Working in the Fourier domain has been a staple of scientific and engineering applications. Learning those interactions rather than just hardcoding them has been fairly widely explored as well - the term to look for is Fourier Neural Operators [1][2]. It turns out you can prove universality even if the nonlinearity remains in the real domain [3].
The concept is fairly mainstream nowadays, to the degree that Jensen talked about it in his GTC keynote in 2021 [4] and there’s even a mainstage TED talk about its applications [5].
A nice property of doing things this way is that your model ends up being resolution-invariant which is particularly interesting for engineering domains. Scaling these methods has sparked the "let’s do a fully deep-learning-based weather model"-race [6][7].
As for using this on text data: my intuition would be that is going to not work as well because of a fairly unique property of text: for image, video and scientific data each individual element is of approximately equal importance, whereas in text you can have discrete tokens like a "not" somewhere in there that change the meaning of everything around it fairly significantly and you’d want that all to all interaction to capture that. Any kind of mixing that smoothes things out is going to inherently be at a disadvantage - probably true to some degree for most of those efficiency saving methods and why we’re seeing more limited adoption on text.
Actually the examples in the text are quite concrete - the few lines would be everything you'd have to write to get such a site working (modulo a bit of http plumbing). Obviously we are nowhere near such a thing. The rest of the text acts as a bit of a pointer what could help to get there.
From my other comment (in this case the example was an amazon like shopping site):
> The thing I would like to get working is telling the net "here is a ton of different buttons, grids, lists and a lot of other UI stuff, and there are all our products - I want to maximize revenue - do whatever you like to get there".
The way of telling a neural network these things would be these few lines of code which would hook up different parts to get it working.
You're gonna have to make a clear distinction between web dev project configuration, and AI for making good choices that optimize meeting sales and marketing goals.
How is your 'project codebase markup' any different from filling in some radio buttons on a template generator or a CMS?
The forth black box paper seems to be quite close actually. The big difference is that I would like to go for higher level tasks. The low level variant - having code missing a few lines/functions and filling those is indeed quite well researched. More interesting would be the opposite: Having blocks of code (called components in my text) and letting the net find a good way to use them. Normally the way a programmer would build something like let's say the amazon website is sketching up a few ideas how it could look - cutting it into small parts and then writing (or reusing) the code for the small parts.
A lot of the "using AI for programming" papers (AI in this case being anything from nets to logic stuff) focus on building these small parts - I'm more interested in the levels above that. The "generating code from natural language/dialogs" also kind of misses my idea as it still assumes a "programmer" to tell it what to do (in the amazon example "display the products on a grid" or "make the buy button bigger"). The programmer would in this case either guess the right thing ("everybody has their products in a grid") or run something like A/B testing against some metric ("items sold" or "$ revenue") and then go back to the conversational programming tool and tell it "change the button". Why not skip the programmer and give the metric directly to the net? The thing I would like to get working is telling the net "here is a ton of different buttons, grids, lists and a lot of other UI stuff, and there are all our products - I want to maximize revenue - do whatever you like to get there".
Regarding the vague ideas: I believe (also totally unsubstantiated - though research in this area would be quite interesting) that writing down ideas first and then looking into them (either by reading other papers or doing own research) is far better than the other way round, because it has a higher probability of leading to something new, because you don't follow assumptions and errors others potentially made which could lead to a dead end. Of course there is a certain trade off, because if you try to reinvent everything you will not get to the point of something new.
> The thing I would like to get working is telling the net "here is a ton of different buttons, grids, lists and a lot of other UI stuff, and there are all our products - I want to maximize revenue - do whatever you like to get there".
As you referenced in your footnote, this goal is a little like the goal of putting a man on the moon, if the year is 1900. One could the make the case that something resembling an AGI agent would be needed to design/manage a merchant website with any degree of introspection and customizability. Your machine would need to understand business objectives, UI patterns, aesthetics, etc before taking the helm, and these things take years to learn even for even humans. This is not to say that AGI isn't an admirable goal, but that it's an obvious one, just as I imagine reaching the moon was in the centuries leading up to 1969.
Scientific research is certainly a massively parallel effort, pushing the boundary of our knowledge in many different direction at once, but rest assured that there are many good men and women working in this specific direction.
(also know that I have a lot of respect for this kind of vision-oriented thinking, especially because it generally leaves the writer vulnerable to criticism like this)
Every ML problem has a lot of algorithms that can be applied to it to solve it. Previously unsolvable problem (e.g. learning playing atari game from pixels) can become solvable if a better ML algorithm is devised, more compute power is applied, or the problem is simplified (maybe with data augmentation or feature engineering, or constraining the domain).
I think the problem you are stating - finding the best configuration of website to extract more ad revenue - can be simplified until it is solvable by currently available algorithms and hardware. Big companies are already doing similar things - Google uses reinforcement learning to recommend new items on its services, Facebook is known to use machine learning to somehow make more profit from users.
Given expert knowledge of the problem and good algorithms, it can be solved in some form, that's my point here.
