Today, I'm going to talk about AI and us. AI researchers have always said that we humans do not need to worry, because only menial jobs will be taken over by machines. Is that really true? They have also said that AI will create new jobs, so those who lose their jobs will find a new one. Of course. But the real question is: How many of those who may lose their jobs to AI will be able to land a new one, especially when AI is smart enough to learn better than most of us?
今天我将和大家聊聊人工智能和我们 曾经,人工智能方面的研究人员 总是告诉我们, 我们人类不需要担心, 因为机器只能从事体力劳动, 可真的是那样吗? 他们还说人工智能可以创造 新的工作机会, 因此,那些因其失去工作的人 可以找到新的工作。 这当然对的。 但是,真正的问题是: 那些因为人工智能出现 而失去工作的人中 有多少人能够找到一份新的工作? 尤其是,当人工智足够聪明到 比大多数人类做得更好时。
Let me ask you a question: How many of you think that AI will pass the entrance examination of a top university by 2020? Oh, so many. OK. So some of you may say, "Of course, yes!" Now singularity is the issue. And some others may say, "Maybe, because AI already won against a top Go player." And others may say, "No, never. Uh-uh." That means we do not know the answer yet, right? So that was the reason why I started Todai Robot Project, making an AI which passes the entrance examination of the University of Tokyo, the top university in Japan.
我想问大家一个问题: 你们中有多少人认为 到2020年,人工智能可以通过 顶尖大学的入学考试。 哦,好多。好的 你们中有人可能会说: "当然了,这是肯定的!“ 现在的问题是什么时候 会到达这个科技奇点。 其他一些人会说:”可能吧, 因为人工智能已经赢了 一个顶级的围棋选手。“ 然后其他人可能说: ”不会的,这不可能发生。“ 这就说明我们现在其实并不知道答案 对不对? 这就是我为什么发起了 “东大”机器人项目, 创造一个可以通过日本顶尖大学 (东京大学)的入学考试 的机器人。
This is our Todai Robot. And, of course, the brain of the robot is working in the remote server. It is now writing a 600-word essay on maritime trade in the 17th century. How does that sound?
这就是我们的“东大”机器人。 当然,机器人的大脑是在远程服务器中工作。 图中他正在写一篇600字的短文, 主题是17世纪的海运贸易。 这听起来怎么样?
Why did I take the entrance exam as its benchmark? Because I thought we had to study the performance of AI in comparison to humans, especially on the skills and expertise which are believed to be acquired only by humans and only through education. To enter Todai, the University of Tokyo, you have to pass two different types of exams. The first one is a national standardized test in multiple-choice style. You have to take seven subjects and achieve a high score -- I would say like an 85 percent or more accuracy rate -- to be allowed to take the second stage written test prepared by Todai.
为什么我要选择入学考试作为 他的衡量标准呢? 因为,我认为我们要研究 人工智能的表现, 就一定要将它与人类进行对比 尤其是在那些我们认为只有人类可以 而且只能通过教育, 才能获得的技能和知识。 为了进入东大——东京大学 你需要通过两个不同类型的考试。 第一个是全国标准测试, 考试是以多项选择的形式。 你需要考7个科目, 并且获得一个较高的分数。 我估计大概有84%的人, 准确的说或者有更多, 能有机会参加第二阶段 由东大准备的笔试。
So let me first explain how modern AI works, taking the "Jeopardy!" challenge as an example. Here is a typical "Jeopardy!" question: "Mozart's last symphony shares its name with this planet." Interestingly, a "Jeopardy!" question always asks, always ends with "this" something: "this" planet, "this" country, "this" rock musician, and so on. In other words, "Jeopardy!" doesn't ask many different types of questions, but a single type, which we call "factoid questions."
