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《人工智能》的错用名称
After China started to promote progress in “artificial intelligence”, Chinese friends who find out that I’m a computer scientist sometimes ask me questions like “what will become of society after AI takes all our jobs?” Perhaps many people all over the world are in fear of “artificial intelligence” because they think of how it is portrayed in science fiction, whereas the entire world’s media has generally failed to explain what it means in reality.
中国开始促进《人工智能》进步后，有些中国朋友听说我是电脑科学家就问我“人工智能取代大家的职业，社会变成怎么样”这样的问题。恐怕世界各地的不少人害怕《人工智能》，因为那句话经常被科幻采用，但全世界的媒体很少清楚解释那个词的现实生活意思。
The 1990s science-fiction television drama “Star Trek The Next Generation” featured an android “Lieutenant Commander Data” who had a “positronic brain” (an idea from 1940s author Isaac Asimov) which apparently gave Data self-awareness or consciousness. The 1979 Star Trek motion picture had an artificial antagonist that used a female robot to confront the protagonists in their spaceship, and the final act showed this antagonist to have originally been a simple space probe, but collecting too much data caused it to become self-aware and gain consciousness (and a misunderstanding that Earth’s inhabitants should be destroyed, so the film can have a plot). According to this kind of science fiction, self-awareness is not only something that we can take for granted is easily made part of AI, but it also something that can spontaneously emerge in a simple computer (and turn against humans). But this kind of fiction also claims that the origin of life is so easy that it happened everywhere and the entire galaxy is brimming full of aliens, whereas evidence suggests our real universe does not have those conditions: life cannot arise so easily, and neither can self-awareness or consciousness.
1990年代科幻电视剧集《星际旅行下一代》有个人形机器人《Data少校》，他有《正子脑》（这个主意来自1940年代作家伊撒克·阿西莫夫），正子脑看来给Data一个自觉意识。1979年的《星际旅行无限太空》电影有一名人造对手，使用女性机器人进入主角的太空船，结局说那对手原来只是个简单的空间探测器而已，但吸收太多资料就给它自觉意识（和误以为该灭絶地球的居民，所以电影有情节）。按照这样个科幻，自觉意识不但是人工智能的理所当然方面，还能在简单的电脑自然发生（和反目为仇）。不过，这样的虚构也声称生命起源这么容易发生，全星系都充满外星人。证据表明我们的真正宇宙没有这样的情况：生命不那么容易起来，自觉意识也不那么容易发生。
MIT computer scientist John McCarthy who coined the term “artificial intelligence” in 1956 was the inventor of the LISP programming language and wrote Chess software (and suggested a form of cloud-computing service in the 1960s), but the things he was describing were not meant to be anything to do with consciousness or self-awareness. A computer’s processor is essentially an electronic circuit: a transistor can let one current switch another current on or off, so we can use particular arrangements of transistors to process binary-number patterns (performing arithmetic, moving data patterns around, etc) by switching. We can arrange transistors such that different binary patterns on the input can cause different parts of the circuit to be switched on, and we can have the system process its input in a sequence, so that we can refer to it as “obeying the software’s instructions” whereas actually it is simply a circuit being manipulated by the software’s binary patterns and it has no understanding. But this kind of machine code is hard to write, so computer scientists have been inventing “high-level languages” which, although still being computer languages, are easier to write; software called “compilers” and “interpreters” can translate these high-level languages into the necessary binary patterns for the machine. But a compiler’s ‘translation’ is most definitely not the same thing as machine translation of natural (human) languages: writing reliable natural-language translation software is very difficult (such software often makes mistakes, as I’ll mention below), whereas “compiling” a computer language is very reliable because these languages have been specifically designed for it. Today there are many high-level languages, and I recommend all computer scientists should at least know Python, C, C++, Java, JavaScript, Bash, Makefile etc. McCarthy hoped that future programming languages can look more like declarations of facts, letting the compiler translate these into commands, but the system still has no real understanding; it just needs a first-rate compiler designer to pull it off. This kind of software behaves like a device reacting to patterns: the programmer says “if this pattern happens, then take this action, or if that pattern happens, then take that action” etc, and has to consider beforehand in great detail what kind of input patterns may happen; overlooking a rare situation can result in the software behaving inappropriately or crashing when that situation happens.
