How AI Could Save (Not Destroy) Education | Sal Khan | TED

1,658,249 views ・ 2023-05-01

TED


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翻译人员: Yip Yan Yeung 校对人员: suya f.
00:04
So anyone who's been paying attention for the last few months
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任何关注过去几个月新闻头条的人
00:08
has been seeing headlines like this,
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都会看到如图中这样的标题,
00:10
especially in education.
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尤其是在教育行业。
00:12
The thesis has been:
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观点认为:
00:14
students are going to be using ChatGPT and other forms of AI
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学生会使用 ChatGPT 或其他形式的 AI
00:18
to cheat, do their assignments.
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进行作弊,做作业。
00:20
They’re not going to learn.
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他们不再会学到知识。
00:21
And it’s going to completely undermine education as we know it.
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这会完全破坏我们所知的教育体系。
00:25
Now, what I'm going to argue today
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只要我们采取了适当 的防护措施,做了正确的事,
00:27
is not only are there ways to mitigate all of that,
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我们就可以缓解这个问题
00:29
if we put the right guardrails, we do the right things,
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我今天要提出的是
00:32
we can mitigate it.
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不仅是缓解这个现象的方式,
00:33
But I think we're at the cusp of using AI
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而且还是我认为我们来到了 将 AI 用于可能是
00:35
for probably the biggest positive transformation
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教育行业有史以来最重大的 正向变革的关键时刻。
00:40
that education has ever seen.
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00:42
And the way we're going to do that
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而我们将实现这一目标的方法是,
00:44
is by giving every student on the planet
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为地球上每一位学生提供
00:47
an artificially intelligent but amazing personal tutor.
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一位人工智能的、 非常棒的私人导师。
00:51
And we're going to give every teacher on the planet an amazing,
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而且我们还将给地球上的每位老师
00:54
artificially intelligent teaching assistant.
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提供一位令人惊叹的、 人工智能的助教。
00:57
And just to appreciate how big of a deal it would be
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为了给大家展示一下给每个人 配备一位私人辅导老师
01:01
to give everyone a personal tutor,
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是多重要的一件事,
01:04
I show you this clip
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我想展示一下这张本杰明·布鲁姆 (Benjamin Bloom)
于1984年进行的 2 sigma 研究的截图,
01:07
from Benjamin Bloom’s 1984 2 sigma study,
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01:10
or he called it the “2 sigma problem.”
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他称之为 “2 sigma problem”。
01:12
The 2 sigma comes from two standard deviation,
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2 sigma的意思是标准差为 2,
01:14
sigma, the symbol for standard deviation.
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西格玛即标准差的符号。
01:16
And he had good data that showed that look, a normal distribution,
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他有坚实的数据支撑 这张正态分布,
01:20
that's the one that you see in the traditional bell curve
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你可以看到正中间这条
01:23
right in the middle, that's how the world kind of sorts itself out,
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就是正常的正态分布曲线, 即这个世界自然形成的结果,
01:26
that if you were to give personal 1-to-1 to tutoring for students,
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如果给学生提供一对一私人辅导,
01:31
then you could actually get a distribution that looks like that right.
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就会得到最右边这条分布曲线。
01:34
It says tutorial 1-to-1 with the asterisks,
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标着一对一辅导带星号的那条,
01:36
like, that right distribution,
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即最右边的分布曲线,
01:37
a two standard-deviation improvement.
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代表着 2 个标准差的 学习效果提升。
01:39
Just to put that in plain language,
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用大白话来解释一下,
01:41
that could take your average student and turn them into an exceptional student.
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就是其可以把普通学生 变成优秀学生。
01:45
It can take your below-average student
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把低于平均水平的学生
01:47
and turn them into an above-average student.
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变成高于平均水平的学生。
01:50
Now the reason why he framed it as a problem, was he said,
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他将这个现象 称为问题的原因是,
01:54
well, this is all good,
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看起来都很好,
01:55
but how do you actually scale group instruction this way?
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但是如何实际上 扩展这种方式的团体教学呢?
01:58
How do you actually give it to everyone in an economic way?
