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搜狗推广王小川:人机大战的启示,人工智能的机

他认为,人工智能的前提是要理解深度学习,机器是模仿人脑去学习。在 1985 年,人类就提出了人工智能,但那时做不到,计算量太大,而现在技术已经成熟,主要表现在计算力的提升。在这样的背景下,要定位好自己。
He thinks, the premise is to understand the deep learning of artificial intelligence, machine is to imitate the human brain to learn. In 1985, humans have put forward the artificial intelligence, but can't do that, then calculate the amount is too big, and now the technology is mature, mainly displays in the ascension of computing power. In this context, to locate yourself. 
 
人机大战的启示,人工智能的机遇与挑战
人工智能但凡与实践结合,是否就会降低人工智能的水平。人工智能有三个层次:
In artificial intelligence and the practice union, whether can reduce the level of artificial intelligence. Artificial intelligence has three levels: 
 
 
1.将传统规则教给机器
1. The traditional rules to teach machines 
2.将答案教给机器记忆学习
2. Teach your answer to machine learning memory 
3.将目标给机器自我学习
3. The target for self learning machine 
 
 
这是一个不断进化的过程
This is a process of constant evolution 
基于这样背景,什么样的人容易被取代?如果人类的工作环境相对封闭,工作结果的更加标准化,信息需求较少,那么这样的工作更加被机器取代。相反,认知边界越宽广,需求信息越多,这样的工作,就不容易被取代。
Based on this background, what kind of person easy to be replaced? If human relatively closed environment, the work is the result of more standardization, information demand is less, then the more replaced by machines. In contrast, the broader cognitive boundary, demand information, the more of this work, it is not easy to be replaced. 
 
 
人最终是否会被取代?
People will eventually be replaced? 
想象力是目前机器取代不了的。但如果科学家,以创造生命的态度去做,那就不一样了,要理解这人工智能和创造生命这是不一样的概念。未来,人类是和机器一起的进步的,如果我们目标清晰、环境封闭,那么机器会代替我们一些繁重的活,把我们解放出来,技术会带来与人的融合,让我们生活水平提高。
Imagination is the machine can't replace. But if scientists to create life attitude is to do it, it won't be the same, to understand the artificial intelligence and create life this is not the same concept. In the future, the progress of human is together with the machine, if we clear goal, the environment is closed, the machine will be instead of some of our hard work, liberating us, technology will bring and the fusion of people, let us raise living standards. 
 
 
以下是王小川分享要点记录:
The following is wang xiaochuan share points record: 
今天我抬头第一眼看到咱们讲的是“新硬件生态”。我们在提一个词叫智能硬件,其实每次提到这个词我反而焦虑,我们有时候弄了没有想明白的词做这个事情,可能带来很多风险。
Today, I looked up first saw is we speak of "ecological" new hardware. We in a word, called intelligent hardware, in fact, every time I mentioned the word I anxiety instead, we want to understand the word sometimes got no do this thing, can bring a lot of risk. 
 
 
就像我们很多公司在做一个技术,这个技术能做什么、不能做什么,没有判断,要么产生恐惧感,要么产生盲目膜拜,不知道有什么意义,可能投资或者做事就偏了。当我们回到“新硬件”反而给我留下空间诠释什么是智能。
As we did a lot of companies in a technology, this technology can do, can't do anything, no judgement, or fear, or produce blind worship, don't know what's the meaning, may the Angle of investment or doing things. When we went back to the "new hardware" instead, give me space to interpret what is intelligence. 
 
 
今年3月8号开始的一周时间有一个Alpha Go李世石的人机大战,人跟最顶尖的科技公司进行了一场较量。这场比赛的赛前很有意思,我们回顾一下,但是Google一发布我就兴奋了,因为两年前我看到了他们的生物科学的发展,很可惜自己没有把气场攒起来,我跟清华的同事和实验室都提到这个想法,但是没有提做下围棋这件事情,说这件事情太难了。
March 8, began a week of time have an Alpha Go lee se-dol man-machine war, along with the top technology companies fought a battle. Before the game is very interesting, we review, but Google released a I am excited, because two years ago, I saw the development of their biological science, it is a pity that you didn't have the aura saved up, my colleagues and with tsinghua lab would talk about the idea, but do not go the matter, said it was too difficult. 
 
