Am I the only one who found those images somewhat disturbing? I wonder if they're triggering something similar to http://www.reddit.com/r/trypophobia
This unpublished one is incredibly creepy. https://i.imgur.com/6ocuQsZ.jpg
Inceptionism: Going Deeper into Neural Networks
121–130 of 166 posts
Re: Inceptionism: Going Deeper into Neural Networks
#122Earlier quoted context omitted.
Perhaps the argument should be steelmanned in that we should generally avoid using algorithms which are so complex that they aren't glass boxes. I doubt the idea to "simply follow gradients" can prove neural networks to be glass boxes because the output of that is still too complex. And we are clearly onto something here. If we can generate artificially hallucinated pictures today, it is not unreasonable to assume th…
> Combining such a hallucination technique with reinforcement learning might be a key to general intelligence. Knowing that the most common parallel effect of induced hallucination via psychotropics is ego-loss (complete loss of subjective self-identity) [0], maybe they need to try completely inverse processes in order to create a sense of ego in a machine... Because what's real intelligence but one's sense of self?…
I would argue that a sense of experience is a necessary precursor for that, and also that intelligence and consciousness are two different things, although (if I read you right) the latter certainly informs the former. Barry Sanders' A is for OX has many well-sourced musings on the emergence of consciousness as a product of literary capability vs. a purely oral tradition which you might find interesting, and of course I think everyone needs to read Jaynes, Dennett, Hofstatder on these topics.
Re: Inceptionism: Going Deeper into Neural Networks
#123Worth reading the comments too. One from Vincent Vanhoucke: "This is the most fun we've had in the office in a while. We've even made some of those 'Inceptionistic' art pieces into giant posters. Beyond the eye candy, there is actually something deeply interesting in this line of work: neural networks have a bad reputation for being strange black boxes that that are opaque to inspection. I have never understood those…
Perhaps the argument should be steelmanned in that we should generally avoid using algorithms which are so complex that they aren't glass boxes. I doubt the idea to "simply follow gradients" can prove neural networks to be glass boxes because the output of that is still too complex. And we are clearly onto something here. If we can generate artificially hallucinated pictures today, it is not unreasonable to assume th…
Great more "ZOMG I'm skirred of AI" FUD.
Stop being so afraid of the future.
Re: Inceptionism: Going Deeper into Neural Networks
#124The reason they look so 'fractal-like' (e.g. trippy!) is because they actually are fractals! In the same way a normal fractal is a recursive application of some drawing function, this is a recursive application of different generation or "recognition -> generation" drawing functions built on top of the CNN. So I believe that, given a random noise image, these networks don't generate the crazy trippy fractal patterns…
Functional iteration is actually a fun way to draw fractal images in the plane. It goes like this (for anyone interested):
1) Pick a function f: R^2 ==> R^2
2) Pick a region of R^2 (this could be the unit square for instance).
3) For each point in the region do the following:
a) Plug the point into f. Then plug f(x) into f. Then plug f(f(x)) into f, etc....
b) The norm of f(f(...f(x)...)) will either run off to infinite or stay bounded.
c) Record for the original point, x, how many iterations it took the process to run off to infinite (or the maximum if the sequence stayed bounded).
Here's the result of this process for the function:
f(x,y) = ( exp(x) * cos(y), exp(x) * sin(y) )
Re: Inceptionism: Going Deeper into Neural Networks
#125Earlier quoted context omitted.
Perhaps the argument should be steelmanned in that we should generally avoid using algorithms which are so complex that they aren't glass boxes. I doubt the idea to "simply follow gradients" can prove neural networks to be glass boxes because the output of that is still too complex. And we are clearly onto something here. If we can generate artificially hallucinated pictures today, it is not unreasonable to assume th…
> I think it is highly unethical that there is almost no democratic control over what is being developed at Google, Facebook et al. in secrecy. The most recent XKCD comic is quite relevant: http://xkcd.com/1539/ I consider myself to be very Left of center, but, I can't imagine what form of 'democratic control' you think is necessary over the research that Google and Facebook does. I do not fault Google or Facebook fo…
It's a tricky ethical area. The Google post cites several research papers that seemt o provide more than enough information to replicate these results or get similar ones, which is good, because I think everyone should be able to explore these tools - I stick by my view from yesterday that this may be a scientific breakthrough.