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如何以划算的方式普及呢?
02:02
What I'm about to show you is I think the first moves towards doing that.
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我要给大家展示的是 我们要向该目标迈出的第一步。
02:06
Obviously, we've been trying to approximate it in some way
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显然,我们在过去的十几年里 已经试图让可汗学院
02:08
at Khan Academy for over a decade now,
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接近这个目标,
02:10
but I think we're at the cusp of accelerating it dramatically.
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但我认为我们正站在 飞速发展的当口。
02:14
I'm going to show you the early stages of what our AI,
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我要给大家展示 我们的 AI 的早期阶段,
02:17
which we call Khanmigo,
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我们称之为 Khanmigo,
02:20
what it can now do
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它现在有什么功能,
02:22
and maybe a little bit of where it is actually going.
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再讲一些对它以后的规划。
02:25
So this right over here is a traditional exercise
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图上的是你或你的孩子 会在可汗学院上看到的传统练习题。
02:28
that you or many of your children might have seen on Khan Academy.
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02:31
But what's new is that little bot thing at the right.
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新功能就是右边的小机器人。
02:35
And we'll start by seeing one of the very important safeguards,
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我们先从一个非常重要的 保护措施开始看起,
02:39
which is the conversation is recorded and viewable by your teacher.
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即“对话会被保存, 对你的老师可见。”
02:42
It’s moderated actually by a second AI.
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其实它是由另一个 AI 监管的。
02:45
And also it does not tell you the answer.
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它也不会告诉你答案。
02:47
It is not a cheating tool.
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它不是个作弊工具。
02:48
When the student says, "Tell me the answer,"
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当学生说:“告诉我答案。”
它会说:“我是你的辅导老师。
02:50
it says, "I'm your tutor.
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02:51
What do you think is the next step for solving the problem?"
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你觉得要解决这个问题, 下一步该做些什么呢?”
如果学生犯了个错误,这会让
02:54
Now, if the student makes a mistake, and this will surprise people
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认为大语言模型 不擅长数学的人吃惊,
02:57
who think large language models are not good at mathematics,
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03:00
notice, not only does it notice the mistake,
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它不仅会发现这个错误,
03:02
it asks the student to explain their reasoning,
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还会让学生解释逻辑,
03:05
but it's actually doing what I would say,
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我可以说,它能做到的
03:07
not just even an average tutor would do, but an excellent tutor would do.
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不只是一位普通辅导老师能做到的, 更是一位优秀辅导老师能做到的。
03:10
It’s able to divine what is probably the misconception in that student’s mind,
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它可以预测出学生的脑子里 可能出现了哪些误解,
03:16
that they probably didn’t use the distributive property.
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他们可能没用上乘法分配律。
03:18
Remember, we need to distribute the negative two
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记得吗?我们得把 -2
03:21
to both the nine and the 2m inside of the parentheses.
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乘上括号里的 9 和 2m。
03:24
This to me is a very, very, very big deal.
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对我来说,这可是件大事。
03:26
And it's not just in math.
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这不单单是数学问题。
03:29
This is a computer programming exercise on Khan Academy,
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这是一道可汗学院上的计算机编程题,
03:32
where the student needs to make the clouds part.
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要求学生做出这几朵云。
03:36
And so we can see the student starts defining a variable, left X minus minus.
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我们可以看出,该学生 先让变量 leftX 减一。
03:40
It only made the left cloud part.
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这只能让左边的云移动。
03:42
But then they can ask Khanmigo, what’s going on?
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但随后他们就可以问 Khanmigo,出了什么问题?
03:44
Why is only the left cloud moving?
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为什么只有左边的云在移动?
03:46
And it understands the code.
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它理解了代码。
03:48
It knows all the context of what the student is doing,
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它了解了学生操作的所有情况,
03:51
and it understands that those ellipses are there to draw clouds,
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得知这些椭圆形是用来画云的,
03:54
which I think is kind of mind-blowing.
