Using AI to Increase Your Intelligence & Enrich Humanity | Dr. Fei-Fei Li
Dr. Fei-Fei Li:
李飞飞博士：
I think the biggest thing humanity never learns is the older generation lamenting about the future generation, as if the future generation doesn't know anything, they're rude, they're forgetting the past.
我认为人类永远学不到的最重要的事情就是老一辈人为下一代人哀叹，好像下一代人什么都不知道，他们很粗鲁，他们忘记了过去。
But if you look at arc of history of humanity, by and large, we advance for the better.
但如果你看看人类的历史轨迹，总的来说，我们会朝着更好的方向前进。
Now, I'm not denying the atrocities, I'm not denying the setbacks, I'm not denying this, but fundamentally, I'm an optimist in humanity.
现在，我不否认暴行，我不否认挫折，我不否认这一点，但从根本上来说，我是一个人性乐观主义者。
I look at kids, they're curious.
我看着孩子们，他们很好奇。
Of course, they get massively entertained by this technology, but they also are starting to use it.
当然，他们从这项技术中获得了巨大的乐趣，但他们也开始使用它。
What I worry about are teachers and some parents, because I think our society today, and especially Silicon Valley, are not doing them a service.
我担心的是老师和一些家长，因为我认为当今的社会，尤其是硅谷，并没有为他们提供服务。
We're forgetting about them.
我们正在忘记他们。
Andrew Huberman:
安德鲁·休伯曼：
Hey everyone.
嘿大家。
To celebrate the launch of my new book entitled "Protocols," I'm pleased to share that I'll be hosting three live events very soon.
为了庆祝我的新书《协议》的推出，我很高兴地告诉大家，我很快将举办三场现场活动。
The first live event is in New York City at Radio City Music Hall on September 17th.
第一场现场活动将于 9 月 17 日在纽约无线电城音乐厅举行。
The second event is in Los Angeles at the Dolby Theatre on October 8th.
第二场活动将于 10 月 8 日在洛杉矶杜比剧院举行。
And the third live event is in San Francisco at the Masonic on October 28th.
第三场现场活动将于 10 月 28 日在旧金山共济会举行。
At each of these events, I'll be discussing topics from the book, and my favorite part, taking questions directly from you, the audience.
在每次活动中，我都会讨论书中的主题以及我最喜欢的部分，并直接回答观众的问题。
To get tickets, you can go to hubermanlab.com/events and use the code PROTOCOLS to get early access.
要获取门票，您可以访问 hubermanlab.com/events 并使用代码 PROTOCOLS 抢先体验。
Again, that's hubermanlab.com/events, and use the code PROTOCOLS to get early access to tickets.
同样，这是 hubermanlab.com/events，并使用代码 PROTOCOLS 来提前获取门票。
Welcome to the Huberman Lab podcast, where we discuss science and science-based tools for everyday life.
欢迎来到休伯曼实验室播客，我们在这里讨论科学和基于科学的日常生活工具。
I'm Andrew Huberman, and I'm a professor of neurobiology and ophthalmology at Stanford School of Medicine.
我是安德鲁·胡伯曼，斯坦福大学医学院神经生物学和眼科教授。
My guest today is Dr. Fei-Fei Li, a computer scientist and professor at Stanford, and one of the pioneers and luminaries of artificial intelligence and computer vision.
今天我的嘉宾是李飞飞博士，他是斯坦福大学计算机科学家、教授，也是人工智能和计算机视觉领域的先驱和杰出人物之一。
As you all know, millions of people use AI chatbots to look up information every single day.
众所周知，每天有数百万人使用人工智能聊天机器人来查找信息。
And of course, many people are concerned about AI, where it's going, and how it might replace certain human jobs, or degrade our experience of life in one way or another.
当然，许多人都担心人工智能的发展方向，以及它如何取代某些人类工作，或以某种方式降低我们的生活体验。
Today, we discuss from a neuroscience perspective what intelligence really is, and the ways that AI can and is being used for good, meaning to truly enhance learning, health, and to enrich rather than diminish the human experience.
今天，我们从神经科学的角度讨论智能到底是什么，以及人工智能可以并且正在被用于好的方面的方式，这意味着真正增强学习、健康，并丰富而不是削弱人类的经验。
We start off by talking about how human brains of all ages learn new information, what rules the brain follows in that process, and how AI, because it is based on the content of the internet, both resembles and falls short of what human brains can learn.
我们首先讨论各个年龄段的人类大脑如何学习新信息，大脑在这个过程中遵循什么规则，以及人工智能如何因为基于互联网内容而与人类大脑可以学习的内容相似但又有所不同。
And we discuss exciting uses of AI and robotics in medicine.
我们还讨论了人工智能和机器人技术在医学中令人兴奋的用途。
To be clear, Fei-Fei acknowledges and addresses the many valid concerns about AI, but as the director of the Stanford Institute for Human-Centered Artificial Intelligence, her goal is to make sure that humans and humanity at large are represented in where AI goes next.
需要明确的是，飞飞承认并解决了对人工智能的许多合理担忧，但作为斯坦福以人为中心的人工智能研究所所长，她的目标是确保人类和整个人类在人工智能的下一步发展中得到代表。
As you'll soon hear, Dr. Fei-Fei Li is an extraordinary scientist and educator.
您很快就会听说，李飞飞博士是一位杰出的科学家和教育家。
She has been called the godmother of AI for her ushering in of AI technologies, but also for her insistence that the ethics and benevolent uses of AI stay central to AI and robotics.
她被称为人工智能教母，因为她引入了人工智能技术，也因为她坚持认为人工智能的道德和仁慈用途仍然是人工智能和机器人技术的核心。
So whether you are young or old, today's conversation will inform and empower you to understand and use AI in ways that truly benefit you and enrich your life.
因此，无论您是年轻还是年长，今天的对话都将告诉您并让您能够理解和使用人工智能，从而真正使您受益并丰富您的生活。
Before we begin, I'd like to emphasize that this podcast is separate from my teaching and research roles at Stanford.
在开始之前，我想强调一下，这个播客与我在斯坦福大学的教学和研究角色是分开的。
It is, however, part of my desire and effort to bring zero cost to consumer information about science and science-related tools to the general public.
然而，我的愿望和努力的一部分是向公众提供有关科学和科学相关工具的零成本消费者信息。
In keeping with that theme, today's episode does include sponsors.
为了与这个主题保持一致，今天的节目确实有赞助商。
And now for my discussion with Dr. Fei-Fei Li.
现在我和李飞飞博士进行了讨论。
Dr. Fei-Fei Li, welcome.
李飞飞博士，欢迎光临。
Dr. Fei-Fei Li:
李飞飞博士：
Thank you.
谢谢你。
I'm excited to be here, Andrew.
我很高兴来到这里，安德鲁。
Andrew Huberman:
安德鲁·休伯曼：
Yeah.
是的。
This is a long time coming, and-
这是一个很长的时间，而且-
Dr. Fei-Fei Li:
李飞飞博士：
Yes
是的
Andrew Huberman:
安德鲁·休伯曼：
...
...
you are a luminary in this AI field, but I also consider you a neuroscientist and computer scientist, and we share a common path through vision science.
您是人工智能领域的杰出人物，但我也认为您是神经科学家和计算机科学家，我们在视觉科学方面拥有共同的道路。
And so I'd like to-
所以我想——
Dr. Fei-Fei Li:
李飞飞博士：
And fellow colleagues
还有各位同事
Andrew Huberman:
安德鲁·休伯曼：
And fellow colleagues-
还有各位同事——
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
at Stanford.
在斯坦福大学。
So I'd like to start in vision.
所以我想从愿景开始。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
What is so special about vision and seeing and light as it pertains to AI and where it's all going?
与人工智能相关的视觉、视觉和光有什么特别之处，以及它的发展方向是什么？
Because I think for most people, those probably sound like very divorced themes, but actually, that's where it all starts.
因为我认为对于大多数人来说，这些可能听起来像是非常分离的主题，但实际上，这就是一切的开始。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
I see vision as a cornerstone of intelligence in almost two parallel way.
我以几乎两种并行的方式将视觉视为智力的基石。
One is what evolution has taught us.
一是进化论告诉我们的。
What's the evolution of vision and animal intelligence and human intelligence?
视觉、动物智力和人类智力的进化是什么？
The other one is computer vision and AI, what that relationship is.
另外一个是计算机视觉和人工智能，两者之间的关系是什么。
So I'll go into each.
所以我将逐一讨论。
Evolution, I always say that 540 million years ago, animals saw the first light.
进化论，我总是说5.4亿年前，动物看到了第一道光。
These are simple sea ocean animals, trilobites and the cousins, and before that, there was very little sensing.
这些是简单的海洋动物，三叶虫及其近亲，在此之前，人们几乎没有感知能力。
Around that same time, tactile and haptics was starting also to emerge in animal bodies.
大约在同一时间，触觉和触觉也开始在动物体内出现。
But there was no hearing, there's no smelling, there's absolutely no nervous system.
但没有听觉，没有嗅觉，完全没有神经系统。
But the first photoreceptive cells created a evolutionary force that propelled animals to evolve, because sensing the external world changes your self-perception, changes the way your relationship with the external world.
但第一个感光细胞创造了一种进化力量，推动动物进化，因为感知外部世界会改变你的自我认知，改变你与外部世界的关系方式。
To put it simply, if you can see food, it changes your life, from a evolution point of view, and you become someone else's food, and also you're actively seeking food, you're actively seeking mates, and all that.
简单来说，如果你能看到食物，从进化的角度来看，它会改变你的生活，你成为别人的食物，你也在积极地寻找食物，你在积极地寻找伴侣，等等。
So really, because of sensing and perception, evolution took a incredibly accelerated pace in terms of animal speciation.
事实上，由于感知和感知，动物物种形成的进化速度令人难以置信地加快。
Fossil studies have told us that 10 million years after the first light for animals was what we call the Big Bang of evolution or Cambrian explosion of animal speciation.
化石研究告诉我们，动物出现第一道光后的 1000 万年就是我们所说的进化大爆炸或动物物种形成的寒武纪爆发。
And fast-forward, I think vision has always played a huge role in not only in the early evolution of animals, but as well as advanced intelligence and how that emerged.
快进来说，我认为视觉不仅在动物的早期进化中，而且在高级智能及其出现过程中始终发挥着巨大作用。
You and I are both vision student and scientist.
你和我都是视觉学生和科学家。
It is estimated half of the cortical activities in human brain is involved in visual function.
据估计，人脑的皮质活动有一半与视觉功能有关。
Children were first visual before they were verbal in development.
孩子们在语言发展之前首先是视觉。
So vision really, to this day, plays a central role in both the evolution of animal intelligence as well as in the daily human life.
因此，直到今天，视觉在动物智力的进化以及人类的日常生活中确实发挥着核心作用。
Now, in parallel, vision as a discipline or as an area of artificial intelligence really played a pivotal role in what we see as this modern AI moment in a couple of ways.
现在，与此同时，视觉作为一门学科或作为人工智能的一个领域，确实在我们所看到的现代人工智能时刻以多种方式发挥了关键作用。
First of all is the algorithms, the neural network algorithms.
首先是算法，神经网络算法。
Neural network algorithms were first computer scientists start dabbling that in the early 1950s.
神经网络算法是计算机科学家在 20 世纪 50 年代初开始涉足的领域。
And Andrew, you might remember what's happening on the neuroscience side in the early 1950s is that neuroscientists like Hubel and Wiesel were starting to record visual cells in mammalian brain and starting to realize there is a hierarchical structure of nervous cells that stack against each other and pass neural information across this hierarchy.
Andrew，你可能还记得 20 世纪 50 年代初神经科学方面发生的事情，像 Hubel 和 Wiesel 这样的神经科学家开始记录哺乳动物大脑中的视觉细胞，并开始意识到神经细胞存在一种层次结构，这些细胞相互堆叠并在这个层次结构中传递神经信息。
And it goes from collecting light from retina all the way to recognizing there is a shape in front of you.
它从收集视网膜的光线一直到识别出你面前有一个形状。
And that very neural architecture that we see in mammalian brain is also part of the inspiration of neural network algorithm.
我们在哺乳动物大脑中看到的神经结构也是神经网络算法的灵感的一部分。
Now, today's neural network algorithm runs on hundreds of billions and even trillions of parameters.
现在，当今的神经网络算法运行在数千亿甚至数万亿个参数上。
It has the complexity that departs from what we recorded in the mammalian brain or the visual pathway.
它的复杂性不同于我们在哺乳动物大脑或视觉通路中记录的内容。
But the origin is very close to each other, about half a century ago, a little more than half a century ago.
但起源却非常接近，大约是半个世纪前，半个多世纪前一点。
That's one aspect of vision's contribution to AI.
这是视觉对人工智能贡献的一方面。
There is another aspect of vision's contribution to AI that is also pivotal, which is through big data.
视觉对人工智能的另一个贡献也很关键，那就是通过大数据。
Is that comes closer to my own work, is that AI around the century was a field of machine learning.
这与我自己的工作更接近的是，本世纪的人工智能是机器学习的一个领域。
A lot of different labs, different research scientists were trying out different algorithms.
许多不同的实验室、不同的研究科学家正在尝试不同的算法。
And it's not just neural network.
这不仅仅是神经网络。
There are other methods, jargon words like Bayesian methods, support vector machine methods.
还有其他方法，行话如贝叶斯方法、支持向量机方法。
It doesn't matter what these methods are, but it's a explorative phase that we're trying to get these algorithms to work so that we can empower the machine to read or to see.
这些方法是什么并不重要，但这是一个探索阶段，我们正在尝试让这些算法发挥作用，以便我们能够赋予机器阅读或查看的能力。
A group of us computer vision scientists were struggling with these algorithms.
我们一群计算机视觉科学家正在努力研究这些算法。
And, I was a very young faculty, first-year faculty, 2006, at Princeton, and my students and I are looking at these algorithms and how little data were fed into these algorithms to learn.
而且，2006 年，我是普林斯顿大学的一名非常年轻的教师，一年级教师，我和我的学生正在研究这些算法，以及输入这些算法进行学习的数据有多么少。
So I turned to cognitive neuroscience literature, namely vision literature, and started to study how much humans learn, how much humans can see.
所以我转向认知神经科学文献，即视觉文献，开始研究人类学到了多少，人类能看到多少。
And the numbers were incredible.
这些数字令人难以置信。
Humans, by age six, can learn tens of thousands of different object categories, and the exposure to visual world is also massive, right?
人类到六岁时，就可以学习数以万计的不同物体类别，并且接触到的视觉世界也很大，对吗？
Babies can see most of the time, the moment they are born.
婴儿在出生的那一刻起，大部分时间都可以看到东西。
So they're inundated with this big data.
所以他们被这些大数据淹没了。
So we conjectured that the lack of data was a huge part of the reason there's a lack of progress in AI.
因此我们推测，数据的缺乏是人工智能缺乏进展的一个重要原因。
So we took a departure from everybody else who are really focusing only on algorithm and said that we need data.
因此，我们与其他真正只关注算法的人不同，我们需要数据。
We need data to drive these algorithms.
我们需要数据来驱动这些算法。
So long story short, we led this ImageNet project that collected the first ever internet-scale large data set for the field of artificial intelligence, but really through the field of vision, because ImageNet is a collection of 50 million images, and the goal of ImageNet was to drive machines to recognize everyday objects, microphones, cups, chairs.
长话短说，我们领导了这个 ImageNet 项目，该项目收集了人工智能领域有史以来第一个互联网规模的大型数据集，但实际上是通过视野，因为 ImageNet 是 5000 万张图像的集合，而 ImageNet 的目标是驱动机器识别日常物体，麦克风，杯子，椅子。
And that work converged with the advances in neural network algorithm as well as in GPU computing.
这项工作与神经网络算法和 GPU 计算的进步融合在一起。
And by 2012, that work, that convergence of the three elements of modern AI became the defining moment of what modern AI is.
到 2012 年，这项工作，即现代人工智能三个要素的融合，成为了现代人工智能的决定性时刻。
Andrew Huberman:
安德鲁·休伯曼：
I recall somewhere around 2012-
我记得大约在2012年的某个地方——
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm
嗯嗯
Andrew Huberman:
安德鲁·休伯曼：
...
...
it seems there was this debate at this vision course at Cold Spring Harbor that was held every other summer.
每隔一个夏天在冷泉港举办的视觉课程中似乎就有这样的争论。
Like could a computer learn to recognize specific faces as well as humans?
就像计算机可以学会像人类一样识别特定的面孔吗？
Now I think most people would say computers are actually much better at it-
现在我想大多数人会说计算机实际上在这方面做得更好 -
Dr. Fei-Fei Li:
李飞飞博士：
Yes
是的
Andrew Huberman:
安德鲁·休伯曼：
...
...
than humans are, even though you have these super recognizer people who are exceptional at this.
比人类还要高，尽管你有这些在这方面表现出色的超级识别者。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
Could you tell us how is it that this technology went from a state basically where it would confuse you and maybe a cousin or even someone that looks somewhat like you-
你能否告诉我们，这项技术是如何从一个基本上会让你、也许是你的表亲，甚至是看起来有点像你的人感到困惑的状态开始的——
Dr. Fei-Fei Li:
李飞飞博士：
A cookie
一块饼干
Andrew Huberman:
安德鲁·休伯曼：
...
...
to the, right, or to the point where now it is exquisitely precise.
到了，向右，或者到了现在非常精确的程度。
Dr. Fei-Fei Li:
李飞飞博士：
How did we get here?
我们是怎么到这里的？
I want to definitely double, triple click on the convergence of this technology.
我绝对希望双击、三次点击这项技术的融合。
I think around the second decade of 21st century, so like you said, around 2012, the huge convergence was the capability of GPU computing, which basically accelerated or parallelized computing so that you can have more flops going through algorithms, right?
我认为在21世纪的第二个十年左右，就像你说的，在2012年左右，巨大的融合是GPU计算的能力，它基本上加速或并行化计算，这样你就可以通过算法有更多的失败，对吗？
You need that speed.
你需要这样的速度。
Then you also have a, after many decades of research, neural network algorithm is getting more mature.
那么你还有一个，经过几十年的研究，神经网络算法越来越成熟。
Starting, as we said, 1950s, people start to create these very simple algorithm that behaves similarly to neurons, but much simpler.
正如我们所说，从 20 世纪 50 年代开始，人们开始创建这些非常简单的算法，其行为与神经元类似，但要简单得多。
Neurons, as you know, are very complex.
如您所知，神经元非常复杂。
But here the idea is that you have one unit of node that takes some input and outputs another input, and within it, it's just a function, a very simple function.
但这里的想法是，你有一个节点单元，它接受一些输入并输出另一个输入，在它内部，它只是一个函数，一个非常简单的函数。
So you stack them together.
所以你把它们堆在一起。
That's what neural network is.
这就是神经网络。
But by the time it's in the, after around 2010-ish, the maturity of these algorithms have gotten to a level that it's becoming really good.
但到了 2010 年左右，这些算法的成熟度已经达到了非常好的水平。
But also, last but not the least, the recognition of big data.
最后但并非最不重要的一点是对大数据的认可。
Internet definitely fueled that.
互联网无疑推动了这一点。
It made data more available, but the reckoning moment of, wow, big data needs to be part of that equation.
它使数据更容易获得，但是，哇，大数据的清算时刻需要成为这个等式的一部分。
We need to use big data to drive this algorithm to learn these patterns.
我们需要使用大数据来驱动这个算法来学习这些模式。
So these convergence of these three things really set off the revolution of AI.
所以这三件事的融合确实引发了人工智能的革命。
The specific moment is also worth mentioning because you mentioned face recognition, is this ImageNet challenge my lab put forward that starting 2010, after we collected this humongous data set, we at that point, GPU was not yet mature, and we put out a public challenge for the research community for multiple years in the role and invited people to solve this major computer vision problem called object recognition.
具体的时刻也值得一提，因为你提到了人脸识别，这个ImageNet挑战是我的实验室提出的，从2010年开始，在我们收集了这个庞大的数据集之后，我们当时GPU还没有成熟，我们向研究界提出了一个公开的挑战，邀请人们解决这个称为对象识别的计算机视觉重大问题。
The task was very easy.
任务非常简单。
We have a data set of 1,000 different categories of objects, and this data set is more than a million images large.
我们有一个包含 1000 个不同类别物体的数据集，这个数据集超过一百万张图像。
It's what we call the testing data set.
这就是我们所说的测试数据集。
And the task for the algorithm is I'll show you a picture, you have to name the main objects inside.
算法的任务是我给你看一张图片，你必须说出里面的主要对象的名称。
And if you guess right, you get a point.
如果你猜对了，你就得到一分。
If you guess wrong, you don't get a point.
如果你猜错了，你就没有得分。
So that ImageNet challenge, we later, a couple of years later, benchmarked human performance by a very smart graduate student at Stanford, and that was roughly 4%.
因此，几年后，我们在 ImageNet 挑战赛上对斯坦福大学一位非常聪明的研究生的人类表现进行了基准测试，结果约为 4%。
So random chance will be one over 1,000.
所以随机概率是千分之一。
Andrew Huberman:
安德鲁·休伯曼：
Mm.
嗯。
Dr. Fei-Fei Li:
李飞飞博士：
Right?
对吗？
So 4% for humans is not that bad.
所以 4% 对人类来说并没有那么糟糕。
The first few years, machines were not as good as humans.
最初几年，机器还不如人类。
The turning point was 2012, the convergence of neural network, ImageNet data set, and GPU.
转折点是2012年，神经网络、ImageNet数据集和GPU的融合。
Even that year, even though the error rate was cut to...
即使那一年，尽管错误率降低到了……
Oh, by the way, the human performance error rate was 4%.
哦，顺便说一下，人类的表现错误率为 4%。
Sorry, I need to correct that.
抱歉，我需要纠正一下。
The error rate was cut down to the tens.
错误率降低到了几十。
It wasn't where human performance was.
这不是人类表现的地方。
Andrew Huberman:
安德鲁·休伯曼：
So this is looking at images and assigning a-
所以这是查看图像并分配-
Dr. Fei-Fei Li:
李飞飞博士：
One out of 1,000 labels.
千分之一的标签。
Andrew Huberman:
安德鲁·休伯曼：
Got it.
明白了。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
But 2012 was so momentous that year because the error rate from previous algorithm dropped a lot by this neural network algorithm.
但2012年是非常重要的一年，因为通过这种神经网络算法，之前算法的错误率下降了很多。
And we know in the research community when something this drastic happens, it means a inflection point.
我们在研究界知道，当这种剧烈的事情发生时，就意味着一个拐点。
But it still took another three years, I remember, by 2012, 2016, for the algorithm to beat humans in naming 1,000 objects.
但我记得又花了三年时间，到 2012 年、2016 年，该算法才在命名 1000 个物体方面击败人类。
Andrew Huberman:
安德鲁·休伯曼：
Could I ask you where this 4% error is coming from in this very smart graduate student?
我能问一下这位非常聪明的研究生这 4% 的错误是从哪里来的吗？
Is it that they don't recognize the objects, or it's a recognition against time pressure, like they have to, they're being fed images fast enough that occasionally they do an incorrect assignment?
