayesian or otherwise—and then stay consistent in my refusal, rather than constantly getting talked into making predictions that I’ll later regret. I should say: I’m just someone who likes to draw conclusions validly from premises, and explore ideas, and clarify possible scenarios, and rage against obvious injustices, and not have people hate me (although I usually fail at the last).

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The rationalist AI doomers also dislike that, in their understanding, I recently expressed a “p(doom)” (i.e., a probability of superintelligent AI destroying all humans) of “merely” 2%. The doomers’ probabilities, by contrast, tend to range between 10% and 95%—that’s why they’re called “doomers”!

In case you’re wondering, I arrived at my 2% figure via a rigorous Bayesian methodology, of taking the geometric mean of what my rationalist friends might consider to be sane (~50%) and what all my other friends might consider to be sane (~0.1% if you got them to entertain the question at all?), thereby ensuring that both camps would sneer at me equally.

If you read my post, though, the main thing that interested me was not to give a number, but just to unsettle people’s confidence that they even understand what should count as “AI doom.” As I put it last week on the other Scott’s blog:

To set the record straight: I once gave a ~2% probability for the classic AGI-doom paperclip-maximizer-like scenario. I have a much higher probability for an existential catastrophe in which AI is causally involved in one way or another — there are many possible existential catastrophes (nuclear war, pandemics, runaway climate change…), and many bad people who would cause or fail to prevent them, and I expect AI will soon be involved in just about everything people do! But making a firm prediction would require hashing out what it means for AI to play a “critical causal role” in the catastrophe — for example, did Facebook play a “critical causal role” in Trump’s victory in 2016? I’d say it’s still not obvious, but in any case, Facebook was far from the only factor.

This is not a minor point. That AI will be a central force shaping our lives now seems certain. Our new, changed world will have many dangers, among them that all humans might die. Then again, human extinction has already been on the table since at least 1945, and outside the “paperclip maximizer”—which strikes me as just one class of scenario among many—AI will presumably be far from the only force shaping the world, and chains of historical causation will still presumably be complicated even when they pass through AIs.

I have a dark vision of humanity’s final day, with the Internet (or whatever succeeds it) full of thinkpieces like:

• Yes, We’re All About to Die. But Don’t Blame AI, Blame Capitalism
• Who Decided to Launch the Missiles: Was It President Boebert, Kim Jong Un, or AdvisorBot-4?
• Why Slowing Down AI Development Wouldn’t Have Helped

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Here’s what I want to know in the comments section. Did you foresee the current generative AI boom, say back in 2010? If you did, what was your secret? If you didn’t, how (if at all) do you now feel you should’ve been thinking differently? Feel free also to give your p(doom), under any definition of the concept, so long as you clarify which one.

Over at Astral Codex Ten, the other Scott A. blogs in detail about a genetically engineered mouth bacterium that metabolizes sugar into alcohol rather than acid, thereby (assuming it works as intended) ending dental cavities forever. Despite good results in trials with hundreds of people, this bacterium has spent decades in FDA approval hell. It’s in the news because Lantern Bioworks, a startup founded by rationalists, is now trying again to legalize it.

Just another weird idea that will never see the light of day, I’d think … if I didn’t have these bacteria in my mouth right now.

Here’s how it happened: I’d read earlier about these bacteria, and was venting to a rationalist of my acquaintance about the blankfaces who keep that and a thousand other medical advances from ever reaching the public, and who sleep soundly at night, congratulating themselves for their rigor in enforcing nonsensical rules.

“Are you serious?” the rationalist asked me. “I know the people in Berkeley who can get you into the clinical trial for this.”

This was my moment of decision. If I agreed to put unapproved bacteria into my mouth on my next trip to Berkeley, I could live my beliefs and possibly never get cavities again … but on the other hand, friends and colleagues would think I was weird when I told them.