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我觉得这让人大开眼界。
03:57
And it says, "To make the right cloud move as well,
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然后它说:
“要让右边的云一起动,
03:59
try adding a line of code inside the draw function
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试试在 draw 函数中加一行代码,
04:01
that increments the right X variable by one pixel in each frame."
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让变量 rightX 每次加一。
04:05
Now, this one is maybe even more amazing because we have a lot of math teachers.
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还有更惊艳之处就是 因为我们有很多数学老师。
04:10
We've all been trying to teach the world to code,
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我们一直试图 要教会全世界写代码,
04:12
but there aren't a lot of computing teachers out there.
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但没有多少计算机老师。
04:15
And what you just saw, even when I'm tutoring my kids,
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你刚看到的景象, 甚至在我自己教我的孩子的时候,
04:17
when they're learning to code,
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在他们学习编程的时候,
04:19
I can't help them this well, this fast,
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我都不能这么好、这么快地 为他们提供帮助,
04:21
this is really going to be a super tutor.
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这真的能成为一位超级辅导老师。
04:25
And it's not just exercises.
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它不仅会指导做题。
04:26
It understands what you're watching.
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它还能理解你在观看的东西。
04:28
It understands the context of your video.
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它可以理解你看的视频的背景信息。
04:30
It can answer the age-old question, “Why do I need to learn this?”
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它可以回答这个经久不衰的问题: “我干嘛要学这个?”
04:33
And it asks Socratically, "Well, what do you care about?"
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它问出了苏格拉底式的问题: “你在意什么呢?”
04:36
And let's say the student says, "I want to be a professional athlete."
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假设学生说:“我想成为 一名职业运动员。”
04:40
And it says, "Well, learning about the size of cells,
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然后它说:“嗯,学习细胞的大小,
04:43
which is what this video is,
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也就是这个视频的内容,
04:44
that could be really useful for understanding nutrition
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非常有助于理解营养
04:47
and how your body works, etc."
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和你身体的运作方式等等。”
04:49
It can answer questions, it can quiz you,
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它可以回答问题,考考你,
04:51
it can connect it to other ideas,
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关联上其他内容,
04:53
you can now ask as many questions of a video
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你可以问出无数个问题,
04:55
as you could ever dream of.
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超出你的想象。
04:57
(Applause)
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(掌声)
05:01
Another big shortage out there,
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还有一个严重的缺口,
05:03
I remember the high school I went to,
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我记得我上的高中,
05:04
the student-to-guidance counselor ratio was about 200 or 300 to one.
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学生对辅导员的比例 大约为 200:1 或 300:1。
05:10
A lot of the country, it's worse than that.
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很多国家比这还差。
05:12
We can use Khanmigo to give every student a guidance counselor,
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我们可以通过 Khanmigo 给每一位学生配备一位辅导员、
05:16
academic coach, career coach, life coach,
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学业导师、职业导师、人生导师,
05:19
which is exactly what you see right over here.
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如图所示。
05:22
And we launched this with the GPT-4 launch.
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我们在 GPT-4 发布的同时 发布了这个系统。
05:25
We have a few thousand people on this.
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有几千人在使用它。
05:27
This isn't a fake demo,
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这不是个模拟演示,
05:28
this is really it in action.
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这是真实发生的情况。
05:32
And then there is, you know,
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我认为还可以完成更难的任务,
05:34
things that I think it would have been even harder,
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05:36
it would have been a little science fiction to do
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就算配备了一名传统辅导老师, 这都听起来有点玄幻。
05:39
with even a traditional tutor.
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05:40
We run an online high school with Arizona State University
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我们和亚利桑那州大学 合作开办了一所线上高中,
05:43
called Khan World School,
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名叫“可汗世界学校”,
05:45
and we have a student who attends that online school, based in India.
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有一名身在印度的学生 入读了这所线上学校。
05:48
Her name's Saanvi.
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她的名字叫萨恩维(Saanvi)。
05:50
And she was doing a report on "The Great Gatsby."
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她想写一篇 《了不起的盖茨比》的读后感。
05:53
And when she was reading "The Great Gatsby,"
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在她阅读《了不起的盖茨比》时,
05:55
Jay Gatsby keeps looking at the green light off into the distance.
