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Differentiable Neural Computers
- IanCal 10y agoDoes anyone have a readcube link/similar for the paper? http://www.nature.com/nature/journal/vaop/ncurrent/full/nature20101.html http://www.nature.com/nature/journal/vaop/ncurrent/full/natu...
- ericjang 10y agohere you go http://rdcu.be/kXdj http://rdcu.be/kXdj
- IanCal 10y agoFantastic, thanks.
- pmatev 10y agoThis is great, thanks. Anyone know how those charts / graphs have been generated?
- hyperbovine 10y agoBy hand, I imagine. There is an author credited specifically with the graphics and nothing else.
- rkaplan 10y agoThis paper builds off of DeepMind's previous work on differentiable computation: Neural Turing Machines. That paper generated a lot of enthusiasm when it came out in 2014, but not many researchers use NTMs today. The feeling among researchers I've spoken to is not that NTMs aren't useful. DeepMind is simply operating on another level. Other researchers don't understand the intuitions behind the architecture well enough to make progress with it. But it seems like DeepMind, and specifically Alex Graves (first author on NTMs and now this), can.
- wiz21c 10y ago>> DeepMind is simply operating on another level. Would you be so kind as to to explain what you mean here ? Thanks !
- outsideline 10y agoThey're taking features that are present in the brain that aren't modeled and are making computational models for them. They're not a gold standard. You can create your own in under an hour. It's not another level. It's bio-inspired computing. Here.. take the 'Axon Hillock' https://en.wikipedia.org/wiki/Axon_hillock https://en.wikipedia.org/wiki/Axon_hillock code up a function for it, attach it to present day neuron models, make it do something fancy, write a white-paper and kazaam you're operating on another level.. Get it?
- wiz21c 10y agook I get it :) Nice little sarcasm, I'm loving it :-)
- visarga 10y agoDeep mind is breaking new ground in number of directions. For example, "Decoupled Neural Interfaces using Synthetic Gradients" is simply amazing - they can make training a net async and run individual layers on separate machines by approximating the gradients with a local net. It's the kind of thing that sounds crazy on paper, but they proved it works. Another amazing thing they did was to generate audio by direct synthesis from a neural net, beating all previous benchmarks. If they can make it work in real time, it would be a huge upgrade in our TTS technology. We're still waiting for the new and improved AlphaGo. I hope they don't bury that project.
- outsideline 10y agoDecoupled Neural Interfaces using Synthetic Gradients is a fancy name for the electro-chemical gradient that lies outside the cell wall of neurons : https://en.wikipedia.org/wiki/Electrochemical_gradient https://en.wikipedia.org/wiki/Electrochemical_gradient It's decoupled yet stores transient local information regarding previous neuron activity. Another bio-inspired copy-pasta.
- partycoder 10y agoI wonder if they will put this to use in their StarCraft bot.
- saguppa 10y agoYeah, games like StarCraft will probably need a working memory component. The task that they solve here with RL is a simple puzzle game. It'll be interesting to see if this works for Atari games or StarCraft.
- bytefactory 10y agoNerdgasm.
- orthoganol 10y agoIt appears they are touting 'memory' as the key new feature, but I know at least in the deep learning NLP world there already exists models with 'memory', like LSTMs or RNNs with dynamic memory or 'attention.' I can't imagine this model is too radically different than the others. Maybe I just feel a bit uneasy with a claim such as: > We hope DNCs provide a new metaphor for cognitive science and neuroscience.
- modeless 10y agoThe "memory" in a typical RNN is akin to a human's short term working memory. It only holds a few things and forgets old things quickly as new things come in. This new memory can hold a large number of things and stores them for an unlimited amount of time, more like a human's long term memory or a computer's RAM. It's a big difference, and the implementation is completely different too.
- orthoganol 10y agoI was not referring to typical RNNs, but LSTMs or RNNs with 'attention'. They are designed to overcome vanishing/ exploding gradient problems and hold arbitrary memory lengths.
- vintermann 10y agoThey can technically be as long as you want them, but in practice there are still severe constraints. LSTMs alleviate the gradient problems, but you still get real trouble with long-term dependencies. Alex Graves and some others in DeepMind have focused a lot in the past year or so on developing practical differentiable data structures, so that the LSTM can read and write to an external memory (and save its precious internal state for more immediate needs) yet still be trainable via backpropagation.