首先,让我向大家解释一下 现代人工智能是如何工作的, 就以”危险边缘!“挑战游戏作例子。 这有一个典型的”危险边缘!" 游戏问题: “莫扎特的最后一部交响曲与这颗星球 有相同的名字。” 有趣的是,“危险边缘!”游戏 总是以提问的方式出现,而且总是以 “这个”什么东西作为结尾。 这颗星球,这个国家, 这个摇滚明星,等等。 换而言之,“危险边缘!"游戏 并不会问不同类型的问题, 而是只有一种形式, 我们称之为“事实问答”。
By the way, do you know the answer? If you do not know the answer and if you want to know the answer, what would you do? You Google, right? Of course. Why not? But you have to pick appropriate keywords like "Mozart," "last" and "symphony" to search. The machine basically does the same. Then this Wikipedia page will be ranked top. Then the machine reads the page. No, uh-uh.
对了,你们知道这个问题的答案吗? 如果你们不知道这个问题的答案, 但是如果你们想知道答案, 你们会怎么做? 你们会用谷歌搜索,对不对? 当然是这样。 为什么不呢? 但是,你必须选择合适的关键字, 比如“莫扎特”,“最后”, “交响曲”来进行搜索。 人工智能基本上也是这样做的。 然后关于这个的维基百科页面 会被排在第一个位置。 接下来,机器将阅读这个页面, 不,不是这样。
Unfortunately, none of the modern AIs, including Watson, Siri and Todai Robot, is able to read. But they are very good at searching and optimizing. It will recognize that the keywords "Mozart," "last" and "symphony" are appearing heavily around here. So if it can find a word which is a planet and which is co-occurring with these keywords, that must be the answer. This is how Watson finds the answer "Jupiter," in this case.
不幸的是,没有任何现存的人工智能 包括 Watson(IBM创造的人工智能), Siri 和东大机器人, 能够进行阅读。 但是,他们非常擅长搜索和 寻找最优解。 他们可以识别到一个位置, 这些关键字 “莫扎特”,“最后”和 “交响曲” 在这个位置附近出现的频率非常高。 因此,如果他可以在这个位置附近 找到一个代表星球的单词, 那么这个词就一定是答案了。 因此,Watson 找到了答案是“木星”。
Our Todai Robot works similarly, but a bit smarter in answering history yes-no questions, like, "'Charlemagne repelled the Magyars.' Is this sentence true or false?" Our robot starts producing a factoid question, like: "Charlemagne repelled [this person type]" by itself. Then, "Avars" but not "Magyars" is ranked top. This sentence is likely to be false. Our robot does not read, does not understand, but it is statistically correct in many cases.
我们的东大机器人工作方式与这个相似,但稍稍聪明点, 对于回答历史方面的“是-否”问题时, 比如,“查理曼击退了马扎尔人。” 这句话是真的还是假的? 我们的机器人自己首先 会提出一个事实问题, 比如:“查理曼击退【……人】” 然后,如果是“阿尔瓦人”而不是 “马扎儿人”被排在了第一个。 这句话就很可能就是错误的。 我们的机器人并不会阅读, 也无法理解, 但是在统计意义上 大多数情况下是对的。
For the second stage written test, it is required to write a 600-word essay like this one:
对于第二阶段的笔试, 要求考试者写一篇600字的短文,比如这个:
[Discuss the rise and fall of the maritime trade in East and Southeast Asia in the 17th century ...]
【讨论一下十七世纪东亚和东南亚 海运贸易的兴起和衰落……】 正如我之前展示的,
and as I have shown earlier, our robot took the sentences from the textbooks and Wikipedia, combined them together, and optimized it to produce an essay without understanding a thing.
我们的机器人从课本和维基百科上 摘抄句子, 并将它们组合在一起, 然后以最优解的方式生成一篇短文, 尽管他自己完全不理解 自己写的是啥。
(Laughter)
(笑声)
But surprisingly, it wrote a better essay than most of the students.
但令人吃惊的是,他比大多数学生 写的文章还要好。
(Laughter)
(笑声)
How about mathematics? A fully automatic math-solving machine has been a dream since the birth of the word "artificial intelligence," but it has stayed at the level of arithmetic for a long, long time. Last year, we finally succeeded in developing a system which solved pre-university-level problems from end to end, like this one. This is the original problem written in Japanese, and we had to teach it 2,000 mathematical axioms and 8,000 Japanese words to make it accept the problems written in natural language. And it is now translating the original problems into machine-readable formulas. Weird, but it is now ready to solve it, I think. Go and solve it. Yes! It is now executing symbolic computation. Even more weird, but probably this is the most fun part for the machine.