 
没有参与这件事情是挺可悲的事情,有很多朋友说你怎么积极的在这件事情,包括2月份就在知乎上写文件说Google会有,没有参与,围观总是可以的,所以这个事情参与了比较多心思。赛前的时候我发现大部分人看好,有两类人不看好,第一类人是围棋选手,尤其是参加世界大赛拿到9段的选手,包括聂卫平。
Not involved in this matter is quite sad things, there are a lot of friends say you how positive in this matter, including in February on zhihu said Google would have write files, didn't participate, onlookers can always, so the things more involved in the state of mind. Before the game when I found that most people, there are two kinds of people, the first kind of people is a go player, especially in the world competition to paragraph 9 players, including wei-ping nie. 
 
 
我在五局棋里面参加了两局,我跟大家讲我对围棋理解不同,虽然知道规则,但是根本没有办法判断这个局面好不好,所以下棋比赛当中我就看一件事情就知道这个比赛人是否会赢。我就看教练的脸色,脸色越难看机器胜算越大,最后教练崩溃了,最后机器赢了。
I attended two innings in five innings chess, I tell you to go I understand different, although know the rules, but there is no way to judge the situation is good, so the chess game I saw one thing will know who will win the game. I will look at the coach's face, and his face more ugly machine the odds, the greater the last coach crash, finally won the machine. 
 
 
我讲这个例子是,我们似乎面临一些威胁,曾经擅长的思考能力开始由机器侵入进来。一个围棋选手的情况就是一个机器把你的成功和引以为豪的东西替代的时候,这是一种什么恐惧。我想以后各位可能多多少少会经历一点,包括腾讯开始改BUG了,我们可能有这种压力。
I speak this example is that we seem to be facing some threats, ever good at thinking began to invade by machines. A go player is a machine to your success and proud of things instead of, this is what a kind of fear. I think you more or less likely to experience a little later, including tencent started to change BUG, we may have this kind of pressure. 
 
 
在这个比赛前,很多互联网的代表人物,甚至有技术代表的人都认为人会赢,机器赢不了,包括我们公司十几个人,我们一一去问,百分之八九十的人给我的答复是未来机器会赢,但是这次人会赢。他们认为下围棋会有区别,认为以后机器没有问题,但是现在时机不成熟,很不幸,最后机器确实战胜了人。
Before the game, a lot is the representative figure of the Internet, and even have a technical representative of people believe that people will win, the machine can't win, a dozen people, including our company we ask, what people give my answer will be the future machines will win, but the people will win. They think go there will be a difference, think later machine, there is no problem, but the timing was not mature now, unfortunately, the machine did win over the people. 
 
 
我们当时记小黑板说不够情怀,即便做科技的人也没有想到这个这么突然,这是这个事情给大家的感受。
When we work to remember that not enough feelings, even if to do science and technology of people did not think of this all of a sudden, so this is the thing to feel. 
 
 
但是我想我们不要有恐惧或者有浪漫性的关怀,我们了解它到底能做什么、不能做什么,这对我们生活态度和工作有帮助。不是说这个东西到底怎么赚钱,很多朋友说到底是不是商业机会,我们究竟怎么理解这件事情,包括人自身的提升,我们对机器的了解可能会更加长远。
But I think we don't have fear or a romantic caring, we know what it can do, can't do anything, it is helpful to our attitude towards life and work. Not to say that this thing how to make money, a lot of friends at the end of the day is business opportunities, how we understand the matter, including one's own ascension, our understanding of the machine could be more in the long run. 
 
 
不知道大家有没有听过“深度学习”这个词。大部分都听过,因为这个词像Alpha Go一样,讲到了一个特别神秘的概念,就是机器的深度学习或者智能。
Don't know if you have heard the word "deep learning". Most have heard, because the word like Alpha Go, speak to a mysterious concept in particular, is the depth of the machine learning or intelligence. 
 