是他们无法识别这些物体，还是这是对时间压力的一种识别，就像他们必须的那样，他们输入图像的速度足够快，以至于偶尔他们会做出不正确的分配？
Dr. Fei-Fei Li:
李飞飞博士：
I don't think the time pressure was the main issue, even though for a graduate student to do this, I don't think they want to do this forever.
我不认为时间压力是主要问题，即使对于研究生来说，我不认为他们想永远这样做。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
But I think the human brain, as you know, has limited memory, whether it's long-term or short-term, right?
但我认为，正如你所知，人脑的记忆能力是有限的，无论是长期记忆还是短期记忆，对吧？
So retaining the patterns of 1,000 object classes, even if some classes you're familiar is not that easy.
因此，即使某些类是您熟悉的，保留 1,000 个对象类的模式也不是那么容易。
Andrew Huberman:
安德鲁·休伯曼：
Got it.
明白了。
Dr. Fei-Fei Li:
李飞飞博士：
So, I think there is the confusion.
所以，我认为存在混乱。
And also, for example, different species of dogs gets really close.
而且，例如，不同种类的狗之间的关系非常接近。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
And that's a challenge.
这是一个挑战。
Andrew Huberman:
安德鲁·休伯曼：
I'd like to take a quick break and acknowledge our sponsor, Lingo.
我想稍微休息一下并感谢我们的赞助商 Lingo。
Lingo is an everyday wearable that tracks your glucose 24/7.
Lingo 是一款日常可穿戴设备，可 24/7 跟踪您的血糖。
Glucose drives a lot of key processes that support energy, body composition, and long-term health.
葡萄糖驱动许多支持能量、身体成分和长期健康的关键过程。
When glucose is constantly spiking and crashing, that's where we can start to see metabolic dysfunction, and over time, that can even progress to pre-diabetes.
当血糖不断飙升和崩溃时，我们就会开始看到代谢功能障碍，随着时间的推移，甚至可能发展为糖尿病前期。
Right now, about 115 million adults in the US have pre-diabetes.
目前，美国约有 1.15 亿成年人患有糖尿病前期。
Most don't know it, and a higher percentage of men have it than women do.
大多数人不知道这一点，而且男性患有此病的比例高于女性。
Often, there aren't clear symptoms of pre-diabetes early on, so people don't tend to look into it.
通常，糖尿病前期并没有明显的症状，因此人们往往不会去关注它。
But the fact is that metabolic health is shaping how your body functions every day, whether you feel it or not.
但事实是，无论您是否感觉到，代谢健康正在塑造您身体的日常运作方式。
Tracking your glucose with Lingo can help you see how food, activity, and stress impact your glucose throughout the day.
使用 Lingo 跟踪您的血糖可以帮助您了解食物、活动和压力如何影响您全天的血糖。
I personally have used Lingo, and it's been an invaluable tool for improving my metabolic health.
我个人使用过 Lingo，它是改善我的代谢健康的宝贵工具。
If you would like to try Lingo, Huberman Lab listeners in the US and UK can save 10% on a four-week plan.
如果您想尝试 Lingo，美国和英国的 Huberman Lab 听众可以在为期 4 周的计划中节省 10%。
Just visit hellolingo.com/huberman for more information.
请访问 hellolingo.com/huberman 了解更多信息。
Terms and conditions apply.
条款和条件适用。
Again, that's hellolingo.com/huberman.
再说一次，这是hellolingo.com/huberman。
Today's episode is also brought to us by Wealthfront.
今天的节目也是由 Wealthfront 为我们带来的。
In today's financial landscape of constant market shifts and chaotic news, it's easy to feel uncertain about how to save and invest your money.
在当今市场不断变化、新闻混乱的金融环境中，人们很容易对如何储蓄和投资感到不确定。
Wealthfront is the solution that helps you take control of your money while managing risk.
Wealthfront 是帮助您在管理风险的同时掌控资金的解决方案。
For nearly a decade, I've trusted Wealthfront to navigate this volatility.
近十年来，我一直相信 Wealthfront 能够应对这种波动。
With the Wealthfront cash account, I can earn 3.3% annual percentage yield, or APY, on my cash from program banks.
通过 Wealthfront 现金账户，我可以从计划银行获得 3.3% 的年化收益率 (APY)。
And I know my money is growing until I'm ready to spend it or invest it.
我知道我的钱会不断增长，直到我准备好花钱或投资为止。
One of the features I love about Wealthfront is that I have access to instant, no-fee withdrawals to eligible accounts 24/7.
我喜欢 Wealthfront 的功能之一是我可以 24/7 即时、免费提款到符合条件的账户。
That means I can move my money where I need it without waiting.
这意味着我可以将资金转移到需要的地方，而无需等待。
And when I'm ready to transition from saving to investing, Wealthfront lets me seamlessly transfer my funds into one of their expert-built portfolios.
当我准备好从储蓄过渡到投资时，Wealthfront 可以让我无缝地将资金转移到他们专家构建的投资组合之一。
For a limited time, Wealthfront is offering the Huberman Lab audience an exclusive 0.75% APY boost over the base rate for three months, meaning you can get up to 4.05% variable APY on up to $150,000 in deposits.
在有限的时间内，Wealthfront 为 Huberman Lab 受众提供为期三个月的独家年利率比基本利率高 0.75% 的优惠，这意味着您最多可在 150,000 美元的存款中获得高达 4.05% 的可变年利率。
Over one million people already trust Wealthfront to save more, earn more, and build long-term wealth with confidence.
超过一百万人已经相信 Wealthfront 能够充满信心地储蓄更多、赚取更多并建立长期财富。
If you'd like to try Wealthfront, you can go to wealthfront.com/huberman to receive the boost offer and start earning 4.05% variable APY today.
如果您想尝试Wealthfront，您可以访问wealthfront.com/huberman 获取提升优惠，并立即开始赚取4.05% 的可变年利率。
That's wealthfront.com/huberman to get started.
请从 Fortunefront.com/huberman 开始。
This is a paid testimonial of Wealthfront.
这是 Wealthfront 的付费推荐。
Client experiences will vary.
客户体验会有所不同。
Wealthfront brokerage is not a bank.
Wealthfront 经纪公司不是银行。
The base APY is as of January 30th, 2026, and subject to change.
基本 APY 截至 2026 年 1 月 30 日，可能会发生变化。
For more information, please see the episode description.
欲了解更多信息，请参阅剧集描述。
I can see the rationale for doing this in the vision domain, but has a similar thing been explored with hearing, with sounds?
我可以在视觉领域看到这样做的基本原理，但是在听觉和声音方面是否也探索过类似的事情？
As humans, we are amazing at recognizing speech inflection, emotional tone, things like that.
作为人类，我们非常擅长识别言语变化、情绪语气等。
But if I had to discriminate even 15 different sound frequencies, I can tell you as a non-musician, it would be very difficult for me.
但如果我必须区分 15 种不同的声音频率，我可以告诉你，作为一个非音乐家，这对我来说将是非常困难的。
Dr. Fei-Fei Li:
李飞飞博士：
Absolutely.
绝对的。
I think that what you see is the floodgate got open.
我认为你看到的是闸门打开了。
And every sub-area of AI, whether it's speech recognition, sound recognition, natural language processing, which is more than recognition, vision, all areas got really a boost in terms of the technology.
人工智能的每一个子领域，无论是语音识别、声音识别、自然语言处理，不仅仅是识别、视觉，所有领域都在技术方面得到了真正的推动。
We have colleagues at Stanford who are studying whale sound, whale songs, using machine learning and AI now.
我们斯坦福大学的同事现在正在使用机器学习和人工智能来研究鲸鱼的声音、鲸鱼的歌曲。
And speech recognition is another area that did so well in the early days of this AI revolution.
语音识别是人工智能革命早期表现出色的另一个领域。
And of course, the technology continues to advance.
当然，技术在不断进步。
By the time the transformer paper was published around 2016, 2017, it quickly showed that it is even more powerful than the early ImageNet, AlexNet algorithm.
到2016年、2017年左右Transformer论文发表时，很快就表明它比早期的ImageNet、AlexNet算法还要强大。
There, it was not the field of computer vision that made the next big progress.
在那里，取得下一个重大进展的并不是计算机视觉领域。
It's the field of natural language processing.
这是自然语言处理领域。
So, because the recipe hasn't changed, now we have an even more powerful neural network algorithm called Transformer, but we have even more data on the internet from, at least more readily available data on the internet in the form of texts.
因此，因为配方没有改变，现在我们有一个更强大的神经网络算法，称为 Transformer，但我们在互联网上有更多的数据，至少是互联网上更容易获得的文本形式的数据。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
And now we have more powerful GPUs.
现在我们拥有更强大的 GPU。
So companies like OpenAI and Google quickly rallied beyond this very important technology, and it still took about five years, from 2017 to 2022, to get to the ChatGPT moment in natural language.
因此，像 OpenAI 和 Google 这样的公司迅速团结起来，超越了这项非常重要的技术，从 2017 年到 2022 年，仍然花了大约五年的时间，才达到了自然语言的 ChatGPT 时刻。
But that's yet another step forward.
但这又向前迈出了一步。
Andrew Huberman:
安德鲁·休伯曼：
So, I think for people who are not computer scientists nor neuroscientists, the natural human experience will perhaps resonate with them, and maybe I can just frame my question through that lens.
因此，我认为对于既不是计算机科学家也不是神经科学家的人来说，人类的自然体验也许会引起他们的共鸣，也许我可以通过这个镜头来提出我的问题。
So, when a child learns that there's something called a kitty cat-
所以，当一个孩子得知有一种叫做小猫的东西时——
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm
嗯嗯
Andrew Huberman:
安德鲁·休伯曼：
...
...
they go, "Oh, cat." Then they usually drop the kitty part.
他们说：“哦，猫。”然后他们通常会放弃小猫的部分。
They might say, "Kitty," and then they learn cat.
他们可能会说“Kitty”，然后他们就会学习“cat”。
And if they have enough interactions with a cat, they'll realize what a cat is, even if they see it from the side, from the back.
如果他们与猫有足够的互动，他们就会意识到猫是什么，即使他们从侧面、背面看到它。
And eventually, if they see a tail that looks a little bit like a cat and it's behind some books, you say, "What is that?" They're very likely to say, "Cat." Even if they've also seen foxes and other animals with tails.
最终，如果他们看到一条看起来有点像猫的尾巴，并且它在一些书后面，你会说，“那是什么？”他们很可能会说“猫”。即使他们也见过狐狸和其他有尾巴的动物。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
Just based on their experience.
只是根据他们的经验。
Dr. Fei-Fei Li:
李飞飞博士：
Uh-huh.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
They're making a probability judgment.
他们正在做一个概率判断。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
And that's essentially what AI can do.
这本质上就是人工智能可以做的事情。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
That's essentially what machine learning can do.
这本质上就是机器学习可以做的事情。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
But it seems to me that there's a ...
但在我看来，有一个...
key moment that had to happen in the progression of from calculators to the AI we have now to be able to see an image of a tail and make the reasonable assumption that it's most likely a cat if it's indoors or something like that because-
从计算器到人工智能的发展过程中必须发生的关键时刻，我们现在能够看到尾巴的图像，并做出合理的假设，如果它在室内或类似的地方，它很可能是一只猫，因为-
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm
嗯嗯
Andrew Huberman:
安德鲁·休伯曼：
...
...
foxes generally aren't indoors, this sort of thing.
狐狸一般不在室内，诸如此类。
So at what point did machine learning and AI gain the ability to do contextual learning and come up with the most likely assignment of what something is?
那么，机器学习和人工智能在什么时候获得了进行情境学习并提出最有可能的事物分配的能力呢？
Because it's one thing to show apples and bananas and oranges.
因为展示苹果、香蕉和橙子是一回事。
They're all fruit.
它们都是水果。
Okay, you could distinguish them.
好吧，你可以区分它们。
You could distinguish those from cars and trucks, et cetera.
您可以将它们与汽车和卡车等区分开来。
But this object constancy piece-
但这个物体恒常片-
Dr. Fei-Fei Li:
李飞飞博士：
Mm
毫米
Andrew Huberman:
安德鲁·休伯曼：
...
...
that if something is moving, you're only getting a partial image, this isn't what most people think of in terms of intelligence, but it's part of what makes our brains and the brains of other animals, but especially our brains, so remarkable-
如果有东西在移动，你只能得到部分图像，这不是大多数人所认为的智力，但它是我们的大脑和其他动物的大脑，尤其是我们的大脑如此非凡的部分原因——
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm
嗯嗯
Andrew Huberman:
安德鲁·休伯曼：
...
...
and why we consider ourselves probably the smartest species on Earth, and if not the smartest, then certainly the best at technology development.
以及为什么我们认为自己可能是地球上最聪明的物种，如果不是最聪明，那么肯定是技术开发方面最好的。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
So when did AI achieve this, and how was that scripted into these computers to allow them to do that?
那么人工智能什么时候实现了这一点，又是如何将其编写到这些计算机中以允许它们做到这一点的呢？
Dr. Fei-Fei Li:
李飞飞博士：
So let's just take the problem very-- You have described it so well, this problem of seeing a glimpse of a cat tail and being able to recognize cat, right?
所以，让我们非常认真地对待这个问题——你已经描述得很好了，这个问题是瞥见猫尾巴并能够识别猫，对吧？
Or assign a high likelihood there is a cat.
或者指定有一只猫的可能性很高。
The interesting thing is, Andrew, generations of machine learning computer scientists have tried this problem.
有趣的是，安德鲁，一代又一代的机器学习计算机科学家都尝试过这个问题。
So before today that machines could reliably do it, there were different algorithm.
因此，在机器能够可靠地做到这一点之前，存在不同的算法。
You can imagine a common sense way of thinking about this is, oh, maybe we should recognize all the furniture to know it's indoors, or it's unlikely to be a fox.
你可以想象一个常识性的思考方式是，哦，也许我们应该认出所有的家具才能知道它在室内，否则它不太可能是一只狐狸。
So there are rules like that, that it was built into previous generations of algorithms.
所以有这样的规则，它被内置到前几代算法中。
There are also rules like, well, instead of guess it's a cat, let's only guess one out of the 10 potential animals, cat being one of them.
还有一些规则，比如，好吧，我们不要猜测它是一只猫，而是只猜测 10 种潜在动物中的一种，猫就是其中之一。
That limits the search or guess space, and that would help.
这限制了搜索或猜测空间，这会有所帮助。
So many ideas were tried.
尝试了很多想法。
So when was the moment it became much more reliable?
那么什么时候它变得更加可靠呢？
Is this current era when the huge data that these algorithms have learned, let's take Gemini or GPT, have learned really created the capability in the machine's learned space, so much knowledge, so much pattern, that when presented with this more or less maybe a new-ish photo of a cat's tail sticking outside of a bookshelf, that pattern activated the learned, what we call learned weights or learned parameters, that put the machine's assessment or guess of this object closer to what it has seen, which is likely to be a cat tail or just tail, because there's just so much data.
在当今时代，这些算法所学习的海量数据（让我们以 Gemini 或 GPT 为例）是否真正在机器的学习空间中创造了能力，如此多的知识，如此多的模式，当呈现出这张或多或少可能是猫尾巴粘在书架外的新照片时，该模式激活了学习的内容，我们称之为学习权重或学习参数，使机器对该物体的评估或猜测更接近它所看到的物体，这可能是一只猫尾部或只是尾部，因为数据太多了。
Andrew Huberman:
安德鲁·休伯曼：
Got it.
明白了。
Dr. Fei-Fei Li:
李飞飞博士：
This is where, Andrew, as neuroscientists, I think we depart from human brain, because that child who learns about, what you say, kitty cat, will not have the chance to download the internet of images of cat.
安德鲁，作为神经科学家，我认为我们与人类大脑的区别在于，因为那个学习你所说的小猫的孩子将没有机会在互联网上下载猫的图像。
They likely have seen three cats, 10 cats at most, but yet they're able to identify that tail as a cat tail instead of a fox tail through a different kind of learning pathway.
他们可能见过三只猫，最多十只猫，但他们能够通过不同类型的学习途径识别出那条尾巴是猫尾巴而不是狐狸尾巴。
These are the mysteries we haven't fully solved.
这些都是我们尚未完全解开的谜团。
But I do want to point out that departure between today's AI algorithm that is learned with a humongous amount of data versus how humans have evolved.
但我确实想指出，当今通过大量数据学习的人工智能算法与人类的进化方式之间存在差异。
Andrew Huberman:
安德鲁·休伯曼：
If we continue to ascend the hierarchy from simple object recognition to what you and I would call higher order brain functions, like moving more towards what most people, they hear the word intelligence, and they just think, oh, it must be some higher order thing, creativity, imagination.
如果我们继续从简单的物体识别到你我所说的更高阶的大脑功能，就像更多地朝着大多数人的方向发展，他们听到智力这个词，他们就会想，哦，这一定是一些更高阶的东西，创造力，想象力。
Let's go to a middle step and then a much further step out.
让我们先进行中间步骤，然后再进一步进行。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
So staying with the cat example, if a computer or a child learns to recognize a cat through the tail, the whole thing, whatever, and they've seen a cat move, it's a very new world at that point for-
因此，以猫为例，如果计算机或孩子学会通过尾巴识别猫，整个事情，无论如何，并且他们看到了猫的移动，那么这就是一个非常新的世界——
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm
嗯嗯
Andrew Huberman:
安德鲁·休伯曼：
...
...
that brain, that child, or that computer.
那个大脑，那个孩子，或者那台电脑。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
Because now they know that the cat generally moves in the direction of its head, not its tail.
因为现在他们知道猫通常会朝头部的方向移动，而不是朝尾巴的方向移动。
These are simple-
这些很简单——
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm
嗯嗯
Andrew Huberman:
安德鲁·休伯曼：
...
...
simple learning rules, right?
简单的学习规则，对吧？
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
It might go after mice, but it might run from dogs.
它可能会追老鼠，但也可能会躲避狗。
Maybe yes, maybe no, and on and on.
也许是，也许不是，等等。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
And so it seems that the next layer up in terms of, quote unquote, intelligence, is to assign likelihoods of direction to move, directions not to move, other objects that that object is likely to interact with.
因此，就智能而言，下一层似乎是分配移动方向、不移动方向以及该对象可能与之交互的其他对象的可能性。
This all sounds very basic to people-
这对人们来说听起来非常基本——
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm
嗯嗯
Andrew Huberman:
安德鲁·休伯曼：
...
...
but this is how brains learn, and this is-
但这就是大脑学习的方式，这是——
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm
嗯嗯
Andrew Huberman:
安德鲁·休伯曼：
...
...
how machines learn.
机器如何学习。
So when was the next big inflection in terms of giving a computer, AI, a picture of a cat and saying, "Animate this cat for me.
那么下一个重大变化是什么时候呢？给计算机、人工智能一张猫的图片，然后说：“为我制作这只猫的动画。
Make it move like a cat," without giving it any specific instructions about how to move its limbs, et cetera.
让它像猫一样移动”，而不给它任何关于如何移动四肢等的具体指令。
But I would imagine that was a pretty quick, but a remarkably important transformation in this whole thing that we call AI, because that's what a brain does.
但我认为，对于我们称之为人工智能的整个事物来说，这是一个相当快但非常重要的转变，因为这就是大脑的作用。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
So it's really funny you ask this.
所以你问这个问题真的很有趣。
You put it beautifully.
你说得很漂亮。
I never thought to put it in this way for a public audience, but that moment came when video Become part of the training data.
我从来没有想过以这种方式向公众展示它，但那一刻到来了，视频成为了训练数据的一部分。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
So see, again, I'm going back to the training data.
所以，再一次，我将回到训练数据。
So around 2023, very shortly after ChatGPT moment, multiple research teams start to put video into the training data.
因此，在 2023 年左右，ChatGPT 之后不久，多个研究团队开始将视频放入训练数据中。
Of course, I'm not going to get into the nuance of the algorithm.
当然，我不会深入讨论该算法的细微差别。
There's a little bit of changes and variations.
有一些变化和变化。
So remember January 2024, Sora was released, and that's where people see a video can be generated.
所以请记住 2024 年 1 月，Sora 发布了，人们就在那里看到了可以生成的视频。
Literally what you just said, people can then type and say, "A cat running towards a mouse." And then a few-second clip would be generated, and there would be a cat moving its leg in a plausible way, running towards a mouse.
正如您刚才所说，人们可以输入并说：“一只猫跑向一只老鼠。”然后会生成几秒钟的剪辑，并且会有一只猫以合理的方式移动它的腿，跑向老鼠。
At that time, there were still mistakes.
那个时候，还是有错误的。
Still, even today, it's not perfect, but things have gotten a lot better.
即使在今天，它仍然不完美，但情况已经好多了。
But that opened the floodgate of video generation, as you described it.
但这打开了视频生成的闸门，正如您所描述的。
So what happened there?
那么那里发生了什么？
What happened there is actually not as revolutionary as you might think.
那里发生的事情实际上并不像你想象的那么具有革命性。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
Because the bottom line is it's still data.
因为底线是它仍然是数据。
As a scientist, I can tell you there are all kinds of algorithm tweaks and changes and improvements and all that.
作为一名科学家，我可以告诉你有各种各样的算法调整、改变和改进等等。
But overall, if you zoom out, it's still part of this great neural network era, right?
但总的来说，如果你缩小视野，它仍然是这个伟大的神经网络时代的一部分，对吗？
But what happened is that we're now able to process video data in a way, again, some clever engineering, tokenize it, whatever you call it.
但发生的事情是，我们现在能够以某种方式处理视频数据，再次，一些巧妙的工程，将其标记化，无论你怎么称呼它。
And now we can generate these short clips of videos, which is frames put together, that look like plausible cat movement.
现在我们可以生成这些视频短片，这些视频片段是拼凑在一起的，看起来像猫的真实动作。
Now, you might ask, does the algorithm know the muscle structure of a cat's legs so that when the algorithm shows that the cat is moving in a plausible way with the paws in a sequence?
现在，您可能会问，算法是否知道猫腿部的肌肉结构，以便当算法显示猫以合理的方式移动爪子时？
I would say the algorithm doesn't.
我想说算法没有。
But what it does have is so many videos, especially cat on the internet, so many videos of cat.
但它确实有这么多的视频，尤其是互联网上的猫，这么多关于猫的视频。
So it learned what it should look like.
所以它知道了它应该是什么样子。
So in a way, humans do that.
所以在某种程度上，人类就是这样做的。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
Most of us, without education, would not know how muscles move in cats.
我们大多数人没有受过教育，不会知道猫的肌肉是如何运动的。
I still don't know.
我还是不知道。
Our colleagues in medical school might know.
我们医学院的同事可能知道。
But we have just got so used to seeing cats moving this way, that we have a plausible idea of how cats move.
但我们已经习惯了猫这样移动，所以我们对猫的移动方式有了一个合理的想法。
So that is similar.
所以这是相似的。
That's how similar AI is.
这就是人工智能的相似之处。
It's the statistics.
这是统计数据。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
It's the large amount of data that shows you what is the plausible generation of cat movements.
