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追求。例如被杨振宁先生称为“世界上0早的计算机”的算盘，直至PC普及之前都是主流的计算工具，上世纪七八十年代，许多中国家长都会送孩子去学习心算和珠算——算盘本身正是被人类赋予了规则、体现了人类智慧的工具，本质上，这与今天的PC、智能手机、平板设备可谓一脉相承。

击败了卡斯珀罗夫的IBM“深蓝”被许多人视为AI研究的里程碑。在对弈时，能想出更多后续杀招、对方可能的应手、由此带来的变化、变化后对应的棋路调整的棋手赢面显然更大，而计算机在此方面的优势不言而喻。人脑只能设想出几步、十几步棋，但机器则能模拟出所有的可能性。也就是说，即便不是“深蓝”，也迟早会有其他的计算机选手挑战人类成功，而且基于当前的信息科技发展水平，如果将国际象棋世界0的人机之争变成每年例行的赛事，那极有可能已无人能够战胜机器对手——哪怕只是一台Windows Phone。当然，计算机棋手短期内还无法攻陷源起于中国的围棋的阵地，这很让我们为老祖宗的深邃智慧感到自豪——有人估算，围棋的变化可能性超出象棋10的122次方倍。计算机下棋的方法是穷举所有的可能性，而人类则可以根据经验选择性地精减（prune）和深入。可以想象，若仅仅通过提升机器性能、存储棋谱、优化算法来作出“判断”，因为需实时处理的计算量太大，现有的0强大的计算机也还是不可能战胜人类大师。

不过，的确计算机不同于人类以往发明的任何工具。这种不同体现在，一是它不是出厂时用途便已固化的专用工具，像脚踏车、影碟机，它的能力取决于用户安装的程序。二是它可以为各种专用工具注入新的生命力，比如近来被热议的“可穿戴设备”，只是将某些计算能力植入腕带、手表、眼镜等“传统工具”，将之与手机、PC之间建立起数据关联而已。

但凡“工具”，皆包含了其人类创造者的智能、经验与巧思。广义的AI是给予制造物以契合事理的功能特性，与人类一起共同完成我们做不到和做不好的事，达到“人+机器=超级人”的效果。就像锤子、斧子是人们手臂的强化和延续，汽车、轮船和飞机是人们腿脚的强化和延续。近年来无人驾驶汽车很受关注，似乎这是一种新形态的智能机器，但无人驾驶的飞机多年以前便已发明——同样无需人来驾驭，飞机和汽车相比，能说哪个更智能呢？

过去的几十年来，计算机硬件性能的演进和软件适用领域的拓展超越了所有人的想象。若是以广义的视角来观察AI的外延，承认所有灌注了人类对世界的思考的工具都体现了某种程度的“智能”，那么可以说生活中已然随处可见智能设备。

让机器用自己的方式思考和成长

“耳聪目明”是对人的夸赞。科学家们一直在尝试让计算机能用人类的方式来了解世界，所以语音识别和计算机视觉始终是AI研究的重点——今天我们已经可以和Cortana对话、或是坐在配有360°无死角摄像头的无人汽车里感受机器驾驶员的技术。

Cortana和无人汽车是机器人的一种吗？某种意义上是的。但如果说“真正的机器人”必须既能像人那样思考，又具备类人的体貌——好吧，谁知道我们要用像人一样的机器来做什么呢？大家对于人形机器的固执迷思的背后，很可能是想找到替自己做粗重工作的帮手吧。

然而现实是已经出现了很多能帮我们做粗重工作的机器，无论是煮咖啡、烧烤还是洗碗、打扫……人们是喜欢一台四肢粗壮的机器人系着白围裙跑来跑去给我们做所有的家务，还是习惯于用各种小巧的设备来完成不同的任务？