 
深度学习讲的是两个概念,第一个概念是机器学里面用神经语言模拟人脑的语言模型做训练或者机器的识别,就是把你的输入变成向量,中间经过迭代做到结果。第二是迭代的网络结果很深,不是一层可以做到,需要多层。后来研究人员不断提升这样的模型。
Deep learning is about two concepts, the first concept is inside the machine learning neural language was used to simulate the brain language model for training or machine identification, is your input into vector, intermediate results through iteration do. The second is the network result of iterative deep, is not a layer can be done, need to multilayer. Later the researchers improve the model. 
 
 
这个概念很早就提出来了,1985年这个理论就已经趋于成熟,它是反向传播,机器怎么进行训练这个已经有了。有了这个机器之后有一个问题,计算量太大,做不到。当时十几个结点的时候机器已经不够用了,但是现在最大的不是理论体系变化,而是计算力的提升。
This concept proposed early in 1985, this theory has been mature, it is a back propagation, the machine how to training the already have. Then there is a problem with this machine, amount of calculation is too big, can't do. Ten several nodes at the time when machine is not enough, but now the largest theoretical system is not change, but the calculations of ascension. 
 
 
这也告诉大家,从人工智能到理论深度学习的做法,包括之前的这些机器的理解能力已经慢慢成熟,今天我们用的方法没有超过当年理论的框架和计算模式。这不是一个新东西。
This also tells us, from the theory of artificial intelligence to the deep learning approach, including the machines before understanding has been mature, today we use the method of no more than when the theoretical framework and computing model. This is not a new thing. 
 
 
发生了什么变化呢?其实变化在两件事情,一件事情是计算力的极大的提升。Alpha Go的机器计算力是深蓝的2.5万倍。第二个是我们采集了大量的数据,数据采集比较困难,现在有大数据之后,数据有多大?其实下围棋没有多大,基本上数据基本上用了30万台曾经下过围棋的用来做训练。
What has changed? Actually change the two things, one thing is a tremendous improvements in the computing power. Alpha Go machine force is calculated by deep blue 25000 times. The second is that we collected a large amount of data, data acquisition is difficult, there are big data, data have how old? Actually go there is not much, basically data basically with 300000 units have been under go for training. 
 
 
没有互联网不敢想,有了互联网之后,国外下围棋的网站上已经有对应的数据,30万台,每一台大概100步的样子,所以一共3000万步棋做了训练。我们怎么把这个理论用来下围棋,这是Google的创新。第一件事情cnn网络(音),用点看图的方法来下棋,以前说棋子是逻辑分析,而不是网络。
Without the Internet did not dare to think, after the Internet, go abroad website have corresponding data, 300000 units, each about 100 steps, so a total of 30 million positions do the training. How do we put this theory to go, this is Google's innovation. First thing CNN network (sound), use the method of point at the picture to play chess, said before pieces is the logical analysis, rather than the network. 
 
 
现在就像看照片一样看棋盘,因此机器有了棋感。最近五年有一个最大的提升是人脸识别,之前是完全不知道怎么样的事情,识别眉毛吗?识别眼睛吗?在座可能有写程序的,你想一下用什么规则去描述人的脸,但是今天我们用CNN图像的感觉做到了。
Just like looking at photographs board now, so the machine have move feeling. The last five years has one of the biggest improvement is face recognition, before is completely don't know how, to identify their eyebrows? Identify the eyes? Everyone here may have write programs, it with what rules do you want to go to describe the person's face, but today we use the feeling of CNN image did. 
 
 
所以Google的第一个创新是用CNN网络对机器进行描述,使得机器有了体感。第二个是把跟深蓝相关的搜索作为理性的方式,跟CNN的感性进行结合,这是第二件创新。第三件事情是用的强大的学习,让机器跟自己下,当机器变得聪明之后自己可以跟自己下,因此在这里面提升。这里面并没有带来理论界的突破,但是在创新应用里面做了很大的贡献。所以Alpha Go的胜利背后融合了工程师的重新能力在。
So Google's first CNN network was carried out on the machine is used to describe the innovation, makes the machine has the feeling of the body. The second is associated with deep search as a rational way, combination with CNN's perceptual, this is the second innovation. The third thing is to use powerful learning, let the machine with himself, as machines become smarter after you can talk with myself, so in this promotion. It does not bring theoretical breakthrough, but made great contribution in innovative applications. So Alpha Go behind the victory is a blend of the engineer's ability in again. 
 
