计算科学(computational science) 和 人工智能(aritificial intelligence) 有什么区别呢？

这里有一个会议论文集《Computational Sciences and Artificial Intelligence in Industry： New Digital Technologies for Solving Future Societal and Economical Challenges》可以供大家参考：下载地址https://disk.pku.edu.cn:443/link/ECE25A3A1DFE2CA073876EE649625B0C

我在网上查这个问题的时候（实际上人工智能目前还缺乏一个严格被普遍任何的定义， 可以认为是计算科学的一部分，也是使用计算机来辅助人类工作（如，认识世界、数据处理、寻找规律、诊断和预测等），它使用的算法和更加传统的方法（如有限元、谱方法、线性规划、FFT等）相比可能要偏向于机器学习方法（如神经网络、贝叶斯网络、决策树、遗传算法等）， 还看到了一个有意思的问题和回答（其中的回答可以说是放之四海而皆准的，实际上没有讨论计算科学与人工智能的差别和联系）。

问题：

大家好，我在匈牙利学习物理，这是理学学士的最后一年。之后，我想在奥地利的维也纳大学（计算科学与工程）或林茨的JKU（人工智能）攻读硕士学位。我的问题是：无论是在工业界还是在学术圈，哪个领域的职业前景更好？我知道这两个有点不同，但我在这两个领域都有一些经验，我喜欢这样做。我在计算天体物理学方面做了3年的研究，除此之外，我现在在一家跨国公司实习，在那里我帮助一个团队进行人工智能开发。所以我想知道哪个领域有更好的未来。有人在过这些领域都有经验吗？提前谢谢！

参考：https://www.physicsforums.com/threads/masters-degree-computational-science-vs-artificial-intelligence.980146/

回答：

寻找吸引你的“闪光”目标。关于这个目标，你不需要被其他人驱使，也不需要担心你的职业前景或赚钱多少。你不断回到的这个目标是因为你觉得它有趣、引人入胜。当你工作的时候，你不会注意到世界其他地方。随着时间的推移，你会继续努力。如果你能找到并努力，你的职业生涯将是丰富而有回报的。你会自动地把注意力集中在工作上。这将比其他任何事情都更能让你进步。

参考：https://www.physicsforums.com/threads/masters-degree-computational-science-vs-artificial-intelligence.980146/

还有一个问题：

What is the difference between artificial intelligence and computational intelligence?

回答：

The book Computational Intelligence: An Introduction (2nd edition, 2007) by Andries P. Engelbrecht, (you can download it from https://disk.pku.edu.cn:443/link/D8E57514D3EBA94F3030F17D70060FC3) which has been cited more than 3000 times, defines artificial intelligence as follows

These intelligent algorithms include artificial neural networks, evolutionary computation, swarm intelligence, artificial immune systems, and fuzzy systems. Together with logic, deductive reasoning, expert systems, case-based reasoning and symbolic machine learning systems, these intelligent algorithms form part of the field of Artificial Intelligence (AI). Just looking at this wide variety of AI techniques, AI can be seen as a combination of several research disciplines, for example, computer science, physiology, philosophy, sociology and biology.

and computational intelligence as follows

This book concentrates on a sub-branch of AI, namely Computational Intelligence (CI) – the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex and changing environments. These mechanisms include those AI paradigms that exhibit an ability to learn or adapt to new situations, to generalize, abstract, discover and associate. The following CI paradigms are covered: artificial neural networks, evolutionary computation, swarm intelligence, artificial immune systems, and fuzzy systems.

He then notes

At this point it is necessary to state that there are different definitions of what constitutes CI. This book reflects the opinion of the author, and may well cause some debate. For example, swarm intelligence (SI) and artificial immune systems (AIS) are classified as CI paradigms, while many researchers consider these paradigms to belong only under Artificial Life. However, both particle swarm optimization (PSO) and ant colony optimization (ACO), as treated under SI, satisfy the definition of CI given above, and are therefore included in this book as being CI techniques. The same applies to AISs.

So, there may be different definitions of CI (given by different people), but, given that this book has been cited so many times, I would just stick to these definitions and use this book as a reference (I have actually consulted it a few times in the past). My university library even contains a copy of it.

To summarise, CI is a sub-field of AI, which studies (or is associated with) the following topics

• artificial neural networks (NN),
• evolutionary computation (EC),
• swarm intelligence (SI),
• artificial immune systems (AIS), and
• fuzzy systems (FS).

which are also part of AI, which additionally studies

• logic,
• deductive reasoning,
• expert systems,
• case-based reasoning, and
• symbolic machine learning systems.

Just to give further credibility to these definitions, Andries P. Engelbrecht has an h-index of 59, has been cited 22557 times, and is an IEEE Senior Member. You can find more info about him here. Note that I have no affiliation with him. I am just providing this information so that people start to follow these definitions (rather than just looking at definitions given by people who have not extensively studied the field). Moreover, note that the definition of CI given by Engelbrecht is consistent with the definition given by IEEE that you are quoting.