正是大量的数据向您展示了猫的运动的合理一代。
Andrew Huberman:
安德鲁·休伯曼：
Yeah, so when people have heard almost certainly that the brain is a prediction machine, it's a learning machine, this is exactly what-
是的，所以当人们几乎肯定听说大脑是一台预测机器、一台学习机器时，这正是——
Dr. Fei-Fei Li:
李飞飞博士：
Yes
是的
Andrew Huberman:
安德鲁·休伯曼：
...
...
you're referring to.
你指的是。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
Let's go to a really far-out there-
让我们去一个非常遥远的地方——
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm
嗯嗯
Andrew Huberman:
安德鲁·休伯曼：
...
...
aspect of brain function that we know exists in humans, which is thoughts-
我们知道人类存在大脑功能的一个方面，那就是思想——
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm
嗯嗯
Andrew Huberman:
安德鲁·休伯曼：
...
...
and creativity.
和创造力。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
Now, there are probably rules for thoughts and creativity.
现在，思想和创造力可能有规则。
They're a little bit harder to tack down than examples from the visual system.
它们比视觉系统中的示例更难处理。
Like if it's a tail and it's indoors, it's likely a cat, this kind of thing.
就像如果它是一条尾巴并且在室内，它很可能是一只猫，这种东西。
But they're there.
但他们就在那里。
The rules are there.
规则就在那里。
Dr. Fei-Fei Li:
李飞飞博士：
If you use apple as an example-
如果你以苹果为例——
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm
嗯嗯
Dr. Fei-Fei Li:
李飞飞博士：
...
...
we could have gone from low level, seeing an apple-
我们本可以从低层出发，看到一个苹果-
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm
嗯嗯
Dr. Fei-Fei Li:
李飞飞博士：
...
...
to mid-level, seeing apple always drop, not fly off.
到了中层，看到苹果总是掉下来，而不是飞掉。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
And the highest level, what is the equation that governs the apple's movement?
最高层，控制苹果运动的方程是什么？
Andrew Huberman:
安德鲁·休伯曼：
Right.
对。
So that's ascending to a higher order-
所以这是上升到一个更高的顺序 -
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
more reductionist analysis.
更加还原主义的分析。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
What do you think about the idea that while AI is indeed intelligent, it can do things that brains can do, maybe even, well, certainly things that individual human brains can't do.
你如何看待这样的想法：虽然人工智能确实是智能的，但它可以做大脑可以做的事情，甚至可能是人类大脑无法做的事情。
We know this by virtue of beating humans at chess and this sort of thing.
我们通过在国际象棋和诸如此类的事情中击败人类而知道这一点。
The idea right now, as I understand it, is that AI is trained on the internet.
据我了解，目前的想法是人工智能是在互联网上训练的。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
Images, discussions, videos, songs, but that's not all of human cognition, right?
图片、讨论、视频、歌曲，但这并不是人类认知的全部，对吧？
So are there aspects of AI that are, whether or not it's Chat or it's Claude, or even the most powerful, not yet released machine learning and AI tools, that don't have access to features of human brain function yet because they've never been uploaded to the internet, at least not in a way that the AI can pull out?
那么，人工智能的某些方面，无论是 Chat 还是 Claude，甚至是尚未发布的最强大的机器学习和人工智能工具，都无法访问人类大脑功能的功能，因为它们从未上传到互联网，至少没有以人工智能可以提取的方式上传？
So for instance, you could put a symphony there, and it follows certain rules of music and mathematics and sound.
例如，你可以在那里放一首交响乐，它遵循一定的音乐、数学和声音规则。
Like that makes sense.
这么说是有道理的。
But you have thoughts all day long, and I have thoughts all day long that don't quite mesh with language in a way that I can just type them out on the internet.
但你整天都有想法，我也整天都有想法，这些想法与语言不太吻合，我可以在互联网上把它们打出来。
Stay with me here.
留在我这里吧。
I know this is a long question, but I feel like this is the one thing you are perfectly poised to answer, and I've been waiting to ask you this for-
我知道这是一个很长的问题，但我觉得这是你完全准备好回答的一件事，我一直在等着问你这个问题-
Dr. Fei-Fei Li:
李飞飞博士：
Let's do it
让我们开始吧
Andrew Huberman:
安德鲁·休伯曼：
...
...
a year and a half since I saw you in Utah.
自从我在犹他州见到你已经一年半了。
In the world of art, we have this thing called abstraction, right?
在艺术的世界里，我们有一种叫做抽象的东西，对吗？
And occasionally, somebody will come up with a painting or a drawing that it doesn't look like anything specific.
有时，有人会画出一幅画或一幅图画，但它看起来并不像任何具体的东西。
This happens in music, too, where you just feel something like there's a fundamental rule or an emotion associated with it.
这种情况也发生在音乐中，你只是感觉到有一个基本规则或与之相关的情感。
Like they've tapped into some aspect of brain function, but you can't say what it is.
就像他们已经利用了大脑功能的某些方面，但你不能说它是什么。
I feel like this is the sort of thing that is complicated for AI, or for me to understand how AI could do, because you can put that piece of art into AI and say, "You know what?
我觉得这对人工智能来说是很复杂的事情，或者对我来说理解人工智能如何做是很复杂的，因为你可以将这件艺术品放入人工智能中并说，“你知道吗？
Fundamental feature of human experience does this reveal?" And it only has access to what's on the internet.
这揭示了人类经验的基本特征吗？”而且它只能访问互联网上的内容。
So how can you capture a complex constellation of feelings and experience with AI?
那么如何利用人工智能捕捉复杂的感受和体验呢？
That seems to be the gap for me, and I'm sure we'll get there with AI, but I'm not seeing From neuroscience to AI in any kind of direct way, the same way we could ratchet through visual motion, sadness, happiness, you could pull out a lot of things, but it's hard to get to these higher order abstract representations that can't be spoken or written down or drawn.
这对我来说似乎是一个差距，我相信我们会通过人工智能实现这一目标，但我没有以任何直接的方式看到从神经科学到人工智能，就像我们可以通过视觉运动、悲伤、快乐来棘轮一样，你可以拉出很多东西，但很难获得这些不能说、写下来或画出来的更高阶的抽象表示。
If I just say, "Give me your example of whatever, nostalgia for your childhood home." You could write about it, but those are just words.
如果我只是说，“给我举个例子，对你童年的家的怀念。”你可以写下来，但这些只是文字。
I can't understand your experience at a first-person level.
我无法以第一人称的角度理解您的经历。
Dr. Fei-Fei Li:
李飞飞博士：
Totally.
完全可以。
Andrew, I know you put a lot of thoughts into this question, and I think it's a very important question.
安德鲁，我知道你对这个问题思考了很多，我认为这是一个非常重要的问题。
And let's peel this one step at a time.
让我们一步一步地揭开这一点。
First of all, TLDR short answer is, I agree with you that we do have to be very careful recognizing what AI can do, is likely to do, not conjecturing over 100 years or whatever.
首先，长话短说，简短的回答是，我同意你的观点，我们必须非常小心地认识到人工智能可以做什么、可能做什么，而不是猜测 100 多年或其他什么。
I recognize what you just said are these extremely nuanced, personalized, hard-to-characterize, or not even captured human cognitive behaviors.
我承认你刚才所说的是这些极其微妙、个性化、难以表征、甚至无法捕捉到的人类认知行为。
And because they were not captured, then they were not uploaded on the internet, and today's AI doesn't have a way to do that.
因为它们没有被捕获，所以它们没有上传到互联网上，而今天的人工智能没有办法做到这一点。
So when you call internet, which is the source of AI's data, let's be very clear what is internet.
所以当你说互联网是AI数据的来源时，我们要清楚什么是互联网。
Internet is not some random thing.
互联网不是随机的东西。
Internet is the biggest collection of human behavior in multimodal forms.
互联网是人类多模式行为的最大集合。
Let's break it down further.
让我们进一步分解它。
Internet has the world's population typing on it for many, at this point, multiple decades.
全世界的人们在互联网上打字已有数十年之久。
That typing is a sensing mechanism that captured everything from teenager chit-chat all the way to deep scientific articles who got digitized and get uploaded.
这种打字是一种传感机制，可以捕获从青少年的闲聊到数字化并上传的深度科学文章的一切内容。
Right?
正确的？
So that capturing human language is what internet is super good at.
因此捕捉人类语言是互联网最擅长的。
Then internet captures images.
然后互联网捕获图像。
How?
如何？
Because we now have digital cameras that's so prevalent in smartphones and digital cameras, so that humans love taking photos from the cat in your house, to selfies, to beautiful BBC-captured photos.
因为我们现在拥有在智能手机和数码相机中非常普遍的数码相机，因此人们喜欢拍摄家里的猫、自拍以及 BBC 拍摄的美丽照片。
Those also got uploaded in our digital sphere.
这些也上传到我们的数字领域。
On top of that, there's videos.
最重要的是，还有视频。
Videos now has sound and has movements.
视频现在有声音和动作。
That also got uploaded to our digital sphere.
这也被上传到我们的数字领域。
On top of that, there's music.
除此之外，还有音乐。
We're not even getting into the legal discussion of copyrights, but let's just table that aside.
我们甚至没有进入版权的法律讨论，但让我们把它放在一边。
I'm just talking about the forms of data.
我只是谈论数据的形式。
The speeches and singing and music and orchestra, that also got uploaded into the digital sphere.
演讲、歌唱、音乐和管弦乐队也被上传到数字领域。
So now we have created this humongous library of human knowledge in words, human behavior in videos, human expressions, or even nature's whatever in sound, and now AI gets trained on that.
所以现在我们已经创建了这个巨大的人类知识库，包括人类的文字知识、视频中的人类行为、人类表达，甚至自然界的声音，现在人工智能正在接受这方面的训练。
That is why it's so powerful.
这就是为什么它如此强大。
This is why, especially in the words front, that AI can recognize patterns, can synthesize patterns, because so much of this is already there.
这就是为什么，特别是在前面的话，人工智能可以识别模式，可以合成模式，因为其中很多已经存在。
But the thing that you just talk about, that when, let's say, Picasso had that incredibly profound thought about that particular way of expressing that portrait of the young woman, that thought has never been captured.
但你刚才谈到的事情是，当毕加索对表达年轻女子肖像的特殊方式产生极其深刻的思考时，这种想法从未被捕捉到。
In fact, as neuroscientists, if I ask you which brain area did that thought come from, you don't know.
事实上，作为神经科学家，如果我问你这个想法来自哪个大脑区域，你不会知道。
Right?
正确的？
Is it Broca?
是布罗卡吗？
Is it V1?
是V1吗？
Is it motor?
是电机吗？
Is it prefrontal?
是前额叶吗？
We don't know.
我们不知道。
Maybe it's diffused everywhere.
也许它已经扩散到各处了。
Because that thought is so personalized, so special, you can call it creativity, you can call it emotion, you can call it whatever you want.
因为这种想法是如此个性化、如此特别，你可以称之为创造力，你可以称之为情感，你可以称之为任何你想要的东西。
You can call it Cat 231, whatever name you can give it.
您可以将其称为 Cat 231，无论您如何命名。
That thought is not captured, therefore, it's not on the internet.
这个想法没有被捕获，因此，它不在互联网上。
Therefore, AI has not seen it.
所以AI还没有看到。
So that is where humans still remain so unique.
这就是人类仍然如此独特的地方。
But we also need to give credit to AI.
但我们也需要相信人工智能。
Because AI has learned so many things, it can combine information in highly creative way.
因为人工智能已经学到了很多东西，所以它可以以高度创造性的方式组合信息。
Did you remember move 37?
你还记得第37步吗？
Andrew Huberman:
安德鲁·休伯曼：
This is AlphaGo, right?
这就是阿尔法狗吧？
Dr. Fei-Fei Li:
李飞飞博士：
Right.
对。
Andrew Huberman:
安德鲁·休伯曼：
Yeah.
是的。
Dr. Fei-Fei Li:
李飞飞博士：
Move 37 has symbolized AI's creativity.
第37步象征着人工智能的创造力。
I think it's both true, but can be taken out of context.
我认为这都是正确的，但可以断章取义。
Because that was a game when AlphaGo was playing Lee Sedol, and in, I think it's a third game out of the five games, that AlphaGo, as a computer algorithm, made a move that the human masters of Go never thought about.
因为那是AlphaGo对阵李世石的一场比赛，我认为这是五场比赛中的第三场比赛，AlphaGo作为计算机算法，做出了人类围棋大师从未想过的棋法。
And that is incredible move, right?
这是令人难以置信的举动，对吧？
Because really, humans collectively, these are the masters, never thought about it.
因为真的，人类集体，这些都是主人，从来没有想过。
But if you really go deep into what AI did there, it was because, first of all, Go is a highly mathematical game.
但如果你真正深入研究人工智能在那里做了什么，那是因为，首先，围棋是一种高度数学化的游戏。
It has very clear mathematical objective, very clear mathematical rules in terms of move.
它有非常明确的数学目标，在走法方面有非常明确的数学规则。
So when AI having a bigger compute and ways to retain how many moves it can remember, it was able to do things that human brains don't typically do.
因此，当人工智能拥有更大的计算能力并能够保留它能记住的动作数量时，它就能够做人类大脑通常不会做的事情。
So is that called creativity?
那么这也叫创造力吗？
I think it is.
我认为是的。
But we do have to recognize that's a special kind of creativity.
但我们必须认识到这是一种特殊的创造力。
I was talking to an incredible mathematician of our time, and I was asking him about the unsolved problem of mathematics and how AI can contribute to that.
我正在与我们这个时代一位令人难以置信的数学家交谈，我向他询问了尚未解决的数学问题以及人工智能如何为此做出贡献。
And he was very positive.
而且他非常积极。
He said there are many problems in today's mathematics.
他说当今的数学存在很多问题。
As hard as they are, even as, say, a Field Medalist, I probably have forgotten there are known methods in math that can solve these problems because I have a human brain.
尽管它们很难，即使作为田径奖章获得者，我可能已经忘记了数学中已知的方法可以解决这些问题，因为我有一个人脑。
I don't remember and I don't know all of math's solutions in the past hundreds of years, even if I were a Field Medalist.
我不记得也不知道过去几百年里所有的数学解决方案，即使我是田径奖章获得者。
So AI can help us to solve these problems.
所以人工智能可以帮助我们解决这些问题。
But as a mathematician, he was also telling me, he said, "I don't know if AI can solve all of math problems, because some of these math problems require solutions that have not been invented, that will push creativity to a whole different level." And this is where we should be curious.
但作为一名数学家，他也告诉我，“我不知道人工智能是否可以解决所有数学问题，因为其中一些数学问题需要尚未发明的解决方案，这会将创造力推向一个完全不同的水平。”这就是我们应该好奇的地方。
Is it going to be a human creativity, or AI would go through its iterations of improvement and get to a point of creativity that humans don't have?
是人类的创造力，还是人工智能会经过迭代改进，达到人类不具备的创造力？
Or is it a combined creativity?
还是综合创意？
My current conjecture is hybrid.
我目前的猜想是混合的。
Andrew Huberman:
安德鲁·休伯曼：
Mm.
嗯。
Dr. Fei-Fei Li:
李飞飞博士：
Is that humans working alongside AI would help us to solve these problems whose solutions have yet to be invented.
人类与人工智能一起工作是否会帮助我们解决这些尚未发明解决方案的问题。
And then what you said, especially you touched on emotion, is even more personalized.
然后你说的话，尤其是你谈到的情感，就更加个人化了。
This is not necessarily logic.
这不一定是逻辑。
This is not necessarily deductive reasoning.
这不一定是演绎推理。
This is maybe, Andrew, you look at this cup and say, "It's a gray cup." What if it evoked an emotion in me, a childhood moment, that a gray cup might mean something that only me and my best friend share?
安德鲁，也许你看着这个杯子说：“这是一个灰色的杯子。”如果它唤起了我的一种情感，一个童年时刻，一个灰色的杯子可能意味着只有我和我最好的朋友分享的东西怎么办？
That is a completely inaccessible piece of information in my brain that is never uploaded on the internet.
这是我大脑中完全无法访问的信息，从未上传到互联网上。
And no matter how mighty AI is today, cannot access that, so that my reaction to this cup and potentially what I would do with it because of that piece of memory can be completely different.
无论今天的人工智能多么强大，都无法访问它，因此我对这个杯子的反应以及由于那段记忆我可能会用它做什么可能完全不同。
You can call it creativity, you can call it expression, you can call it storytelling, you can call it in many ways, but that's where AI cannot access.
你可以称之为创造力，你可以称之为表达，你可以称之为讲故事，你可以用很多方式来称呼它，但这就是人工智能无法访问的地方。
Andrew Huberman:
安德鲁·休伯曼：
I feel like at some point in the not-too-distant future, computers will have access to our brain activity in non-invasive ways.
我觉得在不久的将来的某个时候，计算机将能够以非侵入性的方式访问我们的大脑活动。
Dr. Fei-Fei Li:
李飞飞博士：
Mm.
嗯。
Andrew Huberman:
安德鲁·休伯曼：
So like I might even imagine in five, 10 years, I'm wearing something on my head right now.
就像我想象的那样，五年、十年后，我现在头上戴着一些东西。
Dr. Fei-Fei Li:
李飞飞博士：
Right.
对。
Andrew Huberman:
安德鲁·休伯曼：
You can't see it.
你看不到它。
It's a very, very fine hairnet.
这是一个非常非常精致的发网。
Hairnet makes it sound like it's ...
发网让它听起来像是......
Whatever, like some electrodes that are just there on the outside of my skull, not bothering me, sensing my activity inside the brain, maybe also sensing my heart rate, autonomic activity, how alert I am, and comparing that, yes, to what I'm saying and what I'm doing.
不管怎样，就像我头骨外面的一些电极，不会打扰我，感知我大脑内部的活动，也许还感知我的心率、自主活动、我的警觉程度，并将其与我所说的和所做的进行比较。
This is all totally within reach and is going to happen.
这一切完全是可以实现的，并且将会发生。
You and I both know this.
你我都知道这一点。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
And it's probably already starting to scare people, but let's keep it benevolent, right?
它可能已经开始吓到人们了，但让我们保持仁慈吧？
There's this world where a computer that I own, and I'm not worried about data getting out or anything like that, we've managed that problem, is sensing all these aspects of me and is picking up on the fact that, yes, what I say might be important, but there are aspects of my internal state and brain activity that I'm not even aware of.
在这个世界上，我拥有一台计算机，我不担心数据泄露或类似的事情，我们已经解决了这个问题，正在感知我的所有这些方面，并认识到这样一个事实：是的，我所说的可能很重要，但我的内部状态和大脑活动的某些方面我什至没有意识到。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
And I can decide to collaborate with this aspect of me and say, "Let's come up with a really interesting picture that I've never seen before but comes from some experience of mine that's important based on whatever." And it could reveal that to me-
我可以决定与我的这一方面合作，并说：“让我们想出一张非常有趣的图片，我以前从未见过，但来自我的一些经验，无论基于什么，这些经验都很重要。”它可以向我揭示这一点——
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
because it has access to unconscious-
因为它可以接触无意识-
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm
嗯嗯
Andrew Huberman:
安德鲁·休伯曼：
...
...
features of my brain activity.
我的大脑活动的特征。
Dr. Fei-Fei Li:
李飞飞博士：
Yes.
是的。
Andrew Huberman:
安德鲁·休伯曼：
I think this is very likely to happen-
我认为这种情况很有可能发生——
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
in the not-too-distant future.
在不远的将来。
And perhaps if people thought about it within the bubble of their own experience, like this isn't immediately going to the internet, or it's not going to be used against them, you're actually learning about yourself.
也许，如果人们在自己的经历的泡沫中思考它，比如这不会立即进入互联网，或者它不会被用来对付他们，那么你实际上是在了解自己。
Dr. Fei-Fei Li:
李飞飞博士：
Of course.
当然。
Andrew Huberman:
安德鲁·休伯曼：
And I feel most people have an inherent interest in what's going on for them, also with other people, thank goodness.
我觉得大多数人对他们自己以及其他人所发生的事情有着天生的兴趣，谢天谢地。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
But there, I think, amazing.
但我认为，这太神奇了。
I would love to know why I trip up in certain ways and don't have the best day, or why some days I have the best day, or where ideas come from in me, what states I could kind of elaborate on.
我很想知道为什么我会在某些方面出错并且没有度过最好的一天，或者为什么有些日子我有最好的一天，或者我的想法来自哪里，我可以详细说明哪些状态。
But I'm not going to know how to do that except, okay, one cup of coffee, good.
但我不知道该怎么做，除非，好吧，一杯咖啡，很好。
One and a half, a little better.
一年半了，好一点了。
Two is too much.
两个太多了。
Like right now, if you think about how primitively we go about this, it's kind of crazy.
就像现在，如果你想想我们的做法是多么原始，那就有点疯狂了。
It's crazy.
太疯狂了。
And everyone has a different method.
而且每个人都有不同的方法。
And we all try and get this right, and then you've aged enough by the time you get it right that then you have to update it.
我们都努力把这件事做好，然后当你做对的时候你已经足够老了，你必须更新它。
And like, we're probably not getting the most out of our biology and our brains at all right now.
就像，我们现在可能根本没有充分利用我们的生物学和大脑。
Dr. Fei-Fei Li:
李飞飞博士：
No, we're not.
不，我们不是。
And this is why I keep saying this is why it bothers me when people talk about AI.
这就是为什么我一直说这就是为什么当人们谈论人工智能时我感到困扰。
Some people make it sound like it's replacing humanity, but what you describe is about enhancing and augmenting humanity, right?
有些人听起来像是它正在取代人类，但你所描述的是增强和增强人类，对吗？
This is where it doesn't even have to go as sci-fi as a small hairnet accessing your brainwaves.
在这里，它甚至不需要像一个小发网访问你的脑电波那样科幻。
Just AI learning your patterns of writing-
只是人工智能学习你的写作模式-
Andrew Huberman:
安德鲁·休伯曼：
Mm
毫米
Dr. Fei-Fei Li:
李飞飞博士：
...
...
can already help you to be a better communicator, a more effective communicator, a more efficient communicator.
已经可以帮助你成为一个更好的沟通者，一个更有效的沟通者，一个更高效的沟通者。
And that is an empowering capability that we could unleash in today's AI.
这是我们可以在当今的人工智能中释放的赋能能力。
I think one of the most important thing, Andrew, that as a neuroscientist and also faculty We know is agency is so important for humanity.
我认为最重要的事情之一，安德鲁，作为一名神经科学家和教师，我们知道代理对于人类来说是如此重要。
That boils down to motivation, agency, and dignity at every individual level.
这归结为每个人层面的动机、能动性和尊严。
And I think we need to recognize that we need to think about AI as a tool that helps us in our agency.
我认为我们需要认识到，我们需要将人工智能视为一种可以帮助我们机构的工具。
It should not take away our agency.
它不应该剥夺我们的代理权。
And people who lead in today's AI should not try to talk like that this work will take away agency from people.
当今人工智能领域的领导者不应该试图谈论这项工作将剥夺人们的代理权。
Andrew Huberman:
安德鲁·休伯曼：
Yeah, I think people who are very familiar with the technology, whether it's computers or it's biology or any technology, cars for that matter, we, they, become such nerds of that thing that we forget that it can be scary to people.
