Thank you for this!
It seems that my ignorance is on display here, the fact that these papers are new to me shows just how out of touch with the field I am. I am unsurprised that 'yes it works, mostly, but other approaches are better' is the answer, and should not be surprised that someone went and did it.
It looks like the successful Facebook AI approach is several steps farther down the road than my proposal, so my offer is unlikely to provide any value outside of the intellectual exercise for me, so I'm probably not actually going to go through with it--by the time the price drops that far, I will want to play with the newer tools.
Waifulabs is adorable and awesome. I've mostly been using style transfers on still life photos and paintings, I have human waifu selfie to anime art on my to do list but it has been sitting there for a while.
Are you planning integration with DeepAnime and maybe WaveNet so your perfect waifus can talk? Though you would know if that's a desirable feature for your userbase better than I would...
On the topic, it looks like someone could, today, convert a selfie of a partner into an anime face, train wavenet on a collection of voicemails, and train a generator using an archive of text message conversations, so that they could have inane conversations with a robot, with an anime face reading the messages to them with believable mouth movements.
I guess the next step after that would be to analyze the text for inferred emotional content (simple approaches with NLP might get really close to the target here, pretty sure they're already built), and warp the voice/eyes for emotional expression (I think WaveNet can do this for voice, if I remember correctly?
Maybe a deepfake type approach that transforms the anime girls using a palatte of a set of representative emotion faces? I'd be unsurprised if this has already been done, though maybe it's niche enough that it has not been.
This brings to mind an awful idea: In the future I could potentially make a model of myself and provide it as 'consolation' to someone I am breaking up with. Or worse, announce that the model has already been running for two weeks.
I suspect that today older style, still image heavy anime could probably be crafted entirely using generators (limited editing of the writing, no animators or voice actors), is there a large archive of anime scripts somewhere that a generator could train on, or is that data all scattered across privately held archives?
What do you think?
When TWDNE went up, I asked 'how long will I have to read and mash refresh before I see a cute face with a plot I would probably be willing to watch while bored at 2am' The answer was 'less than 10minutes', and this is either commentary on the effectiveness of the tool, or on my (lack of?) taste.
I have a few pieces of artwork I've made using StyleGAN that I absolutely love, and absolutely could not have made without the tool.
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, I'm pretty enthusiastic about the idea but have not done anything at all to pursue it.
Do you think training a language model, whether it is GPT-2 or a near term successor entirely on math papers could have value?
Here's an example from nature on snake venom that 'won' an evolutionary arms race.
From the abstract: "Examination of the prothrombin target revealed endogenous blood proteins are under extreme negative selection pressure for diversification, this in turn puts a strong negative selection pressure upon the toxins as sequence diversification could result in a drift away from the target. Thus this study reveals that adaptive evolution is not a consistent feature in toxin evolution in cases where the target is under negative selection pressure for diversification."
There are implications here for arms races generally. When you target something 'core' to the target that cannot be easily randomized to develop a diverse and therefore adaptive strategy, it is possible to 'win' an evolutionary arms race in the long term.
Essentially Eliezer's blind idiot god writes itself into a corner when it can no longer randomize a section under attack, and just sort of fails.
Carefully define mathematician. Working definition: one who has obtained a degree in mathematics at some level (is an undergrad a mathematician, or do you need a phd? Do physics degree holders count as mathematicians? What about accounting and finance degrees?).
List number of non degree holders in the usa, list number of degree holders in the usa, list number of mathematics degree holders at the threshold, calculate ratio.
List number of incarcerated people, list number with degrees. Calculate ratio. List number of incarcerated degree holders with math degrees, calculate ratio to degree holders.
From these ratios, you should be able to see if mathematicians are proportionate, under, or overrepresented in incarcerated populations relative to both similarly educated and the general population.
Second approach: submit surveys to known math degree holders and known holders of similar levels of education. Ask 'do you do things you feel to be unethical on a regular basis?' and 'do you do things that a typical person would feel to be unethical, if they understood it, on a regular basis?' along with some lie scales (to determine whether the person is lying to the test to improve their image; these scales are commonly used on psychological tests).
