## Dismantling Quantum Hype with Tim Nguyen

November 22nd, 2022

Happy Thanksgiving to my American readers! While I enjoy a family holiday-week vacation in exotic Dallas—and yes, I will follow up on my old JFK post by visiting Dealey Plaza—please enjoy the following Thanksgiving victuals:

I recently recorded a 3-hour (!) YouTube video with Timothy Nguyen, host of the Cartesian Cafe. Our episode is entitled Quantum Computing: Dismantling the Hype. In it, I teach a sort of extremely compressed version of my undergraduate Intro to Quantum Information Science course, unburdening myself about whatever Tim prompts me to explain: the basic rules of quantum information, quantum circuits, the quantum black-box model, the Deutsch-Jozsa algorithm, BQP and its relationship to classical complexity classes, and sampling-based quantum supremacy experiments. This is a lot more technical than an average podcast, a lot less technical than an actual course, and hopefully just right for some nonempty subset of readers.

Outside of his podcasting career, some of you might recognize Nguyen as the coauthor, with Theo Polya, of a rebuttal of “Geometric Unity.” This latter is the proposal by the financier, podcaster, and leading “Intellectual Dark Web” figure Eric Weinstein for a unified theory of particle physics. Now, I slightly know Weinstein, and have even found him fascinating, eloquent, and correct about various issues. So, in an addendum to the main video, Nguyen chats with me about his experience critiquing Weinstein’s theory, and also about something where my knowledge is far greater: namely, my 2002 rebuttal of some of the central claims in Stephen Wolfram’s A New Kind of Science, and whether there are any updates to that story twenty years later.

Enjoy!

## Reform AI Alignment

November 20th, 2022

Update (Nov. 22): Theoretical computer scientist and longtime friend-of-the-blog Boaz Barak writes to tell me that, coincidentally, he and Ben Edelman just released a big essay advocating a version of “Reform AI Alignment” on Boaz’s Windows on Theory blog, as well as on LessWrong. (I warned Boaz that, having taken the momentous step of posting to LessWrong, in 6 months he should expect to find himself living in a rationalist group house in Oakland…) Needless to say, I don’t necessarily endorse their every word or vice versa, but there’s a striking amount of convergence. They also have a much more detailed discussion of (e.g.) which kinds of optimization processes they consider relatively safe.

Nearly halfway into my year at OpenAI, still reeling from the FTX collapse, I feel like it’s finally time to start blogging my AI safety thoughts—starting with a little appetizer course today, more substantial fare to come.

Many people claim that AI alignment is little more a modern eschatological religion—with prophets, an end-times prophecy, sacred scriptures, and even a god (albeit, one who doesn’t exist quite yet). The obvious response to that claim is that, while there’s some truth to it, “religions” based around technology are a little different from the old kind, because technological progress actually happens regardless of whether you believe in it.

I mean, the Internet is sort of like the old concept of the collective unconscious, except that it actually exists and you’re using it right now. Airplanes and spacecraft are kind of like the ancient dream of Icarus—except, again, for the actually existing part. Today GPT-3 and DALL-E2 and LaMDA and AlphaTensor exist, as they didn’t two years ago, and one has to try to project forward to what their vastly-larger successors will be doing a decade from now. Though some of my colleagues are still in denial about it, I regard the fact that such systems will have transformative effects on civilization, comparable to or greater than those of the Internet itself, as “already baked in”—as just the mainstream position, not even a question anymore. That doesn’t mean that future AIs are going to convert the earth into paperclips, or give us eternal life in a simulated utopia. But their story will be a central part of the story of this century.

Which brings me to a second response. If AI alignment is a religion, it’s now large and established enough to have a thriving “Reform” branch, in addition to the original “Orthodox” branch epitomized by Eliezer Yudkowsky and MIRI.  As far as I can tell, this Reform branch now counts among its members a large fraction of the AI safety researchers now working in academia and industry.  (I’ll leave the formation of a Conservative branch of AI alignment, which reacts against the Reform branch by moving slightly back in the direction of the Orthodox branch, as a problem for the future — to say nothing of Reconstructionist or Marxist branches.)

Here’s an incomplete but hopefully representative list of the differences in doctrine between Orthodox and Reform AI Risk:

(1) Orthodox AI-riskers tend to believe that humanity will survive or be destroyed based on the actions of a few elite engineers over the next decade or two.  Everything else—climate change, droughts, the future of US democracy, war over Ukraine and maybe Taiwan—fades into insignificance except insofar as it affects those engineers.

