It used to be that if you wanted to get a computer to do something new, you would have to program it. Now, programming, for those of you here that haven't done it yourself, requires laying out in excruciating detail every single step that you want the computer to do in order to achieve your goal. Now, if you want to do something that you don't know how to do yourself, then this is going to be a great challenge.
在过去,如果你想让计算机做一件事 你需要设计电脑程序 你们可能从没做过这件事 编程需要排列出你想让电脑做的 每一个细枝末节的小步骤来达到你的目的 假如你自己都不清楚完成这某件事的话 要编写处电脑程序来完成那件事就会显得 比登天还要困难 这也是这个人,亚瑟 塞缪尔,所面临的挑战
So this was the challenge faced by this man, Arthur Samuel. In 1956, he wanted to get this computer to be able to beat him at checkers. How can you write a program, lay out in excruciating detail, how to be better than you at checkers? So he came up with an idea: he had the computer play against itself thousands of times and learn how to play checkers. And indeed it worked, and in fact, by 1962, this computer had beaten the Connecticut state champion.
在1956年,他想让这台电脑和他下国际象棋 你怎样才能罗列出所有的细枝末节, 并且让电脑下象棋比你厉害? 他想出一个办法 它让电脑和自己对战几千次 学习如何下象棋 事实证明他做到了。1962年 这台电脑打败了美国康涅狄克州象棋冠军 亚瑟 塞缪尔是机器学习之父
So Arthur Samuel was the father of machine learning, and I have a great debt to him, because I am a machine learning practitioner. I was the president of Kaggle, a community of over 200,000 machine learning practictioners. Kaggle puts up competitions to try and get them to solve previously unsolved problems, and it's been successful hundreds of times. So from this vantage point, I was able to find out a lot about what machine learning can do in the past, can do today, and what it could do in the future. Perhaps the first big success of machine learning commercially was Google. Google showed that it is possible to find information by using a computer algorithm, and this algorithm is based on machine learning. Since that time, there have been many commercial successes of machine learning. Companies like Amazon and Netflix use machine learning to suggest products that you might like to buy, movies that you might like to watch. Sometimes, it's almost creepy. Companies like LinkedIn and Facebook sometimes will tell you about who your friends might be and you have no idea how it did it, and this is because it's using the power of machine learning. These are algorithms that have learned how to do this from data rather than being programmed by hand.
我非常敬畏他 因为我是机器学习的实践者 我曾是Kaggle的主席 Kaggle是一个拥有200,000机器学习实践者地社区 Kaggle会组织竞赛 让人们尝试解决过去未解决的问题 已成功解决问题几百次 在这个有利环境中,我发现了 机器学习在过去,现在,和将来可以做些什么 第一个机器学习的商业成功案例应该是谷歌 谷歌用计算机算法寻找信息 而且这个算法以计算机学习为基础 从那以后,机器学习得到了很多的商业成功 像亚马逊、网飞这类公司 通过机器学习向你推荐你可能想买的东西 你可能想看的电影 有时候你会被吓一跳 像领英、脸谱这类的公司 有时会告诉你谁会是你的朋友 你根本不知道他们是如何做到的 其实他们正是运用了机器学习的力量 这种运算方法使用数据 而非手动编写程序 这也是IBM的Watson超级计算机 在《危险边缘》里打败两届世界冠军的秘诀
This is also how IBM was successful in getting Watson to beat the two world champions at "Jeopardy," answering incredibly subtle and complex questions like this one. ["The ancient 'Lion of Nimrud' went missing from this city's national museum in 2003 (along with a lot of other stuff)"] This is also why we are now able to see the first self-driving cars. If you want to be able to tell the difference between, say, a tree and a pedestrian, well, that's pretty important. We don't know how to write those programs by hand, but with machine learning, this is now possible. And in fact, this car has driven over a million miles without any accidents on regular roads.
