Yeah, after watching that, I can’t see how anyone reasonable could dislike it. That was awesome.
gwern
Karma: 43,372
When I noticed a reply from ‘gwern’, I admit was mildly concerned that there would be a link to a working webpage and a paypal link
Oh, well, if you want to pay for StyleGAN artwork, that can be arranged.
Do you think training a language model, whether it is GPT-2 or a near term successor entirely on math papers could have value?
No, but mostly because there are so many more direct approaches to using NNs in math, like (to cite just the NN math papers I happened to read yesterday) planning in latent space or seq2seq rewriting. (Just because you can solve math problems in natural language input/output format with Transformers doesn’t mean you should try to solve it that way.)
Feeding in output as input is exactly what is iterative about DeepDream, and the scenario does not change the fact that GPT-2 and DeepDream are fundamentally different in many important ways and there is no sense in which they are ‘fundamentally the same’, not even close.
And let’s consider the chutzpah of complaining about tone when you ended your own highly misleading comment with the snide
But by all means, spend your $1000 on it. Maybe you’ll learn something in the process.
I predict that you think artwork created with StyleGAN by definition cannot have artistic merit on its own.
Which is amusing because when people look at StyleGAN artwork and they don’t realize it, like my anime faces, they often quite like it. Perhaps they just haven’t seen anime faces drawn by a true Scotsman yet.
GPT-2 is best described IMHO as “DeepDream for text.” They use different neural network architectures, but that’s because analyzing images and natural language require different architectures. Fundamentally their complete-the-prompt-using-training-data design is the same.
If by ‘fundamentally the same’ you mean ‘actually they’re completely different and optimize completely different things and give completely different results on completely different modalities’, then yeah, sure. (Also, a dog is an octopus.) DeepDream is a iterative optimization process which tries to maximize the class-ness of an image input (usually, dogs); a language model like GPT-2 is predicting the most likely next observation in a natural text dataset which can be fed its own guesses. They bear about as much relation as a propaganda poster and a political science paper.
There’s something I should note that doesn’t come through in this post: one of the reasons I was interested in Bitcoin in 2011 is because it was obvious to me that the ‘experts’ (economists, cryptographers, what have you) scoffing at it Just Did Not Get It.
The critics generally made blitheringly stupid criticisms which showed that they had not even read the (very short) whitepaper, saying things like ‘what if the Bitcoin operator just rolls back transactions or gets hacked’ or ‘what stops miners from just rewriting the history’ or ‘the deflationary death spiral will kick in any day now’ or ‘what happens when someone uses a lot of computers to take over the network’. (There were much dumber ones than that, which I have mercifully forgotten.) Even the most basic reading comprehension was enough to reveal most of the criticisms were sheer nonsense, you didn’t need to be a crypto expert (certainly I was not, and still am not, either a mathematician or C++ coder, and wouldn’t know what to do with an exponent if you gave it to me). Many of them showed their ideological cards, like Paul Krugman or Charles Stross, and revealed that their objections were excuses because they disliked the potential reduction in state power—I mean, wow, talk about ‘politics is the mind-killer’. I think I remarked on IRC back then that every time I read a blog post or op-ed ‘debunking’ Bitcoin, it made me want to buy even more Bitcoin. (I couldn’t because I was pretty much bankrupt and wound up selling most of the Bitcoin I did have. But I sure did want to.)
Even cryptopunks often didn’t seem to get it, and I wrote a whole essay in 2011 trying to explain their incomprehension and explain to them what the whole point was and why it worked in practice but not their theory (“Bitcoin is Worse is Better”). So, it was quite clear to me that Bitcoin was, objectively, misunderstood, and a ‘secret’ in the Thiel sense.
And in a market where a price is either too low or too high, ‘reversed stupidity’ is intelligence...
(If anyone was wondering: I don’t think this argument really holds right now. Discussions of Bitcoin are far more sophisticated, and the critics generally avoid the dumbest old arguments. They often even manage to avoid making any factual errors—although I’ve wondered about some of the Tether criticisms, which rely on what looks like rather dubious statistics.)