We Reform AI-riskers, by contrast, believe that AI might well pose civilizational risks in the coming century, but so does all the other stuff, and it’s all tied together.  An invasion of Taiwan might change which world power gets access to TSMC GPUs.  Almost everything affects which entities pursue the AI scaling frontier and whether they’re cooperating or competing to be first.

(2) Orthodox AI-riskers believe that public outreach has limited value: most people can’t understand this issue anyway, and will need to be saved from AI despite themselves.

We Reform AI-riskers believe that trying to get a broad swath of the public on board with one’s preferred AI policy is something close to a deontological imperative.

(3) Orthodox AI-riskers worry almost entirely about an agentic, misaligned AI that deceives humans while it works to destroy them, along the way to maximizing its strange utility function.

We Reform AI-riskers entertain that possibility, but we worry at least as much about powerful AIs that are weaponized by bad humans, which we expect to pose existential risks much earlier in any case.

(4) Orthodox AI-riskers have limited interest in AI safety research applicable to actually-existing systems (LaMDA, GPT-3, DALL-E2, etc.), seeing the dangers posed by those systems as basically trivial compared to the looming danger of a misaligned agentic AI.

We Reform AI-riskers see research on actually-existing systems as one of the only ways to get feedback from the world about which AI safety ideas are or aren’t promising.

(5) Orthodox AI-riskers worry most about the “FOOM” scenario, where some AI might cross a threshold from innocuous-looking to plotting to kill all humans in the space of hours or days.

We Reform AI-riskers worry most about the “slow-moving trainwreck” scenario, where (just like with climate change) well-informed people can see the writing on the wall decades ahead, but just can’t line up everyone’s incentives to prevent it.

(6) Orthodox AI-riskers talk a lot about a “pivotal act” to prevent a misaligned AI from ever being developed, which might involve (e.g.) using an aligned AI to impose a worldwide surveillance regime.

We Reform AI-riskers worry more about such an act causing the very calamity that it was intended to prevent.

(7) Orthodox AI-riskers feel a strong need to repudiate the norms of mainstream science, seeing them as too slow-moving to react in time to the existential danger of AI.

We Reform AI-riskers feel a strong need to get mainstream science on board with the AI safety program.

(8) Orthodox AI-riskers are maximalists about the power of pure, unaided superintelligence to just figure out how to commandeer whatever physical resources it needs to take over the world (for example, by messaging some lab over the Internet, and tricking it into manufacturing nanobots that will do the superintelligence’s bidding).

We Reform AI-riskers believe that, here just like in high school, there are limits to the power of pure intelligence to achieve one’s goals.  We’d expect even an agentic, misaligned AI, if such existed, to need a stable power source, robust interfaces to the physical world, and probably allied humans before it posed much of an existential threat.

What have I missed?

## WINNERS of the Scott Aaronson Grant for Advanced Precollege STEM Education!

November 18th, 2022

I’m thrilled to be able to interrupt your regular depressing programming for 100% happy news.

Some readers will remember that, back in September, I announced that an unnamed charitable foundation had asked my advice on how best to donate $250,000 for advanced precollege STEM education. So, just like the previous time I got such a request, from Jaan Tallinn’s Survival and Flourishing Fund, I decided to do a call for proposals on Shtetl-Optimized before passing along my recommendations. I can now reveal that the generous foundation, this time around, was the Packard Foundation. Indeed, the idea and initial inquiries to me came directly from Dave Orr: the chair of the foundation, grandson of Hewlett-Packard cofounder David Packard, and (so I learned) longtime Shtetl-Optimized reader. I can also now reveal the results. I was honored to get more than a dozen excellent applications. After carefully considering all of them, I passed along four finalists to the Packard Foundation, which preferred to award the entire allotment to a single program if possible. After more discussion and research, the Foundation then actually decided on two winners: •$225,000 for general support to PROMYS: the long-running, world-renowned summer math camp for high-school students, which (among other things) is in the process of launching a new branch in India. While I ended up at Canada/USA Mathcamp (which I supported in my first grant round) rather than PROMYS, I knew all about and admired PROMYS even back when I was the right age to attend it. I’m thrilled to be able to play a small role in its expansion.
• $30,000 for general support to AddisCoder: the phenomenal program that introduces Ethiopian high-schoolers to programming and algorithms. AddisCoder was founded by UC Berkeley theoretical computer science professor and longtime friend-of-the-blog Jelani Nelson, and also received$30,000 in my first grant round. Jelani and his co-organizers will be pressing ahead with AddisCoder despite political conflict in Ethiopia including a recently-concluded civil war. I’m humbled if I can make even the tiniest difference.

