Quickies

December 4th, 2017

Updates (Dec. 5): The US Supreme Court has upheld Trump’s latest travel ban. I’m grateful to all the lawyers who have thrown themselves in front of the train of fascism, desperately trying to slow it down—but I could never, ever have been a lawyer myself. Law is fundamentally a make-believe discipline. Sure, there are times when it involves reason and justice, possibly even resembles mathematics—but then there are times when the only legally correct thing to say is, “I guess that, contrary to what I thought, the Establishment Clause of the First Amendment does let you run for president promising to discriminate against a particular religious group, and then find a pretext under which to do it. The people with the power to decide that question have decided it.” I imagine that I’d last about half a day before tearing up my law-school diploma in disgust, which is surely a personality flaw on my part.

In happier news, many of you may have seen that papers by the groups of Chris Monroe and of Misha Lukin, reporting ~50-qubit experiments with trapped ions and optical lattices respectively, have been published back-to-back in Nature. (See here and here for popular summaries.) As far as I can tell, these papers represent an important step along the road to a clear quantum supremacy demonstration. Ideally, one wants a device to solve a well-defined computational problem (possibly a sampling problem), and also highly-optimized classical algorithms for solving the same problem and for simulating the device, which both let one benchmark the device’s performance and verify that the device is solving the problem correctly. But in a curious convergence, the Monroe group and Lukin group work suggests that this can probably be achieved with trapped ions and/or optical lattices at around the same time that Google and IBM are closing in on the goal with superconducting circuits.

As everyone knows, the flaming garbage fire of a tax bill has passed the Senate, thanks to the spinelessness of John McCain, Lisa Murkowski, Susan Collins, and Jeff Flake.  The fate of American higher education will now be decided behind closed doors, in the technical process of “reconciling” the House bill (which includes the crippling new tax on PhD students) with the Senate bill (which doesn’t—that one merely guts a hundred other things).  It’s hard to imagine that this particular line item will occassion more than about 30 seconds of discussion.  But, I dunno, maybe calling your Senator or Representative could help.  Me, I left a voicemail message with the office of Texas Senator Ted Cruz, one that I’m confident Cruz and his staff will carefully consider.

Here’s talk show host Seth Meyers (scroll to 5:00-5:20):

“By 2027, half of all US households would pay more in taxes [under the new bill].  Oh my god.  Cutting taxes was the one thing Republicans were supposed to be good at.  What’s even the point of voting for a Republican if they’re going to raise your taxes?  That’s like tuning in to The Kardashians only to see Courtney giving a TED talk on quantum computing.”

Speaking of which, you can listen to an interview with me about quantum computing, on a podcast called Data Skeptic. We discuss the basics and then the potential for quantum machine learning algorithms.

I got profoundly annoyed by an article called The Impossibility of Intelligence Explosion by François Chollet.  Citing the “No Free Lunch Theorem”—i.e., the (trivial) statement that you can’t outperform brute-force search on random instances of an optimization problem—to claim anything useful about the limits of AI, is not a promising sign.  In this case, Chollet then goes on to argue that most intelligence doesn’t reside in individuals but rather in culture; that there are hard limits to intelligence and to its usefulness; that we know of those limits because people with stratospheric intelligence don’t achieve correspondingly extraordinary results in life [von Neumann? Newton? Einstein? –ed.]; and finally, that recursively self-improving intelligence is impossible because we, humans, don’t recursively improve ourselves.  Scattered throughout the essay are some valuable critiques, but nothing comes anywhere close to establishing the impossibility advertised in the title.  Like, there’s a standard in CS for what it takes to show something’s impossible, and Chollet doesn’t even reach the same galaxy as that standard.  The certainty that he exudes strikes me as wholly unwarranted, just as much as (say) the near-certainty of a Ray Kurzweil on the other side.

I suppose this is as good a place as any to say that my views on AI risk have evolved.  A decade ago, it was far from obvious that known methods like deep learning and reinforcement learning, merely run with much faster computers and on much bigger datasets, would work as spectacularly well as they’ve turned out to work, on such a wide variety of problems, including beating all humans at Go without needing to be trained on any human game.  But now that we know these things, I think intellectual honesty requires updating on them.  And indeed, when I talk to the AI researchers whose expertise I trust the most, many, though not all, have updated in the direction of “maybe we should start worrying.”  (Related: Eliezer Yudkowsky’s There’s No Fire Alarm for Artificial General Intelligence.)

Who knows how much of the human cognitive fortress might fall to a few more orders of magnitude in processing power?  I don’t—not in the sense of “I basically know but am being coy,” but really in the sense of not knowing.

To be clear, I still think that by far the most urgent challenges facing humanity are things like: resisting Trump and the other forces of authoritarianism, slowing down and responding to climate change and ocean acidification, preventing a nuclear war, preserving what’s left of Enlightenment norms.  But I no longer put AI too far behind that other stuff.  If civilization manages not to destroy itself over the next century—a huge “if”—I now think it’s plausible that we’ll eventually confront questions about intelligences greater than ours: do we want to create them?  Can we even prevent their creation?  If they arise, can we ensure that they’ll show us more regard than we show chimps?  And while I don’t know how much we can say about such questions that’s useful, without way more experience with powerful AI than we have now, I’m glad that a few people are at least trying to say things.

But one more point: given the way civilization seems to be headed, I’m actually mildly in favor of superintelligences coming into being sooner rather than later.  Like, given the choice between a hypothetical paperclip maximizer destroying the galaxy, versus a delusional autocrat burning civilization to the ground while his supporters cheer him on and his opponents fight amongst themselves, I’m just about ready to take my chances with the AI.  Sure, superintelligence is scary, but superstupidity has already been given its chance and been found wanting.

Speaking of superintelligences, I strongly recommend an interview of Ed Witten by Quanta magazine’s Natalie Wolchover: one of the best interviews of Witten I’ve read.  Some of Witten’s prouncements still tend toward the oracular—i.e., we’re uncovering facets of a magnificent new theoretical structure, but it’s almost impossible to say anything definite about it, because we’re still missing too many pieces—but in this interview, Witten does stick his neck out in some interesting ways.  In particular, he speculates (as Einstein also did, late in life) about whether physics should be reformulated without any continuous quantities.  And he reveals that he’s recently been rereading Wheeler’s old “It from Bit” essay, because: “I’m trying to learn about what people are trying to say with the phrase ‘it from qubit.'”

