This week I'm at CVPR — the IEEE's Computer Vision and Pattern Recognition Conference, which is a huge AI event. I'm currently rehearsing the timing of my talk one last time, but I wanted to take a minute between run-throughs to link to my co-author Steven Forsyth's wonderful post on the NVIDIA research blog about our paper.
Steven does a fantastic job of describing our work, so head over there to see what he has to say. I couldn't resist putting a post of my own because (a) I love this video we created...
...and (b), Steven left out what I think was the most convincing result we had, which shows that BaRT achieves a Top-1 accuracy on ImageNet that is higher than the Top-5 accuracy of the previous state-of-the-art defense, Adversarial Training.
Accuracy of BaRT under attack by PGD for varying adversarial distances, compared to the previous state-of-the-art.
Also, (c) I am very proud of this work. It's been an idea I've been batting around for almost three years now, and I finally got approval from my client to pursue it last year. It turns out it works exactly how I expected, and I can honestly say that this is the first — and probably only — time in my scientific career that has ever happened.
If you want a copy of the paper, complete with some code in the appendices, ((Our hands are somewhat tied releasing the full code due to the nature of our client relationship with the wonderful Laboratory for Physical Sciences, who funded this work.)) our poster, and the slides for our oral presentation you can find it on the BaRT page I slapped together on my website.
All programming blogs need at least one post unofficially titled “Indisputable Proof That I Am Awesome.” These are usually my favorite kind of read, as the protagonist starts out with a head full of hubris, becomes mired in self-doubt, struggles on when others would have quit, and then ultimately triumphs over evil (that is to say, slow or buggy computer code), often at the expense of personal hygiene and/or sanity.
I'm a fan of the debugging narrative, and this is a fine example of the genre. I've been wrestling with code for mapping projections recently, so I feel Miller's pain specifically. In my opinion the Winkel Tripel is mathematically gross, but aesthetically unsurpassed. Hopefully I'll find some time in the next week or so to put up a post about my mapping project.
A screenshot of a project I've been working on to map geotagged tweets.
File under "all college degrees are not created equal" or perhaps "no, junior, you may not borrow enough to buy a decent house in order to get a BA in psych."
Social cohesion can be thought of as a manifestation of how "iterated" people feel their interactions are, how likely they are to interact with the same people again and again and have to deal with long term consequences of locally optimal choices, or whether they feel they can "opt out" of consequences of interacting with some set of people in a poor way.
Munger links to some very good analysis but it occurs to me that what is really needed is the variance of grades over time and not just the mean. (Obviously these two things are related since the distribution is bounded by [0, 4]. A mean which has gone from 2.25 to 3.44 will almost certainly result in less variance here.)
I don't much care where the distribution is centered. I care how wide the distribution is — that's what lets observers distinguish one student from another. Rankings need inequality. Without it they convey no information.
I share Graur's and Tabarrok's wariness over "high impact false positives" in science. This is a big problem with no clear solutions.
The Graur et al. paper that Tabarrok discusses is entertaining in its incivility. Sometimes civility is not the correct response to falsehoods. It's refreshing to see scientists being so brutally honest with their opinions. Some might say they are too brutal, but at least they've got the honest part.
McCaffrey is completely right. But good luck to him reasoning people out of an opinion they were never reasoned into in the first place.
I do like the neologism "sustainable pricing" that he introduces. Bravo for that.
I would add a sixth reason to his list: accusations of "price gouging" are one rhetorical prong in an inescapable triple bind. A seller has three MECE choices: price goods higher than is common, the same as is common, or lower than is common. These choices will result in accusations of price gouging, collusion, and anti-competitive pricing, respectively. Since there is no way to win when dealing with people who level accusations of gouging, the only sensible thing to do is ignore them.
Executive summary: apiarists have agency, and the world isn't static. If the death rate of colonies increases, they respond by creating more colonies. Crisis averted.
"Betting Therapy" should be a thing. You go to a betting therapist and describe your fears — everything you're afraid will happen if you do X — and then the therapist offers to bet money on whether it actually happens to you or not. After you lose enough money, you stop being afraid.
As a CS guy who's tip-toed into psychology here and there I would offer Morrison & Murphy-Hill this advice: tread very, very lightly when making claims regarding the words "knowledge" and especially "intelligence."
I'm glad I didn't know about this in the winter of 2003, when I engaged in intense bouts of Tetris as a weird form of post-modern zazen. I still remember the guy who used to sit in front of me in Linear Algebra wore a tattersall shirt every single class, and I would see tetrominos cascading down his back.
