回答问题人工智能源码_回答21个最受欢迎的人工智能问题

Artificial intelligence sets the stage for a new era of solutions to be made with computers. It allows us to solve problems that we could not have imagined in the past. It’s a technology with massive potential and it’s also very confusing. To clear up some of the confusion I decided to make this video where I answer the most popular AI questions.

人工智能为计算机解决方案的新纪元奠定了基础。 它使我们能够解决过去无法想象的问题。 这是一项具有巨大潜力的技术，而且也非常令人困惑。 为了消除一些困惑，我决定在这段视频中回答最流行的AI问题。

1. Who Invented Artificial Intelligence and where?
   谁在哪里发明了人工智能？

The ideas about Artificial Intelligence evolved through centuries, starting with greek myths about Intelligent robots (Talos myth), but AI as we know it today only emerged in 1955. The term was coined by Alan Turing, Marvin Minsky, Allen Newell, Herber A.Simon, and John McCarthy.

关于人工智能的思想发展了数百年，从关于智能机器人的希腊神话(Talos神话)开始，但是今天我们所知的AI直到1955年才出现。这个名词是由Alan Turing，Marvin Minsky，Allen Newell，Herber A提出的。西蒙和约翰·麦卡锡。

Alan Turing, famous for his Turing Machine and working on deciphering the German Enigma Code.

艾伦·图灵(Alan Turing)以他的图灵机而闻名，他致力于解密德国《谜语》。

Marvin Minsky’s inventions include the first head-mounted graphical display and the confocal microscope.

Marvin Minsky的发明包括第一个头戴式图形显示器和共聚焦显微镜。

Allen Newell worked on two of the earliest AI programs, the Logic Theory Machine (1956) and the General Problem Solver

艾伦·纽厄尔(Allen Newell)从事两个最早的AI程序：逻辑理论机器(1956)和通用问题解决器

Herber A.Simon proposed a preferential attachment mechanism to explain power-law distributions.

赫伯·西蒙(Herber A.Simon)提出了一种优先依附机制来解释幂律分布。

John McCarthy created a programing language Lisp, invented garbage collection, and organized the Dartmouth conference where Artificial Intelligence was started as a field.

约翰·麦卡锡(John McCarthy)创建了一种编程语言Lisp，发明了垃圾收集，并组织了达特茅斯会议，在此会议上人工智能开始了。

2. How does artificial intelligence work?

2.人工智能如何工作？

Artificial intelligence is an all-encompassing term, which covers a myriad of different intelligent algorithms, of which the most popular is the neural network.

人工智能是一个无所不包的术语，涵盖了无数种不同的智能算法，其中最流行的是神经网络。

Neural Networks are built up by neurons. You can imagine them as a small computer chip that gets an input and based on some formula gives out some output.

神经网络是由神经元建立的。 您可以将它们想象为一个小型计算机芯片，该芯片获得输入，并根据某些公式给出一些输出。

Let’s say for all numbers greater than 10 the neuron gives out 0 and for all numbers less than 10 it gives out 1. So, this could be an example of we have the data for rainy days and we are naively predicting forest fires. If there were less than 10 rainy days this year we predict a forest fire and give out the output of 1. If more than 10 then a forest fire is unlikely and the output is 0.

假设对于所有大于10的数字，神经元给出0，而对于小于10的所有数字给出1。因此，这可能是一个示例，即我们拥有雨天的数据，并且天真地预测着森林大火。 如果今年的雨天少于10天，则我们将预测一场森林大火并给出1的输出。如果多于10天，那么一场森林大火的可能性就很小，输出为0。

Now, with more complex neural networks there are many many layers of such neurons that allow making extremely complex predictions. In that same example, we could have data for the humidity, temperature, amount of people that visited the forest, amount of thunderstorms, etc. All these elements would trigger different neurons and based on their outputs we could make better predictions.

现在，有了更复杂的神经网络，此类神经元的许多层都可以做出极其复杂的预测。 在同一示例中，我们可以获取湿度，温度，参观森林的人数，雷暴的数量等数据。所有这些元素都将触发不同的神经元，并根据它们的输出可以做出更好的预测。

As a more concrete example of a neural network application, let’s take an image, which is a collection of pixel information that can be given as input into a massive neural network.

作为神经网络应用程序的一个更具体的示例，让我们来拍摄一张图像，该图像是像素信息的集合，这些像素信息可以作为输入输入到大型神经网络中。

Let’s say it was an image of a cat. The neural network is trained to look for different characteristics if it finds a cluster of pixels that represent ears it can already give out a prediction that this is some sort of animal. If it recognizes eyes, paws, etc. Other different clusters light up and give the prediction that an image of a cat has been given as an input.

假设这是一只猫的形象。 如果发现发现代表耳朵的像素簇，该神经网络将经过训练以寻找不同的特征，它已经可以给出这种动物的预测。 如果它识别出眼睛，爪子等，则其他不同的群集会亮起，并做出预测，表示已将猫的图像作为输入。

The learning of a neural network works by feeding a lot of inputs into the neural net and g