1956年造了“人工智能”那个词的约翰·麦卡锡（麻省理工学院的电脑科学家）也是LISP编程语言发明家，他写了个早期国际象棋软件（也在1960年代建议云端运算服务）不过他所描述的不是自觉意识。电脑处理器只不过是电子电路：晶体管能容许一个电流开关另一个电流，所以我们会用特定的晶体管架构处理二进制的数字图案（做算术、移动数据图案，等），我们也会安排晶体管所以一个二元码图案打开一个电路，另一个二元码图案打开另一个电路等，和系统能循序打开正确的电路，我们说那系统是“服从软件的命令”但其实只是被二元码图案操作，没有任何悟性。不过，这样的机器语言难写，所以电脑科学家发明一些“高级语言”（仍然是电脑语言但比较容易写）而创造“编译器”等软件能把这些高级语言翻译成机器的二元码图案。但编译器并不是机器翻译系统：写个可靠的“自然语言”翻译软件很难（它们往往犯错，我下面讨论一点），但“编译”个电脑语言很可靠，因为这些语言是为这件事而设计的。今天有多种多样的高级语言，我推荐所有电脑科学家至少认识Python、C、C++、Java、JavaScript、Bash、Makefile等。麦卡锡希望未来的编程语言好像宣告事实而让编译器把这些翻译成命令，但系统仍然没有悟性，只需要非常厉害的编译器设计师。这样的软件好像图案反应器，编制员说“如果有这个图案，就说这个行动；如果有那个图案，就说那个行动”等等。编制员得提前详细地想起一切的可能发生的图案，如果忽略一两情况，而那个情况真的发生，那软件的反应不合适或死机了。
What is called “artificial intelligence” today typically involves a method called “machine learning” but it’s not real learning (the system still has no real understanding)—it’s just a shortcut so that the programmers don’t have to write so many rules themselves. For example, I wrote a piece of software to turn Chinese characters into Pinyin pronunciation. But how do I handle the case where one Chinese character can be pronounced in more than one way? For example, the character 差 can mean “wrong” or “different” or “send” and each of these meanings has a different pronunciation, so when reading it in a sentence, the reader is supposed to judge from the context whether this particular instance of it is “wrong” or “different” or “send” and pronounce it accordingly. (English examples include “lead”, “wind”, “bow” etc.) A real person can interpret the meaning of the surrounding text, but the computer is just a machine, so how can it understand what the sentence is about? So my system’s specific rule (in the November 2020 version) is: “look at the 3 Chinese characters before this point, and the 2 Chinese characters after it, and see if these 5 include any of the following words:
现代的所谓《人工智能》系统往往使用一个“机器学习”方法，但不真的是学习（系统仍然没有悟性），只不过是个近路所以编制员不必自己写所有规则。比如说，我写了一个软件把汉字翻译成汉语拼音。但我怎么处理多音字? 比如“差”有时是chà有时是chā有时是chāi，我该写什么规则? 真人可以明白“如果是关于差别，是chā，如果是关于差来是chāi，如果是关于错误是chà”，但电脑只不过是机器，怎么会了解句子关于什么? 所以，我系统的具体规则（2020年11月版本）是：“看看从3汉字前和2汉字后的上下文，如果那些5汉字包括:
‘I’ or ‘go’ or ‘’s’ or ‘you’ or ‘to’ or ‘then’ or ‘person’ or ‘make’ or ‘-s’ or ‘will’ or ‘and’ or ‘sky’ or ‘child’ or ‘emperor’ or ‘king’ or ‘rub’ or ‘first’ or ‘Je’ or ‘must’ or ‘out’ or ‘big’ or ‘slave’ or ‘stand’ or ‘although’ or ‘personage’ or ‘basis’ or ‘second’ or ‘cultured’ or ‘anger’ or ‘heart’ or ‘time’ or ‘cast’ or ‘lord’ or ‘besides’ or ‘-th’ or ‘sacrifice’ or ‘Mal’ or ‘Ms’ or ‘every’ or ‘send him’
“我”或“去”或“的”或“你”或“到”或“就”或“人”或“使”或“们”或“要”或“和”或“天”或“子”或“帝”或“王”或“摩”或“先”或“耶”或“必”或“出”或“大”或“奴”或“立”或“然”或“位”或“基”或“亚”或“雅”或“怒”或“心”或“次”或“撒”或“主”或“另”或“第”或“祭”或“玛”或“士”或“列”或“差他”