Thanks so much to the Packard Foundation, and to Packard’s talented program officers, directors, and associates—especially Laura Sullivan, Jean Ries, and Prithi Trivedi—for their hard work to make this happen. Thanks so much also to everyone who applied. While I wish we could’ve funded everyone, I’ve learned a lot about programs to which I’d like to steer future support (other prospective benefactors: please email me!!), and to which I’d like to steer kids: my own, once they’re old enough, and other kids of my acquaintance.

I feel good that, in the tiny, underfunded world of accelerated STEM education, the $255,000 that Packard is donating will already make a difference. But of course,$255,000 is only a thousandth of $255 million, which is a thousandth of$255 billion. Perhaps I could earn the latter sort of sums, to donate to STEM education or any other cause, by (for example) starting my own cryptocurrency exchange. I hope my readers will forgive me for not having chosen that route, expected-utility-maximization arguments be damned.

## Sneerers

November 16th, 2022

In the past few weeks, I’ve learned two ways to think about online sneerers that have been helping me tremendously, and that I wanted to share in case they’re helpful to others:

First, they’re like a train in a movie that’s barreling directly towards the camera. If you haven’t yet internalized how the medium works, absolutely terrifying! Run from the theater! If you have internalized it, though, you can sit and watch without even flinching.

Second, the sneerers are like alligators—and about as likely to be moved by your appeals to reason and empathy. But if, like me, you’re lucky enough to have a loving family, friends, colleagues, and a nigh-uncancellable career, then it’s as though you’re standing on a bridge high above, looking down at the gators as they snap their jaws at you uselessly. There’s really no moral or intellectual obligation to go down to the swamp to wrestle them. If they mean to attack you, let them at least come up to the bridge.

## Sam Bankman-Fried and the geometry of conscience

November 13th, 2022

Update (Nov. 16): Check out this new interview of SBF by my friend and leading Effective Altruist writer Kelsey Piper. Here Kelsey directly confronts SBF with some of the same moral and psychological questions that animated this post and the ensuing discussion—and, surely to the consternation of his lawyers, SBF answers everything she asks. And yet I still don’t know what exactly to make of it. SBF’s responses reveal a surprising cynicism (surprising because, if you’re that cynical, why be open about it?), as well as an optimism that he can still fix everything that seems wildly divorced from reality.

I still stand by most of the main points of my post, including:

• the technical insanity of SBF’s clearly-expressed attitude to risk (“gambler’s ruin? more like gambler’s opportunity!!”), and its probable role in creating the conditions for everything that followed,
• the need to diagnose the catastrophe correctly (making billions of dollars in order to donate them to charity? STILL VERY GOOD; lying and raiding customer deposits in course of doing so? DEFINITELY BAD), and
• how, when sneerers judge SBF guilty just for being a crypto billionaire who talked about Effective Altruism, it ironically lets him off the hook for what he specifically did that was terrible.

But over the past couple days, I’ve updated in the direction of understanding SBF’s psychology a lot less than I thought I did. While I correctly hit on certain aspects of the tragedy, there are other important aspects—the drug use, the cynical detachment (“life as a video game”), the impulsivity, the apparent lying—that I neglected to touch on and about which we’ll surely learn more in the coming days, weeks, and years. –SA

Several readers have asked me for updated thoughts on AI safety, now that I’m 5 months into my year at OpenAI—and I promise, I’ll share them soon! The thing is, until last week I’d entertained the idea of writing up some of those thoughts for an essay competition run by the FTX Future Fund, which (I was vaguely aware) was founded by the cryptocurrency billionaire Sam Bankman-Fried, henceforth SBF.