I’m happy to report that a group based mostly in Rome has carried out the first experimental demonstration of PAC-learning of quantum states, applying my 2006 “Quantum Occam’s Razor Theorem” to reconstruct optical states of up to 6 qubits.  Better yet, they insisted on adding me to their paper!

I was at Cornell all of last week to give the Messenger Lectures: six talks in all (!!), if you include the informal talks that I gave at student houses (including Telluride House, where I lived as a Cornell undergrad from 1998 to 2000).  The subjects were my usual beat (quantum computing, quantum supremacy, learnability of quantum states, firewalls and AdS/CFT, big numbers).  Intimidatingly, the Messenger Lectures are the series in which Richard Feynman presented The Character of Physical Law in 1964, and in which many others (Eddington, Oppenheimer, Pauling, Weinberg, …) set a standard that my crass humor couldn’t live up to in a trillion years.  Nevertheless, thanks so much to Paul Ginsparg for hosting my visit, and for making it both intellectually stimulating and a trip down memory lane, with meetings with many of the professors from way back when who helped to shape my thinking, including Bart Selman, Jon Kleinberg, and Lillian Lee.  Cornell is much as I remember it from half a lifetime ago, except that they must’ve made the slopes twice as steep, since I don’t recall so much huffing and puffing on my way to class each morning.

At one of the dinners, my hosts asked me about the challenges of writing a blog when people on social media might vilify you for what you say.  I remarked that it hasn’t been too bad lately—indeed that these days, to whatever extent I write anything ‘controversial,’ mostly it’s just inveighing against Trump.  “But that is scary!” someone remarked.  “You live in Texas now!  What if someone with a gun got angry at you?”  I replied that the prospect of enraging such a person doesn’t really keep me awake at night, because it seems like the worst they could do would be to shoot me.  By contrast, if I write something that angers leftists, they can do something far scarier: they can make me feel guilty!

I’ll be giving a CS colloquium at Georgia Tech today, then attending workshops in Princeton and NYC the rest of the week, so my commenting might be lighter than usual … but yours need not be.

The destruction of graduate education in the United States

November 17th, 2017

If and when you emerged from your happiness bubble to read the news, you’ll have seen (at least if you live in the US) that the cruel and reckless tax bill has passed the House of Representatives, and remains only to be reconciled with an equally-vicious Senate bill and then voted on by the Republican-controlled Senate.  The bill will add about $1.7 trillion to the national debt and raise taxes for about 47.5 million people, all in order to deliver a massive windfall to corporations, and to wealthy estates that already pay some of the lowest taxes in the developed world. In a still-functioning democracy, those of us against such a policy would have an intellectual obligation to seek out the strongest arguments in favor of the policy and try to refute them. By now, though, it seems to me that the Republicans hold the public in such contempt, and are so sure of the power of gerrymandering and voter restrictions to protect themselves from consequences, that they didn’t even bother to bring anything to the debate more substantive than the schoolyard bully’s “stop punching yourself.” I guess some of them still repeat the fairytale about the purpose of tax cuts for the super-rich being to trickle down and help everyone else—but can even they advance that “theory” anymore without stifling giggles? Mostly, as far as I can tell, they just brazenly deny that they’re doing what they obviously are doing: i.e., gleefully setting on fire anything that anyone, regardless of their ideology, could recognize as the national interest, in order to enrich a small core of supporters. But none of that is what interests me in this post—because it’s “merely” as bad as, and no worse than, what one knew to expect when a coalition of thugs, kleptocrats, and white-nationalist demagogues seized control of Hamilton’s and Jefferson’s experiment. My concern here is only with the “kill shot” that the Republicans have now aimed, with terrifying precision, at the system that’s kept American academic science the envy of the world in spite of the growing dysfunction all around it. As you’ve probably heard, one of the ways Republicans intend to pay for their tax giveaway, is to change the tax code so that graduate students will now need to pay taxes on “tuition”—a large sum of money (as much as$50,000/year) that PhD students never actually see, that can easily exceed the stipends they do see, and that’s basically just an accounting trick that serves the internal needs of universities and granting agencies.  Again, to eliminate any chance of misunderstanding: PhD students, who are effectively low-wage employees, already pay taxes on their actual stipends.  The new proposal is that they’ll also have to pay taxes on a whopping, make-believe “X” on their payroll sheet that’s always exactly balanced out by “-X.”