then the meaning is ‘send’ rather than ‘wrong’”. Why did I write such a complicated rule? I didn’t. My computer did. This rule was generated by my “machine learning” system, because I fed it about 4,000 pages of example Pinyin annotations of Chinese characters, which included over 2,000 examples of how this word should be handled, from which my software could tell it had more than one reading, so it processed the context of each example and found the smallest set of rules that would distinguish which instances are read as “send.”
那么这个‘差’是chāi而不是chà。”我为什么写这么复杂的规则? 我不写了。这条规则是我所写的“机器学习”系统建造的。因为我把差不多4千页的汉语拼音书刊进入我的系统，那个资料包括2千多例子示范如何写“差”，而我所写的软件从此能查考“差”是个多音字（因为不同的例子用不同的拼音）所以它就处理每一例子的上下文而找到最少的规则能识别哪的上下文有“chāi”。
Is this really “intelligence”? I doubt it. It seems to work: if I write “I send him to go” in Chinese, the system can correctly infer that this use of the character should be read as “send” because “I” and “go” are there to provide that clue. But if I write the Chinese for “I am wrong” (which also includes “I”), the word should be read as “wrong” but the system still reads it as “send” because of the “I” being there. Of course, once I *know* about this problem, I can easily fix it just by adding the phrase “I am wrong” to the examples corpus and then re-running the processing of that corpus, whereupon it will drop “I” from the list of context words that can be used to differentiate the case and instead select another. But how do I know how reliable my system is? Although I plan to continue adding more examples and make it more and more reliable, I’m afraid that input very different from the example sentences will always find some instances of mistaken results, because this kind of “machine learning” isn’t really learning, it’s just like a conjuring trick to make people think the computer has learned. “Machine learning” can make my life as a programmer easier (writing all those rules by myself is more difficult), but not only is the system sometimes wrong, but also I myself can’t always guess when it is going to be wrong. I still recommend my system to my friends (because I’ve seen other software to add pinyin that is even worse), but I don’t want to say this is real “learning” or “intelligence”. But this kind of thing is the current state of “artificial intelligence!”
这真的是“智能”吗? 我怀疑。看起来运转: 如果我写“我拆他去”，系统可以写wǒ chāi tā qù而不是wǒ chà tā qù因为“我”和“去”好像帮助系统给它线索这个“差”很可能是“chāi”。但假设我写“我很差”，那也包括“我”，所以也是chāi吗? 看来我的系统以为这样。我看出这个之后，我会很容易修改我的系统: 我只得把“我很差=wǒ hěn chà”加入我的例子材料，并让系统再次处理那些材料。这次它不选择“我”做区别的线索，反而选择另一个。不过，我怎么知道我的系统多可靠? 虽然我打算继续加入更多例子，使系统越来越多可靠，但恐怕我们如果输入跟例子句子截然不同的话，一直会找一些错误的结果，因为这样的“机器学习”不真的是学习，只是好像个把戏让人以为电脑学习了。“机器学习”能使我的编写员生活更容易（我自己写所有规则更难），但系统不但是有时候错了，而且我不能总是猜想系统什么时候将会犯错。我仍然向朋友推荐我的系统（因为我看见其他的加拼音系统更错）但我不想说这是真正的“学习”或“智能”。但这是《人工智能》的状态!
