25 ms·
Inceptionism: Going Deeper into Neural Networks
- Animats 11y agoThis is fascinating. And important. We need better ways to see what neural nets are doing. At least for visual processing, we now have some. This might be usable on music. Train a net to recognize a type of music, then run it backwards to see what comes out. Run on the neural nets that do face popout (face/non face, not face recognition), some generic face should emerge. Run on nets for text recognition, letter forms should emerge. Run on financial data vs results ... optimal business strategies? But calling it "inceptionism" is silly. (Could be worse, as with "polyfill", though.)
- userbinator 11y agoRun on the neural nets that do face popout (face/non face, not face recognition), some generic face should emerge https://en.wikipedia.org/wiki/Eigenface https://en.wikipedia.org/wiki/Eigenface
- moyix 11y agoThis 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/image_generated_by_a_convolutional_network/ https://www.reddit.com/r/MachineLearning/comments/3a1ebc/ima...
- userbinator 11y agoIt reminds me of that parrot image that was said to crash human brains, only even more intense. I certainly experienced some effect, as while looking at it and trying to figure out what exactly it was, I felt my head heating up --- probably increased blood flow.
- walterbell 11y agoI stopped after a few seconds of ocular recursion. A new category of warning label :)
- teraflop 11y agoIn case anyone hasn't read the story this is referring to: "BLIT" by David Langford. http://www.infinityplus.co.uk/stories/blit.htm http://www.infinityplus.co.uk/stories/blit.htm
- shard 11y agoAh, so it's Monty Python's funniest joke in the world in visual form: https://m.youtube.com/watch?v=ienp4J3pW7U https://m.youtube.com/watch?v=ienp4J3pW7U
- Houshalter 11y agoThere's also a few sequels: http://ansible.uk/writing/c-b-faq.html http://ansible.uk/writing/c-b-faq.html http://www.lightspeedmagazine.com/fiction/different-kinds-of-darkness/ http://www.lightspeedmagazine.com/fiction/different-kinds-of... And What Happened at Cambridge IV, which I can't find online.
- JonnieCache 11y agoHere's What Happened at Cambridge IV: https://books.google.co.uk/books?id=5d9hHvD-T7gC&lpg=PA264&ots=4iPFt60kwj&dq=What%20Happened%20at%20Cambridge%20IV&pg=PA264#v=onepage&q=What%20Happened%20at%20Cambridge%20IV&f=false https://books.google.co.uk/books?id=5d9hHvD-T7gC&lpg=PA264&o... This last story makes the ML images even more disturbing! Highly recommend these stories. They'd make a great black mirror episode.
- david-given 11y agoGoogle Books previews appears to block pages randomly per user --- I don't see the entire text, unfortunately.
- mhax 11y agoThat 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.
- drcode 11y agoIf I was part of the Google marketing machine and a developer wanted to make that nightmare image public, I'd veto them in a Mountain View minute.
- tdaltonc 11y agoYa the PR team would have probably gone with this one: https://photos.google.com/share/AF1QipPX0SCl7OzWilt9LnuQliattX4OUCj_8EP65_cTVnBmS1jnYgsGQAieQUc1VQWdgQ/photo/AF1QipPq_sGlqxsEk855ZQFhfYvjyqULDduVhbn9-oU7?key=aVBxWjhwSzg2RjJWLWRuVFBBZEN1d205bUdEMnhB https://photos.google.com/share/AF1QipPX0SCl7OzWilt9LnuQliat...
- kzrdude 11y agoThat image is very unpleasant. I get creeps from fractals, but now my whole body is itching. Humans are weird.
- simonster 11y agoThe fractal nature of many of the "hallucinated" images is kind of fascinating. The parallels to psychedelic drug-induced hallucinations are striking.
- dzhiurgis 11y agoI remember reading that drugs block certain parts of the optical nerve, which itself is a bit like fractal. Also, personally, these pictures looked 'trippy' due to color palette that reminds hippy raves, rather than fractals.
- sixQuarks 11y agoIf we want to progress AI, I seriously recommend these researchers try acid or magic mushrooms.
- meemoo 11y agoI'm reminded of Alex Grey's visionary art https://caudallure.files.wordpress.com/2011/09/1217891347094.. https://caudallure.files.wordpress.com/2011/09/1217891347094....
