11–20 of 77 posts
This 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 enou…
This 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 enou…
That's not to say that NTMs are bad or uninteresting! They are super cool and I think have huge potential in natural language understanding, reasoning, and planning. However, I do think that DeepMind will have to prove that they can be used to solve some non-trivial task, one that can't be solved much more efficiently with traditional CS methods, before people will join in to their research.
Also, I think there's a possibility that solving non-trivial problems with NTMs may require more computing power than Moore's law has given us so far. In the same way that NNs didn't really take off until GPU implementations became available, we may have to wait for the next big hardware breakthrough for NTMs to come into their own.
This 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 enou…
In the human brain, Neurons store an incredible amount of information. Neuron models in neural networks only did so with weights.
There is still a lack of understanding on how the human brain does it. Deep Mind grabbed a proven memory model from Alan Turing's work and applied it to the feature barren neuron models in use. Sprinkle magic ...
They are not operating on another level, they're bringing over features that are well documented in the human brain and in white papers from a past period when people actually thought deeply about this problem and applying it.
https://en.wikipedia.org/wiki/Bio-inspired_computing
There is no 'intuition' about the architecture. Study the human brain and copy pasta into the computing realm.
Others are doing this as well. If anyone bothered to read the white papers people publish, you'll see that many people have presented similar ideas over the years.
You can come up with your own neural Turing machine. Take a featureless neuron model, slap a memory module on it and you have a neural turing machine.
This 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 enou…
Deep 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 sy…
It's decoupled yet stores transient local information regarding previous neuron activity.
Another bio-inspired copy-pasta.
This 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 enou…
>> DeepMind is simply operating on another level. Would you be so kind as to to explain what you mean here ? Thanks !
Here.. take the '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?
Maybe I just feel a bit uneasy with a claim such as:
> We hope DNCs provide a new metaphor for cognitive science and neuroscience.
It 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.
It 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.
The "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.
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
Decoupled Neural Interfaces using Synthetic Gradients = 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.
I wonder if they will put this to use in their StarCraft bot.