111–120 of 166 posts
Some 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…
Putting biological consciousness on a pedestal might be blatant sensationalism itself. By consciousness, I specifically mean the behavioral capacity of general intelligence, nothing more. If by consciousness you mean subjective character of experience then yes, there are serious issues with resolving the mind-body problem. But functionally speaking, our brains exist in a physical universe, are massively parallel, and do stochastic computations. Deep learning systems share all three of these traits except that the scale is about 3-5 order of magnitudes smaller (things like incorporating time, biological impulses are missing but if someone claims those features are going to be the dealbreaker then maybe we can have a discussion). And the scale difference is shrinking at lightning speed.
I'm not claiming DNN's are the end-all-be-all of an upcoming general electronic intelligence. But they seem to be doing mind-blowing stuff every few weeks, and it seems we've stumbled upon a radically new aspect of computation.
I'll repeat what I posted on facebook because I thought it was clever: "Yes, but only if we tell them to dream about electric sheep." So, tell the machine to think about bananas, and it will conjure up a mental image of bananas. Tell it to imagine a fish-dog and it'll do its best. What happens if/when we have enough storage to supply it a 24/7 video feed (aka eyes), give a robot some navigational logic (or strap it t…
Neural networks are a relatively simple mathematical model. They don't actually "think" or have a conscience. Neural networks are also regularly fed books, in order to model some properties of natural language. Here's a good introduction: http://colah.github.io/posts/2014-07-NLP-RNNs-Representation...
Now, this isn't to say that the kinds of neural networks we build today are conscious, but I don't think that's because they're based on a simple mathematical model; I think that's because they don't have the network-level properties that conscious humans do, for example, a self-representation.
Does 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…
The detection layer will detect very faint random signals. For example, if you have a unit that's supposed to detect dogs, it might be very faintly activated if by random chance there is a doggish quality to some part of the image. What they do is pick up that faint, random, signal and amplify it. They say: oh you think that cloud is a tiny bit dog-like? Ok, well then find me a small modification to the image that wo…
Earlier quoted context omitted.
The detection layer will detect very faint random signals. For example, if you have a unit that's supposed to detect dogs, it might be very faintly activated if by random chance there is a doggish quality to some part of the image. What they do is pick up that faint, random, signal and amplify it. They say: oh you think that cloud is a tiny bit dog-like? Ok, well then find me a small modification to the image that wo…
They've written though that they have chosen a particular layer in the network, which reads like "independent of the output layer". Features in such a layer correlate with certain classes, but I don't think they have dealt with classes at all. If that's the case, then the question is how they've amplified the detected features.
This appears to be the source of the mysterious image that showed up on Reddit's /r/machinelearning the other day too: https://www.reddit.com/r/MachineLearning/comments/3a1ebc/ima...
That image was so striking and appeared to come out of nowhere. Was it some kind of marketing ploy do you think? I'm glad to have found the source anyway.
This is one of the most astounding things I've ever seen. Some of these images look positively like art. And not just art, but good art.
Worth 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…
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 for planning on time-scales longer than most governments. Governments ought to be doing this level of long-term planning, but are not (at least publicly)
Worth 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…
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?
This is one of the most astounding things I've ever seen. Some of these images look positively like art. And not just art, but good art.
I felt the same. I think the main aspect about these images that makes me like them is how everything feels connected, which, is what the AI is trying to find: connections. Honestly, can anyone tell me where I could order large prints of some of these?