- raldi 11y agoLink's broken.
- teraflop 11y agoFixed: https://caudallure.files.wordpress.com/2011/09/1217891347094.jpg https://caudallure.files.wordpress.com/2011/09/1217891347094...
- plq 11y agoOT, but this is originally a "3D" image. It can be found in the cover art of "10000 Days", an album from American metal band Tool. The original box comes with two magnifying lenses like this: http://s21.photobucket.com/user/Stonergrunge/media/Mis%20cosas/Colecciones%20varias/TOOL-10000_Days-04.jpg.html http://s21.photobucket.com/user/Stonergrunge/media/Mis%20cos... This (and others in the cover) look stunning through these lenses.
- henryl 11y agoI'll be the first to say it. It looks like an acid/shroom trip.
- jarboot 11y agoMaybe there's something to do with how our brains interpret information differently when under the influence of psychoactive drugs. I've been looking at Aldous Huxley's "Doors of Perception" and other psychonautic works recently and he hypothesizes that these sorts of drugs filter out the usual signals from the CNS that shut out the parts of perception that are not important for you to receive for survival. It might be some great leap of armchair psychology, but I think we're due for another psychedelic revival, especially considering the new advances in synthetic psychedelics, legalization of more harmless recreational drugs, new tests in medical research using MDMA/LSD/Psilocybin, and the cultural shift away from the 'War on drugs'.
- meemoo 11y agoTweak image urls for bigger images: Ibis: http://3.bp.blogspot.com/-4Uj3hPFupok/VYIT6s_c9OI/AAAAAAAAAlc/_yGdbbsmGiw/s6400/ibis.png http://3.bp.blogspot.com/-4Uj3hPFupok/VYIT6s_c9OI/AAAAAAAAAl... Seurat: http://4.bp.blogspot.com/-PK_bEYY91cw/VYIVBYw63uI/AAAAAAAAAlo/iUsA4leua10/s6400/seurat-layout.png http://4.bp.blogspot.com/-PK_bEYY91cw/VYIVBYw63uI/AAAAAAAAAl... Clouds: http://4.bp.blogspot.com/-FPDgxlc-WPU/VYIV1bK50HI/AAAAAAAAAlw/YIwOPjoulcs/s6400/skyarrow.png http://4.bp.blogspot.com/-FPDgxlc-WPU/VYIV1bK50HI/AAAAAAAAAl... Buildings: http://1.bp.blogspot.com/-XZ0i0zXOhQk/VYIXdyIL9kI/AAAAAAAAAmQ/UbA6j41w28o/s6400/building-dreams.png http://1.bp.blogspot.com/-XZ0i0zXOhQk/VYIXdyIL9kI/AAAAAAAAAm... I'd love to experiment with this and video. I predict a nerdy music video soon, and a pop video appropriation soon after.
- cing 11y agoAs linked in the last figure caption, there's a Google Photos gallery with high-resolution downloadable versions: https://goo.gl/photos/fFcivHZ2CDhqCkZdA https://goo.gl/photos/fFcivHZ2CDhqCkZdA
- ohashi 11y agoSome of them are truly beautiful
- anon012012 11y agoThere's a video, among them https://photos.google.com/share/AF1QipPX0SCl7OzWilt9LnuQliattX4OUCj_8EP65_cTVnBmS1jnYgsGQAieQUc1VQWdgQ/photo/AF1QipOlM1yfMIV0guS4bV9OHIvPmdZcCngCUqpMiS9U?key=aVBxWjhwSzg2RjJWLWRuVFBBZEN1d205bUdEMnhB https://photos.google.com/share/AF1QipPX0SCl7OzWilt9LnuQliat...
- agumonkey 11y agoHad me sitting down. Felt mesmerizing, like a weird resonnance with my mind. This is how I imagined my brain working, patching bits of stimulus to recreate complex shapes fractally... Seeing it in pictures is ... just amazing.
- deleted 11y ago[deleted]
- pault 11y agoI would love to see what would come out of a network trained to recognize pornographic images using this technique. :)
- anigbrowl 11y agoThat's a really worthwhile question, actually, considering the degree to which the human form and stylished portrayals of sexuality underpin so much of art and design.