Alas, unless you’ve been tucked away on some Caribbean island—or perhaps, especially if you have been—you’ll know that the FTX Future Fund has ceased to exist. In the course of 2-3 days last week, SBF’s estimated net worth went from ~$15 billion to a negative number, possibly the fastest evaporation of such a vast personal fortune in all human history. Notably, SBF had promised to give virtually all of it away to various worthy causes, including mitigating existential risk and helping Democrats win elections, and the worldwide Effective Altruist community had largely reoriented itself around that pledge. That’s all now up in smoke. I’ve never met SBF, although he was a physics undergraduate at MIT while I taught CS there. What little I knew of SBF before this week, came mostly from reading Gideon Lewis-Kraus’s excellent New Yorker article about Effective Altruism this summer. The details of what happened at FTX are at once hopelessly complicated and—it would appear—damningly simple, involving the misuse of billions of dollars’ worth of customer deposits to place risky bets that failed. SBF has, in any case, tweeted that he “fucked up and should have done better.” You’d think none of this would directly impact me, since SBF and I inhabit such different worlds. He ran a crypto empire from the Bahamas, sharing a group house with other twentysomething executives who often dated each other. I teach at a large state university and try to raise two kids. He made his first fortune by arbitraging bitcoin between Asia and the West. I own, I think, a couple bitcoins that someone gave me in 2016, but have no idea how to access them anymore. His hair is large and curly; mine is neither. Even so, I’ve found myself obsessively following this story because I know that, in a broader sense, I will be called to account for it. SBF and I both grew up as nerdy kids in middle-class Jewish American families, and both had transformative experiences as teenagers at Canada/USA Mathcamp. He and I know many of the same people. We’ve both been attracted to the idea of small groups of idealistic STEM nerds using their skills to help save the world from climate change, pandemics, and fascism. Aha, the sneerers will sneer! Hasn’t the entire concept of “STEM nerds saving the world” now been utterly discredited, revealed to be just a front for cynical grifters and Ponzi schemers? So if I’m also a STEM nerd who’s also dreamed of helping to save the world, then don’t I stand condemned too? I’m writing this post because, if the Greek tragedy of SBF is going to be invoked as a cautionary tale in nerd circles forevermore—which it will be—then I think it’s crucial that we tell the right cautionary tale. It’s like, imagine the Apollo 11 moon mission had almost succeeded, but because of a tiny crack in an oxygen tank, it instead exploded in lunar orbit, killing all three of the astronauts. Imagine that the crack formed partly because, in order to hide a budget overrun, Wernher von Braun had secretly substituted a cheaper material, while telling almost none of his underlings. There are many excellent lessons that one could draw from such a tragedy, having to do with, for example, the construction of oxygen tanks, the procedures for inspecting them, Wernher von Braun as an individual, or NASA safety culture. But there would also be bad lessons to not draw. These include: “The entire enterprise of sending humans to the moon was obviously doomed from the start.” “Fate will always punish human hubris.” “All the engineers’ supposed quantitative expertise proved to be worthless.” From everything I’ve read, SBF’s mission to earn billions, then spend it saving the world, seems something like this imagined Apollo mission. Yes, the failure was total and catastrophic, and claimed innocent victims. Yes, while bad luck played a role, so did, shall we say, fateful decisions with a moral dimension. If it’s true that, as alleged, FTX raided its customers’ deposits to prop up the risky bets of its sister organization Alameda Research, multiple countries’ legal systems will surely be sorting out the consequences for years. To my mind, though, it’s important not to minimize the gravity of the fateful decision by conflating it with everything that preceded it. I confess to taking this sort of conflation extremely personally. For eight years now, the rap against me, advanced by thousands (!) on social media, has been: sure, while by all accounts Aaronson is kind and respectful to women, he seems like exactly the sort of nerdy guy who, still bitter and frustrated over high school, could’ve chosen instead to sexually harass women and hinder their scientific careers. In other words, I stand condemned by part of the world, not for the choices I made, but for choices I didn’t make that are considered “too close to me” in the geometry of conscience. And I don’t consent to that. I don’t wish to be held accountable for the misdeeds of my doppelgängers in parallel universes. Therefore, I resolve not to judge anyone else by their parallel-universe doppelgängers either. If SBF indeed gambled away his customers’ deposits and lied about it, then I condemn him for it utterly, but I refuse to condemn his hypothetical doppelgänger who didn’t do those things. Granted, there are those who think all cryptocurrency is a Ponzi scheme and a scam, and that for that reason alone, it should’ve been obvious from the start that crypto-related plans could only end in catastrophe. The “Ponzi scheme” theory of cryptocurrency has, we ought to concede, a substantial case in its favor—though I’d rather opine about the matter in (say) 2030 than now. Like many technologies that spend years as quasi-scams until they aren’t, maybe blockchains will find some compelling everyday use-cases, besides the well-known ones like drug-dealing, ransomware, and financing rogue states. Even if cryptocurrency remains just a modern-day tulip bulb or Beanie Baby, though, it seems morally hard to distinguish a cryptocurrency trader from the millions who deal in options, bonds, and all manner of other speculative assets. And a traditional investor who made billions on successful gambles, or arbitrage, or creating liquidity, then gave virtually all of it away to effective charities, would seem, on net, way ahead of most of us morally. To be sure, I never pursued the “Earning to Give” path myself, though certainly the concept occurred to me as a teenager, before it had a name. Partly I decided against it because I seem to lack a certain brazenness, or maybe just willingness to follow up on tedious details, needed to win in business. Partly, though, I decided against trying to get rich because I’m selfish (!). I prioritized doing fascinating quantum computing research, starting a family, teaching, blogging, and other stuff I liked over devoting every waking hour to possibly earning a fortune only to give it all to charity, and more likely being a failure even at that. All told, I don’t regret my scholarly path—especially not now!—but I’m also not going to encase it in some halo of obvious moral superiority. If I could go back in time and give SBF advice—or if, let’s say, he’d come to me at MIT for advice back in 2013—what could I have told him? I surely wouldn’t talk about cryptocurrency, about which I knew and know little. I might try to carve out some space for deontological ethics against pure utilitarianism, but I might also consider that a lost cause with this particular undergrad. On reflection, maybe I’d just try to convince SBF to weight money logarithmically when calculating expected utility (as in the Kelly criterion), to forsake the linear weighting that SBF explicitly advocated and that he seems to have put into practice in his crypto ventures. Or if not logarithmic weighing, I’d try to sell him on some concave utility function—something that makes, let’s say, a mere$1 billion in hand seem better than \$15 billion that has a 50% probability of vanishing and leaving you, your customers, your employees, and the entire Effective Altruism community with less than nothing.

