Why do we use neural networks in computers?

Question

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?

Answer 1

1. 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).
2. 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.

Answer 2

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.

Answer 3

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.

Answer 4

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!

Answer 5

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.

Answer 6

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.

Answer 7

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.

Answer 8

Neural Networks are still used for two reasons.

1. They are easy to understand for people who don't want to delve into the math of a more complicated algorithm.
2. 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.

Answer 9

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)