- userbinator 11y agoI think doing the same with videos would be far more interesting... and probably creepier.
- deleted 11y ago[deleted]
- calebm 11y agoI think it would look like a Picasso: https://en.wikipedia.org/wiki/Les_Demoiselles_d'Avignon https://en.wikipedia.org/wiki/Les_Demoiselles_d'Avignon
- davesque 11y agoI'm sure that would be extremely disturbing. It would probably look like H.R. Giger's art.
- hliyan 11y agoAm I the only person who is not entirely happy about the overuse of the pop-culture term 'inception' for everything that is remotely nested, recursive or strange-loop-like? In this paper, we will focus on an efficient deep neural network architecture for computer vision, codenamed Inception, which derives its name from the Network in network paper by Lin et al [12] in conjunction with the famous “we need to go deeper” internet meme [1]
- davesque 11y agoI haven't personally noticed any buzz-wordiness about that term lately. Maybe I'm just not looking at the same stuff as you.
- fixermark 11y agoIn other words, you haven't trained your personal neural net with enough Inception-meme instances to be finding it everywhere in the noise? ;)
- return0 11y agoThere is at least one major neuroscientific paper with similar title http://rstb.royalsocietypublishing.org/content/369/1633/20130142 http://rstb.royalsocietypublishing.org/content/369/1633/2013...
- intjk 11y agoI'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 to someone's head), and give it the ability to ask questions, say, below some confidence interval (and us the ability to supply it answers)? What would this represent? What would come out on the other side? A fraction of a human being? Or perhaps just an artificial representation of "the human experience". ...what if we fed it books?
- andor 11y agoNeural 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-Representations/ http://colah.github.io/posts/2014-07-NLP-RNNs-Representation...
- gradys 11y agoNeurons are also relatively simple, at least in comparison to the mind. I don't think the simplicity or complexity of the underlying model has much bearing on the higher-level properties of the network. 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.
- sp332 11y agoIt would have some kind of intelligence, at least able to recall information and form associations between things. But there's no reason to think that it would come out looking human. I mean you can show a dog lots and lots of images and it doesn't turn human.
- IanCal 11y agoThe one generated after looking at completely random noise on the bottom row, second from the right: http://googleresearch.blogspot.co.uk/2015/06/inceptionism-going-deeper-into-neural.html http://googleresearch.blogspot.co.uk/2015/06/inceptionism-go... Reminds me very heavily of The Starry Night https://www.google.com/culturalinstitute/asset-viewer/the-starry-night/bgEuwDxel93-Pg?utm_source=google&utm_medium=kp&hl=en-GB&projectId=art-project https://www.google.com/culturalinstitute/asset-viewer/the-st... Lovely imagery. I never had much luck with generative networks. I did some work putting RBMs on a GPU partly because I'd seen Hinton talk showing starting with a low level description and feeding it forwards, but always ended up with highly unstable networks myself.
- Lewton 11y agohttps://lh3.googleusercontent.com/4jaIlDI1xXGOhxNejib833qA5yVMAvQLaKmmuaQ0PGY=w1200-h800-no https://lh3.googleusercontent.com/4jaIlDI1xXGOhxNejib833qA5y... full resolution image
- IanCal 11y agoThat's great, thank you.
- abrichr 11y agoNeural networks are notoriously difficult to train due to the large number of hyper-parameters that need to be tuned. If your network never converged, it's possible your learning rate was too high, so it kept overshooting the minima of the loss function.
- IanCal 11y agoQuite possibly. The classification results were great, just wasn't good when trying to run things back through the network repeatedly. I did have issues that the learning rates reported in some of the original papers didn't match the ones in the released code.
- nl 11y agoI'd really like to see what an Electric Sheep looks like. Maybe if they did a collaboration with the Android team?
- frankosaurus 11y agoReally cool. You could generate all kinds of interesting art with this. I can't help but think of people who report seeing faces in their toast. Humans are biased towards seeing faces in randomness. A neural network trained on millions of puppy pictures will see dogs in clouds.
- fixermark 11y agoThat's essentially precisely what's happening here. You can see in the different pictures where different sets of training data were used---buildings, faces, animals. Give the machine millions of reference images to work from and then tell it to find those images in noise, and it will succeed (because it literally can't "imagine" anything else for the noise to be).