Machine translation, although it *has* progressed (because engineers continuously add new examples to their “machine learning” systems), nevertheless still makes mistakes, sometimes quite serious. For example, in 2020 Ting Zhang of the local “Crayfish” company wrote (in Chinese) “when I was recently invited to attend a hearing at the House of Lords, I made a strong case to help the lawgivers clearly understand the contribution to the UK from Chinese investors, and the negative effects on Britain’s science and technology sector if Chinese investment were to be prohibited” (my translation). But WeChat’s automatic translation effort came out as the opposite meaning: “When I was recently invited by the House of Lords to attend the hearing, I argued. Let lawmakers know a lot about the contribution of Chinese investors to the UK and the negative impact of Chinese investment [sic!] on the British technology community.” Please note this was WeChat translate, not Google, so please don’t quickly say this error must have been due to American companies being prejudiced against China—what we are seeing here is simply a limitation of “artificial intelligence”. This particular problem can be fixed, but then we’ll still be waiting for the next one to happen. This isn’t really intelligence, and it isn’t really reliable. (And I did not use any machine translation when writing this Gemini page: all Chinese mistakes are my own.)
机器翻译虽然有进步（因为工程师不断把新例子加入他们的“机器学习”系统）但仍然会犯错，有时很严重。比如，本地Crayfish公司张婷2020年写“我最近被英国上议院邀请出席听证会时， 就据理力争，让立法者们很多的了解中国投资人给英国带来的贡献，以及如果阻止中国投资给英国科技界带来的负面影响”微信的英文翻译是相反的意思: “我辩论了，让立法者们知道很多关于中国投资人给英国的贡献和这贡献对英国科技界带来的负面影响”。请注意这是微信翻译，不是谷歌翻译，所以请别快快地说这只不过是美国公司对中国的偏见。原因是“人工智能”的界限。这个问题能够被修改，但我们仍然等候下个问题。这不真的是智能，也不真的可靠。（此外，我不敢用任何机器翻译系统写这个网页，所有汉语错误是我自己犯了。）
A similar situation arises in signal processing. For example, consider products like fitness trackers. How does it count your steps when you walk? How does it know how your sleep was? This is another product of “machine learning” where we do not know the details of the rules because some computer has automatically deduced them from many examples. When a delegation from Suzhou’s Innovation Incubator came to Cambridge in 2019, they gifted Huawei fitness trackers to the audience, including me (which made me feel a little awkward as I’d only come for the “networking” with Chinese research students), and when I got home I found I could trick it into counting steps simply by swinging it like a pendulum from my finger, but after downloading a software update this was no longer possible (it also seems to somehow “know” whether it’s on my wrist or just being held by the fingers, and sends text-message notifications only if it’s actually on the wrist); I don’t know what rules were included in this new version to differentiate between sensor inputs corresponding to these two situations, but I suppose it’s because they collected and merged in more example data. Although this seems very good, I *know* there will be other situations where it still makes mistakes (so in the instruction book they printed that this is not a real medical device): this is a limit of “machine learning.”