For detailed analyses of the impacts, see, e.g. Luca Trevisan’s post or Inside Higher Ed or the Chronicle of Higher Ed or Vox or NPR.  Briefly, though, the proposal would raise taxes by a few thousand dollars per year, or in some cases as much as $10,000 per year (!), on PhD students who already live hand-to-mouth-to-ramen-bowl, with the largest impact falling on students in STEM fields. For many students who aren’t independently wealthy, this could push a PhD beyond the realm of affordability, and cause them to leave academia or to do their graduate work in other countries. “But isn’t there some workaround?” Indeed, financial ignoramus that I am, my first reaction was to ask: if PhD tuition is basically an accounting fiction anyway, then why can’t the universities just declare that the tuition in question no longer exists, or is now zero dollars? Feel free to explain further in the comments if you understand this stuff, but as far as I can tell, the answer is: because PhD tuition is used to calculate how much “tax” the universities can take from professors’ grant money. If universities could no longer take that tax, and they had no other way to make up for it, then except for the richest few universities, they’d have to scale back research and teaching pretty drastically. To avoid that outcome, the universities would be relying on the granting agencies to let them keep taking the overhead they needed to operate, even though the “PhD tuition” no longer existed. But the granting agencies aren’t set up for this: you can’t just throw a bomb into one part of a complicated bureaucratic machine built up over decades, and expect the machine to continue working with no disruption to science. But more ominously: as my friend Daniel Harlow and many others pointed out, it’s hard to look at the indefensible, laser-specific meanness of this policy, without suspecting that for many in Congress, the destruction of American higher education isn’t a regrettable byproduct, but the goal—just another piece of red meat to throw to the base. If so, then we’d expect Congress to direct federal granting agencies not to loosen their rules about overhead, thereby forcing the students to pay the tax, and achieving the desired destruction. (Note that the Trump administration has already made tightening overhead rules—i.e., doing the exact opposite of what would be needed to counteract the new tax—a central focus of its attempt to cut federal research funding.) OK, two concluding thoughts: 1. When Republicans in Congress defended Trump’s travel ban, they at least had the craven excuse that they were only following the lead of the populist strongman who’d taken over their party. Here they don’t even have that. As far as I know, this targeted destruction of American higher education was Congress’s initiative, not Trump’s—which to me, underscores again the feather-thinness of any moral distinction between the Vichy GOP leadership and the administration with which it collaborates. Trump didn’t emerge from nowhere. It took decades of effort—George W. Bush, Sarah Palin, Karl Rove, Rush Limbaugh, Mitch McConnell, and all the rest—to transform the GOP into the pure seething cauldron of anti-intellectual resentment and hatred that we know today. 2. Given the existential risk to American higher education, why didn’t I blog about this earlier? The answer is embarrassing to admit, and reflects no credit on me. It’s simply that I didn’t believe it—even given all the other stuff that could “never happen in the US,” until it happened this past year. I didn’t believe it, not because it was too far from me but because it was too close—because if true, it would mean the crippling of the research world in which I’ve spent most of my life since age 15, so therefore it couldn’t be true. Surely even the House Republicans would realize they’d screwed up this time, and would take out this crazy provision before the full bill was voted on? Or surely there’s some workaround that makes the whole thing less awful than it sounds? There has to be … right? Anyway, what else is there to say, except to call your representative, if you’re American and still have the faith in the system that such an act implies. Review of “Inadequate Equilibria,” by Eliezer Yudkowsky November 16th, 2017 Inadequate Equilibria: Where and How Civilizations Get Stuck is a little gem of a book: wise, funny, and best of all useful (and just made available for free on the web). Eliezer Yudkowsky and I haven’t always agreed about everything, but on the subject of bureaucracies and how they fail, his insights are gold. This book is one of the finest things he’s written. It helped me reflect on my own choices in life, and it will help you reflect on yours. The book is a 120-page meditation on a question that’s obsessed me as much as it’s obsessed Yudkowsky. Namely: when, if ever, is it rationally justifiable to act as if you know better than our civilization’s “leading experts”? And if you go that route, then how do you answer the voices—not least, the voices in your own head—that call you arrogant, hubristic, even a potential crackpot? Yudkowsky gives a nuanced answer. To summarize, he argues that contrarianism usually won’t work if your goal is to outcompete many other actors in a free market for a scarce resource that they all want too, like money or status or fame. In those situations, you really should ask yourself why, if your idea is so wonderful, it’s not already being implemented. On the other hand, contrarianism can make sense when the “authoritative institutions” of a given field have screwed-up incentives that prevent them from adopting sensible policies—when even many of the actual experts might know that you’re right, but something prevents them from acting on their knowledge. So for example, if a random blogger offers a detailed argument for why the Bank of Japan is pursuing an insane fiscal policy, it’s a-priori plausible that the random blogger could be right and the Bank of Japan could be wrong (as actually happened in a case Yudkowsky recounts), since even insiders who knew the blogger was right would find it difficult to act on their knowledge. The same wouldn’t be true if the random blogger said that IBM stock was mispriced or that P≠NP is easy to prove. The high point of the book is a 50-page dialogue between two humans and an extraterrestrial visitor. The extraterrestrial is confused about a single point: why are thousands of babies in the United States dying every year, or suffering permanent brain damage, because (this seems actually to be true…) the FDA won’t approve an intravenous baby food with the right mix of fats in it? Just to answer that one question, the humans end up having to take the alien on a horror tour through what’s broken all across the modern world, from politicians to voters to journalists to granting agencies, explaining Nash equilibrium after Nash equilibrium that leaves everybody worse off but that no one can unilaterally break out of. I do have two criticisms of the book, both relatively minor compared to what I loved about it. First, Yudkowsky is brilliant in explaining how institutions can produce terrible outcomes even when all the individuals in them are smart and well-intentioned—but he doesn’t address the question of whether we even need to invoke those mechanisms for more than a small minority of cases. In my own experience struggling against bureaucracies that made life hellish for no reason, I’d say that about 2/3 of the time my quest for answers really did terminate at an identifiable “empty skull”: i.e., a single individual who could unilaterally solve the problem at no cost to anyone, but chose not to. It simply wasn’t the case, I don’t think, that I would’ve been equally obstinate in the bureaucrat’s place, or that any of my friends or colleagues would’ve been. I simply had to accept that I was now face-to-face with an alien sub-intelligence—i.e., with a mind that fetishized rules made up by not-very-thoughtful humans over demonstrable realities of the external world. Second, I think the quality of the book noticeably declines in the last third. Here Yudkowsky recounts conversations in which he tried to give people advice, but he redacts all the object-level details of the conversations—so the reader is left thinking that this advice would be good for some possible values of the missing details, and terrible for other possible values! So then it’s hard to take away much of value. In more detail, Yudkowsky writes: “If you want to use experiment to show that a certain theory or methodology fails, you need to give advocates of the theory/methodology a chance to say beforehand what they think they predict, so the prediction is on the record and neither side can move the goalposts.” I only partly agree with this statement (which might be my first substantive disagreement in the book…). Yes, the advocates should be given a chance to say what they think the theory predicts, but then their answer need not be taken as dispositive. For if the advocates are taken to have ultimate say over what their theory predicts, then they have almost unlimited room to twist themselves in pretzels to explain why, yes, we all know this particular experiment will probably yield such-and-such result, but contrary to appearances it won’t affect the theory at all. For science to work, theories need to have a certain autonomy from their creators and advocates—to be “rigid,” as David Deutsch puts it—so that anyone can see what they predict, and the advocates don’t need to be continually consulted about it. Of course this needs to be balanced, in practice, against the fact that the advocates probably understand how to use the theory better than anyone else, but it’s a real consideration as well. In one conversation, Yudkowsky presents himself as telling startup founders not to bother putting their prototype in front of users, until they have a testable hypothesis that can be confirmed or ruled out by the users’ reactions. I confess to more sympathy here with the startup founders than with Yudkowsky. It does seem like an excellent idea to get a product in front of users as early as possible, and to observe their reactions to it: crucially, not just a binary answer (do they like the product or not), confirming or refuting a prediction, but more importantly, reactions that you hadn’t even thought to ask about. (E.g., that the cool features of your website never even enter into the assessment of it, because people can’t figure out how to create an account, or some such.) More broadly, I’d stress the value of the exploratory phase in science—the phase where you just play around with your system and see what happens, without necessarily knowing yet what hypothesis you want to test. Indeed, this phase is often what leads to formulating a testable hypothesis. But let me step back from these quibbles, to address something more interesting: what can I, personally, take from Inadequate Equilibria? Is academic theoretical computer science broken/inadequate in the same way a lot of other institutions are? Well, it seems to me that we have some built-in advantages that keep us from being as broken as we might otherwise be. For one thing, we’re overflowing with well-defined problems, which anyone, including a total outsider, can get credit for solving. (Of course, the “outsider” might not retain that status for long.) For another, we have no Institutional Review Boards and don’t need any expensive equipment, so the cost to enter the field is close to zero. Still, we could clearly be doing better: why didn’t we invent Bitcoin? Why didn’t we invent quantum computing? (We did lay some of the intellectual foundations for both of them, but why did it take people outside TCS to go the distance?) Do we value mathematical pyrotechnics too highly compared to simple but revolutionary insights? It’s worth noting that a whole conference, Innovations in Theoretical Computer Science, was explicitly founded to try to address that problem—but while ITCS is a lovely conference that I’ve happily participated in, it doesn’t seem to have succeeded at changing community norms much. Instead, ITCS itself converged to look a lot like the rest of the field. Now for a still more pointed question: am I, personally, too conformist or status-conscious? I think even “conformist” choices I’ve made, like staying in academia, can be defended as the right ones for what I wanted to do with my life, just as Eliezer’s non-conformist choices (e.g., dropping out of high school) can be defended as the right ones for what he wanted to do with his. On the other hand, my acute awareness of social status, and when I lacked any—in contrast to what Eliezer calls his “status blindness,” something that I see as a tremendous gift—did indeed make my life unnecessarily miserable in all sorts of ways. Anyway, go read Inadequate Equilibria, then venture into the world and look for some$20 bills laying on the street.  And if you find any, come back and leave a comment on this post explaining where they are, so a conformist herd can follow you.