- tomlock 11y agoThese paintings remind me of Louis Wain's work when he was mentally ill. Which makes me wonder, are these sophisticated neural nets mentally ill, and what would a course of therapy for them be like?
- anigbrowl 11y agoThis sort of 'illness' should be supported, rather than treated.
- davesque 11y agoThis 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.
- deleted 11y ago[deleted]
- isp 11y agoI'm blown away by this "guided hallucination" technique. It's not a big oversimplification to describe to the layperson as: enter images into neural network; receive as output artwork representing the essence of the images.
- mortenjorck 11y agoThis may sound ridiculous, but I think this has the potential to be a development as foundation-shaking as Modernism itself. There has been plenty of algorithmically-derived art over the past 30 years, but generative pieces inevitably look like math – they are interesting curiosities, sometimes quite beautiful, but they don’t challenge the mind like any of the major movements of the past 150 years. This is different because, while still just math, it’s modeled on the processes of human perception. And when successfully executed, it plays on human perception in ways that were formerly the exclusive domain of humans – Chagall, DiChirico, Picasso – gifted with some sort of insight into that perception. Future iterations of this kind of processing, with even higher-order symbol management could get really weird, really fast.
- return0 11y ago> And not just art, but good art Makes me wonder what passes as good art nowadays. But yeah some of the renderings were particularly aesthetic.
- calebm 11y agoI 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?
- philipn 11y agoThe 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 directly. Instead, that happens by feeding the generated image back to the network over and over again (with e.g. zooming in between). Think of it a bit like a Rorschach test. But instead of ink blots, we'd use random noise and an artificial neural network. And instead of switching to the next Rorschach card after someone thinks they see a pattern, you continuously move the ink blot around until it looks more and more like the image the person thinks they see. But because we're dealing with ink, and we're just randomly scattering it around, you'd start to see more and more of your original guess, or other recognized patterns, throughout the different parts of the scattered ink. Repeat this over and over again and you have these amazing fractals!
- barbs 11y agoThat's really cool. The trippiness is further compounded by the rainbow-ish colour effect produced by the recursive function, which mimics the "shimmering" rainbow effect you commonly get around lights when tripping on LSD. And also, when under the influence of various drugs you tend to see patterns, particularly faces, where there aren't any.
- DanBC 11y ago> The reason they look so 'fractal-like' (e.g. trippy!) is because they actually are fractals! Do they exhibit self-similarity at different zoom levels?
- fixermark 11y agoI believe they do (in the sense that if you take one of these images, zoom it in, and run it through the algorithm again, it'll take the micro-features of the animals it hallucinated and hallucinate more animals on top of them).
- 11y ago
- huskyr 11y agoVery cool. I wonder if there's some example code on Github to generate images like this?
- mraison 11y agoReally nice. I'd be interested in seeing a more in-depth scientific description of how these images were actually generated. Are there any other publications related to this work?
- kriro 11y agoThere's four papers linked in the article. The last three (see below) were pretty good, haven't read the first. http://arxiv.org/pdf/1412.0035v1.pdf http://arxiv.org/pdf/1412.0035v1.pdf http://arxiv.org/pdf/1506.02753.pdf http://arxiv.org/pdf/1506.02753.pdf http://arxiv.org/pdf/1312.6034v2.pdf http://arxiv.org/pdf/1312.6034v2.pdf
- gojomo 11y agoFacial-recognition neural nets can also generate creepy spectral faces. For example: https://www.youtube.com/watch?v=XNZIN7Jh3Sg https://www.youtube.com/watch?v=XNZIN7Jh3Sg https://www.youtube.com/watch?v=ogBPFG6qGLM https://www.youtube.com/watch?v=ogBPFG6qGLM (Or if you want to put them full-screen on infinite loop in a darkened room: http://www.infinitelooper.com/?v=XNZIN7Jh3Sg&p=n http://www.infinitelooper.com/?v=XNZIN7Jh3Sg&p=n http://www.infinitelooper.com/?v=ogBPFG6qGLM&p=n http://www.infinitelooper.com/?v=ogBPFG6qGLM&p=n ) The code for the 1st is available in a Gist linked from its comments; the creator of the 2nd has a few other videos animating grid 'fantasies' of digit-recognition neural-nets.