At the same time, I can see the basis for some anxiety, because it's not hard to imagine proprietary research going a few steps farther and developing some sort of general intelligence or even a limited but extremely high-powered intelligence that would confer an overwhelming commercial advantage, and/or a political one. Suppose, as an exercise, that one developed an algorithm to maximize persuasiveness by first leading readers/listeners into a quiescent, semi-hypnotic state and then making your commercial or political pitch. There's certainly a potential for abuse.
In Europe this sort of thing tends to bring up the precautionary principle, the idea that you shouldn't do something without oversight and demonstrated minimization of risk. I think that's highly limiting, but expect some pushback against Google over this. Of course, I don't think democracy is all that wonderful either but then I'm a bit of a misanthrope.
Re: Inceptionism: Going Deeper into Neural Networks
#126Does anyone have a good sense of what exactly they mean here: >Instead of exactly prescribing which feature we want the network to amplify, we can also let the network make that decision. In this case we simply feed the network an arbitrary image or photo and let the network analyze the picture. We then pick a layer and ask the network to enhance whatever it detected. Each layer of the network deals with features at…
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Re: Inceptionism: Going Deeper into Neural Networks
#127Some comments seem to be appreciating (or getting disgusted by) the aesthetics but I think the "inceptionism" part should not be ignored: We're essentially peeking inside a very rudimentary form of consciousness: a consciousness that is very fragile, very dependent, very underdeveloped, and full of "genetic errors". Once you have a functioning deep learning neural network, you have the assembly language of consciousn…
We're essentially peeking inside a very rudimentary form of consciousness Blatant sensationalism. There is absolutely nothing here that would suggest consciousness. If you have a mask for matching images, you can reverse that mask and imprint it as an image. What we're seeing here is a more complicated version of the same process. Heck, look more closely. Some of those "building" images have obvious chunks of pedestr…
If anything, some of those images are just a more elaborate version of a kaleidoscope.
I put it to you that your 'self' is not much more complicated than any other elaborately iterated strange loop.
Re: Inceptionism: Going Deeper into Neural Networks
#128Earlier quoted context omitted.
Perhaps the argument should be steelmanned in that we should generally avoid using algorithms which are so complex that they aren't glass boxes. I doubt the idea to "simply follow gradients" can prove neural networks to be glass boxes because the output of that is still too complex. And we are clearly onto something here. If we can generate artificially hallucinated pictures today, it is not unreasonable to assume th…
> I think it is highly unethical that there is almost no democratic control over what is being developed at Google, Facebook et al. in secrecy. The most recent XKCD comic is quite relevant: http://xkcd.com/1539/ I consider myself to be very Left of center, but, I can't imagine what form of 'democratic control' you think is necessary over the research that Google and Facebook does. I do not fault Google or Facebook fo…
I'm just questioning whether an autopilot with a profit maximization heuristic is the best tool to guide technological progress. With democratic control I don't necessarily mean our current democratic systems but any kind of decentralization of decision making by voting. Yeah, I know that's vague, but given what appears be at stake it seems unreasonable not to consider alternatives.
Re: Inceptionism: Going Deeper into Neural Networks
#129I understand the theory behind neural networks quite well, but am not so clear on how you feed them with images, eg how do you build a network that can process megapixel images of random aspect ratios or audio files of predictable length?
I', trying to get a sense of how much effort would be involved to replicate these results if Google isn't inclined to share its internal tools, to do a neural network version of Fractint as it were, which one could train oneself. I have no clue which of the 30-40 deep learning libraries I found would be best to start with, or whether my basic instinct (to develop a node-based tool in ab image/video compositing package) is completely harebrained.
Essentially I'm more interested in experimenting with tools to do this sort of thing by trying out different connections and coefficients than in writing the underlying code. Any suggestions?
Re: Inceptionism: Going Deeper into Neural Networks
#130Worth reading the comments too. One from Vincent Vanhoucke: "This is the most fun we've had in the office in a while. We've even made some of those 'Inceptionistic' art pieces into giant posters. Beyond the eye candy, there is actually something deeply interesting in this line of work: neural networks have a bad reputation for being strange black boxes that that are opaque to inspection. I have never understood those…
That's not really fair though, since any deterministic function can be "back-propagated" using the chain rule (or even automatic differentiation), even though it's not really necessary for simpler models such as GMM and SVM since there are much easier ways of inspecting them. Also, I don't feel single input/output pairs really describe the function itself -- knowing cos(0) = 1 doesn't reveal much about the cosine fun…