假设人人都爱机器人，在通往产品的道路上也还是有着许多障碍。比如，从桌上的茶壶里倒杯茶而不打翻杯子或洒出茶水，这对人类小孩来说都不算挑战——孩子们不假思索就可以完成任务。但对0“聪明”的机器人而言，却要经过艰难复杂的运算。首先他要看到桌子，认出茶壶和茶杯，用适当的力度拿起茶壶（手指太粗可能还不成），举起茶壶、以刚刚好的角度对准茶杯，实施倒茶的动作，还得判断怎样才能让杯中的茶水将满不溢。就算碰巧成功了一次，下一轮换全然不同的桌子、茶壶、茶杯，还是可能会失败。

长期以来，从事AI研究的科学家，也包括那些执迷于创造出类人机器的学者，总是梦想着将人类思考、计划、执行的能力移植给机器，但是否人怎样行动，机器就应怎样行动？是否人达成目标的路径是由A到B，机器就应遵循完全一样的路径？这种研究诚然有着非同寻常的科学价值，却也会因“赋予钢铁工具以人的特征才算成功”的偏执而举步维艰。

另一条思路是跳出窠臼，站在机器的角度去模拟和延展人的思维，而不是用人的视角和习惯去限制机器。无人驾驶汽车并非只有“两只眼睛”，而是装备了多个雷达传感器、全景摄像头和激光测距仪。i-Robot清洁机器人也是，她的身材圆润扁平，一点儿也不像人，但吸尘的时候一定比两米高的机器保洁员好用。

0初，AI研究遭遇的瓶颈是，人的逻辑思考模式几乎无法复制给机器，无论是将低阶的声音、影像、气味等信号升华到认知，还是把有共性的现象抽炼成规律，都不是机器所能掌握的技能——机器学习与大数据将AI研究带入春天，0近还出现了深度学习、深度神经网络等新概念。更大规模的数据量和更少的假设、限制可以让机器用自己擅长的方式（数据存储、挖掘、分析）“思考”和成长，进而在实用化路途上走得更快更远。

人机关系：主宰与助手

截至目前，智能机器（包括形形色色的“机器人”）的优点和缺点同样鲜明。它们能够更迅速更高效地完成很多人类难以承担的工作：在实验室、计算中心等需要运算的环境，在工厂流水线、组装车间等辛苦又单调的环境，在核污染现场、深海、太空等人类不宜接触的环境，到处都有智能机器的身影。

处理数据是机器的强项。多年以前分析较大规模的数据需要动员许多具备专业知识的人共同参与，还往往耗时良久，而现在，遍及全球的互联网与传感网时刻都在生成海量的、多维的数据，依靠人脑无法有效处理，而用计算机来分析，也就是一眨眼的事。借助机器的力量，人们可以更快地由现象抽取规律，由规律导出结论。而今，AI与大数据的结合，已表现在每个领域、每个应用中。未来的两三年，初步拥有了看、听、连接能力的多元化的设备会反过来推动AI研究的跃进，因为更多的数据会让机器不断发现更准确的规律和更贴近事实的因果。

但在可见的未来，让机器拥有接近于人的自主选择、判断、创造与决策能力仍不容易。就像聪明的Cortana，在安静的办公室里可以听懂你说什么，并遵照你的指令帮你拨电话、发信息、查影讯、订餐厅，但如果是在嘈杂的公众场合，比如音乐节现场或鸡尾酒会上，Cortana一定会变得不那么聪明，因为太多的声音信号让她无法分辨有用的信息。但换做是人呢？即便现场宾客再多，声音再嘈杂，没法听清楚谈话对象的每一句话，但多数情况下，你仍然能猜对、补足并理解对方发送的信息，因为你的大脑在全神贯注之下，能够去除环境杂音，捕捉到想听的信号，同时基于对谈话对象所处领域和语言习惯的了解，你可以用想象和思维延展填上没听清的语句漏洞，而且准确率相当高。今天的AI可没有这种能力。