- anigbrowl 11y agoThese images are remarkably similar to chemically-enhanced mammalian neural processing in both form and content. I feel comfortable saying that this is the Real Deal and Google has made a scientifically and historically significant discovery here. I'm also getting an intense burst of nostalgia.
- davedx 11y agoWorth 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 charges: any other model (GMM, SVM, Random Forests) of any sufficient complexity for a real task is completely opaque for very fundamental reasons: their non-linear structure makes it hard to project back the function they represent into their input space and make sense of it. Not so with backprop, as this blog post shows eloquently: you can query the model and ask what it believes it is seeing or 'wants' to see simply by following gradients. This 'guided hallucination' technique is very powerful and the gorgeous visualizations it generates are very evocative of what's really going on in the network."
- svantana 11y agoThat'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 function, even though it's a local maximum. Maybe one could extend the technique to show transitions (morphing) between classes as video?
- fugyk 11y agohttps://photos.google.com/share/AF1QipPX0SCl7OzWilt9LnuQliattX4OUCj_8EP65_cTVnBmS1jnYgsGQAieQUc1VQWdgQ/photo/AF1QipOlM1yfMIV0guS4bV9OHIvPmdZcCngCUqpMiS9U?key=aVBxWjhwSzg2RjJWLWRuVFBBZEN1d205bUdEMnhB https://photos.google.com/share/AF1QipPX0SCl7OzWilt9LnuQliat...
- sophcw 11y agohey, where'd you get that?
- 11y ago
- dnr 11y agoAm 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 http://www.reddit.com/r/trypophobia
- anigbrowl 11y agoMy wife found them somewhat too intense to take in rapidly. If viewing these makes you uncomfortable you should probably steer clear of psychedelic drugs, which tend to induce this sort of imagery for hours on end; as you can imagine this would be mentally tiring at the best of times.
- Houshalter 11y agoThis unpublished one is incredibly creepy. https://i.imgur.com/6ocuQsZ.jpg https://i.imgur.com/6ocuQsZ.jpg
- joeyspn 11y agoDefinitely this thing likes dogs =)
- krebby 11y agoThis one too: https://photos.google.com/share/AF1QipPX0SCl7OzWilt9LnuQliattX4OUCj_8EP65_cTVnBmS1jnYgsGQAieQUc1VQWdgQ/photo/AF1QipPVTpDfh2LrPA9ui0CH1Xof_RByCyaa9ce_U60h?key=aVBxWjhwSzg2RjJWLWRuVFBBZEN1d205bUdEMnhB https://photos.google.com/share/AF1QipPX0SCl7OzWilt9LnuQliat...
- userbinator 11y agoI'm only mildly trypophobic but those images did have a minor effect - and I have a possible hypothesis for why it happens: what these images and trypophobia-triggering ones all have in common is a huge number of edges of various shapes and sizes, and it's this "edge overload" stimulating many more neurons than usual that's causing the disturbance. I find that the repetitive, but not-quite-the-same patterns like (organic) holes or other curvy shapes have the greatest effect; in contrast, straight lines don't do much. This makes sense since straight lines probably only trigger neurons that detect one direction, but curves have many "directions" to them.
- jakozaur 11y agoRequest for startup: Neural Network on demand artist. E.g. SaaS that takes your images and use neural network transformations. Can you make a portrait of my that I look like king.
- waffl 11y agoWhile I think this is beautiful, conceptually, I really am a bit terrified of the potential of this in reverse (the neural network for processing/understanding an image). With Google releasing their 'Photos' app, this network is about to get a direct pipeline for machine learning imagery to accelerate everything – my main fear would be the potential for this technology to be employed by weaponized drones able to scan a scene (with, eventually, incredibly high resolution cameras and microphones that far surpass human capability) and identify every single object/person in realtime (also at a rate that humans are incapable of). Of course, there is great utility to be had as well, it just scares me to think about what could be done with this technology, in a mature form, if used for violent purposes.
- visarga 11y agoThis will happen for sure. Such super-perceptive computers will oversee our every movement. Computers can already understand our emotions in writing, voice and from the expression on our faces, they can also estimate pose and understand your movements. They can label thousands of kinds of objects. And they're just starting. They can also build neural nets 10x smaller by compressing a larger neural net while maintaining most of accuracy. That means once a problem such as vision or speech has been solved with a huge net, it can be transferred in a smaller, more efficient net.