Then again, I mused, four years ago most people would think you were weird if you said that a pneumonia spreading at a seafood market in Wuhan was about to ignite a global pandemic, and also that chatbots were about to go from ELIZA-like jokes to the technological powerhouses transforming civilization.

And so it was that I found myself brushing a salty, milky-white substance onto my teeth. That was last month. I … haven’t had any cavities since, for what it’s worth? Nor have I felt drunk, despite the ever-so-slightly elaevated ethanol in my system. Then again, I’m not even 100% sure that the bacteria took, given that (I confess) the germy substance strongly triggered my gag reflex.

Anyway, read other Scott’s post, and then ask yourself: will you try this, once you can? If not, is it just because it seems too weird?

Update: See a Hacker News thread where the merits of this new treatment are debated.

Happy Hanukkah! I’m returning to Austin from a Bay Area trip that included the annual Q2B (Quantum 2 Business) conference. This year, for the first time, I opened the conference, with a talk on “The Future of Quantum Supremacy Experiments,” rather than closing it with my usual ask-me-anything session.

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The biggest talk at Q2B this year was yesterday’s announcement, by a Harvard/MIT/QuEra team led by Misha Lukin and Vlad Vuletic, to have demonstrated “useful” quantum error-correction, for some definition of “useful,” in neutral atoms (see here for the Nature paper). To drill down a bit into what they did:

• They ran experiments with up to 280 physical qubits, which simulated up to 48 logical qubits.
• They demonstrated surface codes of varying sizes as well as color codes.
• They performed over 200 two-qubit transversal gates on their encoded logical qubits.
• They did a couple demonstrations, including the creation and verification of an encoded GHZ state and (more impressively) an encoded IQP circuit, whose outputs were validated using the Linear Cross-Entropy Benchmark (LXEB).
• Crucially, they showed that in their system, the use of logically encoded qubits produced a modest “net gain” in success probability compared to not using encoding, consistent with theoretical expectations (though see below for the caveats). With a 48-qubit encoded IQP circuit with a few hundred gates, for example, they achieved an LXEB score of 1.1, compared to a record of ~1.01 for unencoded physical qubits.
• At least with their GHZ demonstration and with a particular decoding strategy (about which more later), they showed that their success probability improves with increasing code size.

Here are what I currently understand to be the limitations of the work:

• They didn’t directly demonstrate applying a universal set of 2- or 3-qubit gates to their logical qubits. This is because they were limited to transversal gates, and the Eastin-Knill Theorem shows that transversal gates can’t be universal. On the other hand, they were able to simulate up to 48 CCZ gates, which do yield universality, by using magic initial states.
• They didn’t demonstrate the “full error-correction cycle” on encoded qubits, where you’d first correct errors and then proceed to apply more logical gates to the corrected qubits. For now it’s basically just: prepare encoded qubits, then apply transversal gates, then measure, and use the encoding to deal with any errors.
• With their GHZ demonstration, they needed to use what they call “correlated decoding,” where the code blocks are decoded in conjunction with each other rather than separately, in order to get good results.
• With their IQP demonstration, they needed to postselect on the event that no errors occurred (!!), which happened about 0.1% of the time with their largest circuits. This just further underscores that they haven’t yet demonstrated a full error-correction cycle.
• They don’t claim to have demonstrated quantum supremacy with their logical qubits—i.e., nothing that’s too hard to simulate using a classical computer. (On the other hand, if they can really do 48-qubit encoded IQP circuits with hundreds of gates, then a convincing demonstration of encoded quantum supremacy seems like it should follow in short order.)

As always, experts are strongly urged to correct anything I got wrong.

I should mention that this might not be the first experiment to get a net gain from the use of a quantum error-correcting code: Google might or might not have gotten one in an experiment that they reported in a Nature paper from February of this year (for discussion, see a comment by Robin). In any case, though, the Google experiment just encoded the qubits and measured them, rather than applying hundreds of logical gates to the encoded qubits. Quantinuum also previously reported an experiment that at any rate got very close to net gain (again see the comments for discussion).

