What is, and what isn’t AI? Not an easy question!
The popularity of AI in the media is in part due to the fact that people have started using the term when they refer to things that used to be called by other names. You can see almost anything from statistics and business analytics to manually encoded if-then rules called AI. Why is this so? Why is the public perception of AI so nebulous? Let’s look at a few reasons.
什麽是，什麽不是人工智能？不是一個容易的問題！
爲什麽公衆對人工智能的認識如此模糊？讓我們看看幾個原因。
Reason 1: no officially agreed definition
Even AI researchers have no exact definition of AI. The field is rather being constantly redefined when some topics are classified as non-AI, and new topics emerge.
原因 1：沒有官方認可的定義
即使是人工智能研究人員也沒有對人工智能的確切定義。
There´s an old (geeky) joke that AI is defined as “cool things that computers can’t do.” The irony is that under this definition, AI can never make any progress: as soon as we find a way to do something cool with a computer, it stops being an AI problem. However, there is an element of truth in this definition. Fifty years ago, for instance, automatic methods for search and planning were considered to belong to the domain of AI. Nowadays such methods are taught to every computer science student. Similarly, certain methods for processing uncertain information are becoming so well understood that they are likely to be moved from AI to statistics or probability very soon.
有一個古老的笑話說，人工智能被定義爲“計算機做不到的很酷的事情”。諷刺的是，在這個定義下，人工智能永遠無法取得任何進展：一旦我們找到一種方法，用計算機做一些很酷的事情，它就不再是人工智能的問題。然而，在這個定義中有一個事實，例如，50年前，自動搜索和規劃方法被認爲屬于人工智能領域。現在這種方法被教給每一個計算機科學的學生。類似地，處理不確定信息的某些方法正變得非常容易理解，以至于它們很快就會從人工智能轉移到統計或概率。
Reason 2: the legacy of science fiction
The confusion about the meaning of AI is made worse by the visions of AI present in various literary and cinematic works of science fiction. Science fiction stories often feature friendly humanoid servants that provide overly-detailed factoids or witty dialogue, but can sometimes follow the steps of Pinocchio and start to wonder if they can become human. Another class of humanoid beings in sci-fi espouse sinister motives and turn against their masters in the vein of old tales of sorcerers’ apprentices, going back to the Golem of Prague and beyond.
原因 2：科幻小說的遺産
在各種科幻文學和電影作品中，人工智能的出現使人們對人工智能含義的困惑更加嚴重。科幻小說的故事往往以友好的人形仆人爲特色，他們提供了過于詳細的事實或诙諧的對話，但有時可以遵循木偶奇遇記的步驟，開始懷疑他們是否能成爲人類。
Often the robot hood of such creatures is only a thin veneer on top of a very humanlike agent, which is understandable as most fiction – even science fiction – needs to be relatable by human readers who would otherwise be alienated by intelligence that is too different and strange. Most science fiction is thus best read as metaphor for the current human condition, and robots could be seen as stand-ins for repressed sections of society, or perhaps our search for the meaning of life.
通常，這些生物只不過是一個非常人性化的代理人，這是可以理解的，因爲大多數小說，甚至科幻小說，都需要與人類讀者聯系起來，否則他們就會被疏遠，因爲什麽被疏遠呢？不一樣和奇怪的智力。因此，大多數科幻小說最好被解讀爲對當前人類狀況的隱喻，而機器人可以被視爲社會受壓迫階層的替身，或者也許是我們對生命意義的追求。
Reason 3: what seems easy is actually hard…
Another source of difficulty in understanding AI is that it is hard to know which tasks are easy and which ones are hard. Look around and pick up an object in your hand, then think about what you did: you used your eyes to scan your surroundings, figured out where are some suitable objects for picking up, chose one of them and planned a trajectory for your hand to reach that one, then moved your hand by contracting various muscles in sequence and managed to squeeze the object with just the right amount of force to keep it between your fingers.
原因 3：看似簡單其實很難…
理解人工智能的另一個困難是，很難知道哪些任務容易，哪些任務難。環顧四周，拿起手中的一個物體，然後想想你做了什麽：你用你的眼睛掃描你的周圍環境，找出一些合適的物體，選擇其中一個，並計劃一個軌迹，讓你的手達到那個，然後按順序收縮不同的肌肉來移動你的手，並設法用適當的力量擠壓物體，使其保持在手指之間。
It can be hard to appreciate how complicated all this is, but sometimes it becomes visible when something goes wrong: the object you pick is much heavier or lighter than you expected, or someone else opens a door just as you are reaching for the handle, and then you can find yourself seriously out of balance. Usually these kinds of tasks feel effortless, but that feeling belies millions of years of evolution and several years of childhood practice.