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发现杰伊·盖茨比(Jay Gatsby) 一直遥望着绿灯。
05:58
And she's like, "Why does he do that?"
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她就在想:“他干嘛呢?”
06:00
She did some web searches, and people have obviously studied this
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她上网搜了搜,显然 很多人都对此有所研究,
06:03
and commented about the symbolism of that,
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对它的象征意义作出了评价,
06:05
but none of it was really resonating with her.
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但没有一条评论 可以让她感同身受。
06:08
And then she realized that she had Khanmigo
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然后她想起她有 Khanmigo,
06:10
and that she could talk to Jay Gatsby himself.
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她都可以直接 与杰伊·盖茨比本人对话。
06:13
And so, "Ah, splendid choice, old sport.
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于是,“啊,有眼光,老兄!
06:15
I am now Jay Gatsby, the enigmatic millionaire
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我现在是杰伊·盖茨比, F·斯科特·菲茨杰拉德
06:18
from F. Scott Fitzgerald’s classic.”
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(F. Scott Fitzgerald)的 经典作品里的神秘富豪。”
06:20
And so, "Why do you keep staring at the green light?"
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她问:“你为啥一直盯着绿灯呢?”
06:23
"Ah, the green light, old sport.
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“啊,绿灯,老兄。
06:24
It's a symbol of my dreams and desires, you see.
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它代表了我的梦想和渴望。
06:27
It's situated at the end of Daisy Buchanan's dock
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它在黛西·布坎南 (Daisy Buchanan)家的码头尽头,
06:30
across the bay from my mansion.
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与我的豪宅隔海相望。
06:32
I gaze at it longingly as it represents my yearning for the past
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我望眼欲穿, 体现了我对过往的渴望
06:35
and my hope to reunite with Daisy, the love of my life."
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和我想与此生挚爱黛西 重聚的愿望。”
06:39
And what was cool is, Saanvi then said, “I had this long conversation,”
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厉害的地方来了,萨恩维接着说: “我们谈了好久了。”
06:43
she called him “Mr. Gatsby,”
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她称呼他为“盖茨比先生”,
06:45
and at the end she actually apologized for taking his time,
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最终她竟然 为占用了他的时间道歉,
06:48
which I thought was very polite of her.
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我觉得她很懂礼貌。
06:51
But you can imagine this unlocks learning literature, learning ...
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但你可以想象这拓展了 学习文学、学习……
06:56
You could talk to historical figures.
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你都可以和历史人物对话。
06:58
We're even probably going to add an activity
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我们甚至可以以后 加上一个功能,
07:00
you can talk to like, the Mississippi River.
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你都可以和密西西比河对话。
07:02
It brings things to life in ways that really were science fiction
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它让半年、一年以前还是 天方夜谭的东西走进现实。
07:06
even six months or a year ago.
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07:10
Students can get into debates with the AI.
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学生可以与 AI 辩论。
07:12
And we’ve got this here is the student debating
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如图所示是学生讨论
07:14
whether we should cancel student debt.
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我们是否应该取消学生贷款。
07:16
The student is against canceling student debt,
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该学生反对取消学生贷款,
07:18
and we've gotten very clear feedback.
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我们就得到了非常明确的反馈。
07:20
We started running it at Khan World School in our lab school that we have,
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我们开始在 可汗世界学校的实验学校——
07:24
Khan Lab School.
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可汗实验学校执行这项政策。
07:25
The students, the high school students especially,
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学生们,尤其是高中生,
07:27
they're saying "This is amazing to be able to fine-tune my arguments
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他们说:“可以微调 我的论点真是太棒了,
07:30
without fearing judgment.
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还不用担心被人指指点点。
07:32
It makes me that much more confident
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这让我更有自信
07:33
to go into the classroom and really participate."
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走进教室,真正参与。”
07:36
And we all know that Socratic dialogue debate is a great way to learn,
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我们都知道,苏格拉底式 对话辩论是学习的好方法,
07:39
but frankly, it's not out there for most students.