是的，我认为那些非常熟悉这项技术的人，无论是计算机还是生物学或任何技术，汽车，我们，他们，都成为了这件事的书呆子，以至于我们忘记了它可能会让人们感到害怕。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
And that the languaging around it is essential.
围绕它的语言是至关重要的。
Dr. Fei-Fei Li:
李飞飞博士：
It is.
是的。
Andrew Huberman:
安德鲁·休伯曼：
And I remember a time in the early '90s, I'm sure you remember this too, when genetic testing was viewed as-
我记得九十年代初的某个时候，我相信你也记得这一点，当时基因测试被视为——
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
this thing like, would you want to have it?
这样的东西，你想拥有吗？
Dr. Fei-Fei Li:
李飞飞博士：
Mm.
嗯。
Andrew Huberman:
安德鲁·休伯曼：
Would you want to do a blood test because, oh my goodness, you might see something that could really scare you.
您想做血液检查吗，因为天哪，您可能会看到一些真正让您害怕的东西。
And that discussion is happening now around self-elected MRIs and things like that, none of which people have to do.
现在的讨论正在围绕自我选择的核磁共振成像和类似的事情进行，而这些事情人们都不需要做。
But I come from the stance like more information is better, but I've come to understand that not everyone feels that way.
但我的立场是，信息越多越好，但我逐渐明白，并不是每个人都这么想。
Dr. Fei-Fei Li:
李飞飞博士：
And they should have that choice.
他们应该有这样的选择。
Andrew Huberman:
安德鲁·休伯曼：
Some people don't want to know.
有些人不想知道。
They don't want to know.
他们不想知道。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
Yeah.
是的。
Dr. Fei-Fei Li:
李飞飞博士：
But they should have the choice.
但他们应该有选择。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
In the meantime, we should have enough public education and communication to let people know the pros and cons, but not to deny them the choice, and also not to take away and say, "Well, since you don't understand this, let me decide for you what's good." That is not good.
同时，我们应该有足够的公众教育和沟通，让人们知道利弊，但不要否认他们的选择，也不要拿走说：“好吧，既然你不明白这个，那就让我来为你决定什么是好的。”那可不好。
And the rhetoric around AI right now is getting really skewed because people who know what this is tend to talk down at the public.
现在围绕人工智能的言论已经变得非常扭曲，因为知道这是什么的人往往会在公众面前居高临下。
Whether the motivation is a positive one or negative one, there is a rhetoric of, "You guys don't know what this is, and I will tell you, and I will make you whether happy, safe, whatever it is, and I will decide for you." These are not healthy and not helpful.
无论动机是积极的还是消极的，都有一种说辞，“你们不知道这是什么，我会告诉你们，我会让你们快乐，安全，无论是什么，我会为你们决定。”这些都不健康，也没有帮助。
Andrew Huberman:
安德鲁·休伯曼：
Yeah, I agree, and I think one of the reasons for starting this podcast was to showcase the scientists and physicians who really have a benevolence about them, and they have no interest in dumbing things down-
是的，我同意，我认为开始这个播客的原因之一是展示那些真正对他们有仁慈的科学家和医生，他们没有兴趣把事情简单化——
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm
嗯嗯
Andrew Huberman:
安德鲁·休伯曼：
...
...
but they do have an interest in people understanding things.
但他们确实对人们理解事物感兴趣。
Dr. Fei-Fei Li:
李飞飞博士：
Yes.
是的。
Andrew Huberman:
安德鲁·休伯曼：
And many people would feel that health information is among the more important things to understand.
许多人会觉得健康信息是需要了解的更重要的事情之一。
Dr. Fei-Fei Li:
李飞飞博士：
Absolutely.
绝对的。
Andrew Huberman:
安德鲁·休伯曼：
Well, thankfully, you're breaking the mold of the phenotype you just described.
好吧，值得庆幸的是，你正在打破你刚才描述的表型的模式。
And there are a few others, but you've been doing this at the highest levels, really encouraging people to think about the collaboration that is AI, the agency that exists, and whether to use it or not to use it, and so forth.
还有其他一些，但你们一直在最高层这样做，真正鼓励人们思考人工智能的协作、现有的机构，以及是否使用它，等等。
Dr. Fei-Fei Li:
李飞飞博士：
Well, of the agency, I do think it's important for individual humans, whether you're a student, a teacher, doctor, a policymaker, is learn about this.
嗯，关于该机构，我确实认为这对于个人来说很重要，无论你是学生、教师、医生、政策制定者，都要了解这一点。
Not necessarily learn about how to code.
不一定要学习如何编码。
I don't think it's necessary, depending on your job.
我觉得没必要，要看你的工作情况。
So for example, if you're an artist or if you're a teacher or doctor, you don't necessarily need to code, but learn about what this technology is.
例如，如果您是一名艺术家，或者您是一名教师或医生，您不一定需要编码，但需要了解这项技术是什么。
Learn about how you can use it yourself to empower yourself, your learning, or your work, or your expression.
了解如何使用它来增强自己、学习、工作或表达的能力。
By learning, one feels more in control.
通过学习，人们会感觉更有掌控力。
By learning, you're less scared of trying, and by learning, you retain that agency and that dignity, because at the end of the day, no matter how advanced technology is or medicine is, as humans, we want that benevolence that helps us to live better, keep our dignity, and make our community better.
通过学习，你不再害怕尝试，通过学习，你保留了这种能动性和尊严，因为归根结底，无论技术多么先进，医学多么先进，作为人类，我们都希望仁慈能够帮助我们生活得更好，保持我们的尊严，让我们的社区变得更好。
Andrew Huberman:
安德鲁·休伯曼：
I'd like to take a quick break and acknowledge our sponsor, AG1.
我想稍微休息一下并感谢我们的赞助商 AG1。
I'm excited to share that AG1 has just launched their newest formulation, AG1 Pro.
我很高兴与大家分享，AG1 刚刚推出了他们的最新配方 AG1 Pro。
AG1 Pro takes the clinically backed AG1 formula, which is a blend of vitamins, minerals, probiotics, and adaptogens, and adds three important new ingredients, creatine monohydrate, calcium HMB, and zinc carnosine.
AG1 Pro 采用经过临床支持的 AG1 配方，该配方是维生素、矿物质、益生菌和适应原的混合物，并添加了三种重要的新成分：一水肌酸、HMB 钙和肌肽锌。
Each serving has five grams of creatine monohydrate to support muscle strength and performance, as well as brain health.
每份含有五克一水肌酸，可支持肌肉力量和性能以及大脑健康。
Calcium HMB to support muscle recovery and reduce muscle breakdown, and zinc carnosine to support and improve the lining of your gut.
HMB 钙可支持肌肉恢复并减少肌肉分解，肌肽锌可支持和改善肠道内壁。
All three of these ingredients have compelling science to support them, and therefore, I love seeing them added to the existing AG1 formula.
所有这三种成分都有令人信服的科学支持，因此，我喜欢看到它们添加到现有的 AG1 配方中。
As most of you know, I've been taking AG1 every day for nearly 14 years now.
正如你们大多数人所知，我每天服用 AG1 已有近 14 年了。
I started taking it long before I even knew what a podcast was.
早在我知道什么是播客之前我就开始服用它了。
It's a great product, and it's now made even better with the new AG1 Pro formula.
这是一款很棒的产品，现在新的 AG1 Pro 配方让它变得更好。
If you would like to try AG1 Pro, you can go to drinkag1.com/huberman to get a special offer.
如果您想尝试 AG1 Pro，可以访问 Drinkag1.com/huberman 获取特别优惠。
AG1 is giving away a free bottle of omega-3 coenzyme Q10 with your first subscription.
AG1 首次订阅即可免费赠送一瓶 omega-3 辅酶 Q10。
Again, go to drinkag1.com/huberman to get a free bottle of omega-3 coenzyme Q10 with your first AG1 subscription.
同样，请访问 Drinkag1.com/huberman，首次订阅 AG1 即可免费获得一瓶 omega-3 辅酶 Q10。
Today's episode is also brought to us by LMNT.
今天的节目也是LMNT给我们带来的。
LMNT is an electrolyte drink that has everything you need and nothing you don't.
LMNT 是一种电解质饮料，拥有您需要的一切，没有您不需要的东西。
That means the electrolytes, sodium, magnesium, and potassium, all in the correct ratios, but no sugar.
这意味着电解质、钠、镁和钾的比例都正确，但不含糖。
Proper hydration is critical for brain and body function.
适当的水合作用对于大脑和身体功能至关重要。
Even a slight degree of dehydration can diminish your cognitive and physical performance.
即使是轻微程度的脱水也会降低您的认知和身体表现。
It's also important that you get adequate electrolytes.
获得充足的电解质也很重要。
The electrolytes, sodium, magnesium, and potassium, are vital for the functioning of all cells in your body, especially your neurons or your nerve cells.
电解质（钠、镁和钾）对于体内所有细胞（尤其是神经元或神经细胞）的功能至关重要。
Drinking LMNT makes it very easy to ensure that you're getting adequate hydration and adequate electrolytes.
饮用 LMNT 可以轻松确保您获得足够的水分和电解质。
My days tend to start really fast, meaning I have to jump right into work or right into exercise.
我的日子往往开始得很快，这意味着我必须立即投入工作或立即锻炼。
So to make sure that I'm hydrated and I have sufficient electrolytes, when I first wake up in the morning, I drink 16 to 32 ounces of water with an LMNT packet dissolved in it.
因此，为了确保我有足够的水分和足够的电解质，当我早上第一次醒来时，我会喝 16 至 32 盎司的水，其中溶解有 LMNT 包。
I also drink LMNT dissolved in water during any kind of physical exercise that I'm doing, especially on hot days when I'm sweating a lot and losing water and electrolytes.
我还会在进行任何体育锻炼时饮用溶解在水中的 LMNT，尤其是在炎热的日子里，当我大量出汗并失去水和电解质时。
LMNT has a bunch of great-tasting flavors.
LMNT 有很多美味的口味。
In fact, I love them all.
事实上，我爱他们所有人。
I love the watermelon, the raspberry, the citrus, and I really love the lemonade flavor.
我喜欢西瓜、覆盆子、柑橘，而且我真的很喜欢柠檬水的味道。
So if you'd like to try LMNT, you can go to drinklmnt.com/huberman to claim a free LMNT sample pack with any purchase.
因此，如果您想尝试 LMNT，可以访问 Drinklmnt.com/huberman，购买任意商品即可领取免费的 LMNT 样品包。
Again, that's drinklmnt.com/huberman to claim a free sample pack.
再次强调，请访问 Drinklmnt.com/huberman 索取免费样品包。
The idea that technologies can be connectors as opposed to separators, I think, has to sit at the center of the discussion.
我认为，技术可以成为连接器而不是分离器的想法必须成为讨论的中心。
Dr. Fei-Fei Li:
李飞飞博士：
Yes.
是的。
Andrew Huberman:
安德鲁·休伯曼：
And we all know who they are.
我们都知道他们是谁。
There are several of them, but the big names in this field, they are also in a developmental process where they're learning how to be public-facing, and it happens very fast.
他们有好几个，但是这个领域的大牌人物，他们也正处于发展过程中，他们正在学习如何面向公众，而且发生得非常快。
The microscope is on them, and the cameras are on them.
显微镜在他们身上，摄像机在他们身上。
And so every subtle dysfunction is magnified.
因此，每一个细微的功能障碍都会被放大。
So I like to think that they will mature quickly enough to realize that, and I think they are, that some are, that the public needs to hear the correct, the true message, but in a way that makes them understand.
因此，我认为他们会很快成熟起来，认识到公众需要听到正确、真实的信息，但要以一种让他们理解的方式，而且我认为他们确实是这样。
That's the dirty secret of medicine and academia, that you break this mold, I like to think I break this mold, is that there's a power in not sharing how things work.
这是医学和学术界的肮脏秘密，你打破了这个模式，我喜欢认为我打破了这个模式，那就是不分享事物如何运作是有力量的。
Dr. Fei-Fei Li:
李飞飞博士：
Yep.
是的。
Andrew Huberman:
安德鲁·休伯曼：
But it doesn't serve anybody well.
但这对任何人都没有好处。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
At the end of the day, you pull back the veil and let people in, and people feel safer.
一天结束时，你拉开面纱，让人们进来，人们就会感到更安全。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
There is a power in not sharing.
不分享是一种力量。
There's also a power to say, "Just trust me, I will tell you." And neither as educators, that is, we don't go to our lectures and say, "Just trust me, two plus two equals four." We actually say, "Here's how you break it down and learn about it, so next time you can do it yourself." Right?
还有一种力量可以说：“相信我，我会告诉你。”作为教育工作者，也就是说，我们不会去听讲座并说：“相信我，二加二等于四。”我们实际上会说，“这就是你如何分解并了解它的方法，这样下次你就可以自己做。”正确的？
I also think that especially your podcast is so important as part of public communication and education of knowledge.
我还认为，特别是您的播客作为公共传播和知识教育的一部分非常重要。
I also think that we need to hear voices of different background, right?
我也觉得我们需要听到不同背景的声音，对吗？
Because there are plenty of scholars, technologists, builders, thinkers out there who have been dealing with AI, using AI, thinking hard about how to use AI to empower people, and these voices are so important.
因为有很多学者、技术专家、建设者、思想家一直在研究人工智能、使用人工智能，认真思考如何利用人工智能来赋予人们权力，这些声音非常重要。
Andrew Huberman:
安德鲁·休伯曼：
Well, certainly I'll take names of people to host-
好吧，我当然会记下要主持的人的名字-
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
in addition to you, but since you're here, I'm going to go next to something that I think most everybody would agree would be a wonderful thing if it existed, and it's already starting to happen, which is the use of AI to augment health discovery, treatment of disease, and so on.
除了你们之外，但既然你们都在这里，我接下来要谈谈我认为大多数人都会同意的一件事，如果它存在的话，那将是一件美妙的事情，而且它已经开始发生，那就是使用人工智能来增强健康发现、疾病治疗等。
So using the AlphaGo example from before, and people surely still remember the cat example, those just follow certain rules.
所以用以前的 AlphaGo 例子，人们肯定还记得猫的例子，那些只是遵循一定的规则。
AlphaGo is a very complicated set of rules, but if you learn them, there's a constrained set of rules.
AlphaGo 是一套非常复杂的规则，但是如果你学会了它们，就会有一套受约束的规则。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
With the cat, it seems unconstrained, like infinite possibilities, but it's constrained enough that machines and humans can learn it really well.
对于猫来说，它似乎不受限制，就像无限的可能性，但它的限制足以让机器和人类能够很好地学习它。
When you start getting into medicine-
当你开始涉足医学领域时——
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm
嗯嗯
Andrew Huberman:
安德鲁·休伯曼：
...
...
there are rules of medicine, there are rules of science.
有医学规则，有科学规则。
You have a question, you pose a hypothesis.
你有一个问题，你提出一个假设。
You test the hypothesis, you try to rule out your hypothesis, and so on, like the scientific method.
你检验假设，尝试排除你的假设，等等，就像科学方法一样。
And in medicine, every field has its methods.
而在医学上，每个领域都有它的方法。
We observe, we observe disease, we observe who recovers, we have a case report, we do a randomized controlled trial.
我们观察，我们观察疾病，我们观察谁康复，我们有病例报告，我们进行随机对照试验。
So there are rules, and the internet knows these rules.
所以有规则，并且互联网知道这些规则。
So LLMs can be used to mine health information very well because there are constrained rules.
因此，法学硕士可以很好地用于挖掘健康信息，因为有约束规则。
But I think you and I both know, because I also consider you a biologist, that the rules of biology are still revealing themselves to us.
但我想你和我都知道，因为我也认为你是一名生物学家，生物学的规则仍在向我们揭示自己。
Which is not to say that the dermatologists, neurosurgeons, and oncologists don't know what they're doing, but they're doing what they're doing within a constrained set of rules that they learned.
这并不是说皮肤科医生、神经外科医生和肿瘤科医生不知道他们在做什么，而是说他们是在他们学到的一套有限规则内做他们正在做的事情。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
And even if they continue to learn and update them, it's every month it seems now that a discovery comes out that violates the rule.
即使他们继续学习和更新它们，现在似乎每个月都会出现违反规则的发现。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
Like I learned that action potentials are unitary.
就像我了解到动作电位是单一的一样。
They always look the same.
他们看起来总是一样的。
You either fire or not.
你要么开火，要么不开火。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
But there was a paper not but 12 years ago that showed that the shape of an action potential can vary quite a lot.
但不到 12 年前就有一篇论文表明动作电位的形状可以有很大的变化。
Dr. Fei-Fei Li:
李飞飞博士：
Mm.
嗯。
Andrew Huberman:
安德鲁·休伯曼：
It was published in "Nature."
它发表在《自然》杂志上。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
Everyone saw it, and then no one wanted to deal with it.
所有人都看到了，然后就没有人愿意去对付它了。
It's just too much.
实在是太多了。
It changes the rule.
它改变了规则。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
Neurons are supposed to be either graded or all or one, and the all or-- It's in every single textbook.
神经元应该是分级的，或者全部或一个，而全部或——它在每一本教科书中都有。
So now if I take a bunch of neural activity and I give it the rule, oh, well, action potentials can be big, they can be small in the same neuron, it completely confuses everything we understand about neuroscience.
所以现在如果我采取一堆神经活动并给它规则，哦，好吧，动作电位在同一个神经元中可以很大，也可以很小，这完全混淆了我们对神经科学的理解。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
And our understanding of the brain just breaks down to zero.
而我们对大脑的理解就降到了零。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
But if you gave AI the rule that it could be 100 different shapes of the signal, well, AI could probably do a lot more than even the very, very best graduate student at, dare I say, Stanford or, to be fair, MIT or Caltech.
但如果你给人工智能一个规则，它可以是 100 种不同形状的信号，那么，人工智能可能比斯坦福大学（我敢说）或者公平地说，麻省理工学院或加州理工学院的非常非常优秀的研究生做得更多。
I don't think-- It can do it, and it can do it in the duration of this question-
我不认为——它能做到，而且在这个问题的持续时间内它能做到——
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
which admittedly is a bit long.
诚然，这有点长。
So I'd like to get your thoughts on how is it that humans in healthcare, the general public, and AI can collaborate to help solve disease and ideally come up with new rules for discovery so that we can finally understand our biology at a level that can really change the course of humanity for the better.
因此，我想了解一下您对医疗保健领域的人类、公众和人工智能如何能够合作帮助解决疾病，并在理想情况下提出新的发现规则，以便我们最终能够在一定程度上了解我们的生物学，从而真正使人类的进程变得更好。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
No, Andrew, this is probably, perhaps you touched one of the most exciting usage of AI, which is scientific discovery.
不，安德鲁，这可能是，也许你接触到了人工智能最令人兴奋的用途之一，那就是科学发现。
And in the case of biomedicine, scientific discovery directly connects to human health and diseases.
就生物医学而言，科学发现直接关系到人类健康和疾病。
I think we're ready for a complete rewriting of how scientific discovery can be done.
我认为我们已经准备好彻底重写科学发现的方式。
Because for ages, I don't even know how long, it relies on smart humans retaining what they have learned from other smart humans-
因为多年来，我什至不知道多久，它依赖于聪明的人类保留他们从其他聪明的人类那里学到的东西——
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm
嗯嗯
Dr. Fei-Fei Li:
李飞飞博士：
...
...
and doing things at the speed of our own muscles, I guess.
我想，并以我们自己肌肉的速度做事。
Andrew Huberman:
安德鲁·休伯曼：
Yeah.
是的。
Dr. Fei-Fei Li:
李飞飞博士：
Most likely.
最有可能的是。
Of course, there's super colliders and all that.
当然，还有超级对撞机等等。
But by and large, the ways of doing scientific discovery, human brain or scientist brain are the only central character in this process.
但总的来说，进行科学发现的方式、人脑或科学家的大脑是这个过程中唯一的核心特征。
Now we have a new tool whose brain that can retain humongous amount of information, can help us synthesize knowledge, can go across disciplines in ways that you and I cannot go.
现在我们有了一种新工具，它的大脑可以保留大量信息，可以帮助我们综合知识，可以以你我无法做到的方式跨越学科。
So for example, we happen to be both in the vision neuroscience AI domain, I know nothing about olfactory.
举个例子，我们恰好都在视觉神经科学人工智能领域，我对嗅觉一无所知。
Zero.
零。
Like, I don't even know how to spell most of probably these words that our colleagues know, right?
就像，我什至不知道如何拼写我们同事知道的大部分单词，对吧？
So it's so hard for our brain.
所以这对我们的大脑来说太难了。
But now we have a tool that can break open.
但现在我们有了一个可以破解的工具。
So, I think that we need to change.
所以，我认为我们需要改变。
We need to use this tool.
我们需要使用这个工具。
I was just thinking 150 or, I don't know exactly when, years ago, electricity changed everything in our life, right?
我只是在想 150，或者，我不知道几年前，电力到底是什么时候改变了我们生活中的一切，对吗？
I'm sure that's a moment we were thinking about how the changes, the opportunities, the scary moment.
我确信那一刻我们正在思考变化、机遇和可怕的时刻。
I think we have to come to reckon that scientific discovery is one of the most exciting opportunity for AI and for health, right?
我认为我们必须认识到科学发现是人工智能和健康最令人兴奋的机会之一，对吧？
How information can be synthesized, how information can be presented, not only to clinicians, but also to patients, and how patients can participate in that process from diagnosis to treatment, there's just so much we can do now.
如何合成信息，如何向临床医生和患者呈现信息，以及患者如何参与从诊断到治疗的过程，我们现在能做的就这么多。
Andrew Huberman:
安德鲁·休伯曼：
Yeah.
是的。
I mean, AI, I won't say AI is better than all doctors, but AI was able to disambiguate vertigo from low blood pressure for me a few months back, and one of the people who got it wrong is a ENT who works on the vestibular system.
我的意思是，人工智能，我不会说人工智能比所有医生都好，但几个月前，人工智能能够为我区分眩晕和低血压，而犯错的人之一是一位从事前庭系统工作的耳鼻喉科医生。
Dr. Fei-Fei Li:
李飞飞博士：
What information did you provide?
您提供了什么信息？
Just your subjective feeling?
只是你的主观感受？
Andrew Huberman:
安德鲁·休伯曼：
My subjective experience over a day or two.
我一两天的主观体验。
Dr. Fei-Fei Li:
李飞飞博士：
Okay.
好的。
Andrew Huberman:
安德鲁·休伯曼：
Turns out it was a medication that a doctor had prescribed me that I had a mild but adverse event.
原来这是医生给我开的一种药物，我有轻微但不良的反应。
Dr. Fei-Fei Li:
李飞飞博士：
Mm.
嗯。
Andrew Huberman:
安德鲁·休伯曼：
And it's a weird thing to step and feel like the whole world's dropping down-
迈出一步，感觉整个世界都在塌陷，这是一件很奇怪的事情——
Dr. Fei-Fei Li:
李飞飞博士：
Right
右
Andrew Huberman:
安德鲁·休伯曼：
...