This is why I want legislators & regulators who have played some strategy games. I want people making rules who have the habit of thinking, "If I do this, what is the other guy going to do? Surely he won't simply keep doing the things he was doing before I changed the environment. And surely not the exact the thing that I hope he does. What if he responds by...?"
We started with a weird pseudo-equation, manipulated it as if it were meaningful, transformed it into a series of statements that were either meaningless or clearly false, and out popped something that happened to be true. What Blass essentially proved (and Fiore and Leinster generalized) is, in effect, is that this is no coincidence. More specifically, they’ve proved in a very broad context that if you manipulate this kind of equation, pretending that sets are numbers and not letting yourself get ruffled by the illegitimacy of everything you’re doing, the end result is sure to be either a) obviously false or b) true.
My (very briefly stated) problem with p-values is that they combine size-of-effect and effort-in-experiment into one scalar. (This has been in the news a lot lately with the Oregon Medicaid study. Was the effect of Medicaid on blood pressure, glucose levels, etc. insignificant because Medicaid doesn't help much or because the sample size was too small? Unsurprising peoples' answers to this question are perfectly correlated with all of their prior political beliefs.)
One of the pitfalls of computational modeling is that it allows researchers to just keeping churning out simulation runs until their results are "significant." Processor cycles get shoveled into the model's maw until you have enough results to make even a tiny observed effect fit in under that magical p=0.05 limit. In theory everyone knows this isn't kosher, but "in theory" only takes us so far.
Eatock specifically means the use and abuse of the letters "PhD" as a postnominal, and the appellation "Doctor," not uses/abuses of doctoral programs eo ipso.
I am irked by people claiming that a non-medical doctorate is somehow "not real" though. "Doctor," like most words, has several meanings. What kind of semiotic/linguistic authority are they to declare which one is "real" and which isn't? Thanks, but they can leave their self-serving grammatical prescriptivism out of this.
Suppose that... it eventually becomes clear that quantum annealing can be made to work on thousands of qubits, but that it’s a dead end as far as getting a quantum speedup is concerned. Suppose the evidence piles up that simulated annealing on a conventional computer will continue to beat quantum annealing, if even the slightest effort is put into optimizing the classical annealing code. If that happens, then I predict that the very same people now hyping D-Wave will turn around and—without the slightest acknowledgment of error on their part—declare that the entire field of quantum computing has now been unmasked as a mirage, a scam, and a chimera. The same pointy-haired bosses who now flock toward quantum computing, will flock away from it just as quickly and as uncomprehendingly. Academic QC programs will be decimated, despite the slow but genuine progress that they’d been making the entire time in a “parallel universe” from D-Wave.
I think Aaronson is right to worry about that possibility. That's essentially what caused the "AI Winter." I'd hate to see that happen to QC.
You know you are a graduate student, goes one quip, when your office is better decorated than your home and you have a favourite flavour of instant noodle.
Chad Hagen's "Nonsensical Infographic No. 1"True. And true.
Although the first has more to do with my wife and I having diverging opinons about contemporary art. I think Jared Tarbell prints and John Maeda quotes are great things to put on the wall. My wife... feels otherwise.
As to the second, my preference from among the widely-distributed brands is Maruchan Roast Chicken, but most varieties are good with a little extra curry powder, some sriracha, a bit of cilantro or spring onion, and a squeeze of lime.
(Side note: If you want to branch out on your ramen choices, check out Ramenbox.)
Even graduates who find work outside universities may not fare all that well. PhD courses are so specialised that university careers offices struggle to assist graduates looking for jobs, and supervisors tend to have little interest in students who are leaving academia.
That part is true sans caveats. My advisor is supportive of me leaving academia, but neither he now anyone else knows how to help me look for non-academic jobs. There's plenty of support if I wanted to stay in academia, and a fair amount if I wanted to be at a place like Sandia or MSR. But for the types of positions I want, I'm on my own.
I had always thought Mars had a background in architecture. Turns out he actually went to grad school to study genetics. A lot of what he said about studying and learning and why he went to grad school really resonated with me, which is why I'm writing this post. (Besides wanting to evangelize 99% Invisible, which I couldn't recommend more.)
The only complaint I have with the interview came when Mars said that if you simply read a list of his podcast topics without listening to the show you'd think they were the most boring things in the world.
I couldn't disagree more. The topics he chooses are exactly the sort of thing that lead people like me to descend into hour-long Wikipedia spelunking expeditions. (Except his investigations of them have way higher production value and are told much more artfully than the Wikipedia writing-by-committee process produces.) Don't you want to learn about how Gallaudet University designed buildings suitable for the deaf? Or how audio engineers sound-design the Olympics? Yes. Yes you do.
