The Complete Beginners’ Guide to Artificial Intelligence人工智能新手指南

文/伯納德·馬爾 譯/曹莉

By Bernard Marr

Ten years ago, if you mentioned the term “artificial intelligence” in a boardroom there’s a good chance you would have been laughed at. For most people it would bring to mind sentient,sci-fimachines such as 2001: A Space Odyssey’s HAL1電影《2001：太空漫游》中掌控“發現者”號的人工智能，有超強的計算能力，能模擬出大部分人腦的活動。or Star Trek’s Data2《星際迷航》系列中的一個生化人，可不斷進化。.

[2] Today it is one of the hottest buzzwords in business and industry. AI technology is a crucial lynchpin3lynchpin關鍵；關鍵性人物或事物。of much of the digital transformation taking place today as organizations position themselves to capitalize on the ever-growing amount of data being generated and collected.

[3] So how has this change come about? Well partly it is due to the Big Data revolution itself. The glut of data has led to intensified research into ways it can be processed, analyzed and acted upon. Machines being far better suited than humans to this work, the focus was on training machines to do this in as“smart” a way as is possible.

十年前，如果有人在董事會議上提到“人工智能”一詞，很有可能會受到大家的嘲笑。當時，對于大部分人來說 ，人工智能就是像電影《2001：太空漫游》中的哈爾和《星際迷航》中的百科少校一樣有感知力的科幻機器。

[2]如今，人工智能卻成了商界和產業界最熱門的字眼之一。各組織都在利用源源不斷地生成和收集的數據，人工智能技術因此在數字化轉型中至關重要。

[3]那么，這種轉變是如何發生的？部分原因在于大數據革命。大量數據催生了對數據處理、分析以及作用方式的深化研究，而機器遠比人類更能勝任這項研究工作，重點是如何讓機器盡可能“智能”地工作。

[4]隨著學術界、產業界以及開源社區（介于學術界及產業界之間）對該領域研究興趣的增長，人工智能不斷取得突破和進展。而這些突破和進展將會帶來巨大的變化。從醫療保健到無人駕駛汽車，再到預測法律案件判決結果，如今再無人嘲笑人工智能！

[4] This increased interest in research in the field—in academia, industry and among the open source community4又稱開放源代碼社區，根據相應的開源軟件許可證協議公布軟件源代碼的網絡平臺，是編程者自由溝通交流的空間。which sits in the middle—has led to breakthroughs and advances that are showing their potential to generate tremendous change. From healthcare to self-driving cars5self-driving car無人駕駛汽車。to predicting the outcome of legal cases,no one is laughing now!

What is Artificial Intelligence?

[5] The concept of what defines AI has changed over time, but at the core there has always been the idea of building machines which are capable of thinking like humans.

[6] After all, human beings have proven uniquely capable of interpreting the world around us and using the information we pick up to effect change.If we want to build machines to help us do this more efficiently, then it makes sense to use ourselves as a blueprint.

[7] AI, then, can be thought of as simulating the capacity for abstract,creative, deductive thought—and particularly the ability to learn which this gives rise to—using the digital,binary logic of computers.

[8] Research and development work in AI is split between two branches.One is labelled “applied AI” which uses these principles of simulating human thought to carry out one specific task.The other is known as “generalized AI”—which seeks to develop machine intelligences that can turn their hands to any task, much like a person.

人工智能是什么？

[5]關于人工智能的定義，不同時間有不同的理解，但其核心概念始終是關于如何建造能像人一樣思考的機器的。

[6]畢竟，人類已證明是唯一可解釋周圍世界、可利用所掌握的信息改變世界的生物。如果我們想建造機器幫助我們更有效地工作，那么以我們自己為藍圖來進行建造無疑是合理的。

[7]因此，人工智能可以被認為是利用計算機二進制數字邏輯模擬人類抽象、創造及推理思維的能力——特別是由此產生的學習能力。

[8]人工智能的研究和發展有兩個分支：一為被冠以“應用型人工智能”的分支，即通過應用模擬人類思維的原理實施某項特定的任務；二則為“通用型人工智能”，旨在開發能夠像人一樣從事任何任務的機器智能。

[9]有關應用型、專業化的人工智能的研究正在給許多研究領域帶來突破：在量子物理領域，人工智能可模仿和預測由數十億亞原子粒子組成的各系統的表現；在醫學領域，人工智能可基于基因組數據診斷病人。

[10]在產業界，人工智能可用在金融領域中，可提供的服務包括偵測欺詐行為、預測客戶需求以提高服務質量等。在制造業中，人工智能可用來管理勞動力及生產流程，還可提前提示故障，從而進行預測性維護。

[11]在消費領域，我們日常生活中使用的技術越來越多是由人工智能所支持的：從智能手機助手例如蘋果手機的語音助手Siri、谷歌的谷歌助手，到自動駕駛汽車和無人駕駛汽車——這兩種汽車預計在我們有生之年會在數量上超過人工駕駛汽車。

[9] Research into applied, specialized AI is already providing breakthroughs in fields of study from quantum physics where it is used to model and predict the behavior of systems comprised of billions of subatomic particles, to medicine where it being used to diagnose patients based on genomic data.

[10] In industry, it is employed in the financial world for uses ranging from fraud detection to improving customer service by predicting what services customers will need.In manufacturing it is used to manage workforces and production processes as well as for predicting faults before they occur, therefore enabling predictive maintenance.