成功回答了这样一个极其模糊且复杂的问题 [“古代‘尼姆鲁德狮像’于2003年在这个城市的国家博物馆消失(连同其它很多物品)”] 这也是为什么我们现在有了第一台自驾车 如果你想区分一棵树和一个行人 显然这很重要 但是我们不知道如何写这样一个程序 有了机器学习,这就成为了可能 这台自驾车已经行驶了十万英里 在正常路面上零事故
So we now know that computers can learn, and computers can learn to do things that we actually sometimes don't know how to do ourselves, or maybe can do them better than us. One of the most amazing examples I've seen of machine learning happened on a project that I ran at Kaggle where a team run by a guy called Geoffrey Hinton from the University of Toronto won a competition for automatic drug discovery. Now, what was extraordinary here is not just that they beat all of the algorithms developed by Merck or the international academic community, but nobody on the team had any background in chemistry or biology or life sciences, and they did it in two weeks. How did they do this? They used an extraordinary algorithm called deep learning. So important was this that in fact the success was covered in The New York Times in a front page article a few weeks later. This is Geoffrey Hinton here on the left-hand side. Deep learning is an algorithm inspired by how the human brain works, and as a result it's an algorithm which has no theoretical limitations on what it can do. The more data you give it and the more computation time you give it, the better it gets.
我们知道电脑能够学习 学习做一件有时我们自己都不知道怎么做的事情 有时甚至比我们做得更好 我见过机器学习最惊人的例子 是我在Kaggle做的一个项目 一个叫杰弗里 辛顿的人毕业于多伦多大学, 带领一个团队 赢得了一个自动查毒的竞赛 然而真正精彩的不是他们打败了所有默克公司 或者国际学术团体设计的运算 而是他们团队里没有一个人有化学、生物 或者生命科学的背景 却在两个星期内赢得了比赛 他们是如何做到的? 他们应用了一种超凡的算法叫做深度学习 几个星期后纽约时报在其首页 报道了此次的重要成功 在左手边就是杰弗里 辛顿 深度学习是受到人类大脑的启发 也因此这种算法的能力不受任何理论限制 你给它越多的数据和运算时间 它会工作的越好 纽约时报在其文章中 还说明了深度学习的另一非凡之处
The New York Times also showed in this article another extraordinary result of deep learning which I'm going to show you now. It shows that computers can listen and understand.
现在我要展示给你们看 它表明电脑能够听懂信息
(Video) Richard Rashid: Now, the last step that I want to be able to take in this process is to actually speak to you in Chinese. Now the key thing there is, we've been able to take a large amount of information from many Chinese speakers and produce a text-to-speech system that takes Chinese text and converts it into Chinese language, and then we've taken an hour or so of my own voice and we've used that to modulate the standard text-to-speech system so that it would sound like me. Again, the result's not perfect. There are in fact quite a few errors. (In Chinese) (Applause) There's much work to be done in this area. (In Chinese) (Applause)
(视频)理查德 拉希德:现在, 我要做的最后一步是 用汉语和大家说话 在这之前,我们已经通过很多说汉语的人 收集了大量信息 然后形成一个语音合成系统 把汉字转换成汉语言 之后我们收录了一个小时我的声音 使声音合成系统的声音听起来像我 再次,结果并不完美 他们会有不少错误 (中文) (掌声) 在这个领域还有很多工作要做 (中文) (掌声)
Jeremy Howard: Well, that was at a machine learning conference in China. It's not often, actually, at academic conferences that you do hear spontaneous applause, although of course sometimes at TEDx conferences, feel free. Everything you saw there was happening with deep learning. (Applause) Thank you. The transcription in English was deep learning. The translation to Chinese and the text in the top right, deep learning, and the construction of the voice was deep learning as well.