Sure. I think what the OP was playing around with was whether we could generalize from, as you said, "expert knowledge of the problem[s] and good algorithms" so that AI could replace components in the incumbent programming paradigms. Agreed, when the end problem is well-defined and you know how "do" machine learning, the world is your oyster.
I think this is what the future of media will/should look like: building your reader's/consumer's trust in your publication by quality [1] content to make them come back to you when they need advice on a buying decision. The beauty of this model is that this advice can (and will [2]) be honest, because amazon and other retailers/appstores etc. do not care about what the customer actually buys as long as it is from them, which allows the publication to lead the reader to the best product while still making money. Of course there are still imperfections [3], for example sending them to a specific retailer like amazon when another would be better for them, but this is a lot better than advertising for stuff you do not need or one-sided sponsored content.
[1] "quality" is not meant as an absolute value here, but relative to the publication's target - so a "quality" article on Gawker will of course (and rightfully so) be different from one you will find in the New Yorker
[2] as long as they make the same money from two options it is in the interest of the publication to choose the honest one, because they want you to come back
[3] these imperfections are unfortunately the only thing the advertising market lives on, because if everyone could figure out what they need they would buy exactly this from the best/cheapest retailer which would kill every incentive for any kind of advertising/referral money. This will also be a problem google will face some time in the future: if their search engine gets too perfect there is no need to advertise anymore, because if the user would actually want it they would find it anyways and if not the money is wasted
I hope the "future of media" doesn't revolve around optimizing purchasing decisions. I'd rather think of this as the future/now of marketing where the line between content and advertising is increasingly blurred.
We're doing much of this with PricePlow, and the readers DEFINITELY let you know when you take it too far.
A top ten list is exactly what it is. Readers are in buying mode there. But for other content, if you can educate the consumer and cite sources (our last major article has 110 citations), they don't mind you showing some links to stores.
The fact what we're showing up to date price comparisons also helps with trust.
But the second you over shill without backing out up with research... You will hear about it and lose fans.
Readers aren't as stupid as the media loves to believe. Anyways keep that in mind.
In the beginning (after signing up) a bunch of orders are created and sent out to the drivers. The orders technically exist, but the drivers are told to not show up right now. Then, at least 15 minutes later, the user can tap a "show up now" button which tells the driver to actually pick up the user. So in the end you have to wait 15 minutes when using the service for the first time, but after that it is a lot faster because the orders created in the beginning can be "activated" immediately.
You could argue that it's Uber's attempts to "fix" pesky regulations that have got them into this mess in the first place. I doubt trying to circumvent this in the way you describe would really make the regulators any more sympathetic towards Uber.
There's an interesting business case study to be done between Uber and Hailo (based in London, far more popular there than Uber, expanding overseas), the latter of which was founded by cab drivers themselves and has taken a far more 'softly softly' approach to compliance (arguably at the expense of revenue, but the benefit of not having to deal with this kind of thing).
Well the point of circumvention is not to engender sympathy. It's to find a legal loophole wherein even if the regulators hate you, they can't pursue you.
This is an interesting idea; however, it seems that it wouldn't necessarily help.
I'm uncertain whether the orders you speak of are reservations, or phonies with the purpose of circumventing the new bill. If the former is the case, drivers would still be unable to serve the greatest need - spontaneous rides. The latter could potentially work, but surely it would be caught eventually.
http://en.wikipedia.org/wiki/Part-of-speech_tagging
So the "new" "invention" they want to patent is "we can display the result" which basically is nothing more than syntax highlighting? Or did I get something wrong?
the site actually is mostly about "idea 1" (by using 2) to make people actually do the tasks) but I think switching more towards "idea 2" could actually be more useful
I'm not really happy about having people to deposit money but without that there is no other way to make them actually do the task (at least I did not find one by now but I'm open to suggestions)
The concept is fairly mainstream nowadays, to the degree that Jensen talked about it in his GTC keynote in 2021 [4] and there’s even a mainstage TED talk about its applications [5].
A nice property of doing things this way is that your model ends up being resolution-invariant which is particularly interesting for engineering domains. Scaling these methods has sparked the "let’s do a fully deep-learning-based weather model"-race [6][7].
As for using this on text data: my intuition would be that is going to not work as well because of a fairly unique property of text: for image, video and scientific data each individual element is of approximately equal importance, whereas in text you can have discrete tokens like a "not" somewhere in there that change the meaning of everything around it fairly significantly and you’d want that all to all interaction to capture that. Any kind of mixing that smoothes things out is going to inherently be at a disadvantage - probably true to some degree for most of those efficiency saving methods and why we’re seeing more limited adoption on text.
[1] https://arxiv.org/abs/2010.08895
[2] https://www.nature.com/articles/s42254-024-00712-5
[3] https://jmlr.org/papers/v22/21-0806.html
[4] https://www.youtube.com/watch?v=jhDiaUL_RaM&t=2472s
[5] https://www.ted.com/talks/anima_anandkumar_ai_that_connects_...
[6] https://arxiv.org/abs/2202.11214 (Feb 2022)
[7] https://www.wired.com/story/ai-hurricane-predictions-are-sto...
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