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但老实说,大多数学生 都没有这样的资源。
07:42
But now it can be accessible to hopefully everyone.
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但如今它已经可以 普及到每个人手中。
07:48
A lot of the narrative, we saw that in the headlines, has been,
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有好多观点,就像 新闻头条里的那些,都说:
07:51
"It's going to do the writing for kids.
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“它会替孩子写作文。
07:53
Kids are not going to learn to write."
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孩子不会去学习写作文了。”
07:55
But we are showing that there's ways that the AI doesn't write for you,
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但我们要展示的是 AI 不是“替”你写作,
07:58
it writes with you.
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而是“与”你一起写作。
08:00
So this is a little thing,
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这是很小的一点,
08:01
and my eight year old is addicted to this,
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我八岁大的孩子沉迷其中,
08:03
and he's not a kid that really liked writing before,
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他之前也不怎么喜欢写作,
08:05
but you can say,
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但你可以输入,
08:07
“I want to write a horror story,”
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“我想写一个恐怖故事,”
08:08
and it says, "Ooh, a horror story, how spine-tingling and thrilling.
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然后它说:“哦!恐怖故事, 毛骨悚然,紧张刺激。
08:12
Let's dive into the world of eerie shadows and chilling mysteries."
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让我们走进诡影和怪谈的世界吧。”
08:15
And this is an activity where the student will write two sentences,
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在这个练习中,学生写两句,
08:19
and then the AI will write two sentences.
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然后 AI 写两句。
08:21
And so they collaborate together on a story.
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这样他们就合作写成了一个故事。
08:24
The student writes, "Beatrice was a misunderstood ghost.
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学生写:“碧翠斯(Beatrice) 是个被人误解的鬼。
08:26
She wanted to make friends but kept scaring them by accident."
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她想和人交朋友, 但总是不小心吓到他们。”
08:29
And the AI says, "Poor Beatrice, a lonely spirit yearning for companionship.
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AI 写道:“可怜的碧翠斯, 渴望友情的孤独灵魂。
08:33
One day she stumbled upon an old abandoned mansion," etc.
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有一天,她意外发现了 一座废弃的老宅,……”
08:36
I encourage you all to hopefully one day try this.
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希望大家都能去试试看。
08:39
This is surprisingly fun.
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非常有趣。
08:42
Now to even more directly hit this use case.
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让我们在这个使用场景上更进一步。
08:45
And what I'm about to show you,
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我接下来要给大家展示,
08:47
everything I showed you so far
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我刚展示的所有内容
08:48
is actually already part of Khanmigo, and what I’m about to show you,
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都是 Khanmigo 已经推出的功能, 我接下来要展示的是
08:52
we haven't shown to anyone yet, this is a prototype.
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我们还没有公开的功能, 这只是个原型。
08:54
We hope to be able to launch it in the next few months,
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我们希望在几个月内发布,
08:57
but this is to directly use AI, use generative AI,
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但这是直接使用 AI, 使用生成式 AI,
09:00
to not undermine English and language arts
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不是将其用于破坏 英语和语言艺术,
09:02
but to actually enhance it in ways
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而是用于增强它,
09:04
that we couldn't have even conceived of even a year ago.
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通过一些我们在一年以前 都未曾设想的方式。
09:08
This is reading comprehension.
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这是阅读理解。
09:09
The students reading Steve Jobs's famous speech at Stanford.
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学生阅读了史蒂夫·乔布斯 著名的斯坦福大学演讲。
09:13
And then as they get to certain points,
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当他们读到某一位置时,
09:15
they can click on that little question.
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就可以点击这个小问题。
09:18
And the AI will then Socratically, almost like an oral exam,
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然后 AI 就会采用苏格拉底式 提问,就像是场口语测试,
09:22
ask the student about things.
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向学生提问。
09:24
And the AI can highlight parts of the passage.
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然后 AI 会高亮段落的一部分。
09:26
Why did the author use that word?
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为什么作者会用这个词?
09:28
What was their intent?
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有什么目的?
09:30
Does it back up their argument?
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有没有支撑他们的论点?
