If you have relatively few choices and properties are correlated (as of course they are), I’m not sure how much it matters. I did a simulation of this for embryo selection with n=10, and partially randomized the utility weights made little difference.
gwern
Karma: 43,148
(Quite a lot is public outside Google, I’ve found. It’s not necessarily easy to find, but whenever I talk to Googlers or visit, I find out less than I expected. Only a few things I’ve been told genuinely surprised me, and honestly, I suspected them anyway. Google’s transparency is considerably underrated.)
Heh. I’ve sometimes thought it’d be nice to have a copy of Eureqa or the other symbolic tools, to feed the Monte Carlo results into and see if I could deduce any exact formula given their hints. I don’t need exact formulas often but it’s nice to have them. I’ve noticed people can do apparently magical things with Mathematica in this vein. All proprietary AFAIK, though.
You can simulate it out easily, yeah, but the exact answer seems more elusive. I asked on CrossValidated whether anyone knew the formula for ‘probability of the maximum on both variables given a r and n’, since it seems like something that order statistics researchers would’ve solved long ago because it’s interesting and relevant to contests/competitions/searches/screening, but no one’s given an answer yet.
PDFs support hyperlinks: they can define anchors at arbitrary points within themselves for a hyperlink, and they can hyperlink out. You can even specify a target page in a PDF which doesn’t define any usable anchors (which is dead useful and I use it all the time in references): eg https://www.adobe.com/content/dam/acom/en/devnet/acrobat/pdfs/pdf_open_parameters.pdf#page=5
So I guess the issue here is having a tool which parses and edits PDFs to insert hyperlinks. That’s hard. Even if you solve the lookup problem by going through something like Semantic Scholar (the way I use https://ricon.dev/ on gwern.net for reverse citation search), PDFs aren’t made for this: when you look at a bit of text which is the name of a book or paper, it may not even be text, it may just be an image… Plus, your links will die. You shouldn’t trust any of those sites to stay up long-term at the exact URLs they are at.
Calling something a ‘fad’ has many of the same problems as calling something a ‘bubble’. It’s an invitation to selective reasoning. As Sumner likes to point out, most of the things which get called a ‘bubble’ never turn out to be that, it was just an insult and then a bunch of cherrypicked examples and flexible reasoning (think of all the people who called Bitcoin a bubble when it collapsed to a price far higher than when they called it a bubble).
I think you could get something more useful from a more neutral formulation, like specific cultural artifacts. So 2 recent relevant papers which come to mind would be https://www.nature.com/articles/s41467-019-09311-w http://barabasi.com/f/995.pdf . You could do a post hoc analysis and operationalize ‘fad’ as anything which rose and fell with a sufficiently steep average slope. (Obviously, anything which rises rapidly and then never decays, or only slowly decays, doesn’t match what anyone would think of as a ‘fad’, or rises slowly and decays slowly etc.)
I recently read gwern’s excellent “Why Correlation Usually ≠ Causation” notes, and, like any good reading, felt a profound sense of existential terror that caused me to write up a few half-formed thoughts on it. How exciting!
It may not be directly related, but I’d like to highlight that I just added a new section contextualizing the essay as a whole and explaining how it connects to the rest of my beliefs about correlation & causality: https://www.gwern.net/Causality#overview-the-current-situation
As for the broader question of where do our ontologies come from: I’d take a pragmatic point of view and point out that they must have evolved like the rest of us because thinking is for actions.
How would you define ‘fad’ in an objective and non-pejorative way?
You mean stuff like model-predictive control and planning? You can use backprop to do gradient ascent over a sequence of actions if you have a differentiable environment and/or reward model. This also has a lot of application to image CNNs: reversing GANs to encode an image for editing, optimizing to maximize a particular class (like maximally ‘dog’ or ‘NSFW’ images) etc. I cover some of the uses and history in https://www.gwern.net/Faces#reversing-stylegan-to-control-modify-images
My most recent suggestion in this vein was about OA/Christiano’s preference learning. From my notes:
It’s interesting how all my preference learning PPO runs for both music & poetry keep diverging into highly repetitive adversarial instances like a GAN mode collapse.
While waiting for the PPO, I began to wonder: why couldn’t one remoe the blackbox optimizers entirely and optimizing directly? It seems like a waste to go to all this effort to build a differentiable surrogate (reward) model of the environment (the human), and then treat it like just another blackbox. But it’s not, that’s the whole point of preference learning! Since GPT-2 is differentiable, it ought to be possible to backprop through it to do planning and optimization like MPC.
For example, one could generate high-scoring music pieces by generating a random sequence, text-embedding it into the vector for the reward model, and then doing gradient ascent on the vector. No PPO cluster required. Ratings can be done pairwise on the various optimized sequences (random pairs of high-scoring sequences, although before/after comparisons might be more informative) and then the reward model trained.
If gradient ascent is too slow for routine use, then one can just distill the reward model via training the GPT-2 on successively better corpuses in the usual efficient quick likelihood training (imitation learning) way, for a sort of ‘expert iteration’: generate improved versions of a corpus by generating & selecting new datapoints above a threshold (perhaps using a corpus of human datapoints as starting points), and train to generate that.
This could be a lot faster since it exploits the whitebox nature of a learned reward model instead of treating it as a high-variance blackbox.