What sort of luck or cognitive strategy did this require? I think it did require a good deal of luck simply to be in LW circles where we have enough cryptopunk influence to happen to hear about Bitcoin early on. Otherwise, it would be unreasonable to expect people to somehow pluck Bitcoin out of the entire universe of obscure niche products like ‘all penny stocks’. But once you pass that filter, all you really needed was to, while reading about interesting fun developments online, simply not let your brains fall out and notice that the critics were not doing even the most basic due diligence or making logically valid arguments and had clear (bad) reasons for opposing Bitcoin, and understand that implied Bitcoin was undervalued and a financial opportunity. I definitely do not see anything negative about most people for not getting into Bitcoin in 2011, since there’s no good ex ante reason for them to have been interested in or read up on it and doing so in general is probably a bad use of time—but for LWers, we had other reasons for being interested enough in Bitcoin to realize what an opportunity it was, so there it is a bit of a failure to not get involved.
My favorite example is teddy bears:
teddy bears & other toys are much more cuddly and silky
You’ll need a bunch in a single passage. If you don’t need to disambiguate a large hairball of differently-timed people (like in My Best and Worst Mistake), then you probably shouldn’t bother in general.
Would you say that about citations? “Oh, you only use one source in this paragraph, so just omit the author/year/title. The reader can probably figure it out from mentions elsewhere if they really need to anyway.” That the use of subscripts is particularly clear when you have a hairball of references (in an example constructed to show benefits) doesn’t mean solitary uses are useless.
I’m struggling to see how this is an improvement over “on FB” or “on Facebook” for either the reader or the writer, assuming you don’t want to bury-but-still-mention the medium/audience.
It’s a matter of emphasis. Yes, you can write it out longhand, much as you can write out any equation or number long hand as not but “twenty-two divided by two-hundred-and-thirty” if necessary. Natural language is Turing-complete, so to speak: anything you do in a typographic way or a DSL like equations can be done as English (and of course, prior to the invention of various notations, people did write out equations like that, as painful as it is trying to imagine doing algebra while writing everything out without the benefit of even equal-signs). But you usually shouldn’t.
Is the mention of being Facebook in that example so important it must be called out like that? I didn’t think so. It seemed like the kind of snark a husband might make in passing. Writing it out feels like ‘explaining the joke’. Snark doesn’t work if you need to surround it in flashing neon lights with arrows pointing inward saying “I am being sarcastic and cynical and ironic here”. You can modify the example in your head to something which puts less emphasis on Facebook, if you feel strongly about it.
I don’t think they’re confusingly different. See the “A single unified notation...” part. Distinguishing the two typographically is codex chauvinism.
Yes, seems sensible: hard to go wrong if you copy the Pandoc syntax. You’ll need to add a mention of this to the LW docs, of course, because the existing docs don’t mention sub/superscript either way, and users might assume that LW still copies the Reddit behavior of no-support.
It seems worth it in nerdy circles (i.e. among people who’re already familiar with subscripting) for passages that are dense with jumping around in time as in your chosen example, but I’d expect these sorts of passages to be rare, regardless of the expected readership.
But if passages aren’t dense with that or other uses, then you wouldn’t need to use subscripting much, by definition....
Perhaps you meant, “assuming that it remains a unique convention, most readers will have to pay a one-time cost of comprehension/dislike as overhead, and only then can gain from it; so you’ll need them to read a lot of it to pay off, and such passages may be quite rare”? Definitely a problem. A bit less of one if I were to start using it systematically, though, since I could assume that many readers will have read one of my other writings using the convention and had already paid the price.
Also, it’s unclear why “on Facebook” deserves to be compressed into an evidential.
Because it brings out the contrast: one is based on first-hand experience & observation, and the other is later socially-performative kvetching for an audience such as family or female acquaintances. The medium is the message, in this case.
At the very least, “FB” isn’t immediately obvious what it refers to, whereas a date is easier to figure out from context.
I waffled on whether to make it ‘FB’ or ‘Facebook’. I thought “FB” as an abbreviation was sufficiently widely known at this point to make it natural. But maybe not, if even LWers are thrown by it.
On a side note: it really would be nice if we could have normal Markdown subscripts/superscripts supported on LW. It’s not like we don’t discuss STEM topics all the time, and using Latex is overkill and easy to get wrong if you don’t regularly write Tex.