- aoeuasdf1 11y ago> They can also build neural nets 10x smaller by compressing a larger neural net while maintaining most of accuracy. That means once a problem such as vision or speech has been solved with a huge net, it can be transferred in a smaller, more efficient net. This is known as "dark knowledge". Slides from Geoff Hinton: http://www.ttic.edu/dl/dark14.pdf http://www.ttic.edu/dl/dark14.pdf
- guelo 11y agoI'm starting to come around to sama's way of thinking on AI. This stuff is going to be scary powerful in 5-10 years. And it will continue to get more powerful at an exponential rate.
- johnconner 11y agoYou are not alone in your fears. Others have been ringing the alarm for some time. Nick Bostrom's Superintelligence is a good reference.
- antirez 11y agoAre the coefficients of the neurons inside the layers to "trigger" just multiplied by some constant? Not cited in the original article apparently.
- sitkack 11y agoThis was recently posted to HN, http://tjake.github.io/blog/2013/02/18/resurgence-in-artificial-intelligence/ http://tjake.github.io/blog/2013/02/18/resurgence-in-artific... Which mentions running the NN in reverse, quote By far the most interesting thing I’ve learned about Deep Belief Networks is their generative properties. Meaning you can look inside the ‘mind’ of a DBN and see what it’s imagining. Since a deep belief networks are two-way like restricted boltzmann machines you can make hidden inputs generate valid visual inputs. Continuing with our handwritten digit example you can start with the label input say a ‘3’ label and activate it then go reverse through the DBN and out the other end will pop out a picture of a ‘3’ based on the features of the inner layers. This is equivalent to our ability to visualize things using words, go ahead imagine a ‘3’, now rotate it.
- cafebeen 11y agoIt's worth pointing out that a naive bayes or k-nearest-neighbor classifier can similarly generate examples of valid inputs.
- stared 11y agoI am curious what do they see if feed with a screenshot of their own code.
- kriro 11y agoPretty interesting and beautiful. If I was still at my old job I'd love to try and see how helpful this is in teaching NN. My first instinct is that it would be really valuable because they tend to be blackboxy/hard to conceptualize.
- fizixer 11y agoSome 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 consciousness. Then you start playing with it (as this paper did), you create a hello world program, you solve the factorial function recursively, and so on. Somewhere in that universe of possible programs, is hidden a program (or a set of programs) that will be able to perform the thinking process a lot more accurately.
- romaniv 11y agoWe'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 pedestrians embedded, probably because the algorithm was trained on tourist photos. Is it interesting? Yes, from algorithmic point of view. Cool as hell. However, this has nothing to do with consciousness. If anything, some of those images are just a more elaborate version of a kaleidoscope. It's not like they run a network and got a drawing. They were looking for a particular result, did post processing, did pre-processing and tweaked the intermediate steps (by running them multiple times until the image looked interesting). Finally, we as viewers do our share of pattern matching, similar to how we see patterns in Rorschach inkblots. And there are captions that frame what we see and "guide" us to recognizing the right objects.
- fizixer 11y ago> Blatant sensationalism. There is absolutely nothing here that would suggest consciousness. 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.
- mkj 11y agoHas anyone seen an explanation for the why the images end up with that colour palette?
- TheLoneWolfling 11y agoProbably an artifact of the color space used.
- darkFunction 11y agoI don't understand what kind of NN they used on the painting and the photo of the antelopes(?). What was it pre-trained to recognise? EDIT: in clarification, to pick out abstract features of an image, it must obviously be trained on many images. I'm curious about how it picked out seemingly unique characteristics of the painting, and what images it was trained on to get there.
- agumonkey 11y agoDo computers dream about fractal antilopes ?http://i.imgur.com/jZtbz7f.png http://i.imgur.com/jZtbz7f.png
- m-i-l 11y agoThis story has been picked up by The Guardian: http://www.theguardian.com/technology/2015/jun/18/google-image-recognition-neural-network-androids-dream-electric-sheep http://www.theguardian.com/technology/2015/jun/18/google-ima...
