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.
什么是，什么不是人工智能？不是一个容易的问题！
为什么公众对人工智能的认识如此模糊？让我们看看几个原因。
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：没有官方认可的定义
即使是人工智能研究人员也没有对人工智能的确切定义。
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.
有一个古老的笑话说，人工智能被定义为“计算机做不到的很酷的事情”。讽刺的是，在这个定义下，人工智能永远无法取得任何进展：一旦我们找到一种方法，用计算机做一些很酷的事情，它就不再是人工智能的问题。然而，在这个定义中有一个事实，例如，50年前，自动搜索和规划方法被认为属于人工智能领域。现在这种方法被教给每一个计算机科学的学生。类似地，处理不确定信息的某些方法正变得非常容易理解，以至于它们很快就会从人工智能转移到统计或概率。
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：科幻小说的遗产
在各种科幻文学和电影作品中，人工智能的出现使人们对人工智能含义的困惑更加严重。科幻小说的故事往往以友好的人形仆人为特色，他们提供了过于详细的事实或诙谐的对话，但有时可以遵循木偶奇遇记的步骤，开始怀疑他们是否能成为人类。
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，这些生物只不过是一个非常人性化的代理人，这是可以理解的，因为大多数小说，甚至科幻小说，都需要与人类读者联系起来，否则他们就会被疏远，因为什么被疏远呢？不一样和奇怪的智力。因此，大多数科幻小说最好被解读为对当前人类状况的隐喻，而机器人可以被视为社会受压迫阶层的替身，或者也许是我们对生命意义的追求。
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：看似简单其实很难…
理解人工智能的另一个困难是，很难知道哪些任务容易，哪些任务难。环顾四周，拿起手中的一个物体，然后想想你做了什么：你用你的眼睛扫描你的周围环境，找出一些合适的物体，选择其中一个，并计划一个轨迹，让你的手达到那个，然后按顺序收缩不同的肌肉来移动你的手，并设法用适当的力量挤压物体，使其保持在手指之间。
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.
很难理解这一切有多复杂，但有时当出了什么问题时，它就会显现出来：你挑的东西比你预期的要重或轻得多，或者有人在你伸手去拿把手时打开一扇门，然后你会发现自己严重失衡。通常这类任务让人觉得不费吹灰之力，但这种感觉掩盖了数百万年的进化和几年的童年实践。
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.
虽然对你来说很容易，但是机器人抓取物体是非常困难的，这是一个活跃的研究领域。最近的例子包括谷歌的机器人抓取项目和一个花菜采摘机器人。
…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.
……看起来很难的事情其实很容易
相比之下，下棋和解决数学习题的任务似乎非常困难，需要多年的实践才能掌握和涉及到我们的“高级能力”和集中的意识思维。难怪一些最初的人工智能研究都集中在这类任务上，而且在当时，这些研究似乎概括了智能的本质。
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.
后来发现，计算机非常适合下棋，计算机可以遵循相当简单的规则，以每秒数十亿次的计算速度计算出许多可供选择的移动序列。1997年，在著名的深蓝对卡斯帕罗夫比赛中，电脑击败了国际象棋世界冠军。你能想象吗，更难的问题是抓起棋子，在棋盘上移动而不把它打翻！在第二章中，我们将学习象棋或井字游戏中使用的技术。
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.
虽然深入掌握数学需要（看起来像是）人类的直觉和创造力，但典型的高中或大学课程的许多（但不是全部）练习可以通过应用计算器和简单的规则集来解决。
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.
那么，什么是更有用的定义呢？
尝试一个比“计算机还不能做什么”笑话更有用的定义是列出人工智能的特性，自主性和适应性。
Key terminology
关键术语
Autonomy
The ability to perform tasks in complex environments without constant guidance by a user.
自主性
在复杂环境中执行任务的能力，无需用户的不断指导。
Adaptivity
The ability to improve performance by learning from experience.
适应性
通过从经验中学习提高绩效的能力。
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.
在定义和谈论人工智能时，我们必须谨慎，因为我们使用的许多词汇可能会产生误导。常见的例子是学习、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，你很可能会说，一个系统是智能的，也许是因为它提供准确的导航指令，或者在皮肤损伤的照片中检测到黑色素瘤的迹象。当我们听到这样的声音时，“智能”这个词很容易表明系统能够执行人能够执行的任何任务：去杂货店做饭、洗衣服和叠衣服等等。
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).
同样，当我们说计算机视觉系统理解图像是因为它能够将图像分割成不同的物体，如其他汽车、行人、建筑物、道路等，“理解”一词很容易表明，系统也理解，即使一个人穿着印有道路照片的t恤，也不可以在那条道路上开车。
In both of the above cases, we’d be wrong.
在以上两种情况下，我们都错了。
Note
注释
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.
当心“手提箱词汇”
认知科学家、人工智能领域最伟大的先驱之一马文·明斯基（Marvin Minsky）创造了“手提箱”一词，指的是即使我们只打算使用其中一个词，也会带来一大堆不同的含义。使用这样的术语会增加误解的风险，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.
重要的是要认识到，智力并不像温度那样是一维的。你可以把今天的温度和昨天的比较，或者把赫尔辛基的温度和罗马的比较，然后判断哪一个高，哪一个低。我们甚至有一种倾向，认为根据人们的智商对他们进行排名是可能的——这就是智商（IQ）应该做的。然而，在人工智能的背景下，很显然，不同的人工智能系统不能以一个轴或维度来比较它们的智能。下象棋的算法是比垃圾邮件过滤器更智能，还是音乐推荐系统比自动驾驶汽车更智能？这些问题毫无意义。这是因为人工智能是狭义的（我们将在本章末尾回到狭义人工智能的含义）：能够解决一个问题并不意味着能够解决另一个不同的问题。
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.
为什么你能说“一小撮人工智能”而不是“一个人工智能”
人工智能和非人工智能的分类并不是一成不变的二分法：虽然有些方法显然是人工智能，而另一些方法显然不是人工智能，但也有一些方法涉及人工智能的一小撮，比如一小撮盐。
Note
注释
“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”不是一个可数名词
在讨论人工智能时，我们不鼓励使用人工智能作为可数名词：一个人工智能，两个人工智能，等等。人工智能是一门科学学科，就像数学或生物学一样。这意味着人工智能是一个概念、问题和方法的集合。
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).
因为人工智能是一门学科，你不应该说“一个人工智能”，就像我们不说“一个生物学”一样。当你试图说“我们需要更多的人工智能”时，这一点也应该非常清楚。这听起来是不对的，不是吗？
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).
尽管我们很气馁，使用AI作为可数名词还是很常见的。例如，一个标题为：可穿戴设备的数据帮助一个人工智能识别出糖尿病的征兆，这是一个很好的标题，因为它强调了数据的重要性，并且清楚地表明系统只能检测出糖尿病的征兆，而不是做出诊断和治疗决定。
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作为一个可数名词当然不是什么大问题，如果所说的是有意义的，但如果你想谈论 AI 像个专业人士，避免说“an AI”，而是说“an AI method”。