信号处理有类似的情况。比如，考虑一下《运动腕带》那样的产品。你行走它怎么会算步? 怎么知道你的睡眠如何? 这是另一个“机器学习”产品：我们不知道规则的细节，因为某个电脑从多种多样的例子自动推断出来。苏州科技孵化代表团2019年来剑桥送给听众华为运动腕带，包括我在内（我一点尴尬因为我是为了“人脉”而来了），回家时我发觉那个手表如果被我手指摇摆它就计算更多脚步，但下载软件的新版本后它不这么容易计算脚步（它也好像知道是在我的手腕子或者在我的手指，如果不在手腕子它就没有通知我的短信），我不知道这新版本用什么规则识别这两个情况，但我猜想是因为他们加收藏并入更多例子的数据。虽然这看起来很棒，但我知道必然有别的情况仍然能使它犯错（所以他们在说明书说这不是真正的医疗器），这是“机器学习”的限度。
“Computer vision” has similar limits. Although there are more and more good uses of it (such as blind programmer Saqib Shaikh’s helping Microsoft write the Seeing AI app as a computer vision tool for the blind, and Rana el Kaliouby’s work to read emotions from faces which can help some autistic people and with vehicle safety etc), the system still lacks real understanding, and although it is useful it is never quite 100% reliable. Facial recognition in security systems always has a certain error rate, so we’d better not rely on it completely: if my bank wants both a password *and* a face, that might not be too big a problem, but if they use the face *only* then there’s bound to be someone else in the world with a face similar enough to mine to trigger the system into a false recognition, and we won’t completely understand exactly what conditions caused that misread. If the future really has self-driving cars then we must understand their limits.
《计算机视觉》也有类似的限度。虽然有越来越多善用（我所认识的盲人程序员萨基布·谢赫帮微软写个援助盲人的计算机视觉app，也有拉娜·艾尔·卡里奥碧的工作让软件从人脸上的表情猜想他们的感受，这会帮助一些自闭症人士或汽车安全系统等）但系统仍然没有悟性，虽然有所有用但不是百分之百可靠。安保系统的“刷脸”功能都有错误率而我们最好别百分之百可靠那些系统：如果我的银行需要我的密码和我的脸，那可能不是特大的问题，但如果他们只需要我的脸而已，世界必定有其他人的脸能使系统以为他就是我，而我们不百分之百明白什么情况导致这个误解。如果未来真的带来自动驾驶汽车，我们应该了解它们的限度。
I’m not afraid of “artificial intelligence” but I’m afraid of people relying on these systems’ decisions too much. “Automation bias” is when people mistakenly think a computer’s suggestion is more reliable than another human’s suggestion. In the past, complicated software (such as typesetting packages for music or mathematics) were called “expert systems” because some expert (such as mathematician and computer scientist Donald Knuth of Stanford) summarised their knowledge to program the system. The system’s decision was usually better than that of an average non-professional user. These days the ‘buzzword’ is “algorithm” instead of “expert system”, but if you don’t know how the algorithm was written, you don’t *really* know it’s reliable. One of my English friends once visited Cambridge and give me a lift somewhere in his car; the car’s satellite navigation equipment directed us through Silver Street, where I knew there was a barrier, but he said not to worry as the satellites are always right and he’d recently updated the map. So we drove up to the barrier, and I had to explain that although the satellites themselves are usually quite reliable (at least in peacetime), the car equipment’s updates do not always include *all* new information about barriers etc, so we still need our real intelligence. On another occasion, an English friend tried to use a computer voice to learn Chinese, but the computer voice was making a mistake, and he couldn’t believe that a computer voice could possibly make mistakes, so I had to list some examples of Chinese mistakes in computer voices just to show they shouldn’t be taken as an authority. And when I wrote some software to help a local cancer-research group plan their experiments, the software needed to know how many test tubes they wanted to use, so I wrote a question “how many test tubes, 3?” because I needed a ‘default’ answer and I thought 3 was as good a number as any—I was fully expecting the biologist user to say “oh no not 3, we want 16, let’s put that in” or whatever, but I later found out that someone saw my “3” and said “computer says it should be 3—computer says it should be 3—we’ve got to change our setup to 3” (oh dear). So I made the next edition calculate the default more carefully, but this is a clear example of automation bias: if I myself (as a human) had walked into their lab and said “do you want 3 test-tubes here?” that user would not have taken it from *me* nearly as easily as they took it from my “algorithm” on the *computer* which didn’t actually know any better than I did in this instance.