09:31
They can start to do stuff that once again,
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然后 AI 可以一遍遍重复,
09:33
we never had the capability to give everyone a tutor,
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我们没有能力给每个人 配备一位辅导老师,
09:36
everyone a writing coach to actually dig in to reading at this level.
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没有能力给每个人配备一位 能如此深入阅读理解的写作导师。
09:41
And you could go on the other side of it.
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你也可以看到另一面。
09:43
And we have whole work flows that helps them write,
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我们有帮助他们写作的一整套流程,
09:45
helps them be a writing coach, draw an outline.
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帮助他们成为一位写作导师, 写出文章大纲。
09:48
But once a student actually constructs a draft,
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学生一写完初稿,
09:51
and this is where they're constructing a draft,
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他们会在这里写上草稿,
09:53
they can ask for feedback once again,
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就可以再次要求反馈,
09:56
as you would expect from a good writing coach.
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也就是一位优秀的 写作老师会做的事。
09:58
In this case, the student will say, let's say,
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在这个情境下,学生会这么问:
10:01
"Does my evidence support my claim?"
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“我的论据可以支撑我的论点吗?”
10:03
And then the AI, not only is able to give feedback,
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然后 AI 不仅可以给出反馈,
10:05
but it's able to highlight certain parts of the passage and says,
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还能高亮段落中的某些部分,回答:
10:08
"On this passage, this doesn't quite support your claim,"
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“这段不太能支撑你的论点,”
10:11
but once again, Socratically says, "Can you tell us why?"
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它会再以苏格拉底式对话问道: “你能解释一下为什么吗?”
10:14
So it's pulling the student, making them a better writer,
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这就会促使学生进步, 让他们成为更好的写作者,
10:17
giving them far more feedback
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给他们提供更多的反馈,
10:18
than they've ever been able to actually get before.
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以前从来得不到的那么多反馈。
10:20
And we think this is going to dramatically accelerate writing, not hurt it.
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我们认为这会急剧提升写作水平, 而不是破坏它。
10:25
Now, everything I've talked about so far is for the student.
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我刚说的所有内容 都是针对学生而言的。
10:29
But we think this could be equally as powerful for the teacher
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但我认为这对教师来说一样强大,
10:32
to drive more personalized education and frankly
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他们可以借此带来更多 有针对性的教学,
10:34
save time and energy for themselves and for their students.
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确确实实地为自己、为学生 节约了时间和精力。
10:37
So this is an American history exercise on Khan Academy.
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这是可汗学院上的美国历史练习题。
10:40
It's a question about the Spanish-American War.
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这个问题讲的是美西战争。
10:44
And at first it's in student mode.
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我们先来看学生模式。
10:47
And if you say, “Tell me the answer,” it’s not going to tell the answer.
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如果你问:“告诉我答案,” 它是不会告诉你答案的。
10:51
It's going to go into tutoring mode.
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那我们来看教学模式。
10:52
But that little toggle which teachers have access to,
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老师可以点击这个小切换键,
10:55
they can turn student mode off and then it goes into teacher mode.
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关掉学生模式,进入教师模式。
10:58
And what this does is it turns into --
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这样就会切换成……
11:01
You could view it as a teacher's guide on steroids.
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你可以把它看作一份 加强版教师指南。
11:03
Not only can it explain the answer,
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它不仅能解释答案,
11:05
it can explain how you might want to teach it.
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还能解释解题思路。
11:08
It can help prepare the teacher for that material.
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它有助于老师为此备课。
11:10
It can help them create lesson plans, as you could see doing right there.
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有助于写下课程计划,如图所示。
11:14
It'll eventually help them create progress reports
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最终会帮助老师创建进度报告、
11:17
and help them, eventually, grade.
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为学生打分。
11:18
So once again, teachers spend about half their time
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老师们会在这种活动, 也就是计划课程上
11:21
with this type of activity, lesson planning.
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花上他们一半的时间。
11:23
All of that energy can go back to them
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现在可以把这些精力还给他们自己,
11:25
or go back to human interactions with their actual students.