- spot 11y agoa really early version of this: http://draves.org/fuse/ http://draves.org/fuse/ published as open source in the early 90s. not NN but does have the same image matching/searching.
- drcode 11y agoFascinating link, but that was arguably different: In the case of the Google links, the neural network was built for other uses, and the "image fusion" is only a side effect... It is a sort of "proof" that some really interesting things are happening behind the scenes. In the older approaches, the image fusion was the primary intent of the system. Still very cool, but much less impressive IMHO.
- murbard2 11y agoTwo remarks 1) Captain obvious says: the "tripiness" of these images is hardly coincidental, these networks are inspired by the visual cortex. 2) They had to put a prior on the low level pixels to get some sort of image out. This is because the system is trained as a discriminative classifier, and it never needed to learn this structure, since it was always present in the training set. This also means that the algorithm is going to be ignoring all sort of structures which are relevant to generation, but not relevant for discrimination, like the precise count and positioning of body parts for instance. This makes for some cool nightmarish animals, but fully generative training could yield even more impressive results.
- gradys 11y agoDoes 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 a different level of abstraction, so the complexity of features we generate depends on which layer we choose to enhance. For example, lower layers tend to produce strokes or simple ornament-like patterns, because those layers are sensitive to basic features such as edges and their orientations. Specifically, what does "we then pick a layer and ask the network to enhance whatever it detected" mean? I understand that different layers deal with features at different levels of abstraction and how that corresponds with the different kinds of hallucinations shown, but how does it actually work? You choose the output of one layer, but what does it mean to ask the network to enhance it?
- ebetica 11y agoMy thought is that they basically run gradient descent on the image where the loss is the magnitude of one output plane in one of the layers of the neural network. Probably using gradient descent to push up the magnitude of one of the output plane layers or something like that.
- discardorama 11y agoMy understanding: when you're doing standard gradient descent, you push the error down through the layers, modifying the weights at each layer. Now, in "normal" NN training you stop at the input layer; it makes no sense to tweak the error at the input layer, right? But what if you did the following: flow the error down from the outputs to the layer you're interested in, but don't modify the weights of any of the layers above it; just modify the values of this layer in accordance with the error gradient. Added later: I think we should wait till @akarpathy comes along and ELI5's it to us.
- deleted 11y ago[deleted]
- murbard2 11y ago
- patcon 11y agoWow. Funny how those images look like dreamscapes when the trained neural nets process random noise... Kinda make me contemplate more own conscious experience :)
- joeyspn 11y agoThe level of resemblance with a psychotropics' trip is simply fascinating. It's definitely really close to how our brain reacts when is flooded with dopamine + serotonin. I wonder if the engineers at Google can make the same experiment with audio... It'll be funny to listen the results.
- fortyeight 11y agoI'd be interested to see if the results end up looking something like DIPT which is the only known mainly audio hallucinogenics.
- tripzilch 11y agoMight be interesting, although I've always liked the visuals* of psychedelica a lot more than the audio effects (which in my experience, mostly tends to make sounds be perceived really "loud" and "close", rather than "trippy"--unless that's what you associate with "trippy" audio, of course). Dunno if my experience is typical, obviously. * also the particular mind-altering effects, which are hard to describe
- jastr 11y agoIf anyone wants to send this to their non-dev friends, here's the write-up I sent to mine! https://medium.com/@stripenight/seeing-how-computers-might-think-e8ea3d1de081 https://medium.com/@stripenight/seeing-how-computers-might-t... --- tldr: To figure out how computers "think", Google asked one of its artificial intelligence algorithms to look at clouds and draw the things it saw! There's this complex Artificial Intelligence algorithm called a neural network ( https://en.wikipedia.org/wiki/Artificial_neural_network https://en.wikipedia.org/wiki/Artificial_neural_network ). It's essentially code which tries to simulate the neurons in a brain. Over the last few years, there have been some really cool results, like using neural networks to read people's handwriting, or to figure what objects are in a picture. To start your neural network, you give it a bunch of pictures of dogs, and tell it that those pictures contain dogs. Then you give it pictures of airplanes, and say those are airplanes, etc. Like a child learning for the first time, the neural network updates its neurons to recognize what