杰里米 霍华德:这是在一个中国的机器学习会议上 事实上,一般来说,你不会在学术会议上 听到如此热烈的掌声 当然除了TEDx演讲可以随意鼓掌 你所看到的一切都伴随着深入学习 (掌声)谢谢 对英文的转录是深入学习 翻译成汉语以及屏幕右上方的文字是深入学习 声音的合成也是深入学习 深入学习就是这样神奇的事情
So deep learning is this extraordinary thing. It's a single algorithm that can seem to do almost anything, and I discovered that a year earlier, it had also learned to see. In this obscure competition from Germany called the German Traffic Sign Recognition Benchmark, deep learning had learned to recognize traffic signs like this one. Not only could it recognize the traffic signs better than any other algorithm, the leaderboard actually showed it was better than people, about twice as good as people. So by 2011, we had the first example of computers that can see better than people. Since that time, a lot has happened. In 2012, Google announced that they had a deep learning algorithm watch YouTube videos and crunched the data on 16,000 computers for a month, and the computer independently learned about concepts such as people and cats just by watching the videos. This is much like the way that humans learn. Humans don't learn by being told what they see, but by learning for themselves what these things are. Also in 2012, Geoffrey Hinton, who we saw earlier, won the very popular ImageNet competition, looking to try to figure out from one and a half million images what they're pictures of. As of 2014, we're now down to a six percent error rate in image recognition. This is better than people, again.
这个单一的算法似乎可以做任何事情 而且一年前我发现他甚至有视觉 这个名不见经传的德国竞赛 叫做德国交通标志识别基准 深度学习已学得识别这些交通标识 它不仅能够做的比其它算法好 排行榜显示它比人更厉害 是人的准确率的两倍 到2011年,我们有了第一台视力高于人类的电脑 从此更多的电脑也可以做到 在2012年,谷歌宣布让一个深度学习的算法看YouTube视频 收集16,000台电脑上的数据,为期一个月 之后电脑便能仅通过看视频独立识别人和猫 这近似于人类学习的过程 人类不需要被告诉他们看到了什么 而是在自己认知事物的过程中学习 同样在2012年,杰弗里 辛顿,我们之前看到的人 赢了很火的ImageNet比赛 分辨出150万张图片的内容 到2014年,我们已经将图像识别的误差 降低到百分之六 低于人类误差率 这项非凡的工作现在已经用于工业
So machines really are doing an extraordinarily good job of this, and it is now being used in industry. For example, Google announced last year that they had mapped every single location in France in two hours, and the way they did it was that they fed street view images into a deep learning algorithm to recognize and read street numbers. Imagine how long it would have taken before: dozens of people, many years. This is also happening in China. Baidu is kind of the Chinese Google, I guess, and what you see here in the top left is an example of a picture that I uploaded to Baidu's deep learning system, and underneath you can see that the system has understood what that picture is and found similar images. The similar images actually have similar backgrounds, similar directions of the faces, even some with their tongue out. This is not clearly looking at the text of a web page. All I uploaded was an image. So we now have computers which really understand what they see and can therefore search databases of hundreds of millions of images in real time.
比如说,去年谷歌声明 他们在两小时内把法国的每一个地点汇成地图 他们是将街景填入深度学习算法以辨认街道号 可以想象从前这件事要花费多少时间和精力 同样的事情也发生在中国 百度大概类似于中国的谷歌 我们看到左上角 是一张我上传到百度的深度学习系统的图片 下面你可以看到系统理解了这张照片 并且找到了类似的图片 同样的背景 同样的角度 有的甚至也有伸出来的舌头 网页上没有准确的文字 我只是上传了图片 所以说电脑能够真正理解它所看到的事物 进而在数据库的几百万张图片中进行实时搜索
So what does it mean now that computers can see? Well, it's not just that computers can see. In fact, deep learning has done more than that. Complex, nuanced sentences like this one are now understandable with deep learning algorithms. As you can see here, this Stanford-based system showing the red dot at the top has figured out that this sentence is expressing negative sentiment. Deep learning now in fact is near human performance at understanding what sentences are about and what it is saying about those things. Also, deep learning has been used to read Chinese, again at about native Chinese speaker level. This algorithm developed out of Switzerland by people, none of whom speak or understand any Chinese. As I say, using deep learning is about the best system in the world for this, even compared to native human understanding.