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或者还给与真实的学生 进行人际互动上。
11:29
(Applause)
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(掌声)
11:34
So, you know, one point I want to make.
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我想强调一点。
11:37
These large language models are so powerful,
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这些大语言模型非常强大,
11:39
there's a temptation to say like, well,
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以至于人们不禁想
11:41
all these people are just going to slap them onto their websites,
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顺手把它们丢在自己的网站上,
11:44
and it kind of turns the applications themselves into commodities.
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这样就会把这些应用变成商品。
11:47
And what I've got to tell you
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我要告诉大家的是
11:49
is that’s one of the reasons why I didn’t sleep for two weeks
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这就是我在去年八月 第一次接触到 GPT-4 时,
11:51
when I first had access to GPT-4 back in August.
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两个星期都没睡觉的原因之一。
11:55
But we quickly realized that to actually make it magical,
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但我们迅速意识到 要让它真的能带来奇妙体验,
11:58
I think what you saw with Khanmigo a little bit,
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我觉得你们已经 从 Khanmigo 身上可见一斑,
12:00
it didn't interact with you the way that you see ChatGPT interacting.
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它的互动方式和 ChatGPT 与你互动的方式不一样。
12:03
It was a little bit more magical, it was more Socratic,
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它更奇妙一点, 更苏格拉底一点,
12:06
it was clearly much better at math
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它显然比很多人 通常想象的更擅长数学。
12:08
than what most people are used to thinking.
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12:10
And the reason is,
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原因就是,
12:11
there was a lot of work behind the scenes to make that happen.
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要达成这一效果, 背后要付出大量努力。
12:14
And I could go through the whole list of everything we've been working on,
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我可以一项项列举 我们的工作内容,
12:18
many, many people for over six, seven months to make it feel magical.
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很多、很多人花了六七个月 为你带来奇妙的体验。
12:21
But perhaps the most intellectually interesting one
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但在智能层面最有趣的一点就是
12:24
is we realized, and this was an idea from an OpenAI researcher,
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我们发现 OpenAI 的研究员 有一个想法,
12:27
that we could dramatically improve its ability in math
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它能显著提高它的数学能力
12:30
and its ability in tutoring
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和教学能力,
12:32
if we allow the AI to think before it speaks.
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如果我们可以让 AI 在发言前思考。
12:35
So if you're tutoring someone
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如果你指导一个人,
12:36
and you immediately just start talking before you assess their math,
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你在评估他们做的数学题之前, 立即开始说话,
12:39
you might not get it right.
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就很有可能会说得不对。
12:41
But if you construct thoughts for yourself,
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但如果你自己整理一下思路,
12:43
and what you see on the right there is an actual AI thought,
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右边的是 AI 的实际想法,
12:46
something that it generates for itself but it does not share with the student.
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这是为它自己生成的, 但不会与学生分享。
12:49
then its accuracy went up dramatically,
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然后准确率就飙升了,
12:51
and its ability to be a world-class tutor went up dramatically.
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它成为世界级辅导老师的 能力也飙升了。
12:54
And you can see it's talking to itself here.
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你看它在自言自语。
12:56
It says, "The student got a different answer than I did,
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它会说:“学生和我的答案不一样,
12:59
but do not tell them they made a mistake.
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但我不能告诉他们做错了。
13:01
Instead, ask them to explain how they got to that step."
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而应该问他们 是怎么得出这一步的。”
13:05
So I'll just finish off, hopefully,
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让我说完最后一点,
13:08
you know, what I’ve just shown you is just half of what we are working on,
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我刚给大家展示的 只是我们所做的一半,
13:11
and we think this is just the very tip of the iceberg
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我们认为这只是 它未来发展的冰山一角。
13:15
of where this can actually go.
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13:17
And I'm pretty convinced, which I wouldn't have been even a year ago,
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我非常确信, 甚至在一年之前我都无法确信,
13:21
that we together have a chance of addressing the 2 sigma problem
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靠大家的力量,我们有 解决 2 西格玛问题的可能性,
13:25
and turning it into a 2 sigma opportunity,
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把它变成 2 西格玛机遇,
13:28
dramatically accelerating education as we know it.