...
and then kind of spinning, and I thought, "My goodness, this feels like vertigo," but I remember dizzy and lightheaded are different, so I started looking into that.
然后有点旋转，我想，“天哪，这感觉就像眩晕，”但我记得头晕和头晕是不同的，所以我开始研究这个。
Dr. Fei-Fei Li:
李飞飞博士：
Right.
对。
Andrew Huberman:
安德鲁·休伯曼：
And then sure enough, it was a blood pressure issue.
果然，这是血压问题。
Dr. Fei-Fei Li:
李飞飞博士：
Wow.
哇。
Andrew Huberman:
安德鲁·休伯曼：
It brought my blood pressure, excuse me, down too low.
对不起，它让我的血压降得太低了。
But I consulted.
但我咨询过。
We know some smart doctors.
我们认识一些聪明的医生。
None of these were at Stanford, I will say that.
我想说的是，这些都不是斯坦福大学的。
This is the truth.
这是事实。
But it was-
但那是——
Dr. Fei-Fei Li:
李飞飞博士：
No, we should just be intellectually honest.
不，我们应该在理智上保持诚实。
Andrew Huberman:
安德鲁·休伯曼：
But it's just remarkable.
但这真是太了不起了。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
And when I ran it back to them, they were like, "That's really incredible.
当我把它反馈给他们时，他们说：“这真是令人难以置信。
Had you not been on the phone with me and in my clinic, I would've been able to do some additional testing," to be fair.
如果你没有和我通电话并在我的诊所里，我本来可以做一些额外的测试，”公平地说。
But this was zero cost.
但这是零成本。
It took a morning to know if I drank some electrolytes at what I would've thought would be excessive level, that by two hours later, I would be fine.
我花了一个早上才知道我是否喝了一些我认为过量的电解质，两个小时后我就会没事了。
Now, of course, there's the possibility of a placebo effect here, but two hours later, I was fine.
现在，当然，这里可能存在安慰剂效应，但两个小时后，我就没事了。
And so it's also very consoling to the patient-
所以这对病人来说也是非常安慰的——
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
to have this.
拥有这个。
And so it's not to say don't go to a doctor, but it's incredible.
所以这并不是说不要去看医生，但这令人难以置信。
This exists now.
现在就存在这种情况。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
The doctor can use this tooling.
医生可以使用这个工具。
By the way, I have a very interesting example.
顺便说一句，我有一个非常有趣的例子。
You know that we have to reschedule our conversation because my father was going through a surgery at Stanford with an incredible surgeon.
你知道我们必须重新安排我们的谈话，因为我父亲正在斯坦福大学接受一位出色的外科医生的手术。
But the surgery was done by a robot, the da Vinci robot system, because it was a liver surgery, and the surgeon, incredible surgeon, was driving the robot.
但手术是由机器人、达芬奇机器人系统完成的，因为这是肝脏手术，而外科医生，令人难以置信的外科医生，正在驾驶机器人。
So it was a deep human-machine collaboration.
所以这是一次深入的人机协作。
After the surgery, I asked the surgeon, I said, "Do you imagine if, say, you've done a million," which is impossible for a surgeon, but a human surgeon, "but let's collect all of human surgeons for this type of liver surgery data.
手术结束后，我问外科医生，我说，“你想象一下，如果，比如说，你做了一百万，”这对于外科医生来说是不可能的，但是对于人类外科医生来说，“但是让我们收集所有人类外科医生进行此类肝脏手术的数据。
Can we possibly train a automatic AI to do this?" The answer was not clear.
我们能否训练一个自动人工智能来做到这一点？”答案并不清楚。
Andrew Huberman:
安德鲁·休伯曼：
Hmm.
嗯。
Dr. Fei-Fei Li:
李飞飞博士：
So we went a little bit down a rabbit hole because liver is a very complicated organ.
所以我们有点陷入了兔子洞，因为肝脏是一个非常复杂的器官。
It's extremely vascular.
它的血管非常丰富。
It has a lot of vessels, and everybody's liver is very different.
它有很多血管，每个人的肝脏都有很大不同。
So given the reality of how many patients undergo liver surgery per year, even if you aggregate the world's liver patient surgeries, you might not have enough data to train this algorithm.
因此，考虑到每年有多少患者接受肝脏手术的现实，即使您汇总了世界各地的肝脏患者手术，您也可能没有足够的数据来训练该算法。
So this speaks of a very important fact that AI learns from patterns.
所以这说明了一个非常重要的事实：人工智能从模式中学习。
When the patterns are not abundant, then we have to be careful.
当图案不丰富的时候，我们就要小心了。
We have to know how to use AI or how not to use AI.
我们必须知道如何使用人工智能或如何不使用人工智能。
In this case, that having a human collaborating with the robot is way better than a under-learned robot doing the surgery by itself.
在这种情况下，人类与机器人合作比学习不足的机器人独自完成手术要好得多。
But the same issue might be true for surgeons, because how many surgeries a surgeon can get trained on?
但同样的问题对于外科医生来说可能也是如此，因为外科医生可以接受多少次手术培训？
So these are opportunities that humans and AI can totally collaborate with and might reveal the best result.
因此，这些都是人类和人工智能可以完全合作的机会，并且可能会揭示出最好的结果。
Right?
正确的？
Now, the future remains to be seen.
现在，未来仍有待观察。
Can we create a artificial simulation of a liver that we can now train infinite possibility?
我们能否创建一个人工模拟的肝脏，让我们现在可以训练无限的可能性？
These are all incredibly open scientific possibilities that is waiting ahead of us.
这些都是令人难以置信的开放科学可能性正在等待着我们。
But then there are situations like your situation where the vertigo versus low blood pressure probably have been reported so many times that in the database, there's enough of that, that AI has learned that.
但在某些情况下，比如你的情况，眩晕与低血压可能已经被报告了很多次，以至于数据库中已经有足够多的信息，人工智能已经了解到这一点。
So we can then now take advantage of that for people who don't have immediate access to doctors.
因此，我们现在可以为那些无法立即就医的人利用这一点。
Andrew Huberman:
安德鲁·休伯曼：
Amazing.
太棒了。
Your father's surgery went okay?
你父亲的手术顺利吗？
Dr. Fei-Fei Li:
李飞飞博士：
It did.
确实如此。
Andrew Huberman:
安德鲁·休伯曼：
Great.
太棒了。
Dr. Fei-Fei Li:
李飞飞博士：
It actually-
它实际上——
Andrew Huberman:
安德鲁·休伯曼：
I'm happy to hear that
我很高兴听到这个消息
Dr. Fei-Fei Li:
李飞飞博士：
...
...
he lost 10X less blood than a typical surgery, thanks to the laparoscopic capability of a robot surgery.
由于机器人手术的腹腔镜功能，他的失血量比典型手术少 10 倍。
Andrew Huberman:
安德鲁·休伯曼：
Amazing.
太棒了。
I'd like to talk a little bit about some features that we think are uniquely human that may or may not be.
我想谈谈一些我们认为是人类独有的特征，但它们可能是也可能不是。
You'll tell me.
你会告诉我的。
These are genuine questions-
这些都是真实的问题——
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
not loaded questions.
未加载的问题。
And then I'd also like to get educated on how AI is structured to allow these things to happen.
然后我还想了解人工智能的结构如何让这些事情发生。
For instance, intuition.
例如，直觉。
We all like to think of intuition as this mystical, it certainly is powerful, but this thing that we own that no one can take from us, that can't be mimicked, kind of thing.
我们都喜欢认为直觉是神秘的，它当然是强大的，但这种东西是我们拥有的，没有人可以从我们身上夺走，不能被模仿，诸如此类的东西。
But I could also break intuition down to be, well, it's my experience over time, it's a data set, coupled to some bodily and brain sensations, and some prediction cues.
但我也可以将直觉分解为，嗯，这是我随着时间的推移的经验，它是一个数据集，加上一些身体和大脑的感觉，以及一些预测线索。
Like, the last time I felt this, this happened.
就像，上次我有这种感觉时，这件事就发生了。
The last two times I felt that, things didn't work out that way, so I'm going to go this way.
过去两次我觉得事情不会那样发展，所以我要走这条路。
You could assign these rules to a computer.
您可以将这些规则分配给计算机。
But there are other aspects of our deeper self, if I can refer to them that way.
但我们更深层的自我还有其他方面，如果我可以这样说的话。
We don't know where intuition is mapped in the body.
我们不知道直觉在身体中的映射位置。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Yep.
是的。
Andrew Huberman:
安德鲁·休伯曼：
Could do an imaging experiment, but you're not going to collect all the neurons and hormones and everything simultaneously, so we don't really have a location or even a network to point to.
可以做一个成像实验，但你不会同时收集所有神经元和激素以及所有东西，所以我们实际上没有一个位置，甚至没有一个网络可以指向。
Things like creativity, intuition, premonition, the idea that you really sense something is coming on, but it hasn't happened yet.
比如创造力、直觉、预感，你确实感觉到某件事正在发生，但它还没有发生。
What sorts of rules can AI get that could give it these sorts of capabilities?
人工智能可以获得哪些类型的规则来赋予它这些类型的能力？
And here, I want to talk about it in the context, if you will, of energy.
在这里，如果你愿意的话，我想在能源的背景下谈论它。
So whatever this thing is, it's like mitochondria driving cells more around one thing versus another, the same way fear or happiness would, right?
所以无论这东西是什么，它就像线粒体驱动细胞更多地围绕一件事而不是另一件事，就像恐惧或幸福一样，对吗？
We're just talking about energy.
我们只是在谈论能源。
But within AI systems, and I'm not a computer scientist, within AI systems and GPUs, can we actually allocate more energetic flow through particular learning rules?
但在人工智能系统中，我不是计算机科学家，在人工智能系统和 GPU 中，我们实际上可以通过特定的学习规则分配更多的能量流吗？
So we could tell maybe someday, based on everything you know about my sister, who I love, what is your intuition about how our brother-sister relationship will evolve over time?
所以也许有一天，根据你对我爱的妹妹的了解，你对我们兄妹关系将如何随着时间的推移而发展的直觉是什么？
And what is your sense about what would be great for us to do, perhaps, for our birthdays this year that's different than before?
您认为今年的生日与以往不同，我们应该做什么？
And it only has access to the internet.
而且它只能访问互联网。
Can it actually become mind-like or mind-body-like, and come up with a sense of what might actually be worthwhile?
它真的能变得像心智一样或像心身一样，并产生一种真正有价值的感觉吗？
Or does it just need more and more prompts?
还是只是需要越来越多的提示？
It's just going to keep asking me questions, so I'm actually doing the work.
它只会不断问我问题，所以我实际上正在做这项工作。
Dr. Fei-Fei Li:
李飞飞博士：
Such an interesting question, Andrew.
这是一个有趣的问题，安德鲁。
So, I do want to separate intuition from creativity for the sake of argument here, and maybe we'll come back to merging.
所以，为了在这里争论，我确实想将直觉和创造力分开，也许我们会回到合并上来。
So let's talk about this intuition of, given my sibling love, what's going to happen, right?
那么让我们谈谈这种直觉，考虑到我的兄弟姐妹之爱，将会发生什么，对吗？
Is it really intuition?
真的是直觉吗？
So today, when you go to an AI chatbot, you're going to prompt, "I'm a Stanford professor and a neuroscientist.
所以今天，当你使用人工智能聊天机器人时，你会提示：“我是斯坦福大学的教授和神经科学家。
Give me this information." That is already called context.
把这个信息给我。”这已经称为上下文。
I don't know if you'd call it intuition, but because you gave that piece of information, the AI's answer for you is already going to be different if I type that I'm a 14-year-old teenager loving race cars.
我不知道你是否会称之为直觉，但因为你提供了这条信息，如果我输入“我是一个热爱赛车的 14 岁青少年”，人工智能给你的答案就会有所不同。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
Even if we ask the same question, it'll have customized answer.
即使我们问同样的问题，它也会有定制的答案。
That is a mathematical, I wouldn't call it energy.
这是一个数学问题，我不会称之为能量。
That is just a mathematical fact of how these algorithms takes these context and tailor the outputs.
这只是这些算法如何获取这些上下文并定制输出的数学事实。
And it's called context.
这就是所谓的上下文。
It's not that deep in computer science.
计算机科学并不是那么深入。
That's one type of intuition that is fairly shallow because you already are able to use language to describe it.
这是一种相当肤浅的直觉，因为你已经能够使用语言来描述它。
Or you can say, "I'll upload an image," that also is already expressible, and then AI gets it.
或者你可以说，“我将上传一张图像”，这也是可以表达的，然后人工智能就会得到它。
The deeper intuition you just said is like you don't even know where they come from, right?
你刚才说的更深层次的直觉就像你根本不知道它们来自哪里，对吧？
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
Is it because I smell something?
是因为我闻到了什么味道吗？
Is it hormones?
是荷尔蒙吗？
Is it the mixture of food?
是食物的混合物吗？
Is it my breakfast?
这是我的早餐吗？
That intuition, what would AI do with it?
这种直觉，人工智能会用它做什么？
That is what I would say is inaccessible.
这就是我所说的不可接近的。
There's no sensory apparatus yet that can glean that data and feed it to not only AI, cannot even feed it to...
目前还没有任何感官设备可以收集这些数据并将其提供给人工智能，甚至无法将其提供给......
For example, sometimes as a couple, you might have moment that you're just rubbing each other in the wrong way.
例如，有时作为情侣，你们可能会有这样的时刻：你们只是以错误的方式互相摩擦。
Andrew Huberman:
安德鲁·休伯曼：
Never.
从来没有。
No, I'm just kidding.
不，我只是开玩笑。
Dr. Fei-Fei Li:
李飞飞博士：
Right?
对吗？
Andrew Huberman:
安德鲁·休伯曼：
Yeah, of course.
是的，当然。
Dr. Fei-Fei Li:
李飞飞博士：
So if you're really familiar with each other, you kind of can sense it, but you can't quite tell.
所以如果你们真的很熟悉，你可以感觉到，但又说不出来。
Maybe you just leave quietly, leave that person alone.
也许你就静静地离开，让那个人一个人呆着。
So that means whatever that intuition that person has, they could not even express it in words or a gesture to give it to another person to use as a piece of information.
因此，这意味着无论该人有什么直觉，他们甚至无法用言语或手势表达出来，将其提供给另一个人作为信息使用。
So when you cannot even access that, neither a different human nor a machine can do anything about it, because there's no access to that highly individualized intuition.
因此，当你甚至无法访问它时，其他人或机器都无法对此做任何事情，因为无法获得高度个性化的直觉。
There's no technology that can do that till you say we put brainwave collectors or skin conductance sensors.
没有任何技术可以做到这一点，除非你说我们放置了脑电波收集器或皮肤电导传感器。
By the time we do those, maybe they become accessible.
当我们做到这些的时候，也许它们就变得可以访问了。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
So we have to recognize, so what I'm trying to say here is what's not very deep is, is the data accessible?
所以我们必须认识到，所以我在这里想说的是不太深入的是，数据是否可访问？
Either through language or through picture or through imaging or through brainwaves, whatever it is, it needs to be an accessible piece of information.
无论是通过语言、图片、图像还是脑电波，无论是什么，它都必须是易于理解的信息。
If it's accessible, then If we have collected enough of that, you can train machines with, or if a machine is well-trained, it can, like you said, in a private way, forget about privacy breach, but in a private way, the machine can probably use it.
如果它是可访问的，那么如果我们收集了足够的数据，你可以训练机器，或者如果机器训练有素，它可以像你说的那样，以私密的方式忘记隐私泄露，但以私密的方式，机器可能可以使用它。
What I'm trying to do, Andrew, here is not to make it sound mystical.
安德鲁，我想做的并不是让它听起来很神秘。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
But try to give it a scientific process to describe if it were to happen, how would that happen?
但尝试给它一个科学的过程来描述如果它发生，那会如何发生？
Andrew Huberman:
安德鲁·休伯曼：
Yeah, because pattern recognition based on big data sets and rules get us a long way-
是的，因为基于大数据集和规则的模式识别让我们走得很远-
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
is what I'm hearing.
这就是我所听到的。
And earlier, we were talking about where doctors fail and robots and machines perhaps do better, or they collaborate to do better than either one alone.
早些时候，我们讨论了医生失败的地方，而机器人和机器可能做得更好，或者他们合作比单独做的更好。
As a neuroscientist, you spend a lot of time looking at cells at some point in your career, and it's amazing how the electrophysiologists for decades, if not longer, you develop an intuition.
作为一名神经科学家，你在职业生涯的某个阶段会花费大量时间研究细胞，令人惊讶的是，电生理学家几十年（甚至更长时间）如何培养你的直觉。
Dr. Fei-Fei Li:
李飞飞博士：
Yes.
是的。
Andrew Huberman:
安德鲁·休伯曼：
I'm not really a physiologist, but I learned to recognize cells based on these things that were not written up in any papers.
我并不是真正的生理学家，但我学会了根据这些没有写在任何论文中的东西来识别细胞。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
But if there was a straighter edge along this thing, and it had a certain shape and roundness, I'd tell you right now, that's a transient off alpha cell in the retina.
但如果这个东西有一个更直的边缘，并且它有一定的形状和圆度，我现在就告诉你，那是视网膜中短暂的α细胞脱落。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
Eventually, we developed genetic labels to reveal that that was true in every case, but then you also saw some that didn't fit the rule.
最终，我们开发了基因标签来揭示在每种情况下都是如此，但随后您也会看到一些不符合规则的情况。
Machines can learn that, computers can learn that, and with all that information from all those papers, now we have a pretty good parts list-
机器可以学习这一点，计算机也可以学习这一点，并且有了所有这些论文中的所有信息，现在我们有了一个非常好的零件清单 -
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
of the retina.
视网膜的。
Cool.
凉爽的。
That works.
那行得通。
And then you can apply rules like they fire this way, they fire that way.
然后你可以应用规则，比如它们以这种方式触发，他们以那种方式触发。
Okay.
好的。
I'm good with all of that.
我对这一切都很满意。
What I think I was trying to get to with intuition, and I probably didn't give the best example, is what are some internal states of humans that are really hard to imagine machines could recapitulate, but perhaps they can?
我认为我试图用直觉来表达，而且我可能没有给出最好的例子，人类的一些内部状态确实很难想象机器可以重现，但也许它们可以？
Like motivation.
就像动机一样。
Do machines, do robots get motivated?
机器、机器人有动力吗？
We have rules of motivation, like when I'm really motivated to do something, we call that urgency, a state of urgency, and I might move faster to do it, less activation energy.
我们有动机规则，就像当我真正有动力去做某事时，我们称之为紧迫感，一种紧迫状态，我可能会更快地去做这件事，更少的激活能量。
You say, "Let's go," I stand up a little bit faster.
你说：“走吧。”我站得更快了一些。
Machines could go quicker in a certain direction.
机器可以在某个方向上运行得更快。
But can you say, "Hey, I want you to seek this out, but with a heightened level of urgency"?
但你能说，“嘿，我希望你找出这个问题，但要更加紧迫”？
Or are they just constrained by the mathematical rules they can work with?
或者他们只是受到他们可以使用的数学规则的限制？
Dr. Fei-Fei Li:
李飞飞博士：
So you could build this in the mathematics.
所以你可以用数学来构建这个。
So certain things, whether you call it motivation, or in machine learning world, we call them objective functions, you can build certain things into math.
所以某些东西，无论你称之为动机，还是在机器学习世界中，我们称之为目标函数，你都可以将某些东西构建到数学中。
For example, now you go to, say, ChatGPT, it has different mode, like think deeper mode or give me a quick answer mode.
例如，现在你去ChatGPT，它有不同的模式，比如深入思考模式或给我快速回答模式。
If you don't know how this works, you're like, "Oh, this is interesting." One has more urgency that gives me a quicker-
如果你不知道这是如何工作的，你会说，“哦，这很有趣。”一个人有更多的紧迫感，这让我更快——
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm
嗯嗯
Dr. Fei-Fei Li:
李飞飞博士：
...
...
answer.
回答。
The other one has to go deeper into the search, right?
另外一个还得更深入的寻找吧？
And take longer to give me the answer.
并且需要更长的时间才能给我答案。
So as a human, if you anthropomorphize it too much, you might call it urgency or motivation, but the truth is this is just a different kind of objective for the algorithm.
因此，作为一个人，如果你将其过于拟人化，你可能会称之为紧迫性或动机，但事实是，这只是算法的另一种目标。
You can say, well, the one that think quicker has a time limit or token limit.
你可以说，好吧，思考得更快的人有时间限制或令牌限制。
The one that thinks slower can activate a different part of the model that would take longer.
思考速度较慢的人可以激活模型中需要更长时间的不同部分。
So it become actually mathematically very dry and not that deep.
所以它实际上在数学上变得非常枯燥而且没有那么深。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
But for a human, you can call that motivation or urgency.
但对于人类来说，你可以称之为动机或紧迫感。
But let's go deeper, because you're asking something deeper than that, right?
但让我们更深入一点，因为你问的东西比这更深入，对吧？
Is that there are cognitive states that humans, you truly just, whether it's motivation or urgency or fear or love, that is very hard to access and express, and do machines have it?
人类确实有一些认知状态，无论是动机、紧迫感、恐惧还是爱，都很难访问和表达，而机器有吗？
Today, no. Let's make it very clear.
今天，没有。让我们说得非常清楚。
We tend to imagine that the machines feel or-- They're not.
我们倾向于想象机器有感觉，或者——它们没有。
They don't have that data.
他们没有这些数据。
They don't have that mathematical objective function.
他们没有数学目标函数。
So they can say, when the machine says, "I'm sorry you're so sick today," it's very different from how your friend says it to you, because the machine said that because it has learned through pattern.
因此，当机器说“对不起，你今天病得很厉害”时，他们可以说，这与你的朋友对你说的话非常不同，因为机器这么说是因为它是通过模式学习的。
When someone tells it, "I'm sick," you should say, "I'm sorry you're sick," instead of, "I'm so glad you're sick," because that data exists.
当有人告诉它“我病了”时，你应该说“我很抱歉你病了”，而不是“我很高兴你病了”，因为该数据存在。
Whereas your friend who hears that, they generally want your well-being.
而你的朋友听到这句话时，通常会希望你幸福。
They love you.
他们爱你。
They don't want to see you suffer.
他们不想看到你受苦。
They have that empathetic feel of, oh, wow, if you're in pain, I've experienced pain, so it's not mirror neuron, but it's at least a memory of what pain means.
他们有一种同理心，哦，哇，如果你感到疼痛，我也经历过疼痛，所以这不是镜像神经元，但它至少是对疼痛意味着什么的记忆。
The machine doesn't have any of that.
机器没有这些。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
So we do need to make sure we differentiate that.
所以我们确实需要确保我们能够区分这一点。
So a lot of what drives human, what ticks human, what triggers human, doesn't exist in today's machine.
因此，今天的机器中不存在许多驱动人类、刺激人类、触发人类的因素。
We operate fundamentally different from today's AI, and we have to recognize that, respect that, and this is where public communication is so important.