很難理解這一切有多複雜，但有時當出了什麽問題時，它就會顯現出來：你挑的東西比你預期的要重或輕得多，或者有人在你伸手去拿把手時打開一扇門，然後你會發現自己嚴重失衡。通常這類任務讓人覺得不費吹灰之力，但這種感覺掩蓋了數百萬年的進化和幾年的童年實踐。
While easy for you, grasping objects by a robot is extremely hard, and it is an area of active study. Recent examples include Google’s robotic grasping project, and a cauliflower picking robot.
雖然對你來說很容易，但是機器人抓取物體是非常困難的，這是一個活躍的研究領域。最近的例子包括谷歌的機器人抓取項目和一個花菜采摘機器人。
…and what seems hard is actually easy
By contrast, the tasks of playing chess and solving mathematical exercises can seem to be very difficult, requiring years of practice to master and involving our “higher faculties” and concentrated conscious thought. No wonder that some initial AI research concentrated on these kinds of tasks, and it may have seemed at the time that they encapsulate the essence of intelligence.
……看起來很難的事情其實很容易
相比之下，下棋和解決數學習題的任務似乎非常困難，需要多年的實踐才能掌握和涉及到我們的“高級能力”和集中的意識思維。難怪一些最初的人工智能研究都集中在這類任務上，而且在當時，這些研究似乎概括了智能的本質。
It has since turned out that playing chess is very well suited to computers, which can follow fairly simple rules and compute many alternative move sequences at a rate of billions of computations a second. Computers beat the reigning human world champion in chess in the famous Deep Blue vs Kasparov matches in 1997. Could you have imagined that the harder problem turned out to be grabbing the pieces and moving them on the board without knocking it over! We will study the techniques that are used in playing games like chess or tic-tac-toe in Chapter 2.
後來發現，計算機非常適合下棋，計算機可以遵循相當簡單的規則，以每秒數十億次的計算速度計算出許多可供選擇的移動序列。1997年，在著名的深藍對卡斯帕羅夫比賽中，電腦擊敗了國際象棋世界冠軍。你能想象嗎，更難的問題是抓起棋子，在棋盤上移動而不把它打翻！在第二章中，我們將學習象棋或井字遊戲中使用的技術。
Similarly, while in-depth mastery of mathematics requires (what seems like) human intuition and ingenuity, many (but not all) exercises of a typical high-school or college course can be solved by applying a calculator and simple set of rules.
雖然深入掌握數學需要（看起來像是）人類的直覺和創造力，但典型的高中或大學課程的許多（但不是全部）練習可以通過應用計算器和簡單的規則集來解決。
So what would be a more useful definition?
An attempt at a definition more useful than the “what computers can’t do yet” joke would be to list properties that are characteristic to AI, in this case autonomy and adaptivity.
那麽，什麽是更有用的定義呢？
嘗試一個比“計算機還不能做什麽”笑話更有用的定義是列出人工智能的特性，自主性和適應性。
Key terminology
關鍵術語
Autonomy
The ability to perform tasks in complex environments without constant guidance by a user.
自主性
在複雜環境中執行任務的能力，無需用戶的不斷指導。
Adaptivity
The ability to improve performance by learning from experience.
適應性
通過從經驗中學習提高績效的能力。
Words can be misleading
When defining and talking about AI we have to be cautious as many of the words that we use can be quite misleading. Common examples are learning, understanding, and intelligence.
在定義和談論人工智能時，我們必須謹慎，因爲我們使用的許多詞彙可能會産生誤導。常見的例子是學習、理解和智力。
You may well say, for example, that a system is intelligent, perhaps because it delivers accurate navigation instructions or detects signs of melanoma in photographs of skin lesions. When we hear something like this, the word “intelligent” easily suggests that the system is capable of performing any task an intelligent person is able to perform: going to the grocery store and cooking dinner, washing and folding laundry, and so on.
例如，你很可能會說，一個系統是智能的，也許是因爲它提供准確的導航指令，或者在皮膚損傷的照片中檢測到黑色素瘤的迹象。當我們聽到這樣的聲音時，“智能”這個詞很容易表明系統能夠執行人能夠執行的任何任務：去雜貨店做飯、洗衣服和疊衣服等等。
Likewise, when we say that a computer vision system understands images because it is able to segment an image into distinct objects such as other cars, pedestrians, buildings, the road, and so on, the word “understand” easily suggest that the system also understands that even if a person is wearing a t-shirt that has a photo of a road printed on it, it is not okay to drive on that road (and over the person).
同樣，當我們說計算機視覺系統理解圖像是因爲它能夠將圖像分割成不同的物體，如其他汽車、行人、建築物、道路等，“理解”一詞很容易表明，系統也理解，即使一個人穿著印有道路照片的t恤，也不可以在那條道路上開車。
In both of the above cases, we’d be wrong.
在以上兩種情況下，我們都錯了。
Note
注釋
Watch out for ‘suitcase words’
Marvin Minsky, a cognitive scientist and one of the greatest pioneers in AI, coined the term suitcase word for terms that carry a whole bunch of different meanings that come along even if we intend only one of them. Using such terms increases the risk of misinterpretations such as the ones above.