November 15th, 2017

Today, Shtetl-Optimized is extremely lucky to have the special guest blogger poly: the ‘adviser’ in the computational complexity class P/poly (P with polynomial-sized advice string), defined by Richard Karp and Richard Lipton in 1982.

As an adviser, poly is known for being infinitely wise and benevolent, but also for having a severe limitation: namely, she’s sensitive only to the length of her input, and not to any other information about it.  Her name comes from the fact that her advice is polynomial-size, which is the problem that prevents her from simply listing the answers to every possible question in a gigantic lookup table, the way she’d like to.

Without further ado, let’s see what advice poly is able to offer her respondents.

Dear poly,

When my husband and I first started dating, we were going at it like rabbits!  Lately, though, he seems to have no interest in sex.  That’s not normal for a guy, is it?  What can I do to spice things up in the bedroom?

Sincerely,
Frustrated Wife

Dear Frustrated Wife,

Unfortunately, I don’t know exactly what your question is.  All I was told is that the question was 221 characters long.  But here’s something that might help: whenever you’re stuck in a rut, sometimes you can “shake things up” with the use of randomness.  So, please accept, free of charge, the following string of 221 random bits:

111010100100010010101111110010111101011010001
000111100101000111111011101110100110000110100
0010010010000010110101100100100111000010110
111001011001111111101110100010000010100111000
0111101111001101001111101000001010110101101

Well, it’s not really “random,” since everyone else with a 221-character question would’ve gotten the exact same string.  But it’s random enough for many practical purposes.  I hope it helps you somehow … good luck!

Sincerely,
poly

Dear poly,

I’m a 29-year-old autistic male: a former software entrepreneur currently worth about \$400 million, who now spends his time donating to malaria prevention and women’s rights in the developing world.  My issue is that I’ve never been on a date, or even kissed anyone.  I’m terrified to make an advance.  All I read in the news is an endless litany of male sexual misbehavior: Harvey Weinstein, Louis C. K., Leon Wieseltier, George H. W. Bush, Roy Moore, the current president (!), you name it.  And I’m consumed by the urge not to be a pig like those guys.  Like, obviously I’m no more likely to start stripping or masturbating or something in front of some woman I just met, than I am to morph into a koala bear.  But from reading Slate, Salon, Twitter, my Facebook news feed, and so forth, I’ve gotten the clear sense that there’s nothing I could do that modern social mores would deem appropriate and non-creepy—at least, not a guy like me, who wasn’t lucky enough to be born instinctively understanding these matters.  I’m grateful to society for enabling my success, and have no desire to break any of its written or unwritten rules.  But here I genuinely don’t know what society wants me to do.  I’m writing to you because I remember you from my undergrad CS classes—and you’re the only adviser I ever encountered whose advice could be trusted unconditionally.

Yours truly,
Sensitive Nerd

Dear Sensitive Nerd,

I see your that letter is 1369 characters long.  Based on that, here are a few things I can tell you that might be helpful:

• The Riemann Hypothesis is true.
• ZFC set theory is consistent.
• The polynomial hierarchy is contained in PP.

Write me a 3592-character letter the next time, and I’ll give you an even longer list of true mathematical statements!  (I actually know how to solve the halting problem—no joke!—but am condemned to drip, drip, drip out the solutions, a few per input length.)

But I confess: no sooner did I list these truths than I reflected that they, or even a longer list, might not help much with your problem, whatever it might have been.  It’s even possible to have a problem for which no amount of truth helps in solving it.  So, I dunno: maybe try not worrying so much, and write back to let me know if that helped?  (Not that I expect to understand your reply, or would be able to change any of my advice at this point even if I did.)

Good luck!
–poly

Dear poly,

c34;c’y9v3x

Sincerely,
Unhappy in Unary

Dear Unhappy in Unary,

Finally, someone who writes to me in a language I can understand!  Your question is 11 characters long.  I understand that to be a code expressing that you’re bankrupt, and are filing for Chapter 11 bankruptcy protection.  Financial insolvency isn’t easy for anyone.  But here’s some advice: put everything you have into Bitcoin, and sell out a year from now.  Unfortunately, I don’t know exactly when you’re writing to me, but at least at the time my responses were hardwired in, this was some damn good advice.

You’re welcome,
poly

poly’s polynomial-sized advice column is syndicated in newspapers nationwide, and can also be accessed by simply moving your tape head across your advice tape. You’re welcome to comment on this post, but I might respond only to the lengths of the comments, rather than anything else about them. –SA

November 3rd, 2017

(1) My TEDx talk from Dresden, entitled “What Quantum Computing Isn’t,” is finally up on YouTube.  For regular Shtetl-Optimized readers, there’s unlikely to be much that’s new here: it’s basically 15 minutes of my usual spiel, packaged for mass consumption.  But while it went over well with the live audience, right now the only comment on the video is—I quote—“uuuuuuuuuuuuuuu,” from user “imbatman8472.”  So if you feel so inclined, go over there, watch it, and try to start a more contentful discussion!  Thanks so much to Andrés Goens, and everyone else in Dresden, for inviting me there and hosting a great visit.