我们的运作方式与今天的人工智能根本不同，我们必须认识到这一点，尊重这一点，这就是公共沟通如此重要的地方。
We cannot confuse the public about this.
我们不能在这件事上混淆公众。
Andrew Huberman:
安德鲁·休伯曼：
I'd like to take a quick break to acknowledge one of our sponsors, David.
我想稍微休息一下，向我们的赞助商之一大卫表示感谢。
David makes protein bars unlike any other.
大卫制作的蛋白质棒与众不同。
Their newest bar, the Bronze Bar, has 20 grams of protein, only 150 calories, and zero grams of sugar.
他们最新的 Bronze Bar 含有 20 克蛋白质，仅 150 卡路里，零克糖。
I have to say, these are the best-tasting protein bars I've ever had, and I've tried a lot of protein bars over the years.
我不得不说，这些是我吃过的味道最好的蛋白质棒，这些年来我尝试过很多蛋白质棒。
These new David bars have a marshmallow base, and they're covered in chocolate coating, and they're absolutely incredible.
这些新的大卫巧克力棒有一个棉花糖底座，上面覆盖着巧克力涂层，它们绝对令人难以置信。
I, of course, eat regular whole foods.
当然，我经常吃天然食品。
I eat meat, chicken, fish, eggs, fruits, vegetables, et cetera.
我吃肉、鸡肉、鱼、鸡蛋、水果、蔬菜等等。
But I also make it a point to eat one or two David bars per day as a snack, which makes it easy to hit my protein goal of one gram of protein per pound of body weight.
但我也强调每天吃一两块大卫棒作为零食，这样很容易达到我每磅体重一克蛋白质的蛋白质目标。
And that allows me to take in the protein I need without consuming excess calories.
这使我能够摄入所需的蛋白质，而不会消耗过多的热量。
I love all the David Bronze Bar flavors, including Cookie Dough, Caramel Chocolate, Double Chocolate, Peanut Butter Chocolate.
我喜欢大卫青铜棒的所有口味，包括饼干面团、焦糖巧克力、双重巧克力、花生酱巧克力。
They all actually taste like candy bars.
它们实际上尝起来都像棒棒糖。
Again, they're amazing.
再说一次，他们太棒了。
But again, they have no sugar, and they have 20 grams of protein with just 150 calories.
但同样，它们不含糖，并且含有 20 克蛋白质，只有 150 卡路里热量。
If you'd like to try David, you can go to davidprotein.com/huberman.
如果您想尝试 David，可以访问 david Protein.com/huberman。
Right now, David is offering a deal where if you buy four cartons, you get the fifth carton for free.
现在，大卫正在提供一项优惠，如果您购买四箱，您可以免费获得第五箱。
You can also find David on Amazon or in stores such as Target, Walmart, and Kroger.
您还可以在亚马逊或 Target、沃尔玛和克罗格等商店找到 David。
Again, to get the fifth carton for free, go to davidprotein.com/huberman.
同样，要免费获得第五箱，请访问 david Protein.com/huberman。
I feel like people assume there's an emotion, a person, or whatever inside of the AI chatbot because we're so language-oriented.
我觉得人们假设人工智能聊天机器人内部有一种情感、一个人或其他任何东西，因为我们是如此以语言为导向。
Dr. Fei-Fei Li:
李飞飞博士：
Yes.
是的。
Andrew Huberman:
安德鲁·休伯曼：
It's talking to us.
它正在对我们说话。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
It's writing things to me, and we do that more now than we did 30 years ago.
它正在给我写信，我们现在比 30 年前这样做更多。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
Certainly, we've gotten very accustomed to receiving communications in fairly deprived language.
当然，我们已经非常习惯于接收用相当匮乏的语言进行的交流。
Texts are not extensive prose.
文本不是广泛的散文。
Language has changed, modes of communication have changed, more deprived as opposed to more enriched.
语言发生了变化，交流方式也发生了变化，沟通方式变得更加匮乏，而不是更加丰富。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
But at some point soon, I'm guessing faces are going to start to enter the picture.
但在不久的将来，我猜面孔将开始出现。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
No pun intended.
没有双关语的意思。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
How far off are we from if you or I were to text the other person, "Oh, see you on campus for coffee next week at this time." How soon is it that that text is going to be actually a photo or video-like image of you just talking to me, telling me that?
如果你或我给对方发短信“哦，下周这个时候在校园喝咖啡见”，我们离我们还有多远。多久之后，该文本实际上就会成为您刚刚跟我说话、告诉我这些的照片或类似视频的图像？
This would be trivial to do nowadays.
如今，这件事是微不足道的。
Dr. Fei-Fei Li:
李飞飞博士：
The technology is there.
技术就在那里。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
But we have to now zoom out a little bit and think about the social parameters, the legal implications.
但我们现在必须把目光放远一点，思考社会因素和法律影响。
Humans are capable of doing a lot of things with our tools, but we don't do all of them.
人类能够利用我们的工具做很多事情，但我们并不能做所有的事情。
For example, today, any car manufacturer can say, "Every Friday, the brake doesn't work." This is a trivial technology.
例如，今天，任何汽车制造商都可以说：“每个星期五，刹车都不起作用。”这是一项微不足道的技术。
There's a clock in the car's computer, and it just turns off the brake every Friday.
汽车电脑里有一个时钟，每周五它就会关闭刹车。
But we don't do that because it has deeply bad implications to our human society.
但我们不这样做，因为它对我们的人类社会有着深远的不良影响。
That's where rules comes in, laws come in, social norm comes in, morality comes in, and I think this is where we exit the pure technical discussion of AI and need to enter the social discussion of AI.
这就是规则进来的地方，法律进来的地方，社会规范进来的地方，道德进来的地方，我认为这就是我们退出人工智能的纯技术讨论并需要进入人工智能的社会讨论的地方。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Well, let's do that because one thing that I know about biologists or technologists is they like to go fast because it's exciting.
好吧，让我们这样做吧，因为我知道生物学家或技术专家的一件事是他们喜欢走快，因为这令人兴奋。
It's the next edge.
这是下一个边缘。
I remember long ago, I had a friend who was studying viruses and ways of putting-- These weren't infectious disease viruses.
我记得很久以前，我有一个朋友正在研究病毒和放置方式—— 这些不是传染病病毒。
These were viral vectors for getting genes expressed as experimental tools in animals.
这些是用于获得基因表达的病毒载体，作为动物实验工具。
But there came the opportunity to actually put the rabies virus, a modified rabies virus, into Drosophila, into fruit flies.
但现在有机会将狂犬病病毒（一种改良的狂犬病病毒）真正放入果蝇体内。
Dr. Fei-Fei Li:
李飞飞博士：
Oh, my God.
哦，天啊。
Andrew Huberman:
安德鲁·休伯曼：
Now, that's fine and good, in my opinion, if you are absolutely certain, 100% certainty, that that is a non-functional version of the rabies virus because-
现在，在我看来，如果你绝对确定，100%确定，那是狂犬病病毒的非功能版本，那很好，因为——
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
you can put other cargo in there and do all sorts of important experiments on, believe it or not, disease and things like that.
不管你信不信，你可以把其他货物放在那里，对疾病和类似的事情进行各种重要的实验。
But if there's just one fruit fly that somehow-
但如果只有一只果蝇——
Dr. Fei-Fei Li:
李飞飞博士：
Mutated
变异的
Andrew Huberman:
安德鲁·休伯曼：
...
...
is an escape or you get the actual rabies virus, there's the potential it mates with another, and then they eventually find the others.
是一种逃避，或者你感染了真正的狂犬病病毒，它有可能与另一种病毒交配，然后它们最终会找到其他病毒。
I don't know if this would be a dominant or recessive situation, but now you have fruit flies with rabies, and those things move really fast.
我不知道这是显性还是隐性情况，但现在果蝇感染了狂犬病，而且这些情况进展得非常快。
So there's a reason why you don't do that experiment.
所以你不做这个实验是有原因的。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
But it was exciting for them to think about, and then they got denied.
但他们想想就很兴奋，然后就被拒绝了。
For good reason.
有充分的理由。
Dr. Fei-Fei Li:
李飞飞博士：
Yes.
是的。
Andrew Huberman:
安德鲁·休伯曼：
I was grateful, right?
我很感激，对吧？
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
Go to any biology department, you're going to see some fruit flies flying around.
去任何一个生物系，你都会看到一些果蝇飞来飞去。
They love vinegar, by the way.
顺便说一句，他们喜欢醋。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
So they're coming to your salad.
所以他们会来吃你的沙拉。
But the point here is that technologists love to go fast.
但这里的要点是，技术人员喜欢走得快。
They love sensing that next edge of things.
他们喜欢感知事物的下一个边缘。
So how is it that between government, the general public, technologists, and now I'm just leaving out biology here and medicine, how is it that that conversation can occur in a way that's going to satisfy each of those groups enough, not hold us back?
那么，在政府、公众、技术专家之间，现在我在这里忽略了生物学和医学，这种对话如何能够以一种足以满足每个群体而不是阻碍我们的方式进行呢？
Because we're also supposedly in an AI race right now, so that warrants going faster, not slower.
因为我们现在也正处于一场人工智能竞赛中，所以我们需要走得更快，而不是更慢。
How do you think about this?
您对此有何看法？
Dr. Fei-Fei Li:
李飞飞博士：
Andrew, this is why I returned from Google eight years ago back to Stanford and started the Human-Centered AI Institute.
安德鲁，这就是我八年前从谷歌回到斯坦福大学并创办以人为中心的人工智能研究所的原因。
These are profound societal questions we had to face, and back in 2018, there was no ChatGPT, but as an AI scientist, I knew that this is only going to accelerate.
这些是我们必须面对的深刻的社会问题，早在 2018 年，还没有 ChatGPT，但作为一名人工智能科学家，我知道这只会加速。
This is why I went to my colleagues and university leadership and said, "Let's put a framework." But it's not just my framework or Stanford's framework.
这就是为什么我去找我的同事和大学领导说：“让我们制定一个框架。”但这不仅仅是我的框架或斯坦福大学的框架。
The entire society in every way needs to wake up to the social implication, as we have done this in human history, whether it was cars or airplanes or biotech, is that it's multidimensional with multi-stakeholders.
整个社会在各个方面都需要清醒地认识到社会含义，就像我们在人类历史上所做的那样，无论是汽车、飞机还是生物技术，它都是多维的、多利益相关者的。
There is the professional norm.
有专业规范。
For example, you guys, as biologists, don't sneak into the lab and try to put rabies into Drosophila or fruit flies because that's a professional norm in your ethical training.
例如，你们作为生物学家，不要潜入实验室，试图将狂犬病病毒放入果蝇或果蝇中，因为这是你们道德训练中的专业规范。
There is industry rules, for example, IRBs.
有行业规则，例如 IRB。
Every human subject experiment today on university campuses are subject to the IRB regulatory framework so that we can look at this.
如今大学校园内的每项人体实验都受到 IRB 监管框架的约束，以便我们能够对此进行研究。
And then there are laws and regulatory laws, depending on if it's applied to humans versus crops.
然后还有法律和监管法律，具体取决于它是否适用于人类还是农作物。
So AI has to go through the same.
所以人工智能也必须经历同样的过程。
We need to have our professional norms.
我们需要有我们的职业规范。
We need to have education.
我们需要接受教育。
Computer scientists are not educated in ethics and societal studies.
计算机科学家没有接受过伦理学和社会研究方面的教育。
They're starting to.
他们开始这么做了
This is why a number of universities, including Stanford, are feverishly putting that part of curriculum into our education.
这就是为什么包括斯坦福大学在内的许多大学都热衷于将这部分课程纳入我们的教育中。
Now, those are the norms in education, but we also should work with the government, and different kind of governments and society have different kind of norms and traditions and heritage, and look at where the regulatory measure should apply.
现在，这些都是教育方面的规范，但我们也应该与政府合作，不同类型的政府和社会有不同的规范、传统和遗产，看看监管措施应该适用于哪里。
AI, for example, crossing biology, FDA, I think that's a very important area to look at how AI Should be used to help, but also guardrail to harm, so that we can avoid harm.
例如，人工智能跨越生物学、FDA，我认为这是一个非常重要的领域，需要研究人工智能应该如何用来提供帮助，但也要防范伤害，这样我们就可以避免伤害。
What I would not like to see is one person or a few people coming from industry and telling everybody what to do.
我不希望看到一个或几个来自工业界的人告诉每个人该做什么。
I think that would be dangerous because market forces are different from societal norms, and culture and heritage are different from education and ethics.
我认为这很危险，因为市场力量与社会规范不同，文化和遗产与教育和道德不同。
And these are multi-stakeholder problems to solve together.
这些都是需要多方利益相关者共同解决的问题。
Andrew Huberman:
安德鲁·休伯曼：
I love that answer, and it's something that's very timely right now.
我喜欢这个答案，而且现在这个答案非常及时。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
This aspect of our conversation is surely going to expand over time, but you bullseyed it.
我们谈话的这个方面肯定会随着时间的推移而扩大，但你却胡言乱语了。
I'd like to get your thoughts on how the human brain is being shaped on machines and how machines are being shaped by our understanding of the human brain.
我想了解一下您对机器如何塑造人脑以及我们对人脑的理解如何塑造机器的看法。
So first question first.
所以先问第一个问题。
Many people, parents and kids, are thinking, "Oh, my kid is never going to learn anything now.
许多人，包括父母和孩子，都在想：“哦，我的孩子现在永远学不到任何东西了。
They're just going to look everything up on a chatbot." But if you look back in the history of learning, similar arguments were made about calculators and computers and the typewriter and on and on.
他们只会在聊天机器人上查找所有内容。”但如果你回顾学习的历史，就会发现关于计算器、计算机、打字机等等的类似争论。
However, it is an interesting question that this hardware that we have in our heads evolved to process physical things in the world, light, sound, smells, et cetera.
然而，一个有趣的问题是，我们大脑中的硬件进化来处理世界上的物理事物，如光、声音、气味等。
And then it got this really cool piece up front, the prefrontal cortex, that can learn learning rules and can update those learning rules.
然后它在前面得到了这个非常酷的部分，即前额叶皮层，它可以学习学习规则并可以更新这些学习规则。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
So, if anything, we were gifted with a learning to learn machine and updating learning.
所以，如果说有什么不同的话，那就是我们天生具有学习机器和更新学习的能力。
So that's how kids can adjust and use LLMs.
这就是孩子们可以调整和使用法学硕士的方式。
So I, as a generation that grew up with the personal computer, showed up, granted, I grew up in Palo Alto, it was like-
所以，作为伴随着个人电脑长大的一代人，我出现了，当然，我在帕洛阿尔托长大，就像——
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
here's Pong and there's the Apple IIe, and we had them.
这里有 Pong，那里有 Apple IIe，我们都有。
And I think, oh, cool.
我想，哦，酷。
The brain can mature around technology, collaborate with technology in a way that I think my life has been greatly enriched by it.
大脑可以围绕技术成熟，与技术合作，我认为我的生活因此而大大丰富。
But I think the smartphone and perhaps the camera smartphone combination, as people like Jonathan Haidt have pointed out, have created a situation where most people love these technologies for the ease and convenience.
但我认为，正如乔纳森·海特（Jonathan Haidt）等人所指出的那样，智能手机，也许还有拍照智能手机的组合，已经创造了一种情况，大多数人都喜欢这些技术，因为它们的简单和方便。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
But we're all a little bit more aware now, or a lot more aware, that we're giving up something, too.
但我们现在都更加意识到，或者更加意识到，我们也在放弃一些东西。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
And that there are traps that people, in particular young people, can fall down.
人们，尤其是年轻人，可能会掉入一些陷阱。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
So what is the very optimistic, meh, and very pessimistic view in your mind, if three flavors actually exist there, of how young brains can be enriched, are unaffected, or can be harmed by AI as it exists now?
那么，如果确实存在三种观点，那么你心中的非常乐观、无聊和非常悲观的观点是什么，即年轻的大脑如何能够被现在存在的人工智能丰富、不受影响或受到伤害？
Let's just stay with what we've got.
让我们保留现有的吧。
Dr. Fei-Fei Li:
李飞飞博士：
Great question, Andrew, and the answer almost fall out of our previous conversations because you used the word motivation, and I was using the word agency.
安德鲁，这是一个很好的问题，我们之前的谈话几乎没有给出答案，因为你使用了“动机”这个词，而我使用了“代理”这个词。
The absolute bad outcome is that our young generation, their agency and human-level motivation of learning and living is taken away by tools.
绝对糟糕的结果是，我们的年轻一代，他们学习和生活的能动性和人类层面的动力被工具夺走了。
So doom scrolling, passive watching of shorts, all this are not helping agency, human agency.
所以厄运滚动，被动观看短裤，所有这些都没有帮助机构，人类机构。
Learning, fundamentally respecting the hardware you're talking about, takes time, takes effort, sometimes takes some pain.
学习，从根本上尊重你所谈论的硬件，需要时间，需要努力，有时需要一些痛苦。
That is just how our brain is.
我们的大脑就是这样的。
It doesn't matter how transistors move.
晶体管如何移动并不重要。
Our neurons move in certain ways.
我们的神经元以某些方式移动。
Our chemistry, our hormones move in a certain way.
我们的化学反应、荷尔蒙以某种方式变化。
So for young generation, no matter how the society will be different, jobs will be different, our human body needs to go through a deeply developmental phase where learning needs to happen.
所以对于年轻一代来说，无论社会如何不同，工作如何不同，我们的人体都需要经历一个深度发展的阶段，需要学习。
And that agency of learning, that motivation of learning cannot be taken away by anybody, should not be taken away by humans, nor should it be taken away by machines.
学习的主体、学习的动机不能被任何人夺走，不应该被人类夺走，也不应该被机器夺走。
That would be my concern, which is that if AI is not used right, the agency and motivation is taken away, though we are left with generations or generations to come who have not properly developed the brain.
这就是我担心的问题，即如果人工智能使用不当，其作用和动力就会被剥夺，尽管我们的子孙后代仍然没有正确开发大脑。
The other kind of danger is in the name of agency and motivation, the tools are denied to our students because we're worry you cheat.
另一种危险是以代理和动机的名义，我们的学生无法使用这些工具，因为我们担心你作弊。
We're worry you only got your answer from ChatGPT.
我们担心您只能从 ChatGPT 得到答案。
That is very bad as well, because with the proper agency, proper motivation, proper ways of using this tool, we can go a lot deeper with AI than we have ever learned.
这也非常糟糕，因为只要有适当的机构、适当的动机、使用这个工具的正确方法，我们就可以比我们所学到的更深入地研究人工智能。
I was just thinking about, I was a pre-med student for a while.
我只是在想，我当过一段时间的医学预科生。
Man, organic chemistry was hard.
伙计，有机化学很难。
I remembered trying to learn the molecules, their orientations, but the TA hours are too short, or it overlaps with my other class, and my professors only have certain number of office hours.
我记得尝试学习分子及其方向，但助教时间太短，或者与我的其他课程重叠，而且我的教授只有一定的办公时间。
It was just a struggle to learn that, right?
学习这一点很困难，对吧？
If today I were to have an AI companion, I would ask so many questions about organic chemistry because I know where I'm stuck, right?
如果今天我有一个人工智能伴侣，我会问很多关于有机化学的问题，因为我知道我被困在哪里，对吗？
I have the motivation to learn.
我有学习的动力。
I just needed guidance.
我只是需要指导。
That would be such a powerful tool for me to learn.
这对我来说将是一个非常强大的学习工具。
So that we should not deny students from.
所以我们不应该拒绝学生。
So both things worry me is either denying a tool or taking away agency and motivation.
因此，让我担心的两件事要么是拒绝使用工具，要么是剥夺了能动性和动力。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
Of course, the flip side is great, is let's find a way to keep our children and students' motivation and agency.
当然，另一面也很棒，那就是让我们找到一种方法来保持我们的孩子和学生的积极性和能动性。
Let's find a way to give them the access and the right way of using these tools.
让我们找到一种方法来为他们提供访问权限以及使用这些工具的正确方法。
Then this generation, this coming generation, and many generations to come, will be way smarter than us.
那么这一代人，这一代人，以及未来的许多代人，都会比我们聪明得多。
Because they are super-powered.
因为他们拥有超能力。
Andrew Huberman:
安德鲁·休伯曼：
I love that answer.
我喜欢这个答案。
I have great faith in neuroplasticity and the younger generations, too.
我对神经可塑性和年轻一代也充满信心。
Dr. Fei-Fei Li:
李飞飞博士：
Even our own.
甚至我们自己的。
I know we're old, but-
我知道我们老了，但是——
Andrew Huberman:
安德鲁·休伯曼：
Not so old.
没那么老了。
Let's give ourselves some credit.
让我们给自己一些信任。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
Plasticity does exist throughout the lifespan.
可塑性确实存在于整个生命周期。
Yeah.
是的。
Dr. Fei-Fei Li:
李飞飞博士：
Even our own neuroplasticity, right?
甚至我们自己的神经可塑性，对吧？
I find AI a great tool for my learning.
我发现人工智能是我学习的一个很好的工具。
Andrew Huberman:
安德鲁·休伯曼：
For me, it's been a remarkable discovery of what it can do.
对我来说，这是一个关于它的功能的非凡发现。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
But I tend to approach it from the position of consumer if I know nothing about something, and from the position of creator if I have some knowledge set-
但如果我对某件事一无所知，我倾向于从消费者的角度来看待它，如果我有一些知识，我倾向于从创造者的角度来看待它——
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm
嗯嗯
Andrew Huberman:
安德鲁·休伯曼：
...
...
inside of whatever it is I'm asking.
无论我问什么，里面都有。
Dr. Fei-Fei Li:
李飞飞博士：
Well, I actually have another thing.
嗯，其实我还有一件事。
A Stanford undergrad taught me something last year, and I realized before ChatGPT, sometimes I get lazy.
去年，斯坦福大学的一名本科生教了我一些东西，在 ChatGPT 之前我意识到，有时我会变得懒惰。
If I have a question, I ask the person I think is smart next to me.
如果我有问题，我会问旁边我认为聪明的人。
Now I realize I should not ask lazy questions, because it's so much easier to get information before you spend somebody else's time to ask something that's too lazy.
现在我意识到我不应该问懒惰的问题，因为在你花别人的时间问一些懒惰的问题之前，获取信息要容易得多。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
And AI is forcing me not to be too lazy.
而人工智能正在迫使我不要太懒。
Andrew Huberman:
安德鲁·休伯曼：
How essential is the specificity of the prompt to getting the best information out of AI?
为了从人工智能中获取最佳信息，提示的特异性有多重要？
Dr. Fei-Fei Li:
李飞飞博士：
Prompting is very important.
提示非常重要。
It is-
它是——
Andrew Huberman:
安德鲁·休伯曼：
And that's a skill, right?
这是一项技能，对吗？
Dr. Fei-Fei Li:
李飞飞博士：
That is a skill.
那是一种技能。
This is why public education is so important.
这就是为什么公共教育如此重要。
This is why education is so important.
这就是为什么教育如此重要。
I would love to see our schools, K12, teaching prompting.
我很高兴看到我们的学校 K12 进行教学激励。
Here's a quiz.
