Question

学习人工智能AI需要哪些知识?

学习人工智能AI需要哪些最基础的知识?

Answer 1

人工智能入门需要掌握的知识有：自然语言处理、机器学习、计算机视觉、知识表示、自动推理和机器人学。虽然这些领域的侧重点各有不同，但是都需要一个重要的基础，那就是数学和计算机基础。

Answer 2

这是ANU教授Marcus Hunter(Universal Artificial Intelligence的作者）给出的建议

Short AI Reader's Guide

Please find below suggestions for some textbooks which I found most relevant for understanding and modeling intelligent behaviour in general, and for developing the AIXI model in particular. If you are confused by the amount, diversity or complexity of the references below, I suggest you to start with the Reinforcement Learning book by Sutton and Barto. It requires no background knowledge, describes the key ideas, open problems, and great applications of this field. Don't be surprised about the ease of the book, it teaches understanding, not proofs. It gets really tough to make things work in practice and to prove things. The Artificial Intelligence book by Russell and Norvig gives a comprehensive overview over AI in general. TheKolmogorov Complexity book by Li and Vitanyi is an excellent introduction to algorithmic information theory. If you have some background knowledge in decision theory and algorithmic information theory you may be interested in the Theory of Universal Artificial Intelligence.

Long AI Reader's Guide

For the impatient. If you are the sort of impatient student who wants to build super intelligent machines right away without "wasting" time reading or learning too much, well, others have tried in the last 50 years and failed, and so will you. If you can't hold back, at least read Legg (2008) [Leg08]. This is an excellently written non-technical thesis on the necessary ingredients for super intelligent machines.
It will not help you much building one, since in order to properly understand the general theory and to bridge the gap to "narrow" but practical existing AI algorithms, you need a lot more background. Nevertheless, [Leg08] might motivate you to consider reading the books I'll recommend now.

Artificial Intelligence. Russell and Norvig (2003) [RN10] is the textbook to learn about Artificial Intelligence. The book gives a broad introduction, survey, and solid background of all aspects of AI. There is no real alternative. Whatever subarea of AI you specialize later, you should understand all introduced concepts, and have implemented and solved at least some of the exercises.
The textbooks below are relevant for understanding and modeling general intelligent behavior. If you already got attracted to some specific AI applications, they may not be relevant for you. One axis of categorizing AI is into (1) logical (2) planning and (3) learning aspects. CSL@ANU has experts in all 3 areas. Historically, AI research started with (1) in the 1950s, which is still relevant for many concrete practical applications. Since at least in humans, high-level logical reasoning seems to emerge from the more basic learning and planning aspects, it is conceivable that (1) will play no fundamental role in a general AI system. So I will concentrate on (2) and (3). If put together, learning+planning under uncertainty is mainly the domain of reinforcement learning (RL), also called adaptive control or sequential decision theory in other fields.

Reinforcement Learning. Sutton and Barto (1998) [SB98] is the excellent default RL textbook. It requires no background knowledge, describes the key ideas, open problems, and great applications of this field. Don't be surprised about the ease of the book, it teaches understanding, not proofs. It gets really tough to make things work in practice or to prove things [BT96].
If you want to bring order into the bunch of methods and ideas you've learned so far, and want to understand more deeply their connection either for curiosity or to extend the existing systems to more general and powerful ones, you need to learn about some concepts that at first seem quite disconnected and theoretical.

Information theory. Intelligence has a lot to do with information processing. Algorithmic information theory (AIT) is a branch of information theory that is powerful enough to serve as a foundation for intelligent information processing. It can deal with key aspects of intelligence, like similarity, creativity, analogical reasoning, and generalization, which are fundamentally connected to the induction problem and Ockham's razor principle. Li and Vitanyi's (1997) AIT book [LV97] provides an excellent introduction. Kolmogorov complexity, Minimal Description Length, universal Solomonoff induction, universal Levin search, and all that. It requires a background in theoretical computer science in general and computability theory in particular, which can be obtained from the classic textbook [HMU06].

Universal AI. Now you are in a position to read [Hut05]. The book develops a sound and complete mathematical theory of an optimal "intelligent" general-purpose learning agent. The theory is complete in the sense that it gives a complete description of this agent, not just an incomplete framework with gaps to be filled. But be warned, it is only a theory. Like it is a long way from e.g. the minimax theory of optimally playing games like chess to real chess programs, it is a long way from this theory to a practical general purpose intelligent agent [VNHS09].

Peripheral Areas. The other recommended books below can be regarded as further readings that provide more background and deepen your understanding of various important aspects in AI research. Bishop (2006) [Bis06] is the excellent default textbook in statistical machine learning, and should be put on your reading list. Some Bayesian probability book will be useful too [Pre02, Jay03]. How multiple rational agents interact [SLB08] is the domain of game theory [OR96]. Computer vision [FP02], natural language understanding [JJ08], and robotics [TBF05] interfaces abstract agents with the real world. Alchin (2006) [Alc06] gently and broadly introduces you to philosophy of science in general and Earman (1992) [Ear92] to the induction problem in particular.

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链接：http://www.zhihu.com/question/21277368/answer/40591278
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Answer 3

人工智能的定义可以分为两部分，即“人工”和“智能”。“人工”比较好理解，争议性也不大。有时我们会要考虑什么是人力所能及制造的，或着人自身的智能程度有没有高到可以创造人工智能的地步，等等。但总的来说，“人工系统”就是通常意义下的人工系统。
关于什么是“智能”，就问题多多了。这涉及到其它诸如意识(consciousness)、自我(self)、思维(mind)(包括无意识的思维(unconscious_mind)等等问题。人唯一了解的智能是人本身的智能，这是普遍认同的观点。但是我们对我们自身智能的理解都非常有限，对构成人的智能的必要元素也了解有限，所以就很难定义什么是“人工”制造的“智能”了。因此人工智能的研究往往涉及对人的智能本身的研究。其它关于动物或其它人造系统的智能也普遍被认为是人工智能相关的研究课题。

Answer 4

想学人工智能首先应该了解人工智能。
人工智能的定义，核心，现在的发展状况，趋势，前景。这些基础的认知还是要有的。
还有人工智能的基础入门就是先要学会python这门语言。

Answer 5

数据结构，算法理论，编程语言（C之类），优化工具（MatLab、CPLEX等）