Check power of your statistics.
Between those two methods, you should get a reasonable answer. I haven't googled and won't do it myself, but I think this project, at least approach 1, is doable. Without approach two, in the case mathematicians are better at not going to prison than the general population, the results of approach 1 will incorrectly make it look like Eliezer is right.
I do not know Eliezer, but have read a decent amount of his work, though not this. I offer the following counterpoint:
A mathematician who has chosen to use his math talents to sell used cars has probably calculated what he views to be prices that maximize his profits, taking into account anything you the consumer could do to impose costs for selling an overpriced lemon.
With the mathematician, 'market for lemons' economics are in play, and probably well executed, and therefore, I should avoid negotiating with him, as it is likely to go badly for me. A non mathematician may have made errors or been lazy in his pricing, creating an opportunity for deals.
If Eliezer considers himself to be a mathematician, this assertion is inherently even more suspect, as it is a member of a group ascribing positive characteristics to himself on the basis of his group membership. (I'm a Mathematician you can trust me, because Mathematicians don't lie, because they're Mathematicians....and I can prove it using the language of Mathematicians, which is known as Mathematics, something I've studied more than you... you're still skeptical? What do you have against Mathematicians you lunatic?!)
On the other hand, a pure mathematician who is dumping his car on craigslist is a mathematician who may not be happy about having to be a used car salesman, and is in addition to being as honest as anyone else, likely to find the 'applied' process of figuring out an asking price for the car distatesful. If the buyer is lucky, the mathematician will not need to be talked out of an elaborate payment scheme, calculated the value of the car lazily (find the book value, round up to the nearest $100), and actually has the paperwork so the buyer can hand over cash and the whole business can be concluded quickly.
In the usa, professional, drivers, and recreational licenses are revoked for non-payment of child support: https://www.ncsl.org/research/human-services/license-restrictions-for-failure-to-pay-child-support.aspx
It isn't a perfect analogy but it is 'revocation of a credential due to failure to pay a debt'. I hear it works awesome at getting people to pay child support, doesn't expand the prison population, and is on balance a good thing for society.
As I understand it, the initial purpose for student loans is to ensure that the professional classes with long training times are staffed with motivated indentured servants (if only the idle rich could afford to train as surgeons, they would not be able to have skilled surgeons attend to them during their idleness). This initial purpose has been perverted by the entrance into the education market of bad goods (useless degrees that do not actually provide a profession) as a way of exploiting unsophisticated buyers with access to cheap credit.
These unsophisticated buyers would probably respond to a degree repo with 'oh you mean I can't say I went to devry? Oh no... the horror...I guess when my buddy wants to hire me I'll have to tell him my degree got repod and that he'll have to placate HR in order to bring me on staff'.
In my experience, the degree got the first job, which got the second job, and has never gotten me any meaningful status boost after that.
So in your analogy, would the 'seed text' provided to gpt-2 be analogous to a single keyframe, provided to an artist, and gpt-2s output be essentially what happens when you provide an interpolator (I know nothing about the craft of animation and am probably using this word wrong) a 'start' frame but no 'finish' frame?
I would argue that an approach in animation, where a keyframe artist is not sure exactly where to go with a scene, so he draws the keyframe, hands it to interpolative animators with the request to 'start drawing where you think this is going', and looks at the results for inspiration for the next 'keyframe' will probably result in a lot of wasted effort by the interpolators, and is probably inferior (in terms of cost and time) to plenty of other techniques available to the keyframe artist; but also that it has a moderate to high probability of eventually inspiring something useful if you do it enough times.
In that context, I would view the unguided interpolation artwork as 'original' and 'interesting', even though the majority of it would never be used.
Unlike the time spent by animators interpolating, running trained gpt-2 is essentially free. So, in absolute terms, this approach, even if it produces garbage the overwhelming majority of the time, which it will, is moderate to very likely to find interesting approaches with a low, but reasonable for human reviewers, probability (meaning, the human must review dozens of worthless outputs, not hundreds of millions like the monkeys on typewriters).
