Are neural networks really abandonware? - neural-network

I am planning to use neural networks for approximating a value function in a reinforcement learning algorithm. I want to do that to introduce some generalization and flexibility on how I represent states and actions.
Now, it looks to me that neural networks are the right tool to do that, however I have limited visibility here since I am not an AI expert. In particular, it seems that neural networks are being replaced by other technologies these days, e.g. support vector machines, but I am unsure if this is a fashion matter or if there is some real limitation in neural networks that could doom my approach. Do you have any suggestion?
Thanks,
Tunnuz


It's true that neural networks are no longer in vogue, as they once were, but they're hardly dead. The general reason for them falling from favor was the rise of the Support Vector Machine, because they converge globally and require fewer parameter specifications.
However, SVMs are very burdensome to implement and don't naturally generalize to reinforcement learning like ANNs do (SVMs are primarily used for offline decision problems).
I'd suggest you stick to ANNs if your task seems suitable to one, as within the realm of reinforcement learning, ANNs are still at the forefront in performance.
Here's a great place to start; just check out the section titled "Temporal Difference Learning" as that's the standard way ANNs solve reinforcement learning problems.
One caveat though: the recent trend in machine learning is to use many diverse learning agents together via bagging or boosting. While I haven't seen this as much in reinforcement learning, I'm sure employing this strategy would still be much more powerful than an ANN alone. But unless you really need world class performance (this is what won the netflix competition), I'd steer clear of this extremely complex technique.


It seems to me that neural networks are kind of making a comeback. For example, this year there were a bunch of papers at ICML 2011 on neural networks. I would definitely not consider them abandonware. That being said, I would not use them for reinforcement learning.


Neural networks are a decent general way of approximating complex functions, but they are rarely the best choice for any specific learning task. They are difficult to design, slow to converge, and get stuck in local minima.
If you have no experience with neural networks, then you might be happier to you use a more straightforward method of generalizing RL, such as coarse coding.


Theoretically it has been proved that Neural Networks can approximate any function (given an infinite number of hidden neurons and the necessary inputs), so no I don't think the neural networks will ever be abandonwares.
SVM are great, but they cannot be used for all applications while Neural Networks can be used for any purpose.
Using neural networks in combination with reinforcement learning is standard and well-known, but be careful to plot and debug your neural network's convergence to check that it works correctly as neural networks are notoriously known to be hard to implement and learn correctly.
Be also very careful about the representation of the problem you give to your neural network (ie: the inputs nodes): could you, or could an expert, solve the problem given what you give as inputs to your net? Very often, people implementing neural networks don't give enough informations for the neural net to reason, this is not so uncommon, so be careful with that.

Related

Neural network: Just some regression?

I just started reading about neural networks. I thought they are something magic and extremly intelligent, but at the end of the day it just seems to be a large math function with many "undefined" constants? The learning is just another way for some kind of (more or less "stupid") regression? Is this true? For me this seems not to be very brilliant, so i am a bit surprised why this works that well.
Thank you very much

It has been proved that an artificial neural network with just one hidden layer is a universal approximator; that is, under proper parameterisation, it can approximate any continuous function (see universal approximation theorem). More importantly, as the Wikipedia article mentions:
Work by Hava Siegelmann and Eduardo D. Sontag has provided a proof that a specific recurrent architecture with rational valued weights (as opposed to full precision real number-valued weights) has the full power of a Universal Turing Machine using a finite number of neurons and standard linear connections.
This means that, at least in theory, a neural net is as much as clever as your expensive PC. And this is true without taking into account all modern extensions, e.g. as in Long-Short Term Memory networks. As one of the comments mentions, though, the real problem is learnability, i.e. how to find the right set of parameters for the task under consideration.

What do I mutate and crossover in a genetic neural network?

I wrote a neural network and made a small application with things eating other things.
But I don't really know, how to make the thing genetic.
Currently I'm recording all the inputs and outputs from every individual every frame.
At the end of an generation, I then teach every knew individual the data from the top 10 best fitting individuals from prevous generations.
But the problem is, that the recorded data from a a pool of top 10 individuals at 100 generations, is about 50MB large. When I now start a new generation with 20 individuals I have to teach them 20x50MB.
This process takes longer than 3 minutes, and I am not sure if this is what I am supposed to do in genetic neural networks.
My approach works kind of good actually. Only the inefficiency bugs me. (Of course I know, I could just reduce the population.)
And I could't find me a solution to what I have to crossover and what to mutate.
Crossovering and mutating biases and weights is nonsense, isn't it? It only would break the network, would't it? I saw examples doing just this. Mutating the weight vector. But I just can't see, how this would make the network progress reaching it's desired outputs.
Can somebody show me how the network would become better at what it is doing by randomly switching and mutating weights and connections?
Would't it be the same, just randomly generating networks and hoping they start doing what they are supposed to do?
Are there other algorithms for genetic neural networks?
Thank you.