(2) On December 4-6, there’s going to be a new conference in Mountain View, called Q2B (Quantum Computing for Business).  There, if it interests you, you can hear about the embryonic QC industry, from some of the major players at Google, IBM, Microsoft, academia, and government, as well as some of the QC startups (like IonQ) that have blossomed over the last few years.  Oh yes, and D-Wave.  The keynote speaker will be John Preskill; Google’s John Martinis and IBM’s Jerry Chow will also be giving talks.  I regret that another commitment will prevent me from attending myself, but I hope to attend next year’s iteration.  (Full disclosure: I’m a scientific adviser to QC Ware, the firm that’s organizing the conference.)

(3) On October 24, the House Science Committee heard three hours of testimony—you can watch it all here—about the need for quantum information research and the danger of the US falling behind China.  In what I believe is my first entry in the Congressional record, I’m quoted (for something totally incidental) at 1:09.  John Preskill was mostly just delighted that the witness, Jim Kurose, referred to me as a “physicist.”

(4) For several years, people have been asking me whether Bitcoin is resistant against quantum attack.  Now there’s finally an expert analysis, by Aggarwal et al., that looks into exactly that question.  Two-sentence summary: the proof-of-work is probably fine, although Grover’s algorithm can of course be used against it, which might eventually necessitate adjusting the difficulty parameter to account for that, and/or migrating from a pure preimage search task to collision-finding, where my result with Yaoyun Shi showed that quantum computers offer “only” an n2/3 black-box speedup over classical computers, rather than a square-root speedup.  The scheme for signing the transactions, which is currently based on elliptic curve cryptography, is the real danger point, but again one could address that by migrating to a post-quantum signature scheme.  My main comment about the matter is that, if I’d invested in Bitcoin when I first learned about it, I’d be rich now.

(5) In the first significant victory for my plan to spend a whole sabbatical year just writing up unwritten papers, I’ve got a new paper out today: Shadow Tomography of Quantum States.  Comments extremely welcome!

Grad students and postdocs and faculty sought

October 28th, 2017

I’m eagerly seeking PhD students and postdocs to join our Quantum Information Center at UT Austin, starting in Fall 2018.  We’re open to any theoretical aspects of quantum information, although if you wanted to work with me personally, then areas close to computer science would be the closest fit.  I’m also able to supervise PhD students in physics, but am not directly involved with admissions to the physics department: this is a discussion we would have after you were already admitted to UT.

I, along with my theoretical computer science colleagues at UT Austin, am also open to outstanding students and postdocs in classical complexity theory. My wife, Dana Moshkovitz, tells me that she and David Zuckerman in particular are looking for a postdoc in the areas of pseudorandomness and derandomization (and for PhD students as well).

If you want to apply to the UTCS PhD program, please visit here.  The deadline is December 15.  If you specify that you want to work on quantum computing and information, and/or with me, then I’ll be sure to see your application.  Emailing faculty at this stage doesn’t help; we won’t “estimate your chances” or even look at your qualifications until we can see all the applications together.

If you want to apply for a postdoc with me, here’s what to do:

• Email me introducing yourself (if I don’t already know you), and include your CV, your thesis (if you already have one), and up to 3 representative papers.  Do this even if you already emailed me before.
• Arrange for two recommendation letters to be emailed to me.

Let’s set a deadline for postdoc applications of, I dunno, December 15?

In addition to the above, I’m happy to announce that the UT CS department is looking to hire a new faculty member in quantum computing and information—most likely a junior person.  The UT physics department is also looking to hire quantum information faculty members, with a focus on a senior-level experimentalist right now.  If you’re interested in these opportunities, just email me; I can put you in touch with the relevant people.

All in all, this is shaping up to be the most exciting era for quantum computing and information in Austin since a group of UT students, postdocs, and faculty including David Deutsch, John Wheeler, Wojciech Zurek, Bill Wootters, and Ben Schumacher laid much of the intellectual foundation of the field in the late 1970s and early 1980s.  We hope you’ll join us.  Hook ’em Hadamards!

Unrelated Announcements: Avi Wigderson has released a remarkable 368-page book, Mathematics and Computation, for free on the web.  This document surveys pretty much the entire current scope of theoretical computer science, in a way only Avi, our field’s consummate generalist, could do.  It also sets out Avi’s vision for the future and his sociological thoughts about TCS and its interactions with neighboring fields.  I was a reviewer on the manuscript, and I recommend it to anyone looking for a panoramic view of TCS.

In other news, my UT friend and colleague Adam Klivans, and his student Surbhi Goel, have put out a preprint entitled Learning Depth-Three Neural Networks in Polynomial Time.  (Beware: what the machine learning community calls “depth three,” is what the TCS community would call “depth two.”)  This paper learns real-valued neural networks in the so-called p-concept model of Kearns and Schapire, and thereby evades a 2006 impossibility theorem of Klivans and Sherstov, which showed that efficiently learning depth-2 threshold circuits would require breaking cryptographic assumptions.  More broadly, there’s been a surge of work in the past couple years on explaining the success of deep learning methods (methods whose most recent high-profile victory was, of course, AlphaGo Zero).  I’m really hoping to learn more about this direction during my sabbatical this year—though I’ll try and take care not to become another deep learning zombie, chanting “artificial BRAINSSSS…” with outstretched arms.

2^n is exponential, but 2^50 is finite

October 22nd, 2017

Unrelated Update (Oct. 23) I still feel bad that there was no time for public questions at my “Theoretically Speaking” talk in Berkeley, and also that the lecture hall was too small to accomodate a large fraction of the people who showed up. So, if you’re someone who came there wanting to ask me something, go ahead and ask in the comments of this post.

During my whirlwind tour of the Bay Area, questions started pouring in about a preprint from a group mostly at IBM Yorktown Heights, entitled Breaking the 49-Qubit Barrier in the Simulation of Quantum Circuits.  In particular, does this paper make a mockery of everything the upcoming quantum supremacy experiments will try to achieve, and all the theorems about them that we’ve proved?

Following my usual practice, let me paste the abstract here, so that we have the authors’ words in front of us, rather than what a friend of a friend said a popular article reported might have been in the paper.