At any rate, I’d try to impress on him, as I do on anyone reading now, that the choice between linear and concave utilities, between risk-neutrality and risk-aversion, is not bloodless or technical—that it’s essential to make a choice that’s not only in reflective equilibrium with your highest values, but that you’ll still consider to be such regardless of which possible universe you end up in.

## The Caesar problem remains open

November 8th, 2022

If I haven’t blogged until now about the midterm election, it’s because I find the state of the world too depressing. Go vote, obviously, if you’re eligible and haven’t yet. How many more chances will you have?

While I’m (to put it mildly) neither especially courageous nor useful as an infantryman, I would’ve been honored to give up my life for the Israel of Herzl and Ben-Gurion, or for the America of Franklin and Lincoln. Alas, the Israel of Herzl and Ben-Gurion officially ceased to exist last week, with the election of a coalition some of whose members officially endorse discrimination against Israeli Arab citizens, effectively nullifying Ben-Gurion’s founding declaration that the new state would ensure “complete equality of social and political rights to all its inhabitants irrespective of religion, race, or sex.” The America of Franklin and Lincoln might follow it into oblivion starting tonight, with the election of hundreds of candidates who acknowledge the legimitacy of elections only when their party wins.

The Roman Republic lasted until Caesar. Weimar Germany lasted until Hitler (no, the destroyer of democracy isn’t always literally Hitler, but in that instance it was). Hungary lasted until Orbán. America lasted until Trump. Israel lasted until Netanyahu. After two millennia, democracy still hasn’t solved this problem, and it’s always basically the same problem: one individual, one populist authoritarian, who uses the machinery of democracy to end democracy. How would one design a democracy to prevent this the next time around?

## Oh right, quantum computing

October 31st, 2022

These days, I often need to remind myself that, as an undergrad, grad student, postdoc, or professor, I’ve now been doing quantum computing research for a quarter-century—i.e., well over half of the subject’s existence. As a direct result, when I feel completely jaded about a new development in QC, it might actually be exciting. When I feel moderately excited, it might actually be the most exciting thing for years.

With that in mind:

(1) Last week National Public Radio’s Marketplace interviewed me, John Martinis, and others about the current state of quantum computing. While the piece wasn’t entirely hype-free, I’m pleased to report that my own views were represented accurately! To wit:

“There is a tsunami of hype about what quantum computers are going to revolutionize,” said Scott Aaronson, a professor of computer science at the University of Texas at Austin. “Quantum computing has turned into a word that venture capitalists or people seeking government funding will sprinkle on anything because it sounds good.”

Aaronson warned we can’t be certain that these computers will in fact revolutionize machine learning and finance and optimization problems.  “We can’t prove that there’s not a quantum algorithm that solves all these problems super fast, but we can’t even prove there’s not an algorithm for a conventional computer that does it,” he said. [In the recorded version, they replaced this by a simpler but also accurate thought: namely, that we can’t prove one way or the other whether there’s a useful quantum advantage for these tasks.]