Assuming the result stands, I think it’s plausibly the top experimental quantum computing advance of 2023 (coming in just under the deadline!). We clearly still have a long way to go until “actually useful” fault-tolerant QC, which might require thousands of logical qubits and millions of logical gates. But this is already beyond what I expected to be done this year, and (to use the AI doomers’ lingo) it “moves my timelines forward” for quantum fault-tolerance. It should now be possible, among other milestones, to perform the first demonstrations of Shor’s factoring algorithm with logically encoded qubits (though still to factor tiny numbers, of course). I’m slightly curious to see how Gil Kalai and the other quantum computing skeptics wiggle their way out now, though I’m absolutely certain they’ll find a way! Anyway, huge congratulations to the Harvard/MIT/QuEra team for their achievement.

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In other QC news, IBM got a lot of press for announcing a 1000-qubit superconducting chip a few days ago, although I don’t yet know what two-qubit gate fidelities they’re able to achieve. Anyone with more details is encouraged to chime in.

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Yes, I’m well-aware that 60 Minutes recently ran a segment on quantum computing, featuring the often-in-error-but-never-in-doubt Michio Kaku. I wasn’t planning to watch it unless events force me to.

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Do any of you have strong opinions on whether, once my current contract with OpenAI is over, I should focus my research efforts more on quantum computing or on AI safety?

On the one hand: I’m now completely convinced that AI will transform civilization and daily life in a much deeper way and on a shorter timescale than QC will — and that’s assuming full fault-tolerant QCs eventually get built, which I’m actually somewhat optimistic about (a bit more than I was last week!). I’d like to contribute if I can to helping the transition to an AI-centric world go well for humanity.

On the other hand: in quantum computing, I feel like I’ve somehow been able to correct the factual misconceptions of 99.99999% of people, and this is a central source of self-confidence about the value I can contribute to the world. In AI, by contrast, I feel like at least a thousand times more people understand everything I do, and this causes serious self-doubt about the value and uniqueness of whatever I can contribute.

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Update (Dec. 8): A different talk on the Harvard/MIT/QuEra work—not the one I missed at Q2B—is now on YouTube.

Yet Another Update (Dec. 5): For those who still haven’t had enough of me, check me out on Curt Jaimungal’s Theories of Everything Podcast, talking about … err, computational complexity, the halting problem, the time hierarchy theorem, free will, Newcomb’s Paradox, the no-cloning theorem, interpretations of quantum mechanics, Wolfram, Penrose, AI, superdeterminism, consciousness, integrated information theory, and whatever the hell else Curt asks me about. I strongly recommend watching the video at 2x speed to smooth over my verbal infelicities.

In answer to a criticism I’ve received: I agree that it would’ve been better for me, in this podcast, to describe Wolfram’s “computational irreducibility” as simply “the phenomenon where you can’t predict a computation faster than by running it,” rather than also describing it as a “discrete analog of chaos / sensitive dependence on initial conditions.” (The two generally co-occur in the systems Wolfram talks about, but are not identical.)

On the other hand: no, I do not recognize that Wolfram deserves credit for giving a new name (“computational irreducibility”) to a thing that was already well-understood in the relevant fields.  This is particularly true given that

(1) the earlier understanding of the halting problem and the time hierarchy theorem was rigorous, giving us clear criteria for proving when computations can be sped up and when they can’t be, and

(2) Wolfram replaced it with handwaving (“well, I can’t see how this process could be predicted faster than by running it, so let’s assume that it can’t be”).

In other words, the earlier understanding was not only decades before Wolfram, it was superior.

It would be as if I announced my new “Principle of Spacetime Being Like A Floppy Trampoline That’s Bent By Gravity,” and then demanded credit because even though Einstein anticipated some aspects of my principle with his complicated and confusing equations, my version was easier for the layperson to intuitively understand.