With the current rate of progress in quantum computing technologies, 50-qubit systems will soon become a reality.  To assess, refine and advance the design and control of these devices, one needs a means to test and evaluate their fidelity. This in turn requires the capability of computing ideal quantum state amplitudes for devices of such sizes and larger.  In this study, we present a new approach for this task that significantly extends the boundaries of what can be classically computed.  We demonstrate our method by presenting results obtained from a calculation of the complete set of output amplitudes of a universal random circuit with depth 27 in a 2D lattice of 7 × 7 qubits.  We further present results obtained by calculating an arbitrarily selected slice of 237 amplitudes of a universal random circuit with depth 23 in a 2D lattice of 8×7 qubits.  Such calculations were previously thought to be impossible due to impracticable memory requirements. Using the methods presented in this paper, the above simulations required 4.5 and 3.0 TB of memory, respectively, to store calculations, which is well within the limits of existing classical computers.

This is an excellent paper, which sets a new record for the classical simulation of generic quantum circuits; I congratulate the authors for it.  Now, though, I want you to take a deep breath and repeat after me:

This paper does not undercut the rationale for quantum supremacy experiments.  The truth, ironically, is almost the opposite: it being possible to simulate 49-qubit circuits using a classical computer is a precondition for Google’s planned quantum supremacy experiment, because it’s the only way we know to check such an experiment’s results!  The goal, with sampling-based quantum supremacy, was always to target the “sweet spot,” which we estimated at around 50 qubits, where classical simulation is still possible, but it’s clearly orders of magnitude more expensive than doing the experiment itself.  If you like, the goal is to get as far as you can up the mountain of exponentiality, conditioned on people still being able to see you from the base.  Why?  Because you can.  Because it’s there.  Because it challenges those who think quantum computing will never scale: explain this, punks!  But there’s no point unless you can verify the result.

Related to that, the paper does not refute any prediction I made, by doing anything I claimed was impossible.  On the contrary (if you must know), the paper confirms something that I predicted would be possible.  People said: “40 qubits is the practical limit of what you can simulate, so there’s no point in Google or anyone else doing a supremacy experiment with 49 qubits, since they can never verify the results.”  I would shrug and say something like: “eh, if you can do 40 qubits, then I’m sure you can do 50.  It’s only a thousand times harder!”

So, how does the paper get up to 50 qubits?  A lot of computing power and a lot of clever tricks, one of which (the irony thickens…) came from a paper that I recently coauthored with Lijie Chen: Complexity-Theoretic Foundations of Quantum Supremacy Experiments.  Lijie and I were interested in the question: what’s the best way to simulate a quantum circuit with n qubits and m gates?  We noticed that there’s a time/space tradeoff here: you could just store the entire amplitude vector in memory and update, which would take exp(n) memory but also “only” about exp(n) time.  Or you could compute the amplitudes you cared about via Feynman sums (as in the proof of BQP⊆PSPACE), which takes only linear memory, but exp(m) time.  If you imagine, let’s say, n=50 and m=1000, then exp(n) might be practical if you’re IBM or Google, but exp(m) is certainly not.

So then we raised the question: could one get the best of both worlds?  That is, could one simulate such a quantum circuit using both linear memory and exp(n) time?  And we showed that this is almost possible: we gave an algorithm that uses linear memory and dO(n) time, where d is the circuit depth.  Furthermore, the more memory it has available, the faster our algorithm will run—until, in the limit of exponential memory, it just becomes the “store the whole amplitude vector” algorithm mentioned above.  I’m not sure why this algorithm wasn’t discovered earlier, especially since it basically just amounts to Savitch’s Theorem from complexity theory.  In any case, though, the IBM group used this idea among others to take full advantage of the RAM it had available.

Let me make one final remark: this little episode perfectly illustrates why theoretical computer scientists like to talk about polynomial vs. exponential rather than specific numbers.  If you keep your eyes on the asymptotic fundamentals, rather than every factor of 10 or 1000, then you’re not constantly shocked by events, like a dog turning its head for every passing squirrel.  Before you could simulate 40 qubits, now you can simulate 50.  Maybe with more cleverness you could get to 60 or even 70.  But … dude.  The problem is still exponential time.

We saw the same “SQUIRREL!  SQUIRREL!” reaction with the people who claimed that the wonderful paper by Clifford and Clifford had undercut the rationale for BosonSampling experiments, by showing how to solve the problem in “merely” ~2n time rather than ~mn, where n is the number of photons and m is the number of modes.  Of course, Arkhipov and I had never claimed more than ~2n hardness for the problem, and Clifford and Clifford’s important result had justified our conservatism on that point, but, y’know … SQUIRREL!

More broadly, it seems to me that this dynamic constantly occurs in the applied cryptography world.  OMIGOD a 128-bit hash function has been broken!  Big news!  OMIGOD a new, harder hash function has been designed!  Bigger news!  OMIGOD OMIGOD OMIGOD the new one was broken too!!  All of it fully predictable once you realize that we’re on the shores of an exponentially hard problem, and for some reason, refusing to go far enough out into the sea (i.e., pick large enough security parameters) that none of this back-and-forth would happen.

I apologize, sincerely, if I come off as too testy in this post.  No doubt it’s entirely the fault of a cognitive defect on my end, wherein ten separate people asking me about something get treated by my brain like a single person who still doesn’t get it even after I’ve explained it ten times.

The problem with Uber

October 19th, 2017

I just spent a wonderful and exhausting five days in the Bay Area: meeting friends, holding the first-ever combined SlateStarCodex/Shtetl-Optimized meetup, touring quantum computing startups, meeting with Silicon Valley folks about quantum computing, and giving a public lecture for the Simons Institute in Berkeley.  I’ll probably say more about some of these events in future posts, but for now: thanks so much to everyone who helped them happen!

Alas, my experiences getting around the Bay this week convinced me that there’s a real problem with Uber.  And no, I’m not talking about their corporate culture, or the personality of ousted CEO Travis Kalanick, or the hardball lobbying of municipalities to allow ride-sharing, or the taxi companies needing to adapt to survive, or even Uber having an unsustainable business model (they could charge more and I’d still use it…).

The problem is: when you order an Uber, like 2/3 of the time you and the driver can’t find each other without a lot of back and forth.

Firstly, because you can’t specify where you are with enough accuracy.  When you try, the app does this thing where it literally moves the “you are here” pointer to a place where you’re not. And then, even if the little dot correctly indicates your location, for some reason the driver will think you’re somewhere totally different.

Secondly, because Uber cars are typically unmarked.  Yes, the app tells you that it’s a white Ford or whatever—but there’s a lot of white cars, and it’s hard (at least for me) to distinguish models at a distance, so you can then face a stressful “Where’s Waldo?” problem involving hundreds of cars.

Thirdly, because the drivers understandably have their phones mounted on their dashboards—the result being that, when you call to try to figure out where they are, nothing they say can be distinguished from “mmph hrmph mmph.”  And of course they can’t text while driving.