这是一个测验。
Who is humanity's best prompter?
谁是人类最好的提示者？
Andrew Huberman:
安德鲁·休伯曼：
I'm going to flunk this quiz.
这次测验我会不及格。
Dr. Fei-Fei Li:
李飞飞博士：
Socrates, if he were alive.
苏格拉底，如果他还活着的话。
Andrew Huberman:
安德鲁·休伯曼：
Hmm.
嗯。
Dr. Fei-Fei Li:
李飞飞博士：
Because that is the method of prompting.
因为那是提示的方法。
Right?
正确的？
Think about it.
想一想。
What is Socrates' method?
苏格拉底的方法是什么？
It's prompting and seeking truth by asking questions, and we should go back and teaching kids that.
它通过提问来提示和寻求真理，我们应该回去教孩子们这一点。
Andrew Huberman:
安德鲁·休伯曼：
And taking a walk while you have those discussions.
在进行这些讨论时散步。
Dr. Fei-Fei Li:
李飞飞博士：
Yes.
是的。
Andrew Huberman:
安德鲁·休伯曼：
Which actually is a good transition, perhaps, to this notion of embodied AI.
这实际上可能是向嵌入式人工智能概念的一个很好的过渡。
It's a world apart to attach a face speaking to hearing words.
将说话的面孔与听到的话语联系起来是天壤之别。
My good childhood friend, who I hope you'll meet soon because you both would benefit from the conversation so much, and I just want to be a fly on the wall, Dr. Eddie Chang, chair of neurosurgery, bioengineer, and he studies speech and language.
我儿时的好朋友，我希望你们很快就能见到他，因为你们俩都会从这次谈话中受益匪浅，我只想成为墙上的一只苍蝇，艾迪·张博士，神经外科主任、生物工程师，他研究言语和语言。
He and others have figured out the transformation of neural activity to control of the larynx and pharynx, and he's brought people essentially out of locked-in syndrome so they can speak.
他和其他人已经弄清楚了神经活动如何转变为控制喉部和咽部的方法，他使人们基本上摆脱了锁定综合症，从而可以说话。
Dr. Fei-Fei Li:
李飞飞博士：
Wow.
哇。
Andrew Huberman:
安德鲁·休伯曼：
For the first time in 10 years, he has this patient who was sadly paralyzed, and he could speak through a computer.
十年来，他第一次接诊这位严重瘫痪的病人，并且可以通过电脑说话。
He has others, many examples of these, in fact.
事实上，他还有其他的例子，很多这样的例子。
But the incredible thing is, when he started putting an iPad next to this person, one woman in particular who's wheelchair-bound, they had a video of her at her wedding, so they knew her voice, they knew her emotive patterns, they knew a bit about how she moved her body as well.
但令人难以置信的是，当他开始把 iPad 放在这个人旁边时，尤其是一位坐轮椅的女士，他们有一段她在婚礼上的视频，所以他们知道她的声音，他们知道她的情绪模式，他们也知道一些她如何移动她的身体。
And she now speaks through an iPad next to her frozen real face.
现在，她通过 iPad 在她冰冷的真实面孔旁边说话。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
But she can interact with the world, and it can interact with her in a completely different level of depth than if it were just a microphone, the sort of Stephen Hawking thing.
但她可以与世界互动，而且世界可以以完全不同的深度与她互动，而不仅仅是一个麦克风，就像斯蒂芬·霍金那样。
Dr. Fei-Fei Li:
李飞飞博士：
Mm.
嗯。
Andrew Huberman:
安德鲁·休伯曼：
And it's constantly being updated through machine learning.
并且它通过机器学习不断更新。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
And now also paying attention to the people she's speaking to and their responses.
现在还要注意与她交谈的人以及他们的反应。
This is embodiment.
这是体现。
Dr. Fei-Fei Li:
李飞飞博士：
Yes.
是的。
Andrew Huberman:
安德鲁·休伯曼：
It's on a 2D screen-
它在 2D 屏幕上 -
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm
嗯嗯
Andrew Huberman:
安德鲁·休伯曼：
...
...
admittedly, but this is an exponential leap over just robot sound-
诚然，但这与机器人声音相比是一个指数级的飞跃——
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm
嗯嗯
Andrew Huberman:
安德鲁·休伯曼：
...
...
or even accurate sound alone.
甚至仅是准确的声音。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
It's not just embodiment of people, it's embodiment, also embodied AI goes into robotics, right?
这不仅仅是人的体现，也是人工智能进入机器人领域的体现，对吧？
The next frontier of AI, as I have been saying, is beyond language, because again, humans develop first pre-verbally.
正如我所说，人工智能的下一个前沿领域是超越语言的，因为人类首先是在语言之前发展的。
Evolution took 500 million years without verbal-
进化花了 5 亿年，没有语言——
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm
嗯嗯
Dr. Fei-Fei Li:
李飞飞博士：
...
...
communication.
沟通。
And also the world would, in the right version, would be a lot better place with robots helping humans.
而且，在正确的版本中，如果机器人帮助人类，世界将会变得更加美好。
Andrew Huberman:
安德鲁·休伯曼：
Could you give me some examples?
你能给我一些例子吗？
I love this idea, but again, I realize I'm probably a little too deep into the technology rabbit hole, and it's probably scaring some people.
我喜欢这个想法，但我再次意识到我可能对技术兔子洞有点太深了，这可能会吓到一些人。
So robots.
所以机器人。
We've got self-driving cars.
我们有自动驾驶汽车。
Actually, the Waymo always stops for me and my puppy, my beautiful little six-month-old puppy.
事实上，Waymo 总是为我和我的小狗，我美丽的六个月大的小狗停下来。
Dr. Fei-Fei Li:
李飞飞博士：
Oh.
哦。
Andrew Huberman:
安德鲁·休伯曼：
How could you not stop when he wants to cross the street?
当他要过马路时你怎么能不停下来呢？
But a lot of people won't stop.
但很多人不会停下来。
They'll almost run us over in the morning.
早上他们几乎会碾过我们。
The Waymo is very respectful.
Waymo 非常尊重。
Never gets-
永远得不到——
Dr. Fei-Fei Li:
李飞飞博士：
Well, Waymo has to learn the rules right.
嗯，Waymo 必须学习正确的规则。
Andrew Huberman:
安德鲁·休伯曼：
Exactly.
正是如此。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
So there's benevolence there that doesn't always exist in humans, but where do you think this is going to show up first, and what's it going to look like?
所以，人类并不总是存在仁慈，但你认为这首先会出现在哪里？它会是什么样子？
If we zoom out 12 months from now.
如果我们把时间拉远到 12 个月后。
Dr. Fei-Fei Li:
李飞飞博士：
12 months is a little bit too fast for robotics.
对于机器人技术来说，12 个月有点太快了。
Andrew Huberman:
安德鲁·休伯曼：
Two years, three years?
两年，三年？
Dr. Fei-Fei Li:
李飞飞博士：
I would say if we zoom out 30 years.
我想说如果我们把时间拉长30年。
Andrew Huberman:
安德鲁·休伯曼：
30 years, okay.
30年了，好吧。
Dr. Fei-Fei Li:
李飞飞博士：
Okay?
好吧？
I'm not saying that's the first time robots hit the street.
我并不是说这是机器人第一次出现在街上。
We already have robotic cars.
我们已经有了机器人汽车。
I'm just saying it takes longer for, especially a hardware also involved technology to manifest.
我只是说这需要更长的时间，特别是硬件也涉及到技术才能体现出来。
But I would say, hopefully in you and my lifetime, I would love to see robots being part of our society, helping us.
但我想说，希望在你和我的有生之年，我希望看到机器人成为我们社会的一部分，帮助我们。
For example, I'm a single grown-up child taking care of two very advanced aged, very sick parents, and they happen not to speak English either.
例如，我是一个单身成年孩子，照顾两个年迈体弱多病的父母，而他们碰巧也不会说英语。
The amount of work I do is incredible, right?
我所做的工作量令人难以置信，对吧？
So I would love to have help.
所以我很想得到帮助。
It doesn't take away family's responsibility.
这并不能免除家庭的责任。
It doesn't take away love.
它不会带走爱。
It doesn't take away the necessary communication.
它不会剥夺必要的沟通。
But the physical labor would really, certain part, I would love to get help.
但体力劳动确实是一部分，我很想得到帮助。
We live in the state of California.
我们住在加利福尼亚州。
What is the one thing we all experience?
我们都经历过的一件事是什么？
Andrew Huberman:
安德鲁·休伯曼：
Traffic.
交通。
High taxes.
高税收。
Dr. Fei-Fei Li:
李飞飞博士：
Certain part of- ...
某些部分——...
California doesn't have traffic, but wildfires.
加州没有交通，但有野火。
Andrew Huberman:
安德鲁·休伯曼：
Oh, wild- yes.
哦，狂野——是的。
Dr. Fei-Fei Li:
李飞飞博士：
Right.
对。
Who is fighting these wildfires?
谁在扑灭这些野火？
Putting humans in danger of rescue, natural disaster, is not a great idea, right?
将人类置于救援、自然灾害的危险之中，这不是一个好主意，对吧？
So my family and my parents happen to have enough means.
所以我的家人和我的父母恰好有足够的经济能力。
But I was just thinking, elderly living alone, how do they go get grocery?
但我只是在想，独居老人，他们怎么去买菜？
How do they go get medicine?
他们怎么去取药？
Now, there might be some shipping we are starting to see, but what if they want to go for a walk or want to go to a park?
现在，我们可能开始看到一些运输，但如果他们想去散步或想去公园怎么办？
So there are just so many things that...
所以有很多事情...
Oh, by the way, you're in the school of medicine.
哦，顺便说一句，你在医学院。
We don't have an excess of caretakers.
我们没有多余的看护人员。
We have a shortage of caretakers.
我们缺少看护者。
Our nurses are deeply fatigued and overworked.
我们的护士非常疲劳并且过度劳累。
I was literally in the hospital with my dad for the past month and just watching the amount of work nurses do.
过去的一个月里，我确实和父亲一起待在医院里，观察护士所做的大量工作。
We know that on a given shift, nurses walk miles to fetch things, get medicine.
我们知道，在一个特定的轮班期间，护士会步行数英里去取东西、拿药。
There are just so many.
就这么多。
Can you imagine robots helping, right?
你能想象机器人提供帮助吗？
So there are just so many ways that our society can be structured and can benefit from help.
因此，我们的社会可以通过多种方式构建并从帮助中受益。
Andrew Huberman:
安德鲁·休伯曼：
Oh, I love these examples.
哦，我喜欢这些例子。
So many spring to mind based on what you described, crossing guards.
根据你所描述的内容，我脑海中浮现出很多东西，交叉警卫。
Yeah, you imagine with video that somebody who's homebound because of age or illness could navigate to the store and pick things off the shelf.
是的，您可以通过视频想象，由于年龄或疾病而宅在家里的人可以导航到商店并从货架上挑选东西。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
It doesn't have to be so disconnected that they just program and it comes back.
它不必如此断开连接，以至于他们只需编程即可返回。
Dr. Fei-Fei Li:
李飞飞博士：
Right.
对。
Andrew Huberman:
安德鲁·休伯曼：
Although that could be an option, too.
尽管这也可能是一种选择。
Yeah, I think that we have to revise our notions of what this picture looks like, because I think there are a couple things about robots and computers that scare people.
是的，我认为我们必须修改我们对这张图片的看法，因为我认为机器人和计算机有一些让人害怕的东西。
One is their physical hardness.
一是他们的身体硬度。
Dr. Fei-Fei Li:
李飞飞博士：
Mm.
嗯。
Andrew Huberman:
安德鲁·休伯曼：
Right?
对吗？
And so the way we share space with them is very different than the way-
所以我们与他们共享空间的方式与他们分享空间的方式非常不同——
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
we share space with other things.
我们与其他事物共享空间。
Of course, I'm not thinking, oh, like you cuddle with a robot, although some people might think that.
当然，我并不是在想，哦，就像你拥抱一个机器人一样，尽管有些人可能会这么想。
That's not my mindset.
那不是我的心态。
But I am thinking like, okay, if I had a robot that could fold clothes, vacuum, water the plants, and feed my fish.
但我在想，好吧，如果我有一个可以折叠衣服、吸尘、给植物浇水和喂鱼的机器人就好了。
Although I like to feed my fish myself.
虽然我喜欢自己喂鱼。
I really enjoy it.
我真的很喜欢它。
Love seeing them eat.
喜欢看他们吃饭。
I love being tactilely, literally in touch with them.
我喜欢与他们进行触觉上的接触。
They'll eat from my hands.
他们会从我手里吃东西。
Dr. Fei-Fei Li:
李飞飞博士：
Does your puppy like your fish?
你的小狗喜欢你的鱼吗？
Andrew Huberman:
安德鲁·休伯曼：
He does.
他做到了。
He has his own fish tank.
他有自己的鱼缸。
Dr. Fei-Fei Li:
李飞飞博士：
No.
不。
Andrew Huberman:
安德鲁·休伯曼：
I just got him some tropical fish.
我刚给他买了一些热带鱼。
Dr. Fei-Fei Li:
李飞飞博士：
Really?
真的吗？
Andrew Huberman:
安德鲁·休伯曼：
Yeah, right in front of his little thing.
是的，就在他的小东西面前。
Dr. Fei-Fei Li:
李飞飞博士：
And he's taking care of them?
他正在照顾他们？
Andrew Huberman:
安德鲁·休伯曼：
Well, he looks at them.
好吧，他看着他们。
Dr. Fei-Fei Li:
李飞飞博士：
Oh,
哦，
Andrew Huberman:
安德鲁·休伯曼：
He's not equipped to take care of them yet.
他还没有能力照顾他们。
I don't think...
我不认为...
Unfortunately, there's not enough prefrontal cortex in him.
不幸的是，他的前额皮质不够。
And he's a kind, but he's a bulldog mutt.
他很善良，但他是一只杂种斗牛犬。
They're not the smartest breed.
他们不是最聪明的品种。
They only have a few learning rules.
他们只有一些学习规则。
But they're very kind.
但他们非常友善。
Dr. Fei-Fei Li:
李飞飞博士：
Aw.
噢。
Andrew Huberman:
安德鲁·休伯曼：
But if you want a dog that can take care of a fish tank, you probably need like a West Highland Terrier or something like that.
但如果你想要一只可以照顾鱼缸的狗，你可能需要像西部高地梗或类似的东西。
More prefrontal cortex.
更多前额皮质。
Dr. Fei-Fei Li:
李飞飞博士：
Point taken.
点已采取。
Andrew Huberman:
安德鲁·休伯曼：
But the idea here is if one robot is doing one thing and another robot is doing another, it feels like a lot of hardware in my life.
但这里的想法是，如果一个机器人正在做一件事，而另一个机器人正在做另一件事，那感觉就像我生活中的很多硬件。
And I think that's how people feel.
我认为这就是人们的感受。
Dr. Fei-Fei Li:
李飞飞博士：
Right.
对。
Andrew Huberman:
安德鲁·休伯曼：
But you could imagine a multimorphic robot.
但你可以想象一个多态机器人。
Dr. Fei-Fei Li:
李飞飞博士：
Do you know Baymax?
你知道大白吗？
Andrew Huberman:
安德鲁·休伯曼：
I don't.
我不知道。
Dr. Fei-Fei Li:
李飞飞博士：
Disney's robot, probably 10 years ago, 15 years ago.
迪士尼的机器人，大概10年前、15年前。
Google the image.
谷歌图像。
This is the white medicine robot.
这就是白色的医疗机器人。
Andrew Huberman:
安德鲁·休伯曼：
Ah, yeah
啊，是啊
Dr. Fei-Fei Li:
李飞飞博士：
A healthcare robot that is very, not fluffy, it's very spongy.
这是一个非常柔软的医疗机器人，不是蓬松的，而是非常松软的。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
It feels like a big balloon.
感觉就像一个大气球。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Yeah, so-
是啊，所以——
Dr. Fei-Fei Li:
李飞飞博士：
You might like that.
你可能会喜欢这样。
Andrew Huberman:
安德鲁·休伯曼：
Yeah, more contours-
是的，更多轮廓-
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
and more multitasking from the same robot feels like a world that I could adjust to more quickly than the idea of my world filled with robots.
来自同一个机器人的更多多任务感觉就像一个我可以比充满机器人的世界更快适应的世界。
Dr. Fei-Fei Li:
李飞飞博士：
Yes.
是的。
Again, Andrew, I think as we imagine the future and we talk about how we imagine the future, I keep coming back to the word agency.
再次，安德鲁，我认为当我们想象未来并谈论我们如何想象未来时，我不断回到代理这个词。
Humanity should have the agency to decide how we imagine this.
人类应该有权力来决定我们如何想象这一点。
Andrew Huberman:
安德鲁·休伯曼：
Mm.
嗯。
Dr. Fei-Fei Li:
李飞飞博士：
It cannot just be a company or, I don't know, a investor decide that the world should be filled with metal-like robots, right?
它不能只是一家公司，或者，我不知道，投资者决定世界应该充满金属机器人，对吗？
Andrew Huberman:
安德鲁·休伯曼：
Mm.
嗯。
Dr. Fei-Fei Li:
李飞飞博士：
Our society should be collectively, proactively imagining.
我们的社会应该集体地、主动地想象。
And one thing I worry in this AI rhetoric is that the public is put in a position of being reactive-
在人工智能的言论中，我担心的一件事是公众处于被动反应的境地——
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm
嗯嗯
Dr. Fei-Fei Li:
李飞飞博士：
...
...
when it feels some people are just deciding-
当感觉有些人刚刚做出决定时——
Andrew Huberman:
安德鲁·休伯曼：
Mm
毫米
Dr. Fei-Fei Li:
李飞飞博士：
...
...
and the multi-stakeholders are not participating in this designing the future together.
多方利益相关者并没有参与共同设计未来。
Andrew Huberman:
安德鲁·休伯曼：
Like with your example of your father's surgery, to cross the robot with the physician, right?
就像你父亲手术的例子一样，让机器人与医生交叉，对吗？
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
If we cross a problem where there's a vulnerability with a robot that clearly makes things better-
如果我们遇到一个机器人存在漏洞的问题，而机器人显然可以让事情变得更好-
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm
嗯嗯
Andrew Huberman:
安德鲁·休伯曼：
...
...
the picture changes-
画面发生变化——
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
in the right direction.
朝着正确的方向。
So I'm thinking of a few examples off the top of my head.
所以我立即想到了几个例子。
I think most people would agree that if their kids could walk themselves to school and home, it would be great, but you worry about safety.
我想大多数人都会同意，如果他们的孩子可以自己步行去学校和回家，那就太好了，但你会担心安全。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
But if a robot was really a good guardian of your kid-
但如果机器人真的是你孩子的好监护人——
Dr. Fei-Fei Li:
李飞飞博士：
Definitely
绝对
Andrew Huberman:
安德鲁·休伯曼：
...
...
to the point where they could alert the authorities or maybe even physically protect-
以至于他们可以向当局发出警报，甚至可能进行人身保护——
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
your child, that would be awesome.
你的孩子，那真是太棒了。
Dr. Fei-Fei Li:
李飞飞博士：
Yep.
是的。
Andrew Huberman:
安德鲁·休伯曼：
Give them more agency in the world.
给予他们在世界上更多的代理权。
You think about some of the darker but nonetheless unfortunately real predatory behavior online.
你会想到一些更黑暗但不幸的是真正的网上掠夺行为。
Parents can only oversee their kids' behavior so much.
父母只能对孩子的行为进行这么多监督。
Kids are only aware of so much that's happening.
孩子们只知道发生了这么多事情。
But you could imagine an avatar in there with you that's really advocating for you that can-
但你可以想象一个化身在你身边，真正为你辩护，他可以——
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
spot things and keep predators at bay.
发现事物并阻止掠食者。
Dr. Fei-Fei Li:
李飞飞博士：
Here you go.
给你。
That's a great startup idea.
这是一个很棒的创业想法。
Andrew Huberman:
安德鲁·休伯曼：
That would be cool.
那会很酷。
But here's what's missing, I think, from the picture for me.
但我认为，这就是我的图片中缺少的东西。
I remember seeing this incredible guy, I know people some say he was kind of prickly, but this incredible guy walking around downtown Palo Alto when I was a postdoc and when I was a kid growing up working at the Palo Alto Toy and Sport World, and that was Steve Jobs.
我记得看到过这个不可思议的家伙，我知道有人说他有点多刺，但是当我还是一名博士后时，当我还是个在帕洛阿尔托玩具和体育世界工作的孩子时，这个不可思议的家伙在帕洛阿尔托市中心走来走去，那就是史蒂夫·乔布斯。
No shoes, kind of looked like a hippie.
没穿鞋，有点嬉皮士的样子。
Yes, he shouted at people at work, and probably HR wouldn't look too kindly upon him nowadays.
是的，他在工作中对人大喊大叫，现在HR可能不会对他太友善了。
But he understood-
但他明白——
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm
嗯嗯
Andrew Huberman:
安德鲁·休伯曼：
...
...
that these things we call computers needed to have rounded edges.
我们称之为计算机的这些东西需要有圆形边缘。
Dr. Fei-Fei Li:
李飞飞博士：
Yes.
是的。
Andrew Huberman:
安德鲁·休伯曼：
They needed to fit seamlessly in our pocket.
它们需要无缝地放入我们的口袋中。
They needed to have Bob Dylan on the landing page or whatever, so that it softened the relationship to technology.
他们需要让鲍勃·迪伦（Bob Dylan）出现在登陆页面或其他什么地方，这样就可以软化与技术的关系。
Some people would say, "Well, it went too far.
有些人会说：“好吧，这太过分了。
It was a Trojan horse," but I don't think so.
这是一个特洛伊木马，”但我不这么认为。
Somebody who really understands human nature to allow these, what are clearly going to be benevolent collaborations between robots and humans, to happen.
真正了解人性的人会允许机器人和人类之间的这些显然是仁慈的合作发生。
Because as you pointed out, and with total respect to the technologists that have built AI and the scientists that do amazing science, there's a hardness to either the way they're being presented or what they're capable of sharing that is a real separator.
因为正如你所指出的，并且完全尊重构建人工智能的技术专家和从事令人惊叹的科学的科学家，他们的呈现方式或他们能够分享的内容都存在着真正的分隔符。
Dr. Fei-Fei Li:
李飞飞博士：
Yes.
是的。
Andrew Huberman:
安德鲁·休伯曼：
And I'm not a therapist, but if I could wrap my arms around them, I'd be like, "Listen, guys, you're the smartest people in the room." Guys and gals, to be fair.
我不是治疗师，但如果我能拥抱他们，我会说，“听着，伙计们，你们是房间里最聪明的人。”男孩女孩们，公平地说。
"You're the smartest people in the room, but people don't like you because they don't understand you.
“你是房间里最聪明的人，但人们不喜欢你，因为他们不理解你。
And maybe you need a collaborator to help you share your vision in a way that isn't going to allow the press..." Because the media's guilty of building this chasm, because it's like, "These technologists are coming for us." I think that's a total trick of media, too.