那把人格智能应用到数学方面会怎么样呢? 自从“人工智能”这个词诞生开始, 完全自动的解决数学问题的机器, 就一直是人们梦寐以求的。 但是他已经停留在只能做算术的水平 很长时间了。 去年,我们终于成功开发了一个系统 它可以从头到尾求解 像这样的大学入学水准的题目。 这道题是由日语写的, 我们需要教它2000个数学公理, 和8000日文单词, 它才能识别用我们用日文写的问题。 现在他正将原始的问题 翻译成机器可识别的公式。 这听起来有点怪, 但我想它现在可以解这道题了。 开始解这道题。 好的,现在它在正在进行符号计算。 这可能看起来可能更奇怪。 但对于这台机器而言, 这可能是最有趣的部分。
(Laughter)
(笑声)
Now it outputs a perfect answer, though its proof is impossible to read, even for mathematicians. Anyway, last year our robot was among the top one percent in the second stage written exam in mathematics.
现在它输出了一份完美的答案。 尽管他的证明过程对数学家来说 也是不可能读懂的。 但不管怎么说,我们的机器人去年在 第二阶段的数学笔试中, 排名前1%。
(Applause)
(鼓掌)
Thank you.
谢谢!
So, did it enter Todai? No, not as I expected. Why? Because it doesn't understand any meaning. Let me show you a typical error it made in the English test.
那么,他考入东大了吗? 没有,并不象我期望的那样。 为什么呢? 因为它无法理解任何意义。 让我给你们展示一个它在英文考试中 犯的一个典型错误。
[Nate: We're almost at the bookstore. Just a few more minutes. Sunil: Wait. ______ . Nate: Thank you! That always happens ...]
【Nate:我们再过几分钟就要到书店了。 Sunil: 等一下。_______. Nate: 谢谢! 这经常发生……。】
Two people are talking. For us, who can understand the situation --
两个人在谈话。 对于我们这些能够理解语境的人,
[1. "We walked for a long time." 2. "We're almost there." 3. "Your shoes look expensive." 4. "Your shoelace is untied."]
A.”我们走了很长时间了。“ B. ”我们马上到那了。“ C.”你的鞋看起来很贵。“ D.”你的鞋带开了。“
it is obvious number four is the correct answer, right? But Todai Robot chose number two, even after learning 15 billion English sentences using deep learning technologies. OK, so now you might understand what I said: modern AIs do not read, do not understand. They only disguise as if they do.
很明显D是正确答案,对吧? 但是,东大机器人选了B答案。 而且还是在使用深度学习技术 学习了150亿个英文句子之后。 好的, 你们现在应该能理解我之前说的: 现代人工智能不能阅读, 无法理解意义。 他们只是装作他们好像能这样做。
This is the distribution graph of half a million students who took the same exam as Todai Robot. Now our Todai Robot is among the top 20 percent, and it was capable to pass more than 60 percent of the universities in Japan -- but not Todai. But see how it is beyond the volume zone of to-be white-collar workers.
这是一张50万学生成绩的分布图, 他们参加了和东大机器人一样的考试。 我们的东大机器人排在前20%, 而且他现在可以通过 超过60%的日本大学的考试, 除了东京大学。 但是,我们可以看到 他处在能够成为白领的人群比例 之上。
You might think I was delighted. After all, my robot was surpassing students everywhere. Instead, I was alarmed. How on earth could this unintelligent machine outperform students -- our children? Right? I decided to investigate what was going on in the human world. I took hundreds of sentences from high school textbooks and made easy multiple-choice quizzes, and asked thousands of high school students to answer.