 
这件事情真正重要的在于什么地方?不是在于技术本身,而是所有人在关心我们自己的定位。我把这一周的活动比拟成原来几十年文艺复兴的结果。这样一个星期过去,我恍如隔世。大家知道一个星期机器是什么理解和态度,一个星期之后有了很大的变化。
It's really important is what place? Is not the technology itself, but to all people in the care of our own positioning. I compare the activities of this week as the original decades the results of the Renaissance. A week past, I like a lifetime ago. You know what a week machine understanding and attitude, has changed a lot after a week. 
 
 
我们人和人的关系,包括我们看《圣经》,人和人平等了,距离拉近了。现在我们怎么看这台机器?比赛之前大家认为机器比较笨,什么干不了,比赛之后有两个重要变化,第一个是我们对机器的能力有了更高的评价,机器可以战胜人了。之前说我们看病的时候,你拍一个片子,机器告诉你做诊断没有什么病,我们难以接受,很不相信。
Our relationship between person and person, including we read the bible and human equality, closer. Now how do we see the machine? Before the game we think machines are stupid and what to do, after the game there are two important changes, the first is the ability of our machine with higher evaluation, the machine can be overcome. Said when we see a doctor, before you make a film, machine diagnosis, there is nothing to tell you do we find it difficult to accept, very don't believe it. 
 
 
现在机器告诉你一个什么结果,我们可能觉得比人还要准。但是能想到这个变化吗?我们机器因为这一个事件之后,对能力有一个巨大的认可。这使得我们更多的工程师、更多的创业公司、更多的资本会投向人工智能。
Machine now tell you what a result, we may feel better than people. But can think of this change? Our machine because after this event, the ability to have a great recognition. This makes it more engineers and more entrepreneurial company, more capital to artificial intelligence. 
 
 
我开玩笑讲A股人工智能概念可能延续了好几个涨停板,我们看到了人工智能的信仰。但是很巧的是Alpha Go不是五局都胜利的,输了一局,但是大家还是转不过劲儿来。它代表了整个围棋界的共同的胜利,就是机器变成另外的门派,我们还是有自己的尊严。
I joke that a-share artificial intelligence concepts may continue for several harden board, we see the faith of artificial intelligence. But very skillful is Alpha Go not five innings are victory, lost the game, but everyone still turn to. It represents the whole excel common victory, is the machine into the other factions, we still have their own dignity. 
 
 
想到二十年前的电影《独立日》,当时人类飞行员面临太空船的时候,把飞船开进去把太空船破坏掉,所以面对机器我们还有尊严。更多人,我们很多年轻人开始乘Alpha Go叫狗狗。我们想到年轻人90后或者00后会觉得机器人成为朋友,还有人叫阿老师。
Think of the movie "independence day" twenty years ago, when human pilots are faced with the spacecraft, the spacecraft in the spacecraft eroded, so in the face of the machine we have dignity. More people, many of our young people begin to take Alpha Go call a dog. 90 00 or after we think young people will think robot will become friends, there are also called the teacher. 
 
 
其实机器并没有到不可战胜的时候,第一是要相信它,第二要接受它。拒绝它很难,我们要接受它。这是我们整理人机大战的关键,到底我们怎么用、怎么跟它交朋友,后面会提到,所以这场启蒙运动很重要。
Machine is not actually to invincible, the first is to believe in it, the second to accept it. To reject it is very difficult, we have to accept it. This is our key to finishing the man-machine war, how we use and how to make friends with it, later, so it's very important to the enlightenment. 
 