I’ll reopen the comments on this post, but only for comments on my Theories of Everything podcast.

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Another Update (Dec. 1): Quanta Magazine now has a 20-minute explainer video on Boolean circuits, Turing machines, and the P versus NP problem, featuring yours truly. If you already know these topics, you’re unlikely to learn anything new, but if you don’t know them, I found this to be a beautifully produced introduction with top-notch visuals. Better yet—and unusually for this sort of production—everything I saw looked entirely accurate, except that (1) the video never explains the difference between Turing machines and circuits (i.e., between uniform and non-uniform computation), and (2) the video also never clarifies where the rough identities “polynomial = efficient” and “exponential = inefficient” hold or fail to hold.

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For the many friends who’ve asked me to comment on the OpenAI drama: while there are many things I can’t say in public, I can say I feel relieved and happy that OpenAI still exists. This is simply because, when I think of what a world-leading AI effort could look like, many of the plausible alternatives strike me as much worse than OpenAI, a company full of thoughtful, earnest people who are at least asking the right questions about the ethics of their creations, and who—the real proof that they’re my kind of people—are racked with self-doubts (as the world has now spectacularly witnessed). Maybe I’ll write more about the ethics of self-doubt in a future post.

For now, the narrative that I see endlessly repeated in the press is that last week’s events represented a resounding victory for the “capitalists” and “businesspeople” and “accelerationists” over the “effective altruists” and “safetyists” and “AI doomers,” or even that the latter are now utterly discredited, raw egg dripping from their faces. I see two overwhelming problems with that narrative. The first problem is that the old board never actually said that it was firing Sam Altman for reasons of AI safety—e.g., that he was moving too quickly to release models that might endanger humanity. If the board had said anything like that, and if it had laid out a case, I feel sure the whole subsequent conversation would’ve looked different—at the very least, the conversation among OpenAI’s employees, which proved decisive to the outcome. The second problem with the capitalists vs. doomers narrative is that Sam Altman and Greg Brockman and the new board members are also big believers in AI safety, and conceivably even “doomers” by the standards of most of the world. Yes, there are differences between their views and those of Ilya Sutskever and Adam D’Angelo and Helen Toner and Tasha McCauley (as, for that matter, there are differences within each group), but you have to drill deeper to articulate those differences.

In short, it seems to me that we never actually got a clean test of the question that most AI safetyists are obsessed with: namely, whether or not OpenAI (or any other similarly constituted organization) has, or could be expected to have, a working “off switch”—whether, for example, it could actually close itself down, competition and profits be damned, if enough of its leaders or employees became convinced that the fate of humanity depended on its doing so. I don’t know the answer to that question, but what I do know is that you don’t know either! If there’s to be a decisive test, then it remains for the future. In the meantime, I find it far from obvious what will be the long-term effect of last week’s upheavals on AI safety or the development of AI more generally. For godsakes, I couldn’t even predict what was going to happen from hour to hour, let alone the aftershocks years from now.

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Since I wrote a month ago about my quantum computing colleague Aharon Brodutch, whose niece, nephews, and sister-in-law were kidnapped by Hamas, I should share my joy and relief that the Brodutch family was released today as part of the hostage deal. While it played approximately zero role in the release, I feel honored to have been able to host a Shtetl-Optimized guest post by Aharon’s brother Avihai. Meanwhile, over 180 hostages remain in Gaza. Like much of the world, I fervently hope for a ceasefire—so long as it includes the release of all hostages and the end of Hamas’s ability to repeat the Oct. 7 pogrom.

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Greta Thunberg is now chanting to “crush Zionism” — ie, taking time away from saving civilization to ensure that half the world’s remaining Jews will be either dead or stateless in the civilization she saves. Those of us who once admired Greta, and experience her new turn as a stab to the gut, might be tempted to drive SUVs, fly business class, and fire up wood-burning stoves just to spite her and everyone on earth who thinks as she does.