你们可能认为我应该感到高兴。 毕竟我的机器人 超过了多有地方的学生。 但是相反地,我感到恐慌。 这个不智能的机器到底为什么 比我们的学生,我们的孩子, 表现的还好? 是不是? 我决定调查一下我们这到底是为什么。 我从高中的课本中摘了几百段句子, 制作了简单的多项选择问卷, 然后让上千名学生回答。
Here is an example:
这是有一个例子:
[Buddhism spread to ... , Christianity to ... and Oceania, and Islam to ...]
[佛教传播到...., 基督教传播到....和大洋洲, 伊斯兰教传播到....,]
Of course, the original problems are written in Japanese, their mother tongue.
当然,问题都是用日文写的, 他们的母语。
[ ______ has spread to Oceania. 1. Hinduism 2. Christianity 3. Islam 4. Buddhism ]
[ ________传播到了大洋洲。 A.印度教 B. 基督教 C.伊斯兰教 D. 佛教】
Obviously, Christianity is the answer, isn't it? It's written! And Todai Robot chose the correct answer, too. But one-third of junior high school students failed to answer this question. Do you think it is only the case in Japan? I do not think so, because Japan is always ranked among the top in OECD PISA tests, measuring 15-year-old students' performance in mathematics, science and reading every three years.
很明显,答案是基督教,对不对? 上边写着呢。 而东大机器人也选择了正确的答案。 但是有1/3的初中学生 并没有回答对。 你认为这种情况只发生在日本吗? 我不这么认为, 因为日本人总是在经合组织的 国际学生评估测试中名列前茅。 这个项目每3年 会评估15岁学生 在数学、科学和阅读方面的表现。
We have been believing that everybody can learn and learn well, as long as we provide good learning materials free on the web so that they can access through the internet. But such wonderful materials may benefit only those who can read well, and the percentage of those who can read well may be much less than we expected. How we humans will coexist with AI is something we have to think about carefully, based on solid evidence. At the same time, we have to think in a hurry because time is running out.
我们一直相信 每个人都能够学习 而且能够学好, 只要我们在网上提供 免费的,好的学习材料, 这样他们就可以通过网络 获取这些资料。 但是这些优质的学习资料可能 只会使那些理解能力强的人受益, 而那些理解能力强的人所占的比例 可能远远比我们认为的要少。 基于真实的证据, 我们将如何与人工智能和平共存? 是我们人类需要认真思考一个问题。 同时,我们要赶快思考这个问题, 因为,留给我们的时间十分有限。
Thank you.
谢谢!
(Applause)
(鼓掌)
Chris Anderson: Noriko, thank you.
Chris Anderson: Noriko, 谢谢你。
Noriko Arai: Thank you.
Noriko Arai: 谢谢!
CA: In your talk, you so beautifully give us a sense of how AIs think, what they can do amazingly and what they can't do. But -- do I read you right, that you think we really need quite an urgent revolution in education to help kids do the things that humans can do better than AIs?
CA: 你的出色的演讲, 让我们感受到了人工智能是如何思考 它们能够做什么令人吃惊的事, 以及什么事情是它们做不了。 但是,我不知道我理解的对不对 你认为我们在教育方面急切地 需要进行改革, 以帮助孩子们从事那些人类能够比 人工智能做的更好的工作。
NA: Yes, yes, yes. Because we humans can understand the meaning. That is something which is very, very lacking in AI. But most of the students just pack the knowledge without understanding the meaning of the knowledge, so that is not knowledge, that is just memorizing, and AI can do the same thing. So we have to think about a new type of education.
NA:是, 是的。 因为我们人类能够理解其中的意义。 这对人工智能来说非常缺乏的。 但是,大多数学生仅仅是 被动的接受知识(填鸭式教育), 而并没有理解知识背后的意义。 因此,那些根本不是知识, 那些只是记忆而已。 而人工智能也能做同样的事。 因此,我们要思考一种 新的教育方式。
CA: A shift from knowledge, rote knowledge, to meaning.
CA: 从死记硬背转向理解意义。
NA: Mm-hmm.
NA: 嗯嗯。
CA: Well, there's a challenge for the educators. Thank you so much.
CA: 好的,这是对现场教育者 的一个挑战。非常感谢你。
NA: Thank you very much. Thank you.
NA: 谢谢你们。
(Applause)