計算科學(xué)(computational science) 和 人工智能(aritificial intelligence) 有什么區(qū)別呢？

這里有一個會議論文集《Computational Sciences and Artificial Intelligence in Industry： New Digital Technologies for Solving Future Societal and Economical Challenges》可以供大家參考：下載地址https://disk.pku.edu.cn:443/link/ECE25A3A1DFE2CA073876EE649625B0C

我在網(wǎng)上查這個問題的時候（實際上人工智能目前還缺乏一個嚴(yán)格被普遍任何的定義， 可以認(rèn)為是計算科學(xué)的一部分，也是使用計算機來輔助人類工作（如，認(rèn)識世界、數(shù)據(jù)處理、尋找規(guī)律、診斷和預(yù)測等），它使用的算法和更加傳統(tǒng)的方法（如有限元、譜方法、線性規(guī)劃、FFT等）相比可能要偏向于機器學(xué)習(xí)方法（如神經(jīng)網(wǎng)絡(luò)、貝葉斯網(wǎng)絡(luò)、決策樹、遺傳算法等）， 還看到了一個有意思的問題和回答（其中的回答可以說是放之四海而皆準(zhǔn)的，實際上沒有討論計算科學(xué)與人工智能的差別和聯(lián)系）。

問題：

大家好，我在匈牙利學(xué)習(xí)物理，這是理學(xué)學(xué)士的最后一年。之后，我想在奧地利的維也納大學(xué)（計算科學(xué)與工程）或林茨的JKU（人工智能）攻讀碩士學(xué)位。我的問題是：無論是在工業(yè)界還是在學(xué)術(shù)圈，哪個領(lǐng)域的職業(yè)前景更好？我知道這兩個有點不同，但我在這兩個領(lǐng)域都有一些經(jīng)驗，我喜歡這樣做。我在計算天體物理學(xué)方面做了3年的研究，除此之外，我現(xiàn)在在一家跨國公司實習(xí)，在那里我?guī)椭粋€團隊進行人工智能開發(fā)。所以我想知道哪個領(lǐng)域有更好的未來。有人在過這些領(lǐng)域都有經(jīng)驗嗎？提前謝謝！

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

回答：

尋找吸引你的“閃光”目標(biāo)。關(guān)于這個目標(biāo)，你不需要被其他人驅(qū)使，也不需要擔(dān)心你的職業(yè)前景或賺錢多少。你不斷回到的這個目標(biāo)是因為你覺得它有趣、引人入勝。當(dāng)你工作的時候，你不會注意到世界其他地方。隨著時間的推移，你會繼續(xù)努力。如果你能找到并努力，你的職業(yè)生涯將是豐富而有回報的。你會自動地把注意力集中在工作上。這將比其他任何事情都更能讓你進步。

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

還有一個問題：

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.