就现在而言,电脑的视力意味着什么呢? 事实上不仅仅是电脑能够看见 深度学习其实可以做得更多 像这样一个细小复杂的语句 对深度学习来说是相对易于理解的 你可以看到 斯坦福基础系统显示上面的红点指出 这个语句表达的是否定语气 深度学习在理解语句内容方面已经接近人类水平 同样,深度学习在用于阅读汉语上已经相当于中国本土人水平 这个算法开发于瑞士 没有一个人懂汉语 要我说,深度学习是比较于人类 做这件事最好的系统 这个系统是在我们公司建立的
This is a system that we put together at my company which shows putting all this stuff together. These are pictures which have no text attached, and as I'm typing in here sentences, in real time it's understanding these pictures and figuring out what they're about and finding pictures that are similar to the text that I'm writing. So you can see, it's actually understanding my sentences and actually understanding these pictures. I know that you've seen something like this on Google, where you can type in things and it will show you pictures, but actually what it's doing is it's searching the webpage for the text. This is very different from actually understanding the images. This is something that computers have only been able to do for the first time in the last few months.
它要把这些东西集合起来 这些图片没有文字描述 随着我在这输入文字 同时它会了解这些图片 理解它们是关于什么的 然后找出和这些相似的图片 所以你看,他真正在理解我的文字 理解这些图片 我知道你在谷歌上看到过类似的 你可以输入文字,它会提供给你图片 但实际上它是在网页上搜索文字 这和理解图片是有很大不同的 理解图片是电脑在过去几个月里才刚刚会做的事情 电脑不仅有视力,而且能够阅读
So we can see now that computers can not only see but they can also read, and, of course, we've shown that they can understand what they hear. Perhaps not surprising now that I'm going to tell you they can write. Here is some text that I generated using a deep learning algorithm yesterday. And here is some text that an algorithm out of Stanford generated. Each of these sentences was generated by a deep learning algorithm to describe each of those pictures. This algorithm before has never seen a man in a black shirt playing a guitar. It's seen a man before, it's seen black before, it's seen a guitar before, but it has independently generated this novel description of this picture. We're still not quite at human performance here, but we're close. In tests, humans prefer the computer-generated caption one out of four times. Now this system is now only two weeks old, so probably within the next year, the computer algorithm will be well past human performance at the rate things are going. So computers can also write.
而且当然,电脑也能理解所听到的 也许并不意外,我现在要告诉你们,电脑也可以写 这是我昨天用深度学习算法写的文字 这些是斯坦福的算法做的 每一句话都是深度学习算法对图片进行的描述 算法没见过一个穿黑衣服的男人弹吉他 它见过男人,见过黑色 见过吉他 它便自己对这个图片作出了这样的描述 我们还做不到完全和人类同等水平, 但我们已经很接近了 统计表明,四分之一的人更喜欢电脑做的图片说明 目前这个系统刚被开发两周之久 所以按这个速度,估计明年 电脑算法会超过人类水平 电脑会写 我们把这些都放在一起,会发现一个令人兴奋的机遇
So we put all this together and it leads to very exciting opportunities. For example, in medicine, a team in Boston announced that they had discovered dozens of new clinically relevant features of tumors which help doctors make a prognosis of a cancer. Very similarly, in Stanford, a group there announced that, looking at tissues under magnification, they've developed a machine learning-based system which in fact is better than human pathologists at predicting survival rates for cancer sufferers. In both of these cases, not only were the predictions more accurate, but they generated new insightful science. In the radiology case, they were new clinical indicators that humans can understand. In this pathology case, the computer system actually discovered that the cells around the cancer are as important as the cancer cells themselves in making a diagnosis. This is the opposite of what pathologists had been taught for decades. In each of those two cases, they were systems developed by a combination of medical experts and machine learning experts, but as of last year, we're now beyond that too. This is an example of identifying cancerous areas of human tissue under a microscope. The system being shown here can identify those areas more accurately, or about as accurately, as human pathologists, but was built entirely with deep learning using no medical expertise by people who have no background in the field. Similarly, here, this neuron segmentation. We can now segment neurons about as accurately as humans can, but this system was developed with deep learning using people with no previous background in medicine.