(2) I don’t like to use this blog to toot my own research horn, but on Thursday my postdoc Jason Pollack and I released a paper, entitled Discrete Bulk Reconstruction. And to be honest, I’m pretty damned excited about it. It represents about 8 months of Jason—a cosmologist and string theorist who studied under Sean Carroll—helping me understand AdS/CFT in the language of the undergraduate CS curriculum, like min-cuts on undirected graphs, so that we could then look for polynomial-time algorithms to implement the holographic mapping from boundary quantum states to the spatial geometry in the bulk. We drew heavily on previous work in the same direction, especially the already-seminal 2015 holographic entropy cone paper by Ning Bao et al. But I’d like to think that, among other things, our work represents a new frontier in just how accessible AdS/CFT itself can be made to CS and discrete math types. Anyway, here’s the abstract if you’re interested:

According to the AdS/CFT correspondence, the geometries of certain spacetimes are fully determined by quantum states that live on their boundaries — indeed, by the von Neumann entropies of portions of those boundary states. This work investigates to what extent the geometries can be reconstructed from the entropies in polynomial time. Bouland, Fefferman, and Vazirani (2019) argued that the AdS/CFT map can be exponentially complex if one wants to reconstruct regions such as the interiors of black holes. Our main result provides a sort of converse: we show that, in the special case of a single 1D boundary, if the input data consists of a list of entropies of contiguous boundary regions, and if the entropies satisfy a single inequality called Strong Subadditivity, then we can construct a graph model for the bulk in linear time. Moreover, the bulk graph is planar, it has O(N2) vertices (the information-theoretic minimum), and it’s “universal,” with only the edge weights depending on the specific entropies in question. From a combinatorial perspective, our problem boils down to an “inverse” of the famous min-cut problem: rather than being given a graph and asked to find a min-cut, here we’re given the values of min-cuts separating various sets of vertices, and need to find a weighted undirected graph consistent with those values. Our solution to this problem relies on the notion of a “bulkless” graph, which might be of independent interest for AdS/CFT. We also make initial progress on the case of multiple 1D boundaries — where the boundaries could be connected via wormholes — including an upper bound of O(N4) vertices whenever a planar bulk graph exists (thus putting the problem into the complexity class NP).

(3) Anand Natarajan and Chinmay Nirkhe posted a preprint entitled A classical oracle separation between QMA and QCMA, which makes progress on a problem that’s been raised on this blog all the way back to its inception. A bit of context: QMA, Quantum Merlin-Arthur, captures what can be proven using a quantum state with poly(n) qubits as the proof, and a polynomial-time quantum algorithm as the verifier. QCMA, or Quantum Classical Merlin-Arthur, is the same as QMA except that now the proof has to be classical. A fundamental problem of quantum complexity theory, first raised by Aharonov and Naveh in 2002, is whether QMA=QCMA. In 2007, Greg Kuperberg and I introduced the concept of quantum oracle separation—that is, a unitary that can be applied in a black-box manner—in order to show that there’s a quantum oracle relative to which QCMA≠QMA. In 2015, Fefferman and Kimmel improved this, to show that there’s a “randomized in-place” oracle relative to which QCMA≠QMA. Natarajan and Nirkhe now remove the “in-place” part, meaning the only thing still “wrong” with their oracle is that it’s randomized. Derandomizing their construction would finally settle this 20-year-old open problem (except, of course, for the minor detail of whether QMA=QCMA in the “real,” unrelativized world!).

(4) Oh right, the Google group reports the use of their superconducting processor to simulate non-abelian anyons. Cool.

## On Bryan Caplan and his new book

October 28th, 2022

Yesterday I attended a lecture by George Mason University economist Bryan Caplan, who’s currently visiting UT Austin, about his new book entitled Don’t Be a Feminist. (See also here for previous back-and-forth between me and Bryan about his book.) A few remarks:

(1) Maybe surprisingly, there were no protesters storming the lectern, no security detail, not even a single rotten vegetable thrown. About 30 people showed up, majority men but women too. They listened politely and asked polite questions afterward. One feminist civilly challenged Bryan during the Q&A about his gender pay gap statistics.

(2) How is it that I got denounced by half the planet for saying once, in a blog comment, that I agreed with 97% of feminism but had concerns with one particular way it was operationalized, whereas Bryan seems to be … not denounced in the slightest for publishing a book and going on a lecture tour about how he rejects feminism in its entirety as angry and self-pitying in addition to factually false? Who can explain this to me?