I suspect that a mathematician with the tool I proposed could type in a thesis, see what emerges, and have a moderate to high probability of eventually encountering some text that inspires something like the following thought: 'well, this is clearly wrong, but I would not have thought to associate this thesis with that particular technique, let me do some work of my own and see if there is anything to this'.
I view the output in that particular example to be 'encountering something interesting', and the probability if it occurring at least once if my proposed tool were to be developed to be moderate to high, and that the cost in terms of time spent reviewing outputs would not be high enough to make the approach have negative value to the proposed user community.
I price the value of bringing this tool into existence in terms of the resources available to me personally as 'worth a bit less than $1000 usd'.
The disagreement is about whether 'remixing' can result in 'originality'.
We are in agreement about the way gpt-2 works, and the types of outputs it produces, just disagreeing about whether they meet our criteria for 'interesting' or 'original'. I believe that our definitions of those two things necessarily include a judgement call about the way we feel about 'orginality' and 'insight' as a human phenomenon.
Some attempts to explicate this agreement to see if I understand your position:
I argue that this track, which is nothing but a mashup of other music, stands as an interesting creative work in its' own right. I suspect that you disagree, as it is just 'remixing': https://www.youtube.com/watch?v=YFg5q2hSl2E&app=desktop
I would also believe that gpt-2, properly trained on the whole of the Talmud (and nothing else), with the older stuff prioritized, could probably produce interesting commentary, particular if specific outputs are seeded with statements like 'today this <thing> happened so therefore'.
I think you would ascribe no value to such commentary, due to the source being a robot remixer, rather than a scholar, regardless of the actual words in the actual output text.
If I remember the gpt-2 reddit thread correctly, most comments were trash, some of them made reading the rest of it worthwhile to me.
Just like a 'real' reddit thread.
My standard for interesting poetry is clearly different from (inferior to?) yours. If I understand you correctly, I predict that you think artwork created with StyleGAN by definition cannot have artistic merit on its own.
So we appear to be at an impasse. I do not see how you can simultaneously dismiss the value of the system for generating things with artistic merit (like poetry, mathematics, or song lyrics), and simultaneously share the anxieties of the developers about its' apparent effectiveness at generating propaganda.
AI systems have recently surprised people by being unusually good at strange things, I think opimism for a creative profession like pure math is warranted. In short, the potential payoff (contributions to pure math) is massive, the risk is just an amount of money that in this industry is actually fairly small and the egos of people who believe that 'their' creative field (math) could not be conquered by ML models that can only do 'derivative' things.
I assert that at some point in the next two years, there will exist an AI engine which when given the total body of human work in mathematics and a small prompt (like the one used in gpt-2), is capable of generating mathematical works that humans in the field find interesting to read, provided of course that someone bothers to try.
If the estimated cost for actually training the model I described above, and thus ending this discussion, drops below $1000, and it has not been done, I will simply do it.
I assert that if gpt-2 can write interesting looking poetry, it can probably do interesting mathematics.
I think that there is a wide space between 'boring and useless' and 'groundbreakingly insightful', and that this particular system can generate things in that space.
I think my view here is 'less than cautious' optimism. I am not sure what it takes to justify the expenditure to openai to test this assertion. It sounds like a fairly expensive project (data collection, training time), so better people than I will have to decide to throw money at it, and that decision will be made using criteria that are opaque to me.
More awesome than my puny mind can imagine.
I'd like the raw model to be trained on raw copies of as many mathematical papers and texts as possible, with 'impact factor' used as weights.
I'd also, while I'm dreaming, like to see it trained on only the math, without the prose, and a second model trained to generate the prose of math papers solely from the math contained within.
I think math papers are a better source than reddit news articles because pure mathematics is systematic, and all concepts, at least in theory, can be derived from first principles covered in other places.
Ideally, the system would generate papers with named authors and citation lists that help guide the operators to humans capable of reviewing the work.
If you believe that one single useful mathematical insight could be found with my proposed approach, it's borderline criminal to not devote effort to getting it built.