我不害怕《人工智能》，我所害怕的是人类过度依靠这些系统做决定。所谓的“自动化偏误”是，人以为电脑的建议比其他人的建议更可靠。以前，复杂的软件（比如五线谱或数学排版系统）被成为“专家系统”（因为某个专家，比如史丹佛大学电脑科学家高德纳，概述了自己的知识程序那个系统），往往比非专业的用户更可靠。这几天的潮词是“算法”，但你如果不知道这算法是如何写的，你不真的知道是可靠的。以前我的一个英国朋友拜访剑桥而用他的汽车给我接送某个地方，不过他的卫星导航设备说我们得用Silver Street我知道有路障，他说“路障不可能，放心那些卫星一直是对的而我刚才更新了地图”。所以我们来到了路障，我得解释虽然卫星本身很可靠，但车里设备的更新不总是包括所有路障等，所以我们仍然需要真正智能。在另一个情况，我有英国朋友试试用机器声音学汉语，不过他所用的机器声音犯错，他不相信机器声音可以犯错，所以我不得不列出一些汉语机器声音错误的例子。还有，我曾经写软件帮助一个本地的癌症研究团体打算他们的实验，不过那软件需要知道他们想用多少试管，所以我写个问题“多少试管?”而我需要个“默认选项”所以我说“3?”期待生物学家的用户看这个说“哦，不是3，我输入16”，但我听说某人看见我的“3”就说“电脑说应该是3，电脑说应该是3，我们得改变为3”。所以我使下个版本更仔细的计算合适的默认选项，但这清楚是自动化偏误：如果我本人进的去他们的实验室说“你想用3个吗?”那个用户必定不那么容易信赖我的建议，但如果那个建议来自“算法”他不质疑算法的正确性。
“Artificial intelligence” has knowledge but not understanding—it’s still useful, but we with understanding had better check its suggestions. “Behaviourists” like Dennett claim that consciousness is merely an illusion to help us prioritise which signals to process, and Dennett uses the example of “blindsight”, but I can confidently say I know more than Dennett about blindsight because I’ve *experienced* it—I have cortical visual impairment (my brain does not fully process visual signals), and I have experienced both with-qualia vision *and* without-qualia blindsight-guessing (although I can’t rely on either system all the time), and I *know* there’s a difference between the two (at least in my brain) so I think there’s something wrong with Dennett’s theory that it’s all the same (but we *don’t* know if Galton is right to speculate that Dennett himself has no qualia). Yes Dehaene’s afraid too much focus on qualia can lead to dualist philosophies like the ancient Babylonian idea of man’s immortal soul, but we don’t *have* to go there—I’d rather just say we are material beings but we still don’t understand how self-awareness and consciousness *really* works (all of today’s theories have problems) so we simply *can’t* create a machine with genuine understanding. This is not the argument from Searle’s “Chinese room” thought experiment: he just said “suppose a worker in the room doesn’t understand Chinese but follows some rules that let him choose correct Chinese character cards to display the correct answers to questions” and the refutation is simply that the *worker’s* understanding of Chinese is not relevant to the question of whether the *system as a whole* (including the rules) understands Chinese. But I claim that such a system isn’t even *possible*: if machine translation is anything to go by, *no* set of rules can *always* produce the kind of answer that a person with understanding would provide, so the “Chinese room” is an impossible supposition.
