Some important concepts of Artificial Intelligence！

As Artificial Intelligence has been more and more popular among college students who are pursuing the latest and coolest technology, there seems not to be a clear definition of AI. For our college students, even the most basic concepts of this field are hard to distinguish. If you are extremely enthusiastic about AI but confused by its complex definitions, the following part of this passage will help you a lot.

当人工智能在那些追求最新最酷技术的大学生群体中越来越流行，人工智能似乎还没有一个明确的定义。对于我们大学生来说，即使是这个领域最基本的概念也很难区分。如果你对人工智能非常狂热，但对它的复杂定义感到困惑，接下来的部分将对你有很大帮助。

Artificial Intelligence&

Machine learning&Deep Learning

When you search a book about AI on the internet, you always find some connected with Machine Learning or Deep Learning. You may know there is some relation between these words, but you can’t make it sense. The left picture shows us the relation--just an inclusion one.

当你在网上搜索一本关于人工智能的书时，你总会发现一些与机器学习或深入学习有关的东西。你可能知道这两个词之间有一些关系，但你却无法弄清楚。左图给我们展示了它们之间的关系——只是一个包含。

As you know, AI is a method which enable robots to imitate the activities of human’s minds. However, Machine Learning is not a new field，but one of many specific ways to make AI a reality, that is to say, included in the larger range of AI. Similarly, Deep Learning is a more specific way to make Machine Learning a reality using a special structure which is same as the nerve net in your brain.

正如你所知道的，人工智能可以使机器人能够模仿人类思维活动。然而，机器学习不是一个新的领域，而是使人工智能成为现实的许多具体方法之一，也就是说，包括在更大范围的人工智能中。同样，深层学习是一种使机器学习成为现实更具体的方法，它使用一种特殊的结构，就像你大脑中的神经网络一样。

The process of Machine Learning

Unlike the traditional programming, Machine learning’s results are not certain before you put your data in. You may heard about the AlphaGo who can train itself by practicing more and more which is a best example for Machine Learning. Based on statistical theories, Machine learning uses the old data to build its structure’s foundation, or model, which can solve problems according to the possibility. Once it has solved a question, the database will be renewed and the possibility it depends will be more accurate. In a word, Machine Learning is making a task more effective with increasing experience.

与传统编程不同的是，在将数据放入之前，机器学习的结果是不确定的。你可能听说过AlphaGo的人可以训练自己通过练习更多的是机器学习的一个最好的例子。机器学习以统计理论为基础，利用旧的数据建立其结构的基础或模型，根据可能的问题解决问题。一旦它解决了一个问题，数据库将被更新，它所依赖的可能性将更加精确。总之，机器学习使一项任务随着经验的增加而变得更加有效。