To be clear, these gripes arise only because ride-sharing apps generally work so damn well, and are such an advance over what preceded them, that they’ve changed our expectations about the convenience of getting from place to place.  Because of Uber and Lyft and so on, it’s tempting to plan your life around the assumption that you can be anywhere in a greater metro area, and within 3 minutes a car will magically arrive to take you to wherever else in that area you need to be—while your brain remains uncluttered with transportation logistics, among the most excruciating of all topics.  This is a problem borne of success.

But—good news, everyone!—I have an idea to solve the problem, which I hereby offer free of charge to any ride-sharing service that wants to adopt it.  Namely, when you order a ride, why doesn’t the app—with your explicit permission, of course—use your phone’s camera to send a selfie of you, together with the location where you’re waiting, to the driver?  Is there some obvious reason I’m missing why this wouldn’t work?  Have any ride-sharing companies tried it?  (I only learned today that I can update my Uber profile to include my photo.  Hopefully that will help drivers find me—but a photo of the intersection, or the side of the building where I am, etc. could help even more.)

Not the critic who counts

October 11th, 2017

There’s a website called Stop Timothy Gowers! !!! —yes, that’s the precise name, including the exclamation points.  The site is run by a mathematician who for years went under the pseudonym “owl / sowa,” but who’s since outed himself as Nikolai Ivanov.

For those who don’t know, Sir Timothy Gowers is a Fields Medalist, known for seminal contributions including the construction of Banach spaces with strange properties, the introduction of the Gowers norm, explicit bounds for the regularity lemma, and more—but who’s known at least as well for explaining math, in his blog, books, essays, MathOverflow, and elsewhere, in a remarkably clear, friendly, and accessible way.  He’s also been a leader in the fight to free academia from predatory publishers.

So why on earth would a person like that need to be stopped?  According to sowa, because Gowers, along with other disreputable characters like Terry Tao and Endre Szemerédi and the late Paul Erdös, represents a dangerous style of doing mathematics: a style that’s just as enamored of concrete problems as it is of abstract theory-building, and that doesn’t even mind connections to other fields like theoretical computer science.  If that style becomes popular with young people, it will prevent faculty positions and prestigious prizes from going to the only deserving kind of mathematics: the kind exemplified by Bourbaki and by Alexander Grothendieck, which builds up theoretical frameworks with principled disdain for the solving of simple-to-state problems.  Mathematical prizes going to the wrong people—or even going to the right people but presented by the wrong people—are constant preoccupations of sowa’s.  Read his blog and let me know if I’ve unfairly characterized it.

Now for something totally unrelated.  I recently discovered a forum on Reddit called SneerClub, which, as its name suggests, is devoted to sneering.  At whom?  Basically, at anyone who writes anything nice about nerds or Silicon Valley, or who’s associated with the “rationalist community,” or the Effective Altruist movement, or futurism or AI risk.  Typical targets include Scott Alexander, Eliezer Yudkowsky, Robin Hanson, Michael Vassar, Julia Galef, Paul Graham, Ray Kurzweil, Elon Musk … and with a list like that, I guess I should be honored to be a regular target too.

The basic SneerClub M.O. is to seize on a sentence that, when ripped from context and reflected through enough hermeneutic funhouse mirrors, can make nerds out to look like right-wing villains, oppressing the downtrodden with rays of disgusting white maleness (even, it seems, ones who aren’t actually white or male).  So even if the nerd under discussion turns out to be, say, a leftist or a major donor to anti-Trump causes or malaria prevention or whatever, readers can feel reassured that their preexisting contempt was morally justified after all.

Thus: Eliezer Yudkowsky once wrote a piece of fiction in which a character, breaking the fourth wall, comments that another character seems to have no reason to be in the story.  This shows that Eliezer is a fascist who sees people unlike himself as having no reason to exist, and who’d probably exterminate them if he could.  Or: many rationalist nerds spend a lot of effort arguing against Trumpists, alt-righters, and neoreactionaries.  The fact that they interact with those people, in order to rebut them, shows that they’re probably closet neoreactionaries themselves.

When I browse sites like “Stop Timothy Gowers! !!!” or SneerClub, I tend to get depressed about the world—and yet I keep browsing, out of a fascination that I don’t fully understand.  I ask myself: how can a person read Gowers’s blog, or Slate Star Codex, without seeing what I see, which is basically luminous beacons of intellectual honesty and curiosity and clear thought and sparkling prose and charity to dissenting views, shining out far across the darkness of online discourse?

(Incidentally, Gowers lists “Stop Timothy Gowers! !!!” in his blogroll, and I likewise learned of SneerClub only because Scott Alexander linked to it.)

I’m well aware that this very question will only prompt more sneers.  From the sneerers’ perspective, they and their friends are the beacons, while Gowers or Scott Alexander are the darkness.  How could a neutral observer possibly decide who was right?

But then I reflect that there’s at least one glaring asymmetry between the sides.

If you read Timothy Gowers’s blog, one thing you’ll constantly notice is mathematics.  When he’s not weighing in on current events—for example, writing against Brexit, Elsevier, or the destruction of a math department by cost-cutting bureaucrats—Gowers is usually found delighting in exploring a new problem, or finding a new way to explain a known result.  Often, as with his dialogue with John Baez and others about the recent “p=t” breakthrough, Gowers is struggling to understand an unfamiliar piece of mathematics—and, completely unafraid of looking like an undergrad rather than a Fields Medalist, he simply shares each step of his journey, mistakes and all, inviting you to follow for as long as you can keep up.  Personally, I find it electrifying: why can’t all mathematicians write like that?

By contrast, when you read sowa’s blog, for all the anger about the sullying of mathematics by unworthy practitioners, there’s a striking absence of mathematical exposition.  Not once does sowa ever say: “OK, forget about the controversy.  Since you’re here, instead of just telling you about the epochal greatness of Grothendieck, let me walk you through an example.  Let me share a beautiful little insight that came out of his approach, in so self-contained a way that even a physicist or computer scientist will understand it.”  In other words, sowa never uses his blog to do what Gowers does every day.  Sowa might respond that that’s what papers are for—but the thing about a blog is that it gives you the chance to reach a much wider readership than your papers do.  If someone is already blogging anyway, why wouldn’t they seize that chance to share something they love?