makes up a dog or an airplane. Afterwords, you can give it a picture and ask if the pic contains a dog or an airplane. The problem is that WE DON'T NOW HOW IT KNOWS! It could be using the shape of a dog, or the color, or the distance between it's legs. We don't know! We just can't see what the neurons are doing. Like a brain, we don't quite know how it recognize things. Google had a big neural network to figure out what's in an image, and they wanted to know what it did. So, they gave the neural net a picture, but stopped the neural net at different points, before it could finish deciding. When, they stopped it, they asked it to "enhance" what is just recognized. Eg. if it just saw the outline of a dog, the net would return the picture with the outline a bit thicker. Or, if it saw the colors similar to a banana, it would return the picture with those colors looking more like a banana's colors. This seems like a simple idea, but it's actually really complex, and really insightful! Amazing images here - https://photos.google.com/share/AF1QipPX0SCl7OzWilt9LnuQliattX4OUCj_8EP65_cTVnBmS1jnYgsGQAieQUc1VQWdgQ?key=aVBxWjhwSzg2RjJWLWRuVFBBZEN1d205bUdEMnhB https://photos.google.com/share/AF1QipPX0SCl7OzWilt9LnuQliat... Original article - http://googleresearch.blogspot.com/2015/06/inceptionism-going-deeper-into-neural.html http://googleresearch.blogspot.com/2015/06/inceptionism-goin...
- anigbrowl 11y agoI 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?
- xtacy 11y agoYou could try Torch libraries. There are a few examples on how to (almost) replicate some of Google's neural network models on Imagenet. Check https://github.com/torch/torch7/wiki/Cheatsheet#demos https://github.com/torch/torch7/wiki/Cheatsheet#demos.
- imh 11y agoIt would be interesting to know what happens if instead of tweaking it to better match a banana, they tweaked it to better match a banana and NOT match everything else.
- ghoul2 11y agoThis is brilliant! I did something similar when I was trying to learn about neural networks a long long time ago. The results were fascinating. I was writing a neural network trainer - to recognize simple 2D images. This was on a 300MHz desktop PC(!) so the network had to be pretty small. Which implied that the input images were just compositions of simple geometric shapes - a circle within a rectangle, two circles intersecting, etc. When I tried "recalling" the learnt image after every few X epochs of training, I noticed the neural network was "inventing" more complex curves to better fit the image. Initially, only random dots would show up. Then it would have invented straight lines and would try to compose the target image out of one and more straight lines. What was absolute fun to watch was, at some point, it would stop trying to compose a circle with multiple lines and just invent the circle. And then proceed to deform the circle as needed. During different runs, I could even see how it got stuck into various local minima. To compose a rectangle, mostly the net would create four lines - but having the lines terminate was obviously difficult. As an alternative, sometimes the net would instead try a circle, which it would gradually elongate, straighten out the circumference, slowly to look more and more like a rectangle. I was only an undergrad then, and was mostly doing this for fun - I do believe I should have written it up then. I do not even have the code anymore. But good to know googlers do the same kinda goofy stuff :-)
- tzs 11y agoUnderstanding what is going on in a neural network (or any other kind of machine learning mechanism) when it makes a decision can be important in real world applications. For example, suppose you are a bank and you have used built a neural network to decide if credit applications should be approved. The lending laws in the US require that if you reject someone you tell them why. Your neural network just gives a yes/no. It doesn't give a reason. What do you tell the applicant? I have an idea how to deal with that, but I have no idea if it would satisfy the law. My approach is to run their application through multiple times, tweaking various items, until you get one that would be approved. You can then tell them it was that item that sunk them. For instance, suppose that if you raise their income by $5k, you get approval. You can tell them they were rejected for having income that is too low.
- nitrogen 11y agoI have an idea for a company related to this concept, but for hiring and job training.
- bearzoo 11y agoThey are doing nothing but starting with random noise, and then learning a representation of an image that will maximize the probability in the output layer (by suggesting to the network that this noise should have actually been recognized as a banana or what have you) and back propagating changes into the input layer. Essentially, this has been happening since 2003 in the natural language processing world where we learn 'distributed representations' of words by starting with random representations of words, and learning them by context by back propagating changes into the input layer. Very cool though.
- djfm 11y agoNow I'm thinking about all those google cars, quietly resting in dark garages, dreaming about streets.