- modeless 10y agoThe memory provided by an attention architecture is immutable and has only one addressing mode. The memory in this paper is mutable (hence "Neural Turing Machine") and has several different addressing modes.
- outsideline 10y agohttps://en.wikipedia.org/wiki/Bio-inspired_computing https://en.wikipedia.org/wiki/Bio-inspired_computing Present day Neuron models lack an incredible number of functional features that are clearly present in the human brain. NTMs = representing memory that is stored in neurons https://en.wikipedia.org/wiki/Neuronal_memory_allocation https://en.wikipedia.org/wiki/Neuronal_memory_allocation Decoupled Neural Interfaces using Synthetic Gradients = https://en.wikipedia.org/wiki/Electrochemical_gradient https://en.wikipedia.org/wiki/Electrochemical_gradient Differentiable Neural Computers = Won't specify what natural aspect of the brain this derives from. Pick an aspect of a neuron or the brain that isn't modeled, write a model... Bleeding edge + Operating on another level The fact that someone is going out of there way to remove points from my posts so that this doesn't see tomorrow's foot traffic instead of replying and critiquing me just goes to show how truthful these statements are. Anyone can create such models. No one has a monopoly or patent on how the brain functions. Thus, expect many models and approaches.. Some better than others. You can down-vote all you want. The better model and architecture wins this game. It would help the community if people were honest about what's going on here but people instead want to believe in magic and subscribe to the idea that only a specific group of people are writing biologically inspired software and are capable authoring a model of what is clearly documented in the human brain. Interesting that this is the reception.
- gjm11 10y agoYou're not getting downvoted for being mean about DeepMind, you're getting downvoted for making overconfident pronouncements about things you don't understand. "Neural Turing machines" are not the same thing as neuronal memory allocation: NTMs' memory is external and neuronal memory allocation is all about how memory is stored in neurons in the brain. The "synthetic gradients" in that paper have nothing to do with the electrochemical gradients you mention other than the name. No one is claiming that the DeepMind guys are "operating on another level" because they do bio-inspired things. They are claiming that because they are getting more impressive results than anyone else. Now: Are they really? If so, is that enough justification for such a grand-sounding claim. I don't know. That would be an interesting discussion to have. But "Boooo, these people are just copying things present in the brain, there's nothing impressive about that" is not, especially when the parallels between the brain-things and the DeepMind-things are as feeble as in your examples.
- dharma1 10y agoWaiting for Schmidhuber to pipe up that he wrote about something similar in -93 and Alex Graves was his student anyway
- singham 10y agoExactly. I saw his webpage and was overawed until I read about him on reddit. That guy is full of himself.
- dharma1 10y agoHe has done a lot of pioneering work, to be honest. I recommend seeing him talk (or watch a video), I think his humour comes across better that way
- chriswarbo 10y agoIt's interesting to think that Schmidhuber's actually applying machine learning methods to the field of machine learning, e.g. see the opening of http://people.idsia.ch/~juergen/deep-learning-conspiracy.html http://people.idsia.ch/~juergen/deep-learning-conspiracy.htm... If AGI is the goal and machine learning research is the search algorithm, then Schmidhuber's attempting to perform backpropagation by pushing rewards back along the connections :)
- Senji 10y agoWe should use back propagation with government.
- tim333 10y agoI wonder how close these differentiable neural computers are functionally to cortical columns in the brain that are "are often thought of as the basic repeating functional units of the neocortex." (https://en.wikipedia.org/wiki/Neocortex#Cortical_columns https://en.wikipedia.org/wiki/Neocortex#Cortical_columns)
- carapace 10y ago(What the hell with the thin grey sans-serif body text font? Seriously, do you hate your readers' eyes that much?)
- tvural 10y agoThe idea of using neural networks to do what humans can already write code to do seems a bit wrong-headed. Why would you take a system that's human-readable, fast, and easy to edit, and make it slow, opaque, and very hard to edit? The big wins for ml have all been things that people couldn't write code to do, like image recognition.
- AgentME 10y agoEven if it only works for problems that are trivially solved by people, the fact that it can be done automatically is useful. A system could react to changes and some new problems automatically by continually retraining itself.
- JeffreyKaine 10y agoI think they just want to teach the system to crawl before it can walk, run then eventually fly. Doing something that would be easy for a human to code makes it easy for a human to see what's going on and help train the system to think like a human.