(3) For purposes of his argument, Bryan defines feminism as “the view that women are generally treated less fairly than men,” rather than (say) “the view that men and women ought to be treated equally,” or “the radical belief that women are people,” or other formulations that Bryan considers too obvious to debate. He then rebuts feminism as he’s defined it, by taking the audience on a horror tour of all the ways society treats men less fairly than women (expectations of doing dirty and dangerous work, divorce law, military drafts as in Ukraine right now, …), as well as potentially benign explanations for apparent unfairness toward women, to argue that it’s at least debatable which sex gets the rawer deal on average.

During the Q&A, I raised what I thought was the central objection to Bryan’s relatively narrow definition of feminism. Namely that, by the standards of 150 years ago, Bryan is obviously a feminist, and so am I, and so is everyone in the room. (Whereupon a right-wing business school professor interjected: “please don’t make assumptions about me!”)

I explained that this is why I call myself a feminist, despite agreeing with many of Bryan’s substantive points: because I want no one to imagine for a nanosecond that, if I had the power, I’d take gender relations back to how they were generations ago.

Bryan replied that >60% of Americans call themselves non-feminists in surveys. So, he asked me rhetorically, do all those Americans secretly yearn to take us back to the 19th century? Such a position, he said, seemed so absurdly uncharitable as not to be worth responding to.

Reflecting about it on my walk home, I realized: actually, give or take the exact percentages, this is precisely the progressive thesis. I.e., that just like at least a solid minority of Germans turned out to be totally fine with Nazism, however much they might’ve denied it beforehand, so too at least a solid minority of Americans would be fine with—if not ecstatic about—The Handmaid’s Tale made real. Indeed, they’d add, it’s only vociferous progressive activism that stands between us and that dystopia.

And if anyone were tempted to doubt this, progressives might point to the election of Donald Trump, the failed insurrection to maintain his power, and the repeal of Roe as proof enough to last for a quadrillion years.

Bryan would probably reply: why even waste time engaging with such a hysterical position? To me, though, the hysterical position sadly has more than a grain of truth to it. I wish we lived in a world where there was no point in calling oneself a pro-democracy anti-racist feminist and a hundred other banal and obvious things. I just don’t think that we do.

## Explanation-Gödel and Plausibility-Gödel

October 12th, 2022

Here’s an observation that’s mathematically trivial but might not be widely appreciated. In kindergarten, we all learned Gödel’s First Incompleteness Theorem, which given a formal system F, constructs an arithmetical encoding of

G(F) = “This sentence is not provable in F.”

If G(F) is true, then it’s an example of a true arithmetical sentence that’s unprovable in F. If, on the other hand, G(F) is false, then it’s provable, which means that F isn’t arithmetically sound. Therefore F is either incomplete or unsound.

Many have objected: “but despite Gödel’s Theorem, it’s still easy to explain why G(F) is true. In fact, the argument above basically already did it!”

[Note: Please stop leaving comments explaining to me that G(F) follows from F’s consistency. I understand that: the “heuristic” part of the argument is F’s consistency! I made a pedagogical choice to elide that, which nerd-sniping has now rendered untenable.]

You might make a more general point: there are many, many mathematical statements for which we currently lack a proof, but we do seem to have a fully convincing heuristic explanation: one that “proves the statement to physics standards of rigor.” For example:

• The Twin Primes Conjecture (there are infinitely many primes p for which p+2 is also prime).
• The Collatz Conjecture (the iterative process that maps each positive integer n to n/2 if n is even, or to 3n+1 if n is odd, eventually reaches 1 regardless of which n you start at).
• π is a normal number (or even just: the digits 0-9 all occur with equal limiting frequencies in the decimal expansion of π).
• π+e is irrational.

And so on. No one has any idea how to prove any of the above statements—and yet, just on statistical grounds, it seems clear that it would require a ludicrous conspiracy to make any of them false.

Conversely, one could argue that there are statements for which we do have a proof, even though we lack a “convincing explanation” for the statements’ truth. Maybe the Four-Color Theorem or Hales’s Theorem, for which every known proof requires a massive computer enumeration of cases, belong to this class. Other people might argue that, given a proof, an explanation could always be extracted with enough time and effort, though resolving this dispute won’t matter for what follows.

You might hope that, even if some true mathematical statements can’t be proved, every true statement might nevertheless have a convincing heuristic explanation. Alas, a trivial adaptation of Gödel’s Theorem shows that, if (1) heuristic explanations are to be checkable by computer, and (2) only true statements are to have convincing heuristic explanations, then this isn’t possible either. I mean, let E be a program that accepts or rejects proposed heuristic explanations, for statements like the Twin Prime Conjecture or the Collatz Conjecture. Then construct the sentence

S(E) = “This sentence has no convincing heuristic explanation accepted by E.”