比如说,在医药业 一个波士顿团队宣布 他们发现了肿瘤的几十种临床表现 帮助医生预测癌症 同样的,在斯坦福 一个团队宣布通过用放大镜观察组织 开发了一个基于机器学习的系统 可以比病理学家更有效地预测癌症患者的幸存率 在这两个例子中,不仅预测更加准确 而且他们创造了新的科学视角 在放射学中 新视角是人类可以明白的新临床表现 在病理学中 电脑发现癌细胞周围的细胞 在诊断中同癌细胞一样重要 这和病理学家几十年来的教学是相反的 这两个案例中的系统都是由 医学专家和机器学习专家共同开发的 去年我们就已经超过了这个水平 这个是用显微镜识别组织癌变区的例子 所显示的这个系统能够与病理学专家同样准确地识别癌变区 甚至比病理专家更准确 但是建立系统的都是深度学习的专家 没有一个医学专家 类似的,这是神经细胞分裂 我们已经可以和人类一样准确地分裂细胞 但这是个深度学习系统 没有一个开发者拥有医学背景
So myself, as somebody with no previous background in medicine, I seem to be entirely well qualified to start a new medical company, which I did. I was kind of terrified of doing it, but the theory seemed to suggest that it ought to be possible to do very useful medicine using just these data analytic techniques. And thankfully, the feedback has been fantastic, not just from the media but from the medical community, who have been very supportive. The theory is that we can take the middle part of the medical process and turn that into data analysis as much as possible, leaving doctors to do what they're best at. I want to give you an example. It now takes us about 15 minutes to generate a new medical diagnostic test and I'll show you that in real time now, but I've compressed it down to three minutes by cutting some pieces out. Rather than showing you creating a medical diagnostic test, I'm going to show you a diagnostic test of car images, because that's something we can all understand.
对于我这个完全没有医学背景的人来说 看起来我也完全可以开一个医药公司 我确实这么做了 我开始有点不知所措 但理论上说这件事是可行的 用这些数据分析技术制作医药 所幸的是,反响非常好 不仅是媒体的,包括医药行业 都很支持 理论表明我们可以将制药的中间过程 充分转换成数据分析 让医生去做他们最擅长的 我有一个例子 制作一个医学诊断测试需要十五分钟 我会给你们实际展示 但是我去掉了一部分,把它压缩到了三分钟 不要医学诊断试验 我要给你们展示制作一个汽车图片的诊断测试 因为这个我们都能懂
So here we're starting with about 1.5 million car images, and I want to create something that can split them into the angle of the photo that's being taken. So these images are entirely unlabeled, so I have to start from scratch. With our deep learning algorithm, it can automatically identify areas of structure in these images. So the nice thing is that the human and the computer can now work together. So the human, as you can see here, is telling the computer about areas of interest which it wants the computer then to try and use to improve its algorithm. Now, these deep learning systems actually are in 16,000-dimensional space, so you can see here the computer rotating this through that space, trying to find new areas of structure. And when it does so successfully, the human who is driving it can then point out the areas that are interesting. So here, the computer has successfully found areas, for example, angles. So as we go through this process, we're gradually telling the computer more and more about the kinds of structures we're looking for. You can imagine in a diagnostic test this would be a pathologist identifying areas of pathosis, for example, or a radiologist indicating potentially troublesome nodules. And sometimes it can be difficult for the algorithm. In this case, it got kind of confused. The fronts and the backs of the cars are all mixed up. So here we have to be a bit more careful, manually selecting these fronts as opposed to the backs, then telling the computer that this is a type of group that we're interested in.