Yes, this relies heavily on the fact that subscripts are small/compact and can borrow meaning from their STEM uses. Doing it as superscripts, for example, probably wouldn’t work as well, because we don’t use superscripts for this sort of thing & already use superscripts heavily for other things like footnotes, while some entirely new symbol or layout is asking to fail & would make it harder to fall back to natural language. (If you did it as, say, a third column, or used some sort of 2-column layout like in some formal languages.)
How are you doing inflation adjustment? I mocked up a bunch of possibilities and I wasn’t satisfied with any of them. If you suppress one of the years, you risk confusing the reader given that it’s a new convention, but if you provide all the variables, it ensure comprehension but is busy & intrusive.
Subscripting Typographic Convention For Citations/Dates/Sources/Evidentials: A Proposal
You’re welcome. I didn’t want to write it because I don’t find it that interesting and such a post always takes a frustrating amount of time to write because one has to dig into the details & jailbreak a lot of papers etc, and I’d rather write more constructive things like about all our GPT-2 projects than yet another demoralizing criticism piece, but people kept mentioning it as if it wasn’t awful, so… At least it should spare anyone else, right?
Sometimes it may take a thief to catch a thief. If it was written in 1592, Rayleigh was at his height then, and had much opportunity to see inside the institutions he attacks.
I’m reminded of a book review I wrote last week about famed psychologist Robert Rosenthal’s book on bias and error in psychology & the sciences.
Rosenthal writes lucidly about how experimenter biases can skew results or skew the analysis or cause publication bias (which he played a major role in raising awareness of & developing meta-analysis), gives many examples, and proposes novel & effective measures like result-blind peer review. A veritable former day Ioannidis, you might say. But in the same book, he shamelessly reports some of the worst psychological research ever done, like the ‘Pygmalion effect’, which he helped develop meta-analysis to defend (despite its nonexistence), and the book is a tissue of unreplicable absurd effects from start to finish, and Rosenthal has left a toxic legacy of urban legends and statistical gimmicks which are still being used to defend psi, among other things.
Something something the line goes through every human heart...
Dec 2019 gwern.net newsletter
Drexler’s recent AI whitepaper had some arguments in a similar vein about functional equivalence and necessary compute and comparing CNNs with the retina or visual cortex, so you might want to look at that.
One idea occurred to me that I haven’t heard anyone suggest: the trial didn’t have to be 50-50. With a large enough group, you could hold back a smaller subset as the control (80-20?). Again, you need statistics here to tell you how this affects the power of your test.
You can see that as just a simple version of an adaptive trial, with one step. I don’t think it in any way resolves the basic problem people have: if it’s immoral to give half the sample the placebo, it’s not exactly clear why giving a fifth the sample the placebo is moral.
So, the tests had to be more than scientifically sound. They had to be politically sound. The trials had to be so conclusive that it would silence even jealous critics using motivated, biased reasoning. They had to prove themselves not only to a reasoning mind, but to a committee. A proper RCT was needed for credibility as much as, or more than, for science.
This is an important point. One thing I only relatively recently understood about experiment design was something Gelman has mentioned in passing on occasion: an ideal Bayesian experimenter doesn’t randomize!
Why not? Because, given their priors, there is always another allocation rule which still accomplishes the goal of causal inference (the allocation rule makes its decisions independent of all confounders on average, like randomization, so estimates the causal effect) but does so with the same or lower variance, such as using alternating-allocation (so the experimental and control group always have as identical n as possible, while simple randomization one-by-one will usually result in excess n in one group—which is inefficient). These sorts of rules pose no problem and can be included in the Bayesian model of the process.
The problem is that it will then be inefficient for observers with different priors, who will learn much less. Depending on their priors or models, it may be almost entirely uninformative. By using explicit randomization and no longer making allocations which are based on your priors in any way, you sacrifice efficiency, but the results are equally informative for all observers. If you model the whole process and consider the need to persuade outside observers in order to implement the optimal decision, then randomization is clearly necessary.
Emphasizing the point even more—word didn’t get to me. I just thought to myself, ‘the layout might not be good on mobile. I ought to check.’ (It was not good.)