If nothing else, I think it would help with the adversarial instances. Part of the problem with them is that each PPO run seems to collapse into a single specific adversarial instance. I do a bunch of ratings which penalizes that instance and fixes it, but then I wait another day or two, and then that run collapses into a new single adversarial instance. The reward model seems to gradually get better and the adversarial instances seem to gradually increase in complexity, but the process is very slow and serial. The gradient ascent approach may also run into the problem that it will find adversarial instances for the reward model, but at least it will do so in parallel: if I can run a minibatch n=11 of GPT-2-117M reward models each starting with a different random initial sequence being optimized and do gradient ascent on each in parallel, they will probably find several different adversarial instances in parallel, while the PPO would only find the one it collapses on. So one would get a lot more useful adversarial instances to rate per run.
Nov 2019 gwern.net newsletter
And MuZero, which applies to ALE very well?
Verifying the behavioural effects is much harder
Not really. There’s scads of behavioral measures you can collect passively.
you’d need to avoid unblinding,
No you don’t, and blinding is easy if you think about it for a few seconds, see the comment I left well before yours.
and ideally have several different people with varying levels of age, fitness etc,
No, you don’t, you are letting perfect be the enemy of better
and then you’d get affected by weather, unless your house is very well sealed...
This is a feature, not a bug.
I have the relevant air sensor, it’d be really hard to blind it because it makes noise, and the behavioral effects thing is a good idea, thank you.
Just randomizing would be useful; obviously, your air sensor doesn’t care in the least if it is ‘blinded’ or not. And if it’s placed in a room you don’t go into, that may be enough. As well, maybe you can modify it to have a flap or door or obstruction which opens or closes, greatly changing the rate of CO2 absorption, and randomize that; or if you have someone willing to help, they can come in every n time units to replace the filler or not, giving you both blinded & randomized comparisons between high-CO2-removal vs low-CO2-removal conditions based on whether they pulled out the used filler or not, since the fan presumably still makes the same noise regardless of whether it has brand-new filler removing CO2 at maximum rates or expired tired filler removing only a little CO2. (Remember, experiments work fine comparing 100% removal rates to, say, 10% removal rates; it doesn’t have to be exactly ‘on’/‘off’, that’s just a bit more statistically-efficient because it has a slightly larger effect size, and you have to remember the estimate is a bit lower than the ‘true’ estimate because the ‘off’ condition has 10% of the benefits of the ‘on’.)
It’s good that you built it, but it seems to me that now you have a prototype, before you start investing in a patent or business to sell scaled-up versions, it’d make more sense to invest $100 in a CO2 air sensor and a Raspberry Pi with a switch to randomly turn it on/off: to verify that it decreases CO2 as much and as long as expected, whether you can tell when CO2 levels have been lowered, and whether this has any measurable behavioral effects. The value of such information is very high: there is no point in scaling up a design which isn’t working at its basic task of lowering CO2 levels, and commercialization will be difficult if it does nothing observable (especially given our questions about how and whether CO2 does anything) and, perhaps more importantly from a marketing perspective, if the user can’t feel it doing something.
GS is just an automated web scraping and search engine service. It can’t be picky the way WP is. If you use the necessary
<meta>tags (which can be auto-generated from the existing user+title metadata), it’s unclear why it wouldn’t be picked up by GS eventually.Serendipitously, I added those
<meta>tags togwern.netjust last week after a Twitter discussion about how and whether to get DOIs. At least in theory, I shouldn’t need to get DOIs to get better visibility, but as a backup, I also registered a GS profile which may or may not let me enter pages manually. In my experience, GS updates quite slowly (I think because GS seems to be a skeleton crew passion project and not integrated into regular Google Search), so we’ll see in a few months if any of this did any good.
As far as WP goes, I’m not sure what can be done. As a group blog anyone can post to, being a LW post can never confer any particular notability or RSness on its own. It has to be a case by case basis. Given the ever more deletionist approach of remaining WP editors, that will be difficult even for cases where a LW post is a very good writeup on something and would make an excellent External Link addition, unless it is something like an official statement or interview etc. (Obviously, if a MIRI staffer posts an official piece of MIRI news, there would be no problem citing it in the MIRI article. Stuff like that.) Wikipedians crave status, and so the best way really is to somehow promote pieces into formal publications in somewhere (anywhere) Peer-Reviewed™ and treat a LW post as a preprint.
Meta-learning and transfer learning. You take over 100 million different simulated worlds, and the actual real world is a doddle.
I proposed this idea years back as dynamic or extended flashcards. Because spaced repetition works for learning abstractions, which studies presumably entail learning from a set of testing flashcards to a set of validation flashcards, there doesn’t seem to be any reason to expect SRS to fail when the testing flashcard set is itself very large or randomly-generated. Khan Academy may be an example of this: they are supposed to use spaced repetition in scheduling reviews, apparently based on Leitner, and they also apparently use randomly generated or at least templated questions in some lessons (just mathematics?).
(Incidentally, while we’re discussing spaced repetition variations, I’m also pleased with my idea of “anti-spaced repetition” as useful for reviewing notes or scheduling media consumption.)
Since you mention physics, it’s worth noting Feynman was a big proponent of this for physics, and seemed to have multiple reasons for it.