现在我们有150万张汽车图片 我想要根据拍照的角度对他们进行分类 这些图片完全没有标签,所以我要先对他们进行简单描述 有深度学习算法 它可以自动识别图片的结构要素 令人高兴的是人和电脑可以合作 你可以看到,这个人 正在告诉电脑什么是感兴趣的要素 为之后电脑用来完善算法 现在,这些深度学习算法处在16,000维空间中 所以你看到电脑让他们在这个空间中旋转 尝试找到新的结构要素 当他成功时 开车的人就可以指出感兴趣的要素 现在电脑成功找出这些要素 比如,角度 我们在这个过程中 逐渐的告诉电脑更多 我们想寻找的结构 你可以想象一个诊断测试 这就像是病理学家识别病态区域 或者放射学专家找出潜在的问题囊肿 有时候这对算法来说有些难度 我们的例子就比较麻烦 车的正面和背面全部混淆了 所以我们要仔细一些 人工地选出正面和背面 人后告诉电脑这是我们所感兴趣的一类 做这件事花了一些时间,所以我们跳过
So we do that for a while, we skip over a little bit, and then we train the machine learning algorithm based on these couple of hundred things, and we hope that it's gotten a lot better. You can see, it's now started to fade some of these pictures out, showing us that it already is recognizing how to understand some of these itself. We can then use this concept of similar images, and using similar images, you can now see, the computer at this point is able to entirely find just the fronts of cars. So at this point, the human can tell the computer, okay, yes, you've done a good job of that.
之后我们用这几百个东西训练机器学习算法 希望他会有很大进步 你能看到,它正在消退一些图片 说明他已经开始可以自己理解这些图片了 我们可以用相似图片的概念 用相似的图片,你可以看到 电脑现在能够只找出正面的车 在这个时候,人可以告诉电脑 对的,没错,你做的很好
Sometimes, of course, even at this point it's still difficult to separate out groups. In this case, even after we let the computer try to rotate this for a while, we still find that the left sides and the right sides pictures are all mixed up together. So we can again give the computer some hints, and we say, okay, try and find a projection that separates out the left sides and the right sides as much as possible using this deep learning algorithm. And giving it that hint -- ah, okay, it's been successful. It's managed to find a way of thinking about these objects that's separated out these together.
当然,有时,即使在这个阶段 分组仍然是很困难的 像我们这里,让电脑在这里旋转了一段时间了 我们还是看到左面的和右面的图片有混淆 所以我们可以再一次给电脑一些提示 我们让它通过深度学习算法尽可能分离出左面和右面的图片 有了这个指示——好的,它已经完成了 它要想办法分开这一部分 你现在知道了
So you get the idea here. This is a case not where the human is being replaced by a computer, but where they're working together. What we're doing here is we're replacing something that used to take a team of five or six people about seven years and replacing it with something that takes 15 minutes for one person acting alone.
这不是电脑取代人类 而是一起合作 我们在做的是将过去需要五六人的团队 用七年时间做的事情 变成只需一个人花十五分钟就能完成 这个过程需要四到五次反复
So this process takes about four or five iterations. You can see we now have 62 percent of our 1.5 million images classified correctly. And at this point, we can start to quite quickly grab whole big sections, check through them to make sure that there's no mistakes. Where there are mistakes, we can let the computer know about them. And using this kind of process for each of the different groups, we are now up to an 80 percent success rate in classifying the 1.5 million images. And at this point, it's just a case of finding the small number that aren't classified correctly, and trying to understand why. And using that approach, by 15 minutes we get to 97 percent classification rates.
你可以看到我们已经将150万张图片的62%正确分类 现在我们就可以快速地检查整个分组 确保没有错误 如果哪里有错误,我们可以告诉电脑 每个分组我们都这样做 现在这150万张图片已经达到80%的成功率 现在这个阶段 只需要找出几个不正确的分类 并让电脑明白为什么 到了这个步骤 十五分钟后我们达到了97%的正确率 这种技术能帮助我们解决一个问题
So this kind of technique could allow us to fix a major problem, which is that there's a lack of medical expertise in the world. The World Economic Forum says that there's between a 10x and a 20x shortage of physicians in the developing world, and it would take about 300 years to train enough people to fix that problem. So imagine if we can help enhance their efficiency using these deep learning approaches?
医疗专家不足的问题 世界经济论坛表明,在发展中国家, 内科医生有十倍到二十倍的短缺 而弥补这一短缺需要300年的时间 所以想象一下,是否我们能够用深度学习的方法 帮助他们提高效率?