If S(E) is true, then it’s an example of a true arithmetical statement without even a convincing heuristic explanation for its truth (!). If, on the other hand, S(E) is false, then there’s a convincing heuristic explanation of its truth, which means that something has gone wrong.

What’s happening, of course, is that given the two conditions we imposed, our “heuristic explanation system” was a proof system, even though we didn’t call it one. This is my point, though: when we use the word “proof,” it normally invokes a specific image, of a sequence of statements that marches from axioms to a theorem, with each statement following from the preceding ones by rigid inference rules like those of first-order logic. None of that, however, plays any direct role in the proof of the Incompleteness Theorem, which cares only about soundness (inability to prove falsehoods) and checkability by a computer (what, with hindsight, Gödel’s “arithmetization of syntax” was all about). The logic works for “heuristic explanations” too.

Now we come to something that I picked up from my former student (and now AI alignment leader) Paul Christiano, on a recent trip to the Bay Area, and which I share with Paul’s kind permission. Having learned that there’s no way to mechanize even heuristic explanations for all the true statements of arithmetic, we could set our sights lower still, and ask about mere plausibility arguments—arguments that might be overturned on further reflection. Is there some sense in which every true mathematical statement at least has a good plausibility argument?

Maybe you see where this is going. Letting P be a program that accepts or rejects proposed plausibility arguments, we can construct

S(P) = “This sentence has no argument for its plausibility accepted by P.”

If S(P) is true, then it’s an example of a true arithmetical statement without even a plausibility argument for its truth (!). If, on the other hand, S(P) is false, then there is a plausibility argument for it. By itself, this is not at all a fatal problem: all sorts of false statements (IP≠PSPACE, switching doors doesn’t matter in Monty Hall, Trump couldn’t possibly become president…) have had decent plausibility arguments. Having said that, it’s pretty strange that you can have a plausibility argument that’s immediately contradicted by its own existence! This rules out some properties that you might want your “plausibility system” to have, although maybe a plausibility system exists that’s still nontrivial and that has weaker properties.

Anyway, I don’t know where I’m going with this, or even why I posted it, but I hope you enjoyed it! And maybe there’s something to be discovered in this direction.

## Postdocs, matrix multiplication, and WSJ: yet more shorties

October 7th, 2022

I’m proud to say that Nick Hunter-Jones and Matteo Ippoliti—both of whom work at the interface between quantum information science and condensed-matter physics (Nick closer to the former and Matteo to the latter)—have joined the physics faculty at UT Austin this year. And Nick, Matteo, and I are jointly seeking postdocs to start in Fall 2023! Please check out our call for applications here. The deadline is December 1; you apply through AcademicJobsOnline rather than by emailing me as in past years.

The big news in AI and complexity theory this week was DeepMind’s AlphaTensor, and its automated discovery of new algorithms for matrix multiplication. (See here for the Nature paper.) More concretely, they’ve used AI to discover (among other things) an algorithm for multiplying 4×4 matrices, over finite fields of characteristic 2, using only 47 scalar multiplications. This beats the 49=7×7 that you’d get from Strassen’s algorithm. There are other improvements for other matrix dimensions, many of which work over fields of other characteristics.

Since I’ve seen confusion about the point on social media: this does not improve over the best known asymptotic exponent for matrix multiplication, which over any field, still stands at the human-discovered 2.373 (meaning, we know how to multiply two N×N matrices in O(N2.373) time, but not faster). But it does asymptotically improve over Strassen’s O(N2.81) algorithm from 1968, conceivably even in a way that could have practical relevance for multiplying hundreds-by-hundreds or thousands-by-thousands matrices over F2.

Way back in 2007, I gave a talk at MIT CSAIL’s “Wild and Crazy Ideas Session,” where I explicitly proposed to use computer search to look for faster algorithms for 4×4 and 5×5 matrix multiplication. The response I got at the time was that it was hopeless, since the search space was already too huge. Of course, that was before the deep learning revolution.

This morning, the Wall Street Journal published an article by Karen Hao about competition between China and the US in quantum computing. Unfortunately paywalled, but includes the following passage:

Meanwhile, American academics say it’s gotten harder for Chinese students to obtain visas to conduct quantum research in the U.S. “It’s become common knowledge that when Chinese students or postdocs come to the U.S., they can’t say they’re doing quantum computing,” says Scott Aaronson, director of the Quantum Information Center at the University of Texas, Austin.