同样道理，机器翻译工具可以给出词语的释义，甚至帮我们逐字逐句翻译每句话，但如果是现场即听即译的情况下，逐字逐句翻译既没有必要，也不太可能，因为倾听、辨识、翻译、选择词句都需要思考，但倘若翻译者很了解发言者，也知道之前他曾经讲过类似的话题，就会比较省力，很多时候，发言者讲了很长一段话，翻译者只用一两句成语就能概括与传达准确的意旨；反之，发言者只是说了一个与学术相关的句子，翻译者可能既要表达原意，还要添加注释，来让周边的非专业听众能够明白——这是专属于人的Generate and Test（半猜测半验证）能力，AI并不具备。

结合各种感官捕获的信号与过往的知识积淀去处理信息、判断并做出决策，这是人的专长。机器的优势是数据处理、模式识别，而不是判断、创造与综合。所以我相信，无论AI科技发展得多么迅速，人与机器之间，依然会是主宰与助手的关系。

总结一下，我们需要什么样的机器人？

真正有用的机器人不一定是人的形象，人形机器有趣但不实用。试想一下，当你站在一台高大强壮的人形机器旁，会不会油然而生恐惧感呢？客观地说，粗壮又庞大的机器人只适合工厂和工地，我们可以幻想一种普遍适用且长得与人相像的全能机器，但这种设备的拥有成本一定很高，此外还有空间和能耗等现实问题。现实中，已开始帮助我们做各种工作的机器大多是小巧和悦目的，未来我们的办公室、我们的家都会变得越来越智能，但“智能”会无形地隐藏在吊灯里、电视中、墙壁上，更像是人类生活在智能机器中，而不大可能只是以人的形象提供服务的机器人。

研发有类人情感的机器，对科学家而言或许是值得投入心血的课题，但其实用意义远不及科学意义——而今生活中已经有很多智能机器，虽然它们没有情感，但这能说是坏事吗？假设你的机器人既能干又爱你，但爱的反面不正是沮丧、愤怒等负面情绪？这样的机器人，可能会在情绪不好时拒绝你的指令，还可能希望自己也有权像人一样工作五天休息两天，这恐怕不是我们想要看到的。Common sense analysis technology and characteristics of CI801-EACI801 equipment



pursuit. For example, the abacus, which Mr. Yang Zhenning called "the early computer in the world", was a mainstream computing tool until the popularization of PC. In the 1970s and 1980s, many Chinese parents sent their children to learn mental arithmetic and abacus. The abacus itself was a tool endowed with rules and reflecting human wisdom. In essence, it came down in the same line with today's PCs, smartphones and tablet devices.



IBM "Deep Blue", which defeated Kasperov, is regarded by many as a milestone in AI research. When playing chess, the chess players who can come up with more subsequent moves, the possible response of the opponent, the changes brought about by the changes, and the corresponding adjustment of the chess path after the changes are obviously more successful, and the advantages of computers in this respect are self-evident. The human brain can only imagine a few or more moves, but the machine can simulate all the possibilities. That is to say, even if it is not "Dark Blue", there will be other computer players who will challenge human success sooner or later. And based on the current level of information technology development, if the human-computer competition in the chess world becomes a regular event every year, it is very likely that no one can defeat the machine opponent - even a Windows Phone. Of course, computer chess players cannot capture the position of Go originating from China in the short term, which makes us proud of the profound wisdom of our ancestors. Some people estimate that the probability of change of Go is 122 times greater than that of chess 10. The computer chess method is to exhaust all possibilities, while human beings can selectively prune and deepen according to experience. It can be imagined that if we only make "judgments" by improving machine performance, storing chess scores, and optimizing algorithms, because the amount of computation that needs to be processed in real time is too large, the existing powerful computers cannot defeat the human masters.





However, it is true that computers are different from any tools previously invented by humans. This difference is reflected in the following aspects: First, it is not a special tool that has been solidified when it leaves the factory, such as a bicycle and a DVD player. Its ability depends on the program installed by the user. Second, it can inject new vitality into various special tools. For example, the recently discussed "wearable device" just embeds some computing power into "traditional tools" such as wristbands, watches, glasses, etc. to establish data association with mobile phones and PCs.