So I'm very excited about the opportunities. I'm also concerned about the problems. The problem here is that every area in blue on this map is somewhere where services are over 80 percent of employment. What are services? These are services. These are also the exact things that computers have just learned how to do. So 80 percent of the world's employment in the developed world is stuff that computers have just learned how to do. What does that mean? Well, it'll be fine. They'll be replaced by other jobs. For example, there will be more jobs for data scientists. Well, not really. It doesn't take data scientists very long to build these things. For example, these four algorithms were all built by the same guy. So if you think, oh, it's all happened before, we've seen the results in the past of when new things come along and they get replaced by new jobs, what are these new jobs going to be? It's very hard for us to estimate this, because human performance grows at this gradual rate, but we now have a system, deep learning, that we know actually grows in capability exponentially. And we're here. So currently, we see the things around us and we say, "Oh, computers are still pretty dumb." Right? But in five years' time, computers will be off this chart. So we need to be starting to think about this capability right now.
我对这个机会表示很激动 我同样的担心一些问题 问题是在这张地图上的蓝色区域内 服务占就业的80%以上 什么是服务? 这些是服务 这些也是电脑才刚刚开始学习的事情 也就是说世界上发达国家的80%的就业 是电脑刚开始学习的 这是什么意思? 其实也没什么大不了的,他们会被其他职业替代 比如说会有更多的数据学家 也不尽然 数据学家不需要太久的时间做这些事 比如这四个算法都是同时一个人开发的 如果你认为这些曾经都发生过 我们看到过新的事物出现 然后被新的职业所取代 那这些新的职业又会是什么? 很难做出估计 因为人的能力以这个均匀的速度增长 但是现在我们有了深度学习系统 它的能力以指数方式增长 我们现在在这 目前,我们看周围的事物 会说:“电脑还是很笨。”对吧? 但是在五年内,电脑会超出这张图 所以我们现在要开始考虑这样的能力了
We have seen this once before, of course. In the Industrial Revolution, we saw a step change in capability thanks to engines. The thing is, though, that after a while, things flattened out. There was social disruption, but once engines were used to generate power in all the situations, things really settled down. The Machine Learning Revolution is going to be very different from the Industrial Revolution, because the Machine Learning Revolution, it never settles down. The better computers get at intellectual activities, the more they can build better computers to be better at intellectual capabilities, so this is going to be a kind of change that the world has actually never experienced before, so your previous understanding of what's possible is different.
当然,我们曾经见过这个 在工业革命时期 发动机让生产力迈进一大步 然而问题是,一段时间之后,形势转平了 是由于社会的破坏 但当发动机被普遍应用时 一切都稳定下来了 机器学习革命 将和工业革命有很大不同 因为机器学习革命不会停止 电脑越擅长智能活动 它们越能制造出更加擅长智能活动的电脑 这将会是世界从未经历过的改变 所以你之前理解的可能性是不一样的
This is already impacting us. In the last 25 years, as capital productivity has increased, labor productivity has been flat, in fact even a little bit down.
这正在影响我们的生活 在过去的25年里,随着资本生产力的增加 劳动生产力在变缓,甚至下降
So I want us to start having this discussion now. I know that when I often tell people about this situation, people can be quite dismissive. Well, computers can't really think, they don't emote, they don't understand poetry, we don't really understand how they work. So what? Computers right now can do the things that humans spend most of their time being paid to do, so now's the time to start thinking about how we're going to adjust our social structures and economic structures to be aware of this new reality. Thank you. (Applause)
所以我希望可以发起大家的讨论 我知道当我和人们讲述这样的处境时 人们往往表现出不以为然 电脑不会思考 它们没有情感,也不懂诗 它们甚至都不知道自己是如何运作的 那又怎样? 电脑现在可以做 人类用大部分有偿的劳动时间做的事情 所以现在该到我们思考 我们将如何调整我们的社会结构和经济结构 来应对新形势 谢谢 (鼓掌)