Similar comments apply to Slate Star Codex versus r/SneerClub.  When I read an SSC post, even if I vehemently disagree with the central thesis (which, yes, happens sometimes), I always leave the diner intellectually sated.  For the rest of the day, my brain is bloated with new historical tidbits, or a deep-dive into the effects of a psychiatric drug I’d never heard of, or a jaw-dropping firsthand account of life as a medical resident, or a different way to think about a philosophical problem—or, if nothing else, some wicked puns and turns of phrase.

But when I visit r/SneerClub—well, I get exactly what’s advertised on the tin.  Once you’ve read a few, the sneers become pretty predictable.  I thought that for sure, I’d occasionally find something like: “look, we all agree that Eliezer Yudkowsky and Elon Musk and Nick Bostrom are talking out their asses about AI, and are coddled white male emotional toddlers to boot.  But even granting that, what do we think about AI?  Are intelligences vastly smarter than humans possible?  If not, then what principle rules them out?  What, if anything, can be said about what a superintelligent being would do, or want?  Just for fun, let’s explore this a little: I mean the actual questions themselves, not the psychological reasons why others explore them.”

That never happens.  Why not?

There’s another fascinating Reddit forum called “RoastMe”, where people submit a photo of themselves holding a sign expressing their desire to be “roasted”—and then hundreds of Redditors duly oblige, savagely mocking the person’s appearance and anything else they can learn about the person from their profile.  Many of the roasts are so merciless that one winces vicariously for the poor schmucks who signed up for this, hopes that they won’t be driven to self-harm or suicide.  But browse enough roasts, and a realization starts to sink in: there’s no person, however beautiful or interesting they might’ve seemed a priori, for whom this roasting can’t be accomplished.  And that very generality makes the roasting lose much of its power—which maybe, optimistically, was the point of the whole exercise?

In the same way, spend a few days browsing SneerClub, and the truth hits you: once you’ve made their enemies list, there’s nothing you could possibly say or do that they wouldn’t sneer at.  Like, say it’s a nice day outside, and someone will reply:

“holy crap how much of an entitled nerdbro do you have to be, to erase all the marginalized people for whom the day is anything but ‘nice’—or who might be unable to go outside at all, because of limited mobility or other factors never even considered in these little rich white boys’ geek utopia?”

For me, this realization is liberating.  If appeasement of those who hate you is doomed to fail, why bother even embarking on it?

I’ve spent a lot of time on this blog criticizing D-Wave, and cringeworthy popular articles about quantum computing, and touted arXiv preprints that say wrong things.  But I hope regular readers feel like I’ve also tried to offer something positive: y’know, actual progress in quantum computing that actually excites me, or a talk about big numbers, or an explanation of the Bekenstein bound, whatever.  My experience with sites like “Stop Timothy Gowers! !!!” and SneerClub makes me feel like I ought to be doing less criticizing and more positive stuff.

Why, because I fear turning into a sneerer myself?  No, it’s subtler than that: because reading the sneerers drives home for me that it’s a fool’s quest to try to become what Scott Alexander once called an “apex predator of the signalling world.”

At the risk of stating the obvious: if you write, for example, that Richard Feynman was a self-aggrandizing chauvinist showboater, then even if your remarks have a nonzero inner product with the truth, you don’t thereby “transcend” Feynman and stand above him, in the same way that set theory transcends and stands above arithmetic by constructing a model for it.  Feynman’s achievements don’t thereby become your achievements.

When I was in college, I devoured Ray Monk’s two-volume biography of Bertrand Russell.  This is a superb work of scholarship, which I warmly recommend to everyone.  But there’s one problem with it: Monk is constantly harping on his subject’s failures, and he has no sense of humor, and Russell does.  The result is that, whenever Monk quotes Russell’s personal letters at length to prove what a jerk Russell was, the quoted passages just leap off the page—as if old Bertie has come back from the dead to share a laugh with you, the reader, while his biographer looks on sternly and says, “you two think this is funny?”

For a writer, I can think of no higher aspiration than that: to write like Bertrand Russell or like Scott Alexander—in such a way that, even when people quote you to stand above you, your words break free of the imprisoning quotation marks, wiggle past the critics, and enter the minds of readers of your generation and of generations not yet born.

Update (Nov. 13): Since apparently some people didn’t know (?!), the title of this post comes from the famous Teddy Roosevelt quote:

It is not the critic who counts; not the man who points out how the strong man stumbles, or where the doer of deeds could have done them better. The credit belongs to the man who is actually in the arena, whose face is marred by dust and sweat and blood; who strives valiantly; who errs, who comes short again and again, because there is no effort without error and shortcoming; but who does actually strive to do the deeds; who knows great enthusiasms, the great devotions; who spends himself in a worthy cause; who at the best knows in the end the triumph of high achievement, and who at the worst, if he fails, at least fails while daring greatly, so that his place shall never be with those cold and timid souls who neither know victory nor defeat.

Coming to Nerd Central

October 8th, 2017

While I’m generally on sabbatical in Tel Aviv this year, I’ll be in the Bay Area from Saturday Oct. 14 through Wednesday Oct. 18, where I look forward to seeing many friends new and old.  On Wednesday evening, I’ll be giving a public talk in Berkeley, through the Simons Institute’s “Theoretically Speaking” series, entitled Black Holes, Firewalls, and the Limits of Quantum Computers.  I hope to see at least a few of you there!  (I do have readers in the Bay Area, don’t I?)

But there’s more: on Saturday Oct. 14, I’m thinking of having a first-ever Shtetl-Optimized meetup, somewhere near the Berkeley campus.  Which will also be a Slate Star Codex meetup, because Scott Alexander will be there too.  We haven’t figured out many details yet, except that it will definitively involve getting fruit smoothies from one of the places I remember as a grad student.  Possible discussion topics include what the math, CS, and physics research communities could be doing better; how to advance Enlightenment values in an age of recrudescent totalitarianism; and (if we’re feeling really ambitious) the interpretation of quantum mechanics.  If you’re interested, shoot me an email, let me know if there are times that don’t work; then other Scott and I will figure out a plan and make an announcement.

On an unrelated note, some people might enjoy my answer to a MathOverflow question about why one should’ve expected number theory to be so rife with ridiculously easy-to-state yet hard-to-prove conjectures, like Fermat’s Last Theorem and the Goldbach Conjecture.  As I’ve discussed on this blog before, I’ve been deeply impressed with MathOverflow since the beginning, but never more so than today, when a decision to close the question as “off-topic” was rightfully overruled.  If there’s any idea that unites all theoretical computer scientists, I’d say it’s the idea that what makes a given kind of mathematics “easy” or “hard” is, itself, a proper subject for mathematical inquiry.