當心“手提箱詞彙”
認知科學家、人工智能領域最偉大的先驅之一馬文·明斯基（Marvin Minsky）創造了“手提箱”一詞，指的是即使我們只打算使用其中一個詞，也會帶來一大堆不同的含義。使用這樣的術語會增加誤解的風險，比如上面提到的那些。
It is important to realize that intelligence is not a single dimension like temperature. You can compare today’s temperature to yesterday’s, or the temperature in Helsinki to that in Rome, and tell which one is higher and which is lower. We even have a tendency to think that it is possible to rank people with respect to their intelligence – that’s what the intelligence quotient (IQ) is supposed to do. However, in the context of AI, it is obvious that different AI systems cannot be compared on a single axis or dimension in terms of their intelligence. Is a chess-playing algorithm more intelligent than a spam filter, or is a music recommendation system more intelligent than a self-driving car? These questions make no sense. This is because artificial intelligence is narrow (we’ll return to the meaning of narrow AI at the end of this chapter): being able to solve one problem tells us nothing about the ability to solve another, different problem.
重要的是要認識到，智力並不像溫度那樣是一維的。你可以把今天的溫度和昨天的比較，或者把赫爾辛基的溫度和羅馬的比較，然後判斷哪一個高，哪一個低。我們甚至有一種傾向，認爲根據人們的智商對他們進行排名是可能的——這就是智商（IQ）應該做的。然而，在人工智能的背景下，很顯然，不同的人工智能系統不能以一個軸或維度來比較它們的智能。下象棋的算法是比垃圾郵件過濾器更智能，還是音樂推薦系統比自動駕駛汽車更智能？這些問題毫無意義。這是因爲人工智能是狹義的（我們將在本章末尾回到狹義人工智能的含義）：能夠解決一個問題並不意味著能夠解決另一個不同的問題。
Why you can say “a pinch of AI” but not “an AI”
The classification into AI vs non-AI is not a clear yes–no dichotomy: while some methods are clearly AI and other are clearly not AI, there are also methods that involve a pinch of AI, like a pinch of salt. Thus it would sometimes be more appropriate to talk about the “AIness” (as in happiness or awesomeness) rather than arguing whether something is AI or not.
爲什麽你能說“一小撮人工智能”而不是“一個人工智能”
人工智能和非人工智能的分類並不是一成不變的二分法：雖然有些方法顯然是人工智能，而另一些方法顯然不是人工智能，但也有一些方法涉及人工智能的一小撮，比如一小撮鹽。
Note
注釋
“AI” is not a countable noun
When discussing AI, we would like to discourage the use of AI as a countable noun: one AI, two AIs, and so on. AI is a scientific discipline, like mathematics or biology. This means that AI is a collection of concepts, problems, and methods for solving them.
“AI”不是一個可數名詞
在討論人工智能時，我們不鼓勵使用人工智能作爲可數名詞：一個人工智能，兩個人工智能，等等。人工智能是一門科學學科，就像數學或生物學一樣。這意味著人工智能是一個概念、問題和方法的集合。
Because AI is a discipline, you shouldn’t say “an AI“, just like we don’t say “a biology“. This point should also be quite clear when you try saying something like “we need more artificial intelligences.“ That just sounds wrong, doesn’t it? (It does to us).
因爲人工智能是一門學科，你不應該說“一個人工智能”，就像我們不說“一個生物學”一樣。當你試圖說“我們需要更多的人工智能”時，這一點也應該非常清楚。這聽起來是不對的，不是嗎？
Despite our discouragement, the use of AI as a countable noun is common. Take for instance, the headline Data from wearables helped teach an AI to spot signs of diabetes, which is otherwise a pretty good headline since it emphasizes the importance of data and makes it clear that the system can only detect signs of diabetes rather than making diagnoses and treatment decisions. And you should definitely never ever say anything like Google’s artificial intelligence built an AI that outperforms any made by humans, which is one of the all-time most misleading AI headlines we’ve ever seen (note that the headline is not by Google Research).
盡管我們很氣餒，使用AI作爲可數名詞還是很常見的。例如，一個標題爲：可穿戴設備的數據幫助一個人工智能識別出糖尿病的征兆，這是一個很好的標題，因爲它強調了數據的重要性，並且清楚地表明系統只能檢測出糖尿病的征兆，而不是做出診斷和治療決定。
The use of AI as a countable noun is of course not a big deal if what is being said otherwise makes sense, but if you’d like to talk like a pro, avoid saying “an AI”, and instead say “an AI method”.
使用AI作爲一個可數名詞當然不是什麽大問題，如果所說的是有意義的，但如果你想談論 AI 像個專業人士，避免說“an AI”，而是說“an AI method”。