Typically, genetic algorithms for neural networks are used as an alternative to training with back-propagation. So there is no training phase (trying to combine various kinds of supervised training with evolution is an interesting idea, but isn't done commonly enough for there to be any standard methods that I know of).
In this context, crossover and mutation of weights and biases makes sense. It provides variation in the population. A lot of the resulting neural networks (especially early on) won't do much of anything interesting, but some will be better. As you keep selecting these better networks, you will continue to get better offspring. Eventually (assuming your task is reasonable and such) you'll have neural networks that are really good at what you want them to do. This is substantially better than random search, because evolution will explore the search space of potential neural networks in a much more intelligent manner.
So yes, just about any genetic neural network algorithm will involve mutating the weights, and perhaps crossing them over as well. Some, such as NEAT, also evolve the topology of the neural network and so allow mutations and crossovers that add or remove nodes and connections between nodes.

Deep Learning Neural Networks for Time Series Prediction

I'm starting a work on Internet traffic prediction (time series prediction) using artificial neural networks, but I have few experience with the matter.
Does anyone knows which method is the best for that? (which type
of neural network to use for time series prediction)
Is Deep Learning with unsupervised training a good idea for time
series learning?

You can do time-series prediction with neural nets, but it can get pretty tricky.
1) The obvious choice is a recurrent neural network (RNN). However, these can be really difficult to train, and I would not recommend RNNs if this is your first time using neural nets. Recently there has been some interesting work on easing the training of RNNs (e.g. Hessian-free optimization), but again - it's probably not for beginners ;-) Alternatively, you could try a scheme where you use a standard neural net (i.e. not a RNN), and try to predict the next frame of data from the previous? That might work.
2) This question is too general, there is no categorical right answer. Yes, you can use unsupervised feature learning as part of your solution (e.g. pre-training your model), but if your end goal is time-series prediction you will need to do some supervised learning too.
Good luck!

Neuroph Vs Encog

I have decided to use a feed-forward NN with back-propagation training for my OCR application for Handwritten text and the input layer is going to be with 32*32 (1024) neurones and at least 8-12 out put neurones.
I found Neuroph easy to use by reading some articles at the same time Encog is few times better in performance. Considering the parameters in my scenario which API is the most suitable one. And I appreciate if u can comment on the number of input nodes i have taken, is it too large value (Although it is out of the topic)

First my disclaimer, I am one of the main developers on the Encog project. This means I am more familiar with Encog that Neuroph and perhaps biased towards it. In my opinion, the relative strengths of each are as follows. Encog supports quite a few interchangeable machine learning methods and training methods. Neuroph is VERY focused on neural networks and you can express a connection between just about anything. So if you are going to create very custom/non-standard (research) neural networks of different typologies than the typical Elman/Jordan, NEAT, HyperNEAT, Feedforward type networks, then Neuroph will fit the bill nicely.

Why do we use neural networks in computers?

Why do we use neural networks? It's biologic. Aren't there any more solutions that're more "suitable" for computers?
In other words: Why do we use the human brain as a model for inspiration for artifical intelligence?

Neural networks aren't really very biological. They resemble, at a very general level, the architecture of neurons, but it's a great exaggeration to say that they work "just like the brain" (an exaggeration that's encouraged by some neural-net advocates, alas).
Neural nets are mostly used for fuzzy, difficult problems that don't yield to traditional algorithmic approaches. IOWs, there are more "suitable" solutions for computers, but sometimes those solutions don't work, and in those cases one approach is a neural network.

Why do we use neural networks?
Because they're simple to construct, and often appear to be a good approach to certain classes of problems, such as pattern recognition.
Aren't there any more solutions that're more "suitable" for computers?
Yes, implementations that more closely match a computer's architecture can be more suitable for the computer, but then can be less suitable for an effective solution.
Why do we use the human brain as a model for inspiration for artifical intelligence?
Because our brain is the superior example we have of something intelligent.

Neural Networks are still used for two reasons.
They are easy to understand for people who don't want to delve into the math of a more complicated algorithm.
They have a really good name. I mean when you role into a CEO's office to sell him your model which would you rather say, Neural Network or Support Vector Machine. When he asks how it works you can just say "just like the neurons in your brain", which is something most people understand. If you try and explain a support vector machine Mr. CEO is going to be lost (Not because he is dumb but because SVMs are harder to understand).
Sometimes they are still useful however I think that the training time is often just too long.