所谓《人工智能》有知识但没有悟性，仍然有用但我们有悟性的人最好验证它的建议。“行为主义者”（比如丹尼特）声称自觉意识是个错觉，只不过是个心理模型计算我们优先处理的信号。丹尼特使用“盲视”的例子，但我比他更知道皮层性视损伤是如何：我的脑筋不百分之百明白眼睛的信号，但我不百分之百是盲人，我会看一些，此外我有时候有一点“盲视”（正确的猜测情况但没有视觉的“感质”）但我的那个系统也时断时续。丹尼特声称“盲视”跟视觉的“感质”一样，但我经验两个现象我知道他们不一样，所以我恐怕丹尼特的理论有个问题（高尔顿觉得是因为丹尼特自己没有“感质”但我们不知道丹尼特的感觉，但我知道我有什么感觉）。德阿纳害怕把“感质”放在首位能使我们相信二元论（比如“人有不死的灵魂”那个古巴比伦的主意），但那不一定，我宁可相信我们只是物质但我们还不明白到底什么是自觉意识和思考力，今天的理论都有问题，所以我们根本不会创造一个真正有悟性的机器。这不是希尔勒的“中文房间”思考实验：他只是说“假设房间里面有不懂中文的工人，但服从某些规则能够选择正确的汉字卡显示正确的答案”，反驳是“那工人不懂中文不相干的，因为全系统（包括规则）是懂中文的系统”，但我声称这思考实验的假定也不可能：如果机器翻译系统是典型的例子，没有任何规则能总是产生好像有悟性的人一样的回答，所以“中文房间”是个不可能的假设。
People sometimes ask me to predict the future of “artificial intelligence” but I don’t know. My guesses are:
有些人问我预测《人工智能》的未来，不知道我会不会但一下是我的猜想:
1. A really novel algorithm will be invented by someone on the autism spectrum with strong lateral-thinking ability.
1. 自闭症人士要发明新奇的算法。（因为有些的水平思考很强）
2. Exam markers will experience “Turing tests” as quality control: computers will generate fake student scripts and the marker has to show they won’t be fooled by fake understanding.
2. 阅卷老师要经验《图灵测试》的质量控制：电脑写假试卷，看看老师会不会被没有悟性的“智能”欺骗
3. Poor-quality managers had better be careful not to be replaced by artificial intelligence. (*Good* managers know the work and the workers and understand both, but if some manager can only say things like “What do you need to do? What are the sub-tasks? When will you finish? Have you finished yet? Finished yet? Be quick!” without even knowing what the work is, then they’d better be careful to show they’re bringing more value than some piece of software that says those things.)
3. 劣等的管理人得小心不会被人工智能取代。（优质的管理人真的明白任务和职工，但如果他们只会说“你得做什么? 什么子任务? 什么时候结束? 已经结束了吗? 结束了吗? 快点!” 等等，但不真的明白所提到的任务到底是什么，他们得小心如何证明比人工智能还好。）
4. If there’s too much hype, we might get another “AI winter” as governments and companies get disappointed and abandon AI, but if people increase their understanding of the *limitations* of “artificial intelligence” then perhaps that can be averted.
4. 如果有太多天花乱坠的宣传，下一个“人工智能低谷”（政府和公司被失望而放弃人工智能）可能会发生，不过如果人更多理解《人工智能》的限度，也许能避免激发这个“低谷”
5. The industry will still require “traditional” (not machine-learning) programmers, because people want to understand the reasons behind the rules. (Although I hope “explainable AI” progresses, I doubt it will ever be able to completely replace traditional human programmers.)
5. 产业继续需要“传统”（非“机器学习”）的编写员，因为人想理解规则的原因（希望“可解释的人工智慧”有进步但我怀疑它能百分之百取代程式编写员）
6. I hope everyone will eventually learn to avoid automation bias.
6. 希望大家最终能学会避免自动化偏误
All material © Silas S. Brown unless otherwise stated. Google is a trademark of Google LLC. Huawei is a trademark of Huawei Technologies Co., Ltd registered in China and other countries. Java is a registered trademark of Oracle Corporation in the US and possibly other countries. Microsoft is a registered trademark of Microsoft Corp. Python is a trademark of the Python Software Foundation. Star Trek is a trademark of CBS Studios Inc. / Paramount. WeChat is a trademark of Tencent Holdings Limited. Any other trademarks I mentioned without realising are trademarks of their respective holders.