为什么我们在计算机中使用神经网络？
我们为什么使用神经网络？这是生物性的。难道就没有更多更“适合”计算机的解决方案了吗？
换句话说：为什么我们要用人脑作为人工智能的灵感模型？
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?
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因为它们构建起来很简单，并且通常似乎是解决某些类别问题（例如模式识别）的好方法。
是的，与计算机体系结构更加匹配的实现可能更适合计算机，但可能不太适合有效的解决方案。
因为我们的大脑是我们所拥有的智能事物的最佳例子。
Because they're simple to construct, and often appear to be a good approach to certain classes of problems, such as pattern recognition.
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.
Because our brain is the superior example we have of something intelligent.
神经网络仍然被使用有两个原因。
有时它们仍然有用，但我认为训练时间往往太长。
Neural Networks are still used for two reasons.
Sometimes they are still useful however I think that the training time is often just too long.
我不明白这个问题。神经网络适用于某些功能，而不适用于其他功能。对于各种其他类型的算法来说也是如此，无论它们可能受到什么启发。
如果我们对某事物有很多输入，并且想要一些输出，并且我们有一组具有已知所需输出的示例输入，并且我们不想自己计算函数，那么神经网络就非常有用。我们输入示例输入，将输出与示例输出进行比较，并以自动方式调整神经网络的内部工作原理，以使神经网络输出更接近所需的输出。
这种函数推导在各种形式的模式识别和一般分类中非常有用。当然，这不是万能药。它没有解释力（因为你无法查看内部结构来了解为什么它以特定方式对某些事物进行分类），它不能保证一定范围内的正确性，验证它的工作效果很困难，并且收集足够多的示例用于训练和验证可能会很昂贵，甚至是不可能的。诀窍是知道何时使用神经网络以及使用哪种类型。
当然，有些人过度吹捧这些东西作为某种超级解决方案，甚至是对人类思想的解释，你可能会对他们做出反应。
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.
神经网络只是受到我们大脑的神经结构的“启发”，但它们甚至还没有接近真实神经元行为的复杂性（迄今为止，还没有神经元模型可以捕获单个神经元的复杂性，不要这样做）甚至不用考虑神经元群体……）
尽管“神经”、机器“学习”和其他“伪生物”（如“遗传算法”）术语非常“酷”，但这并不意味着它们实际上是基于真实的生物过程。
只是它们可能非常近似地让人想起一种生物情况。
注意：当然这并不意味着它们毫无用处！它们在许多领域都非常非常重要！
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!
神经网络已经存在了一段时间，最初是为了模拟我们当时对神经元在大脑中工作方式的理解而开发的。它们代表神经元网络，因此称为“神经网络”。由于计算机和大脑在硬件方面非常不同，因此用计算机实现类似大脑的任何东西都会相当笨重。然而，正如其他人到目前为止所指出的那样，神经网络对于一些模糊的事物可能很有用，例如模式识别、面部识别和其他类似用途。它们作为神经元如何连接的基本模型仍然很有用，并且经常用于认知科学和人工智能的其他领域，以试图了解复杂的人脑的小部分如何做出简单的决定。不幸的是，一旦神经网络“学习”了一些东西，就很难理解它实际上是如何做出决定的。
当然，神经网络存在许多误用，并且在大多数非研究应用中，已经开发了更准确的其他算法。如果一款商业软件自豪地宣称它使用神经网络，那么它很可能不需要它，并且可能会使用它来低效地执行可以以更简单的方式执行的任务。除非软件实际上是即时“学习”（这种情况非常罕见），否则神经网络几乎毫无用处。即使软件正在“学习”，有时神经网络也不是最好的方法。
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.
虽然我承认，我对神经网络进行了修补，因为我希望创建高级人工智能，但是，您可以将神经网络视为不仅仅是人脑的人工表示，而是作为一种数学构造。
例如，假设您有一个函数
y = f(x)或更抽象地y = f(x1, x2, ..., xn-1, xn)，神经网络本身充当函数，甚至是一组函数，接收大量输入并产生一些输出[y1, y2, ..., yn-1, yn] = f(x1, x2, .. ., xn-1, xn)此外，它们不是静态的，而是可以不断适应和学习，并最终推断（预测）有趣的事情。他们的抽象性甚至可以使他们针对尚未想到的问题提出独特的解决方案。例如，TDGammon 程序学会了玩双陆棋并击败了世界冠军。世界冠军表示，该节目打出了他从未见过的独特残局。 （如果你问我考虑神经网络的复杂性，那真是太棒了）
然后当你看看循环神经网络（即可以有内部反馈循环，或者将它们的输出返回到它们的输入，同时消耗新的输入）时，它们可以解决更多问题有趣的问题，并绘制更复杂的函数。
简而言之，神经网络就像一个非常非常抽象的高维函数，能够映射/学习非常有趣的东西，否则这些东西不可能以编程方式编程。例如，计算大量物体上的总净重力所需的能量非常大（您必须针对每个物体计算每个物体的能量），但是一旦神经网络学会了如何映射它，它们就可以这些以指数或组合方式运行的复杂计算？多项式时间中的时间。只要看看当你做梦时你的大脑处理物理数据、空间数据/图像/声音的速度有多快。这就是神经网络的潜在计算能力。还要提一下它们存储数据的方式也非常聪明（在突触模式中，即记忆）
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 abstractlyy = 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)
人工智能是计算机科学的一个分支，致力于使计算机更加“生物化”。当您希望计算机执行人类（生物）操作（例如下棋或模仿休闲对话）时，这非常有用。
人脑在某些方面比最强大的计算机更高效、更强大，因此尝试模仿生物处理信息的方式是有意义的。
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.
我所知道的大多数神经网络只不过是灵活的插值器。错误的反向传播既简单又快速，这里有一些可能的用途：
没有什么特别相关的到人类的智慧。神经网络还有其他用途，我见过一种联想记忆的实现，它允许退化而不会（大量）数据丢失，就像大脑看到一些神经元随着时间的推移而死亡一样。
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 :
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.