I don't understand the question. Neural nets are suitable for certain functions, and not others. The same is true for various other sorts of classes of algorithms, regardless of what they might have been inspired by.
If we have a good many inputs to something, and we want some outputs, and we have a set of example inputs with known desired outputs, and we don't want to calculate a function ourselves, neural nets are excellent. We feed in the example inputs, compare the output to the example outputs, and adjust the inner workings of the NN in an automatic fashion, to make the NN output closer to the desired output.
This sort of function derivation is very useful in various forms of pattern recognition and general classification. It isn't a panacea, of course. It has no explanatory power (in that you can't look at the innards to see why it classifies something in a particular way), it doesn't offer guarantees of correctness within certain limits, validating how well it works is difficult, and gathering enough examples for training and validation can be expensive or even impossible. The trick is to know when to use a NN and what sort to use.
There are, of course, people who oversell the things as some sort of super solution or even an explanation of human thought, and you might be reacting to them.

Neural network are only "inspired" by the neural structure of our brain, but they are not even close to the complexity of the behaviour of a real neuron (to date there is no neuron model that captures the complexity of a SINGLE neuron, don't even think about a neuronal population...)
Although "neural", machine "learning" and other "pseudo-bio" (like "genetic algorithms") terms are very "cool", that does not mean that they are actually based on real biological processes.
Just that they may very approximatively remind of a biological situation.
NB: of course this does not make them useless! They're very very important in many fields!

Neural networks have been around for a while, and originally were developed to model as close an understanding as we had at the time to the way neurons work in the brain. They represent a network of neurons, hence "neural network." Since computers and brains are very different hardware-wise, implementing anything like a brain with a computer is going to be rather clunky. However, as others have stated so far, neural networks can be useful for some things that are vague such as pattern recognition, facial recognition, and other similar uses. They are also still useful as a basic model of how neurons connect and are often used in Cognitive Science and other fields of artificial intelligence to try to understand how small parts of the complex human brain might make simple decisions. Unfortunately, once a neural network "learns" something, it is very difficult to understand how it actually makes its decisions.
There are, of course, many misuses of neural networks and in most non-research applications, other algorithms have been developed that are much more accurate. If a piece of business software proudly proclaims it uses a neural network, chances are it probably doesn't need it, and might be using it to inefficiently perform a task that could be performed in a much easier way. Unless the software is actually "learning" on the fly, which is very rare, neural networks are pretty much useless. And even when the software is "learning", sometimes neural networks aren't the best way to go.

While I admit, I tinker with Neural Networks because of my hopes in creating high level AI, however, you can look at a Neural Network as being more than just just an artificial representation of a human brain, but as a Mathematical construct.
For example Let's say you have a function y = f(x) or more abstractly y = f(x1, x2, ..., xn-1, xn), Neural networks themselves act as functions, or even a set of functions, taking in a large input and producing some output [y1, y2, ..., yn-1, yn] = f(x1, x2, ..., xn-1, xn)
Furthermore, they are not static, but instead can continue adapting and learning and eventually extrapolate(predict) interesting things. Their abstractness can even result in them coming up with unique solutions to problems that haven't haven't been thought up yet. For example the TDGammon program learned to play backgammon and beat the world champion. The world champion stated that the program play a unique end game that he had never seen. (that's pretty awesome if you ask me considering the complexity of NNs)
And then when you look at recurrent neural networks (i.e. can have internal feedback loops, or pipe their output back into their input, while consuming new input) they can solve even more interesting problems, and map even more complex functions.
In a nutshell Neural Networks are like a very very abstract high dimensional function and capable of mapping/learning very interesting things that would be otherwise impossible to program programmatically. For example, the energy needed to calculate the total net Forces of Gravity on a large number of objects is intense (you have to calculate it for each object, and against each object), but once a neural network learns how to map it they can do these complex calculations that would run in exponential or combinatoric? time in polynomial time. Just look at how fast your brain processes physics data, spatial data/ images / sound when you dream. That's the potential computation power of Neural Networks. And to also mention the way they store data is very clever as well (in synaptics patterns, i.e. memories)

Artificial intelligence is a branch of computer science devoted to making computers more 'biologic.' This is useful when you want a computer to do human(biologic) things like play chess, or imitate casual conversation.
Human brains are much more efficient and powerful in some ways than the most powerful computers, so it makes sense to try to imitate a biological way of processing information.

Most neural networks I'm aware of are nothing more than flexible interpolators. Backpropagating of errors is easy and fast, here are some possible uses :
Classification of data
Some games (modern backgammon AIs beat the best players in the world, the evaluation function is a neural net)
Pattern recognition (OCR ?)
There is nothing particularly related to human intelligence. There are other uses of neural nets, I have seen an implementation of associative memory which allowed for degradation without (much) data loss, pretty much like the brain which sees some neurons die with time.