[11] In the consumer world more and more of the technology we are adopting into our everyday lives is becoming powered by AI—from smartphone assistants like Apple’s Siri and Google’s Google Assistant, to self-driving and autonomous cars6autonomous car自動駕駛汽車。which many are predicting will outnumber manually driven cars within our lifetimes.

[12] Generalized AI is a bit futher off—to carry out a complete simulation of the human brain would require both a more complete understanding of the organ than we currently have, and more computing power than is commonly available to researchers. But that may not be the case for long, given the speed with which computer technology is evolving. A new generation of computer chip technology known as neuromorphic processors are being designed to more efficiently run brain-simulator code.And systems such as IBM’s Watson cognitive computing platform use highlevel simulations of human neurological processes to carry out an ever-growing range of tasks without being specifically taught how to do them.

[12]通用型人工智能則要更復雜一些——完全模擬人腦需要我們比現在更為徹底地了解人腦，需要研究者掌握更強的計算能力。 但考慮到計算機技術發展的速度，不久這些都將不成其為問題。新一代的計算機芯片技術神經形態處理器正在設計當中，可更高效地運行人腦模擬器代碼。一些類似IBM沃森認知計算平臺的系統可高度模擬人類神經過程，無須人類具體指揮就可執行海量任務。

人工智能有哪些主要進展？

[13]人工智能在致力于模仿人腦思維過程中不斷取得進步。我們所熟知的“機器學習”領域近幾年已取得豐碩的研究成果。事實上，該領域對于當代人工智能來說不可或缺，因此，“人工智能”與“機器學習”這兩個術語有時可以互換使用。

[14]但這種表達是不精確的，最恰當的說法應該是：機器學習代表著更廣范圍內人工智能現階段的最高水準。機器學習的根本并非一步一步來教授機器知識，而是通過編寫程序讓機器能夠像人一樣思考，通過觀察、分類、從錯誤中吸取教訓來學習如何工作，恰如我們人類一樣。

What are the key developments in AI?

[13] All of these advances have been made possible due to the focus on imitating human thought processes.The field of research which has been most fruitful in recent years is what has become known as “machine learning”.In fact, it’s become so integral to contemporary AI that the terms “artificial intelligence” and “machine learning” are sometimes used interchangeably.

[14] However, this is an imprecise use of language, and the best way to think of it is that machine learning represents the current state-of-the-art in the wider field of AI. The foundation of machine learning is that rather than have to be taught to do everything step by step,machines, if they can be programmed to think like us, can learn to work by observing, classifying and learning from its mistakes, just like we do.

[15] The application of neuroscience to IT system architecture7architecture構建；架構。has led to the development of artificial neural networks—and although work in this field has evolved over the last half century it is only recently that computers with adequate power have been available to make the task a dayto-day reality for anyone except those with access to the most expensive,specialized tools.

[16] Perhaps the single biggest enabling factor has been the explosion of data which has been unleashed since mainstream society merged itself with the digital world. This availability of data—from things we share on social media to machine data generated by connected industrial machinery—means computers now have a universe of information available to them, to help them learn more efficiently and make better decisions.

[15]神經科學應用于信息技術系統構建，促進了人工神經網絡的發展。盡管該領域的研究工作在過去的半個世紀中不斷取得進展，但直到最近才研制出具有充足電源的計算機。這種計算機使得普通人——那些本身就擁有最昂貴的專業工具的人除外——運用計算機工作成為日常現實。

[16]也許促成上述一切的唯一最大因素就是數據爆炸。主流社會與數字世界的融合帶來了數據大爆炸，而數據的可獲取性——從我們在社交媒體上分享的各種信息到由聯合工業機械產生的機器數據——意味著計算機現在擁有可幫助它們更有效學習及更好決策的信息來源。

人工智能的未來是什么？

[17]關于這個問題的答案，取決于你問的是誰。仁者見仁，智者見智！

What is the future of AI?

[17] That depends on who you ask,and the answer will vary wildly!

[18] Real fears that development of intelligence which equals or exceeds our own, but has the capacity to work at far higher speeds, could have negative implications for the future of humanity have been voiced, and not just by apocalyptic sci-f i such as The Matrix or The Terminator, but respected scientists like Stephen Hawking.

[19] Even if robots don’t eradicate us or turn us into living batteries, a less dramatic but still nightmarish scenario is that automation of labour (mental as well as physical) will lead to profound societal change—perhaps for the better,or perhaps for the worse.

[20] This understandable concern has led to the foundation last year,by a number of tech giants including Google, IBM, Microsoft, Facebook and Amazon, of the Partnership in AI. This group will research and advocate for ethical implementations of AI, and to set guidelines for future research and deployment of robots and AI. ■

[18]人們擔心，人工智能發展到一定地步，其智能可與人類相當甚至超過人類，且其工作速度遠超人類，由此可能會對人類的未來產生負面影響。這種擔憂在末日科幻電影例如《黑客帝國》和《終結者》中都有所體現，而一些受人敬重的科學家如史蒂芬·霍金也表達了相同的擔憂。

[19]即使機器人不會消滅人類或者僅將人類當成活電池，勞動自動化（無論是體力還是腦力）也將會帶來深刻的社會變化——可能朝著好的方面，也可能朝著壞的方面變化。這種情況雖不像人類消亡那樣夸張，但卻一樣夢魘般可怕。

[20]正是因為這種擔憂，去年，谷歌、IBM、微軟、臉書以及亞馬遜等五家科技巨頭成立了人工智能聯盟。該組織將研究并倡導人工智能應用中的倫理問題，為未來機器人和人工智能的研究和利用制定指導方針。 □