 
技术的进步有三个层次,不管是从软件硬件。这三个层次是什么呢?传统最早的是智能,其实是把规则交给机器。举个例子,我们做一个电饭锅、智能冰箱,它来干嘛呢?我们程序员要写程序,当温度到103度的时候我就跳闸。实际上我们可以把足够复杂的东西交给机器,把人类的智慧交给机器。很不幸的时候,老师讲学生如果把规则给他,他的能力会下降,所以这时候机器比人落后,智力少于人。
Technological progress has three levels, no matter from software to hardware. What is the three level? Traditional earliest is intelligent, is actually the rules to the machines. For example, we do an electric rice cooker, refrigerator, intelligent it doing here? Our programmers to write programs, when the temperature to 103 degrees when I tripped. In fact we can put things complicated enough to the machines, the wisdom of human to the machines. Unfortunately, teachers tell students if give his rules, his ability will decline, so this time machine behind people, intelligence less than one. 
 
 
还有一种情况,我们自己都不知道规则是什么,我们是用感觉。就像刚才讲的人脸识别,这是一个非常经典的问题。每个人都觉得很简单,可能脸盲吃力一点,但是大部分没有问题,不像大家学外语这么难,只是可能记忆力不好,识别没有问题。当把这个问题给机器的时候我们遇到了障碍,几十年里面在图像识别方面我们举步维艰,国外就有做过把这种数据分大类,每年进展非常缓慢,搞图像的人基本上找不到工作,因为不实用。
There is a kind of situation, we do not know what are the rules, we are feeling. Like just about face recognition, this is a classic problem. Everyone thinks is very simple, may face blindness struggling a bit, but there is no problem, most don't like everyone to learn a foreign language so hard, just may be memory, recognition: no problem. When the problem to the machine we encountered obstacles, decades in the aspect of image recognition we struggling, abroad have done the data points, progress is very slow, every year make images of people basically couldn't find a job, because is not practical. 
 
 
很长时间里面人工智能跟理论是脱节的。我们前几年跟清华做人工智能的院士聊,说人工智能但凡跟实践结合就拉低水平,因为连接不上,但是现在不是,现在连接到一块了,为什么呢?因为我们到了第二个阶段,我们开始不用跟机器讲规则。深度学习的美妙之处是我们把问题和答案对应的交给机器,告诉他这是张三的脸,那个脸是李四的,不用告诉机器为什么他是张三或者李四。
For a long time with artificial intelligence theory is disjointed. A few years ago we chat with tsinghua do academician of artificial intelligence, said the artificial intelligence in combination with practice is low, because the connection is not on, but not now, now connected to a, why? Because we came to the second stage, we began to speak rules with the machine. Depth study of the beauty is corresponding to the machines, we put the question and answer told him it was zhang SAN's face, the face is li si, don't tell why he is zhang SAN and li si machine. 
 
 
机器通过大量的数据训练就能够学会。就像我们教小孩一样,通过这些方法一步一步的展开,而在这里我们一并给到机器,图像和声音领域已经非常好了。去年开始图像人脸识别机器超过了人,准确率超过了人一倍。我们可以告诉机器答案,机器可以自己学习。这还不够,甚至有一些问题我们连答案还不够。我们说学围棋有3000万步答案,机器就学会了下棋的基本规则,把6段到9段的方法告诉它,它达到了6段水平。
Through a large amount of data training can learn to machine. Just like we teach children, through these methods, step by step, and here we give to the machine, along with all the images and sound field has been very good. Last year began to face recognition machine over the images, the accuracy of more than one times. We can tell the machine, the machine can learn myself. It is not enough, there are even some problems we even the answer is not enough. We said 30 million steps on go to learn the answer, the basic rule of machine learns to play chess, take 6 to 9 paragraphs way to tell it, it reached the 6 segment level. 
 
 
后来Google让两个机器随机下,下完之后不告诉你赢了还是输了,机器通过自己优化算法找到更好的答案。第二件事情是把答案给机器,第三个是告诉你机器给你一个答案,我评价答案是好或者不好。这是三个层次的进度。特别是第三件事情,像Google团队或者微软顶级的人里面,甚至可能有宗教色彩,当我们给机器一个目标,机器是否自己学会找规则和答案,我只给他目标,而不是告诉他怎么做,这是演进中很重要的一步。
Google later for the two random machine, after not tell you win or lose, machine through optimization algorithm to find a better answer. The second thing is the answer to the machine, the third is to tell you machine to give you an answer, I evaluate the answer is good or bad. This is three levels of progress. Especially the third thing, people like Google or Microsoft's top team, and may even have a religious color, when we give the machine a goal, a machine if I learn to find rules and answers, I just give him a target, instead of telling him what to do, this is very important step in evolution. 
 