All "tools" contain the intelligence, experience and ingenuity of their human creators. In a broad sense, AI is to give reasonable functional characteristics to products, and work with human beings to accomplish things that we cannot and cannot do well, so as to achieve the effect of "human+machine=superman". Just like hammers and axes are the strengthening and continuation of people's arms, cars, ships and planes are the strengthening and continuation of people's legs and feet. In recent years, driverless cars have attracted much attention. It seems that this is a new form of intelligent machine, but unmanned aircraft has been invented many years ago - no one is required to control it. Compared with cars, which one is more intelligent?



In the past decades, the evolution of computer hardware performance and the expansion of software application fields have exceeded everyone's imagination. If we observe the extension of AI from a broad perspective, and recognize that all the tools that infuse human thinking about the world reflect a certain degree of "intelligence", we can say that intelligent devices are already everywhere in life.



Let the machine think and grow in its own way



"Hearing and seeing" is a compliment to people. Scientists have been trying to make computers understand the world in a human way, so voice recognition and computer vision have always been the focus of AI research - today we can talk to Cortana, or sit in an unmanned car equipped with a 360 ° dead angle camera to feel the technology of the robot driver.



Are Cortana and driverless cars a kind of robot? In a sense, yes. But if we say that a "real robot" must be able to think like a human and have a humanoid appearance - well, who knows what we want to do with a machine like a human? Behind everyone's stubborn obsession with humanoid machines, it is likely that they want to find a helper to do heavy work for themselves.



However, the reality is that there have been many machines that can help us do heavy work, whether it is coffee making, barbecue or washing dishes, cleaning... Do people like a sturdy robot running around with a white apron to do all the housework for us, or are they used to using various small equipment to complete different tasks?



If everyone loves robots, there are still many obstacles on the way to products. For example, pouring a cup of tea from the teapot on the table without overturning the cup or spilling tea is not a challenge for human children - children can complete the task without thinking. But for 0 "smart" robots, they have to go through difficult and complicated calculations. First of all, he should look at the table, recognize the teapot and teacup, pick up the teapot with appropriate strength (fingers are too thick, which may not be enough), lift the teapot, aim at the teacup at a just angle, perform the tea pouring action, and also judge how to make the cup full of tea. Even if it happens to succeed once, the next round of completely different tables, teapots and cups may still fail.



For a long time, scientists engaged in AI research, including those who are obsessed with creating humanoid machines, have always dreamed of transplanting human thinking, planning, and execution capabilities to machines. But how should machines act as people act? Is the path for people to achieve goals from A to B, and the machine should follow the same path? This kind of research is indeed of extraordinary scientific value, but it will also be difficult because of the paranoia that "giving steel tools human characteristics is considered successful".



Another idea is to jump out of the rut and stand in the perspective of machines to simulate and extend human thinking, rather than using human perspectives and habits to limit machines. The driverless car is not only equipped with two eyes, but also equipped with multiple radar sensors, panoramic cameras and laser rangefinders. The i-Robot cleaning robot is also round and flat, which is not human at all, but it must be easier to use than a two meter high machine cleaner when vacuuming.



At the beginning of the 20th century, the bottleneck of AI research was that people's logical thinking mode could hardly be copied to machines. Whether it was to sublimate low-level sound, image, smell and other signals to cognition, or to distill common phenomena into rules, it was not a skill that machines could master - machine learning and big data brought AI research into the spring, and new concepts such as deep learning and deep neural networks had emerged recently. A larger amount of data and fewer assumptions and restrictions can enable machines to "think" and grow in the way they are good at (data storage, mining, analysis), and then go faster and farther on the road of practicality.