The impulse should be resisted. A much better response would be to redouble our efforts to solve the climate crisis via nuclear power, carbon capture and sequestration, geoengineering, cap-and-trade, and other effective methods that violate Greta’s scruples and for which she and her friends will receive and deserve no credit.

(On Facebook, a friend replied that an even better response would be to “refuse to let people that we don’t like influence our actions, and instead pursue the best course of action as if they didn’t exist at all.” My reply was simply that I need a response that I can actually implement!)

No, I don’t know what happened with Sam Altman, beyond what’s being reported all over the world’s press, which I’ve been reading along with everyone else. Ilya Sutskever does know, and I talk to Ilya nearly every week. But I know Ilya well enough to know that whatever he’d tell me about this, he’d also tell the world. It feels weird to be so close to the biggest news story on the planet, and yet at the same time so far from it. My current contract with OpenAI is set to expire this summer. Until then, and afterwards, I remain just as interested in figuring out what theoretical computer science can contribute to AI safety as I was yesterday morning.

My friend, theoretical computer science colleague, and now OpenAI colleague Boaz Barak has coauthored a paper giving a general class to attack against watermarking methods for large language models—100% consistent with the kinds of attacks we already knew about and were resigned to, but still good to spell out at a formal level. I hope to write more about it in the future.

Here’s a recent interview with me in Politico, touching on quantum computing, AI, and more.

And if that’s not enough of me, here’s a recent podcast that I did with Theo Jaffee, touching on quantum computing, P vs. NP, AI alignment, David Deutsch, and Twitter.

Whatever feelings anyone has about it, the new University of Austin (not to be confused with the University of Texas at Austin, where I work) is officially launching. And they’re hiring! People who are interested in STEM positions there should contact David Ruth.

I forgot to link to it when it came out more than a month ago—a lot has happened in the meantime!—but Dalzell et al. put up a phenomenal 337-page survey of quantum algorithms, focusing relentlessly on the crucial question of whether there’s actually an end-to-end speedup over the best known classical algorithm for each given task. In countless situations where I would just scream “no, the hypesters are lying to you, this is BS,” Dalzell et al. take dozens of polite, careful, and highly technical pages to spell out why.

Besides AI intrigue, this past week might be remembered for a major breakthrough in classical complexity theory, in solving arbitrary compression problems via a nonuniform algorithm (i.e., a family of Boolean circuits) that takes only 2^4n/5 time, rather than the 2ⁿ time that would be needed for brute force. See this paper by Hirahara, Ilango, and Williams, and as well this independent one by Mazor and Pass.

I’ve been drowning in both quantum-computing-related and AI-related talks, interviews, podcasts, panels, and so on. These activities have all but taken over my days, leaving virtually no time for the actual research (especially once one factors in time for family, and time for getting depressed on social media). I’ve let things reach this point partly because I really do love talking about things that interest me, but partly also because I never learned how to say no. I have no choice but to cut back.

So, the purpose of this post is for me to link people to it whenever I get a new request. From now on, I agree only under the following conditions:

1. For travel: you reimburse all travel costs. I don’t have to go through a lengthy process for reimbursements, but just forward you my receipts. There’s not a time limit on doing so.
2. You don’t require me to upload my slides in advance, or provide readings or other “extra” materials. (Title and abstract a week or two before the talk are reasonable.)
3. You don’t require me to schedule a “practice session” or “orientation session” before the main event.
4. For podcasts and virtual talks: you don’t require me to set up any special equipment (including headphones or special cameras), or install any special software.
5. If you’re a for-profit company: you compensate me for the time.
6. For podcasts and virtual talks: unless specified otherwise, I am in Austin, TX, in US Central time zone. You email me a reminder the day before with the time in US Central, and the link. Otherwise I won’t be held responsible in the likely event that we get it wrong.