 
我看到一个文献,他们想重新训练一台Alpha Go的机器,一开始不是让它学习人怎么下的,一开始是两张白纸的Alpha Go,自己跟自己下,只告诉它目标是赢棋、输棋,看能不能训练出新的棋手。我觉得这件事情很有意义。
I saw a literature, they want to training a Alpha Go machine, a began to not let it learn how, starting with two pieces of white paper of Alpha Go, with himself, only tell it goal is to win, check, can see the training of new players. I think it is very meaningful. 
 
 
一个人在中原学会了所有武功,把它融汇贯通,然后再进行提高。另外一个人从来没有来过地球和中原,来学武功,你说它的武功跟人一样吗?这是人类好奇的,看重新会长出什么智慧来。这是三个层次做的事情。
A person learned all powers in central plains, it will converge, and then to improve. There was another man who had never been to the earth and the central plains, to learn martial arts, you said it's skill with people? This is the curious to see what wisdom will grow again. This is made of three layers. 
 
 
基于刚才三个层次,我们来想,什么样的职业、什么样的人本身的工作更容易被取代?我们可以看作是机会,也可以看作对自身的挑战。
Based on three levels just now, let's think, what kind of person what kind of occupation, work itself is more likely to be replaced? We can be regarded as a chance, also can be seen as a challenge to itself. 
 
 
容易取代的有两点,第一是你的工作和环境封闭。意味着做决策的时候,决策来源的信息是封闭的、有限的甚至是结构化的。比如说下围棋这件事情决策很封闭,只需要知道期盼上的规定就可以下决定。
There are two easy to replace, the first is closed your work and environment. Means that when making decisions, decision making is closed and limited sources of information and even is structured. Such as chess this decision is very closed, just need to know the rules of the anticipation on can be decided. 
 
 
医生会难很多,医生会知道病人的病史和病人当前的状态;但是作为一个老师可能会面临更复杂的环境,做决策的时候信息来源可能足够开放,你的答案越标准越容易被机器取代。第二件事情定的是目标,第一件事情是你处理的信息的开放性或者封闭性。从这一点我们知道,有的决策信息需要的少机器就容易做,信息需要的做机器也能做。
The doctor will be a lot of hard, your doctor will know the patient's medical history and the current state of the patient; But as a teacher may face more complex environment and make decisions that source may be enough to open, your answer the standard the more likely it is to be replaced by machines. The second thing is the goal, the first thing is you deal with the information of the open or closed. From this we know, some decision-making information need machine is easy to do less, information need for the machine can do. 
 
 
我们做好一个翻译或者一个作家,需要很多生活阅历,作家就是读万卷书、行万里路的做法。如此开放的环境对机器就是很大的挑战。反过来,如果这个开放答案跟你有关,机器就容易做到。这就是机器是否能做好、人是否会取代的一个标准。
We do a translation or a writer, need a lot of life experience, the author is to read thousands of books, the view of practice. So open environment for machine is a big challenge. On the other hand, if the open the answer has to do with you, the machine is easy to do. That is whether the machine can do a good job, people will replace a standard. 
 
 
回到人是否会被取代的问题,人是什么概念?人的目标是为了自己的生存或者繁衍,机器更简单,我做诊断,下一个棋,或者做一个语言识别。人已经到了很大的扩展空间,我们的机器只是在局限的空间去工作,主要看机器的训练空间多大,如果算法再好也不能脱离目标和机器适应的范围,所以今天的技术还远不到这一步,第二是我们不会去造一台机器给它设定一个目标怎么生存,也不会说适应环境有特别大的空间。
Back to the question of whether people will be replaced, what is the concept? Person's goal is to own survival or reproduction, the machine is more simple, I do the diagnosis, the next chess, or to do a speech recognition. Man has come to the expansion of the large space, our machines are only in a limited space to work, basically see the training of the machine space is how much, if the algorithm is again good also cannot from the target and the scope of the machine to adapt so today's technology is far less than this step, the second is we're not going to build a machine to set a goal how to survive, will not say to adapt to the environment has a particularly large space. 
 