Man machine relationship: master and assistant



Up to now, the advantages and disadvantages of intelligent machines (including all kinds of "robots") are equally distinct. They can more quickly and efficiently complete many tasks that are difficult for human beings to undertake: there are intelligent machines everywhere in laboratories, computing centers and other computing environments, in factories, assembly lines and other laborious and monotonous environments, in nuclear pollution sites, deep sea, space and other environments that are not accessible to human beings.



Processing data is a machine's strength. Many years ago, it took a long time to mobilize many people with professional knowledge to participate in the analysis of large-scale data. Now, the Internet and sensor networks all over the world are generating massive, multi-dimensional data all the time, which can not be effectively processed by relying on human brains, but it is a blink of an eye to analyze with computers. With the help of machines, people can quickly extract laws from phenomena and draw conclusions from laws. Today, the combination of AI and big data has been shown in every field and application. In the next two or three years, the diversified equipment initially equipped with the ability to see, listen and connect will in turn promote the leap forward of AI research, because more data will enable the machine to constantly discover more accurate rules and more realistic causes and effects.



But in the visible future, it is still not easy for machines to have the ability of independent selection, judgment, creation and decision-making close to that of humans. Like the smart Cortana, you can understand what you say in a quiet office, and follow your instructions to help you dial the phone, send messages, check videos, and book restaurants. But if it is in a noisy public place, such as a music festival or a cocktail party, Cortana will certainly become less smart, because too many sound signals make her unable to distinguish useful information. But what about people? Even if there are more guests on the scene and the voice is noisy, you can't hear every word of the interviewee clearly, but in most cases, you can still guess, supplement and understand the information sent by the other party, because your brain can remove the environmental noise, capture the signal you want to hear, and at the same time, based on the understanding of the field and language habits of the interviewee, you can fill in the loopholes in the unread sentences with imagination and thought extension, And the accuracy is quite high. Today's AI does not have this ability.



In the same way, machine translation tools can give definitions of words and even help us to translate every sentence word by word. However, if we listen and translate on the spot, it is neither necessary nor possible to translate word by word, because listening, identifying, translating, and selecting words and sentences all require thinking. However, if the translator knows the speaker well and knows that he has talked about similar topics before, it will save effort. Many times, The speaker spoke for a long time, and the translator could summarize and convey the exact meaning with only one or two idioms; On the contrary, the speaker just said a sentence related to the academic. The translator may not only express the original meaning, but also add notes to make the surrounding non professional audience understand - this is a person specific Generate and Test ability, which AI does not have.



The combination of signals captured by various senses and past knowledge accumulation to process information, judge and make decisions is a human specialty. The advantages of machines are data processing and pattern recognition, rather than judgment, creation and integration. So I believe that no matter how fast AI technology develops, the relationship between people and machines will still be the relationship between the master and the assistant.



To sum up, what kind of robots do we need?



A really useful robot is not necessarily the image of a person. A humanoid machine is interesting but not practical. Just imagine, when you stand next to a tall and strong humanoid machine, will you feel a sense of fear? Objectively speaking, the sturdy and huge robot is only suitable for factories and construction sites. We can imagine a universal and human like all-around machine, but the cost of ownership of this kind of equipment must be high. In addition, there are real problems such as space and energy consumption. In reality, most of the machines that have started to help us do all kinds of work are small and pleasing to the eye. In the future, our offices and homes will become more and more intelligent, but "intelligence" will be hidden invisibly in chandeliers, televisions, and walls, more like human beings living in intelligent machines, rather than just robots providing services in the image of people.



It may be worthwhile for scientists to develop machines with human like emotions, but their practical significance is far less than scientific significance. Nowadays, there are many intelligent machines in life. Although they have no emotions, is this a bad thing? Suppose your robot is capable and loves you, but the opposite of love is not just depression, anger and other negative emotions? Such robots may refuse your instructions when they are in a bad mood. They may also hope that they have the right to work for five days and rest for two days like people. I'm afraid this is not what we want to see.