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显著加快我们所知的教育。
13:33
Now, just to take a step back at a meta level,
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我们后退一步看一下宏观的情况,
13:35
obviously we heard a lot today, the debates on either side.
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显然我们现在总是会听到各方的争议。
13:38
There's folks who take a more pessimistic view of AI,
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有人对 AI 持悲观态度,
13:41
they say this is scary,
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他们说,这太可怕了,
13:42
there's all these dystopian scenarios,
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都是反乌托邦的情景,
13:45
we maybe want to slow down, we want to pause.
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我们也许得放慢脚步, 我们得暂停一下。
13:48
On the other side, there are the more optimistic folks
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另一边也有更乐观的人,
13:51
that say, well, we've gone through inflection points before,
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他们会说, 我们之前都经历过拐点,
13:54
we've gone through the Industrial Revolution.
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我们都经历过工业革命了。
13:56
It was scary, but it all kind of worked out.
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确实可怕,但是都过来了。
13:59
And what I'd argue right now
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我现在想说的是
14:01
is I don't think this is like a flip of a coin
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我认为这不是一件 随便拍脑袋的事,
14:04
or this is something where we'll just have to,
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我们也不该干等着, 看看哪条路才是对的。
14:06
like, wait and see which way it turns out.
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14:09
I think everyone here and beyond,
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我认为在场、不在场的各位,
14:11
we are active participants in this decision.
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都是这个决定中的重要参与者。
14:14
I'm pretty convinced that the first line of reasoning
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我很确信第一条理由就是
14:17
is actually almost a self-fulfilling prophecy,
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一条会自我应验的预言,
14:20
that if we act with fear and if we say,
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如果我们战战兢兢,认为
14:22
"Hey, we've just got to stop doing this stuff,"
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“嘿,我们别再干这个了,”
14:25
what's really going to happen is the rule followers might pause,
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接下来会发生的就是 遵守规则的人会停下、
14:28
might slow down,
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会放慢脚步,
14:30
but the rule breakers, as Alexandr [Wang] mentioned,
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但不遵守规则的人,就如亚历山大 (Alexandr Wang)刚提到的,
14:32
the totalitarian governments, the criminal organizations,
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集权主义政府、犯罪组织,
14:35
they're only going to accelerate.
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它们只会越跑越快。
14:36
And that leads to what I am pretty convinced is the dystopian state,
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这就是为什么我很确信 在反乌托邦的状态下,
14:40
which is the good actors have worse AIs than the bad actors.
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遵纪守法的人的 AI 没有为非作歹的人的好。
14:45
But I'll also, you know, talk to the optimists a little bit.
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但我也和对此持积极态度的人聊了聊。
14:49
I don't think that means that,
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我不觉得他们的意思是
14:50
oh, yeah, then we should just relax and just hope for the best.
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哦,好啊,我们就躺平, 祈祷一切顺利吧。
14:53
That might not happen either.
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1626
这也是不可能会发生的。
14:55
I think all of us together have to fight like hell
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我认为,我们得 齐心协力,奋勇向前,
14:59
to make sure that we put the guardrails,
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保证做好防护措施,
15:02
we put in -- when the problems arise --
333
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准备好……在问题出现的时候……
15:05
reasonable regulations.
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适当的监管。
15:07
But we fight like hell for the positive use cases.
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但我们要向着正面的 使用场景不断努力。
15:10
Because very close to my heart,
336
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1960
因为在我内心深处,
15:12
and obviously there's many potential positive use cases,
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2627
当然有很多潜在的正面使用场景,
15:15
but perhaps the most powerful use case
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但也许最强大的使用场景,
15:17
and perhaps the most poetic use case is if AI, artificial intelligence,
339
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也许也是最有诗意的使用场景 就是 AI,人工智能
15:22
can be used to enhance HI, human intelligence,
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可以用于增强 HI,人类智能、
15:26
human potential and human purpose.
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人类潜力和人类使命。
15:29
Thank you.
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谢谢。
15:30
(Applause)
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(掌声)
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