 
我们即便有能力,也没有动力去制造一个能取代人的机器,我们不认为机器自己会演化出一种生存能力来。反过来,如果有野心勃勃的科学家要做一件事情,说要创造一个智能机器,这个机器有生存的概念,可以面对整个地球环境。
We even have the ability, also have no incentive to make a person to replace the machine, we don't think the machine itself will evolve to a survival ability. On the other hand, if have ambitious scientists to do one thing, to say to want to create a smart machine, this machine has the concept of survival, can in the face of the whole earth environment. 
 
 
其实我们不是在做人工智能,我们其实是在创造一种生命。所以这个概念大家想清楚,如果你朝着创造生命的态度去做,机器可能有一种生命意识,知道自己的存在。反过来,今天我们的做法大可以放心,我们做这些事情目标足够的简单,比如说Alpha Go机器,把棋盘从19×19变成20×20,人类可以理解和学习,但是环境变了,Alpha Go变得什么都不会了。
Artificial intelligence in fact we are not doing, we are creating a kind of life. So you want to clear the concept, if your attitude toward the creation of life to do, the machine may have a kind of life consciousness, know their presence. In turn, today we can rest assured, our goal to do these things simple enough, such as Alpha Go machine, the board from 19 lines into 20 x 20, human beings can understand and learn, but the environment changed, Alpha Go nothing. 
 
 
另外一个问题是想象力,区别于人和动物的。有一本书叫《人类简史》,这种历史发展是相关的,这也是一个路径,这是我认为人不会被取代的核心的两个判断标准。
Another problem is the imagination, the difference in human and animal. There is a book called "a brief history of mankind, this is related to the historical development, it is also a path, this is I don't think people will be replaced at the core of the two judgment standard. 
 
 
人工智能和人是怎样的关系呢?有技术我们可能变得更强大了,但是也有可能技术让我们变得更弱了。在座很多人戴眼镜,眼镜是一种技术,当用了眼镜之后我们视力变得更好、更强大,但是离开眼镜我们更弱。
Artificial intelligence and what is the relationship? Have the technology we may become more powerful, but it is also possible technology let us become more weak. Here a lot of people wear glasses, glasses is a technology, when using the glasses after our vision to become better, more powerful, but leave the glasses we are weaker. 
 
 
我们要抛离技术之后看自己孤立的行不行,放下手机、pad、汽车交通工具是不是变强了?我们发现是变弱了。我们由于机器变得弱化,我们掌握了能源之后体力被取代了,今天的种植也被机器取代了。
We're leaving technology after the isolated line not line, put down the phone, pad, car transport is better? We found to be weak. We become weakened due to machine, we mastered the energy strength was replaced, today's planting is replaced by a machine. 
 
 
以后目标清晰之后,环境相对封闭,机器能做的时候我们可以交给它,我们可以利用Google做搜索引擎,通过手机变成千里眼、顺风耳,这是一个趋势。未来穿戴设备可能会变成植入,像Google眼镜,很多人眼睛不近视也会有很多人尝试,包括还有年轻的女孩子会整容,这些东西会带来新的植入。
After goals clear, relatively closed environment, the machine can do we can to it, we can do it using Google search engine, by mobile phone into a clairvoyant clairaudient, it is a trend. Future apparel equipment may become implanted, like Google glasses, a lot of people eye myopia also can have a lot of people try, not including the young girl will cosmetic, these things will bring new implants. 
 
 
从这一点我认为人工智能与人融合会带来新的物种,你不用害怕,你问一个猴子你会变成人吗?通过我们对人工智能的理解和对技术的理解,技术可以带来与人的融合,可以把人的能力提升了,也可能把人的能力降低了,这是我们未来的进化。
From the artificial intelligence fusion with people that I think will bring new species, you need not fear, you ask a monkey you will become a person? Through our understanding of the artificial intelligence and the understanding of technology, technology can bring with the fusion of people, can put the person's ability to ascend, may also reduce the person of ability, it is our future evolution. 

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