也许你需要一个合作者来帮助你以一种不允许媒体报道的方式分享你的愿景……”因为媒体对造成这种鸿沟感到内疚，因为它就像是，“这些技术专家正在为我们而来。”我认为这也是媒体的诡计。
That's just to put money in their pocket.
那只是为了把钱放进他们的口袋里。
There's a lot going on right now.
现在发生了很多事情。
So who's the Steve Jobs or the Stacy whoever it's...
那么谁是史蒂夫·乔布斯或史黛西，无论是谁......
It could be a man, could be a woman, someone who really understands human nature.
这个人可能是一个男人，也可能是一个女人，一个真正了解人性的人。
Dr. Fei-Fei Li:
李飞飞博士：
There are many of them.
其中有很多。
Andrew Huberman:
安德鲁·休伯曼：
But-
但是——
Dr. Fei-Fei Li:
李飞飞博士：
There are many of us.
我们有很多人。
Stanford started Human-Centered AI Institute, right?
斯坦福大学成立了以人为中心的人工智能研究所，对吗？
Andrew Huberman:
安德鲁·休伯曼：
Well, there's you.
嗯，有你。
There's you.
有你。
Dr. Fei-Fei Li:
李飞飞博士：
Okay, but there are many.
好吧，但是有很多。
Andrew Huberman:
安德鲁·休伯曼：
Yeah.
是的。
Dr. Fei-Fei Li:
李飞飞博士：
There are plenty of entrepreneurs who are doing incredible startups on AI for drug discovery, AI for healthcare, AI for aging, AI for mental health.
有很多企业家正在人工智能药物发现、人工智能医疗保健、人工智能老龄化、人工智能心理健康等领域开展令人难以置信的初创公司。
These people care about AI, right?
这些人关心人工智能，对吗？
There are many designers and product managers who are trying to.
有很多设计师和产品经理正在尝试这样做。
I do think the megaphone is too much focused on people pumping their chest-
我确实认为扩音器过于关注人们的胸部活动——
Andrew Huberman:
安德鲁·休伯曼：
Mm
毫米
Dr. Fei-Fei Li:
李飞飞博士：
...
...
and talking about tech in a certain particular way.
并以某种特定的方式谈论技术。
So, even this podcast is making a positive difference, I hope, is-
所以，即使这个播客也能产生积极的影响，我希望——
Andrew Huberman:
安德鲁·休伯曼：
I would hope so
我希望如此
Dr. Fei-Fei Li:
李飞飞博士：
...
...
to put that human angle, the rounded human angle, human perspective, human future into these conversations.
将人类的角度、圆润的人类角度、人类的视角、人类的未来纳入这些对话中。
I don't feel despair, Andrew.
我并不感到绝望，安德鲁。
I'm a educator, I'm a builder, I'm a technologist.
我是一名教育家，我是一名建设者，我是一名技术专家。
I see many people around me, including my entire startup.
我看到我周围有很多人，包括我的整个初创公司。
These brilliant young technologists could join any startup or company they want, but they come to World Labs because they want to empower people, right?
这些才华横溢的年轻技术专家可以加入他们想要的任何初创公司或公司，但他们来到世界实验室是因为他们想赋予人们权力，对吗？
So, I see many people, but I don't think there's enough.
所以，我见了很多人，但我认为还不够。
You're right.
你说得对。
I don't think the public discourse is balanced right now.
我认为现在公众的言论并不平衡。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
And there's too much extreme rhetoric, either in terms of extreme doomerism and lack of safety.
而且有太多极端言论，要么是极端的厄运论，要么是缺乏安全感。
It's just freaking people out.
这简直把人们吓坏了。
Or extreme utopian, as if technology can do no wrong, and then that's disingenuous.
或者极端的乌托邦，好像技术不会犯错，那就是不诚实的。
Andrew Huberman:
安德鲁·休伯曼：
Right.
对。
Dr. Fei-Fei Li:
李飞飞博士：
People would say, "Well, okay, you're the haves.
人们会说：“好吧，你是有钱人。
Of course you say that." So, I think we should come to the middle and talk about what this technology is, how to use it, how we can collectively have that agency to guide the future.
你当然这么说。”因此，我认为我们应该走到中间，讨论这项技术是什么、如何使用它、我们如何共同拥有该机构来引导未来。
Andrew Huberman:
安德鲁·休伯曼：
Yeah, one thing that was pointed out to me by one of my podcast colleagues that should've been obvious, but wasn't, and clearly this is something that you, for lack of a better word, you embody, among many other things, is people don't really want to hear stories about machines.
是的，我的一位播客同事向我指出了一件事，这本来应该是显而易见的，但事实并非如此，显然，由于缺乏更好的词，你所体现的，除其他外，就是人们并不真正想听关于机器的故事。
But people love hearing that some person cured their dog's cancer or their-
但人们喜欢听到有人治愈了他们的狗的癌症或他们的——
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
child that was experiencing crazy symptoms.
正在经历疯狂症状的孩子。
They had no clue.
他们毫无头绪。
The doctors had no clue.
医生们毫无头绪。
And their fingertips, AI, solved the problem.
而他们的指尖，人工智能，解决了这个问题。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
These are the stories that really need amplification, because I think that we can relate to them, and they're beautiful stories.
这些故事确实需要放大，因为我认为我们可以与它们产生共鸣，而且它们都是美丽的故事。
They're incredible stories, but they're not getting nearly as much attention as the other stuff.
它们都是令人难以置信的故事，但它们并没有像其他故事那样受到那么多的关注。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
And it's a challenge.
这是一个挑战。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
Traditional media doesn't really care about the long arc of things.
传统媒体并不真正关心事情的长线。
They are on a 12- to 24-hour cycle.
它们以 12 至 24 小时为一个周期。
But other names, perhaps, of people who are really trying to talk about the benevolent use of AI, these collaborations that we should be aware of?
但也许还有其他人的名字，他们真正试图谈论人工智能的善意使用，以及我们应该意识到的这些合作？
Dr. Fei-Fei Li:
李飞飞博士：
Stanford HAI's newsletter, our website, our seminars, we promote a lot of those work.
斯坦福大学 HAI 的时事通讯、我们的网站、我们的研讨会，我们推广了很多这样的工作。
Andrew Huberman:
安德鲁·休伯曼：
I would love to learn more about your startup, because you don't pick projects haphazardly.
我很想更多地了解您的初创公司，因为您不会随意选择项目。
So, what is the project?
那么，这个项目是什么？
What's the goal?
目标是什么？
Dr. Fei-Fei Li:
李飞飞博士：
So, my startup, co-founded with a couple of other co-founders, is called World Labs.
因此，我与其他几位联合创始人共同创立的初创公司被称为“世界实验室”。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
We co-founded it at the beginning of 2024.
我们于 2024 年初共同创立了它。
It really is, for me, my life's work.
对我来说，这确实是我一生的工作。
We both come from vision, and the recognition of there's more beyond language intelligence is what really motivated me to think hard about what's the next chapter of AI frontier.
我们都来自视觉，对语言智能之外还有更多东西的认识真正促使我认真思考人工智能前沿的下一章是什么。
And we recognize that unlocking spatial and physical intelligence is really the next chapter, that it's not excluding languages.
我们认识到，解锁空间和物理智能确实是下一章，它并不排除语言。
Of course, the language technology is incredible.
当然，语言技术是不可思议的。
It's where we can devote more Time to build models or build, eventually, products that can help unlocking capabilities in spatial intelligence, like generating 3D, 4D worlds that are deeply useful for creators, for robot training, for architecture design, or to enable those interactive environments, whether you're talking about healthcare usage or education usage or robotics usage or industry usage.
在这里，我们可以投入更多时间来构建模型或最终构建有助于解锁空间智能功能的产品，例如生成对创作者、机器人培训、架构设计或支持这些交互式环境非常有用的 3D、4D 世界，无论您是在谈论医疗保健用途、教育用途、机器人用途还是行业用途。
These capabilities goes beyond language-
这些能力超越了语言——
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm
嗯嗯
Dr. Fei-Fei Li:
李飞飞博士：
...
...
per se.
本身。
And so World Labs was founded based on that premise.
因此世界实验室就是基于这个前提成立的。
We are still a young company.
我们仍然是一家年轻的公司。
We're very much a model-focused company where we're building this foundation model, and we're started by a lot of PhDs.
我们是一家非常注重模型的公司，我们正在构建这个基础模型，并且我们是由很多博士发起的。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
But now we're starting to build products.
但现在我们开始制造产品。
And so it's still the beginning.
所以这仍然是一个开始。
It's very exciting, and as a technologist, I feel deep in my heart, I'm a builder.
这是非常令人兴奋的，作为一名技术人员，我内心深处感到，我是一名建设者。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
Maybe it's because also I'm an immigrant, so that rolling your sleeves up and just get in with the young generation that is so incredibly smart and just build something from scratch is just so exciting.
也许是因为我也是一名移民，所以卷起袖子，加入年轻一代的行列，他们非常聪明，从头开始建立一些东西，真是令人兴奋。
Andrew Huberman:
安德鲁·休伯曼：
I recall a time not, but what, 15, 20 years ago when there were cars driving around-
我不记得有一次，但是，15、20年前，当时有汽车在周围行驶——
Dr. Fei-Fei Li:
李飞飞博士：
Oh, yeah
哦，是的
Andrew Huberman:
安德鲁·休伯曼：
...
...
taking images.
拍摄图像。
Dr. Fei-Fei Li:
李飞飞博士：
Still driving around.
还在开车兜风。
Andrew Huberman:
安德鲁·休伯曼：
Still driving around, taking images.
仍然开车四处走动，拍照。
But I imagine that there, and there are certainly aerial views as well, but you can imagine little tiny drones, like the type that could fly through a neuron, and just look at everything, so to speak, or drones picking up information about every nook and cranny of the fjords in Norway.
但我想象那里，当然也有鸟瞰图，但你可以想象小型无人机，就像可以飞过神经元的那种，可以说，只是观察一切，或者无人机收集有关挪威峡湾每个角落和缝隙的信息。
Dr. Fei-Fei Li:
李飞飞博士：
Right.
对。
Andrew Huberman:
安德鲁·休伯曼：
Has that been done to map the three-dimensional world?
这是为了绘制三维世界吗？
Dr. Fei-Fei Li:
李飞飞博士：
First of all, let's not make it sound scary that drones are getting to people's homes and properties.
首先，我们不要让无人机进入人们的住宅和财产听起来很可怕。
I think that the ability to capture imageries of the world is really rapidly advanced, right?
我认为捕捉世界图像的能力确实正在迅速发展，对吗？
Our cell phones are incredible sensors.
我们的手机是令人难以置信的传感器。
They're not drones, but people take a lot of photos.
它们不是无人机，但人们拍了很多照片。
And of course, our camera technology has improved.
当然，我们的相机技术也有所进步。
What World Labs is doing is not just taking real-world images.
世界实验室所做的不仅仅是拍摄真实世界的图像。
It's we allow people to imagine what's in their mind's eye.
我们让人们想象他们脑海中的想法。
As long as you can type a sentence or show a picture or a sketch of what you imagine, we try to turn that into worlds-
只要您能输入一个句子或展示您想象的图片或草图，我们就会尝试将其变成世界-
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm
嗯嗯
Dr. Fei-Fei Li:
李飞飞博士：
...
...
and environments.
和环境。
Why is it useful?
为什么它有用？
Because entertainment industry will use it.
因为娱乐业会用到它。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
Design industry will use it.
设计行业会使用它。
Robotics industry very much would use it for training environments and all that.
机器人行业非常愿意将其用于培训环境等。
So the combination of capturing what's in the real world as well as capturing what's in your imagined world is the new frontier.
因此，捕捉现实世界中的内容与捕捉想象世界中的内容的结合是新的领域。
Andrew Huberman:
安德鲁·休伯曼：
If you don't mind, I'd like to just take a couple of more minutes and talk about this moving from imagination to-
如果你不介意的话，我想再花几分钟谈谈从想象到——
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm
嗯嗯
Andrew Huberman:
安德鲁·休伯曼：
...
...
something.
某事。
Because this is Los Angeles, it occurred to me that a lot of people write scripts.
因为这里是洛杉矶，所以我想到有很多人写剧本。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
And then they try and get their movie made.
然后他们尝试制作他们的电影。
Dr. Fei-Fei Li:
李飞飞博士：
Mm-hmm.
嗯嗯。
Andrew Huberman:
安德鲁·休伯曼：
But with AI, in theory, you could take a script and give it to AI, and it could make the movie, in theory.
但从理论上讲，有了人工智能，你可以把剧本交给人工智能，理论上它就可以制作电影。
Right?
正确的？
Going from words to pictures to video, and you could maybe edit it a little bit here and there where it needed help-
从文字到图片再到视频，你也许可以在需要帮助的地方进行一些编辑-
Dr. Fei-Fei Li:
李飞飞博士：
Right
右
Andrew Huberman:
安德鲁·休伯曼：
...
...
of course.
当然。
Has that been done?
已经做到了吗？
Has a successful movie been made start to finish using AI?
一部成功的电影从头到尾都使用了人工智能吗？
Dr. Fei-Fei Li:
李飞飞博士：
So this is a very nuanced topic.
所以这是一个非常微妙的话题。
This is where we also get into people's wariness of AI and creativity when, if not careful, it might sound like we're taking away from storytellers' and creators' job, right?
这也是人们对人工智能和创造力持谨慎态度的地方，如果不小心的话，听起来我们可能会剥夺讲故事者和创作者的工作，对吧？
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
So let's separate this job conversation from the technology conversation a little bit, even though they're entangled.
因此，让我们将工作对话与技术对话稍微分开，尽管它们相互纠缠在一起。
Technology has advanced enough that taking scripts and generating shots, video shots, is getting really good.
技术已经足够先进，拍摄脚本和生成镜头、视频镜头都变得非常好。
We have seen short movies, even almost feature-length films, being assembled by AI tools.
我们已经看到短片，甚至几乎是长片，都是由人工智能工具组装而成的。
We have.
我们有。
And there are many companies, US companies, Asian companies, creating technology.
还有很多公司，美国公司，亚洲公司，正在创造技术。
But what remains deeply human, and that is important, is every part of storytelling and story creation, there are humans behind it with their unique emotion, story, technique, how they see the world, how they move the cameras, how they characterize characters.
但仍然具有深刻的人性，这一点很重要，那就是讲故事和故事创作的每一个部分，背后都有人类，有他们独特的情感、故事、技术，他们如何看待世界，他们如何移动摄像机，他们如何塑造角色。
A lot of that is what Hollywood and novel writers is about.
其中很多都是好莱坞和小说作家的主题。
So how do we meet the human need and human desire of storytelling with modern tools is actually a challenge because there is a fear, very much coming from Hollywood, that AI is taking over, and storytellers and actors and screenwriters, the jobs are being impacted.
因此，我们如何用现代工具满足人类讲故事的需求和愿望实际上是一个挑战，因为人们担心人工智能正在接管，而讲故事的人、演员和编剧的工作正在受到影响，这种恐惧很大程度上来自好莱坞。
And I think it is.
我认为是的。
But how is it being impacted?
但它是如何受到影响的呢？
What are we doing about it?
我们正在做什么呢？
Who is working in a constructive way?
谁在以建设性的方式工作？
This is not my industry per se, but I would love to see much more nuanced work-
这本身不是我的行业，但我希望看到更细致的工作 -
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm
嗯嗯
Dr. Fei-Fei Li:
李飞飞博士：
...
...
in this and also nuanced public discussion about that.
在此以及对此进行细致入微的公开讨论。
But I do think, just like healthcare, we were talking about how AI can rapidly change and disrupt the- ...
但我确实认为，就像医疗保健一样，我们正在讨论人工智能如何迅速改变和颠覆……
old ways of doing healthcare.
进行医疗保健的旧方法。
I think AI is absolutely changing the way we're doing storytelling.
我认为人工智能绝对会改变我们讲故事的方式。
So one story, speaking of which, I have a co-founder whose name is Ben, and Ben and I met with Ben Affleck.
有一个故事，说到这里，我有一位联合创始人，他的名字叫本，我和本遇到了本·阿弗莱克。
So I was joking, Ben meeting Ben, who is also thinking very avant-garde about using AI tools about filmmaking.
所以我开玩笑说，Ben 遇见了 Ben，Ben 也非常前卫地思考如何使用 AI 工具进行电影制作。
So having conversations between technologists and storytellers or movie makers at this moment is critical.
因此，此时此刻，技术人员与故事讲述者或电影制作人之间的对话至关重要。
Andrew Huberman:
安德鲁·休伯曼：
Yeah.
是的。
I feel like in every example of technology, there's some crossover point-
我觉得在每一个技术例子中，都有一些交叉点——
Dr. Fei-Fei Li:
李飞飞博士：
Yes
是的
Andrew Huberman:
安德鲁·休伯曼：
...
...
that when somebody who's truly an insider embraces a technology, and then it just takes off.
当真正的内部人士接受一项技术时，它就会起飞。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
Steven Spielberg or something like that-
史蒂文·斯皮尔伯格或类似的人-
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
or these probably aren't the best examples, but the Steve Jobs-Wozniak crossover, a designer, technology-curious guy, and a real, forgive me to the Jobs fan, but a real computer scientist.
或者这些可能不是最好的例子，但史蒂夫·乔布斯和沃兹尼亚克的交叉，一个设计师，一个对技术好奇的人，一个真正的，请原谅我的乔布斯迷，但一个真正的计算机科学家。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
That merge, these collaborations are really key.
这种合并、这些合作确实很关键。
So you need an insider and an outsider to do it right because you have to understand both cultures-
所以你需要一个内部人士和一个外部人士才能把事情做好，因为你必须了解两种文化——
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
and how to include-
以及如何包括-
Dr. Fei-Fei Li:
李飞飞博士：
Yeah
是啊
Andrew Huberman:
安德鲁·休伯曼：
...
...
the industry, the people.
产业、人。
Dr. Fei-Fei Li:
李飞飞博士：
So I really hope, because World Labs works with VFX industry as well.
所以我真的很希望，因为世界实验室也与视觉特效行业合作。
It's so important for me that our customers and users feel empowered.
让我们的客户和用户感到被赋予权力对我来说非常重要。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
It's not that technology should be taking their jobs away.
这并不是说技术应该夺走他们的工作。
Technology should be making their jobs better, super-powering their creativity.
技术应该让他们的工作变得更好，增强他们的创造力。
And that's how I see this technology, and that's how I would like to work with the users and customers.
这就是我对这项技术的看法，这就是我希望与用户和客户合作的方式。
Andrew Huberman:
安德鲁·休伯曼：
It's wild to think that when I was a kid on California Avenue in Palo Alto, there was this store, Keable & Shuckett, and it was just a photograph store and camera store.
当我还是个孩子的时候，在帕洛阿尔托的加州大道上，有一家叫 Keable & Shuckett 的商店，而且它只是一家照片店和相机店，这真是令人难以置信。
Dr. Fei-Fei Li:
李飞飞博士：
Yes.
是的。
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
You go in there, you get your film developed, and there were all these guys behind the counter, and they'd tell you all this stuff.
你进去，你的胶卷冲洗出来，柜台后面有很多人，他们会告诉你所有这些东西。
You could rent a long-distance lens and this kind of thing.
你可以租一个长距离镜头之类的东西。
None of that exists anymore.
这些都不再存在了。
Everything went digital.
一切都数字化了。
But there are still camera stores, so industries can morph.
但相机商店仍然存在，因此行业可以发生变化。
They don't always get obliterated.
它们并不总是被消灭。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
I think-
我认为——
Dr. Fei-Fei Li:
李飞飞博士：
It morphs.
它变形了。
People also get re-skilled, up-skilled.
人们也会重新掌握技能、提高技能。
We are working with a lot of creators who are using AI tools because they see where technology is going, and they want to re-skill and up-skill themselves.
我们正在与许多使用人工智能工具的创作者合作，因为他们看到了技术的发展方向，并且希望重新掌握和提高自己的技能。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
So I think moments of change is moment of both opportunity and loss.
所以我认为变革的时刻既是机遇也是损失的时刻。
Andrew Huberman:
安德鲁·休伯曼：
Mm-hmm.
嗯嗯。
Dr. Fei-Fei Li:
李飞飞博士：
We need to really be thoughtful about that.
我们需要认真考虑这一点。
Andrew Huberman:
安德鲁·休伯曼：
My last question is about the young generation.
我的最后一个问题是关于年轻一代的。
How do they feel about AI?
他们对人工智能有何看法？
Because there is this-
因为有这个——
Dr. Fei-Fei Li:
李飞飞博士：
How young are you talking about?
你说的是几岁？
Andrew Huberman:
安德鲁·休伯曼：
I'm talking about kids between the age of seven and 20.
我说的是七岁到二十岁之间的孩子。
Dr. Fei-Fei Li:
李飞飞博士：
Okay.
好的。
Andrew Huberman:
安德鲁·休伯曼：
Are they-
他们是——
Dr. Fei-Fei Li:
李飞飞博士：
That's literally my kids.
这确实是我的孩子们。
Andrew Huberman:
安德鲁·休伯曼：
Yeah.
是的。
So I might have asked that question for a reason.
所以我问这个问题可能是有原因的。
How do they feel about it?
他们对此有何感想？
Are they excited by it?
他们对此感到兴奋吗？
Because there is this phenomenon where computers come along, and your handwriting teacher is getting nervous that-
因为电脑出现了这样一个现象，你的书法老师开始紧张了——
Dr. Fei-Fei Li:
李飞飞博士：
Yes
是的
Andrew Huberman:
安德鲁·休伯曼：
...
...
people aren't just typing now.
人们现在不只是打字。
They're all writing with their fingertips, and no one's going to know how to write, and we wrote for...
他们都用指尖写作，没有人知道如何写作，而我们写作是为了……
These stories have been around for a long time about how we're just going to dissolve into a puddle of our own neurons if we don't embrace the past as much as the future.
这些故事已经流传了很长一段时间，讲述的是如果我们不拥抱过去而不是拥抱未来，我们将如何融入自己的神经元的水坑中。
And I like to think some of both is what's important.
我认为两者中的一些都是重要的。
But how do the kids feel?
但孩子们感觉如何？
What do they think?
他们怎么想？
Dr. Fei-Fei Li:
李飞飞博士：
This is actually my pet project as an educator and technologist.
这实际上是我作为教育者和技术专家的最爱项目。
Everywhere I go, I try to talk to students, parents, and teachers because I think that is the most forgotten population.
无论我走到哪里，我都会尝试与学生、家长和老师交谈，因为我认为他们是最被遗忘的人群。
Our policymakers and our technologists and our investors, they don't talk about teachers, parents, and students.
我们的政策制定者、技术专家和投资者，他们不谈论教师、家长和学生。
They all have opinions, and they all have kids, but they don't talk about it.
他们都有自己的观点，也都有孩子，但他们都不谈论。
I always have hope for kids, maybe because I'm an educator, because I think the biggest thing humanity never learns is the older generation lamenting about the future generation as if the future generation doesn't know anything.
我一直对孩子们抱有希望，也许因为我是一名教育家，因为我认为人类永远学不到的最大的事情就是老一辈哀叹下一代，好像下一代什么都不知道。
They're rude.
他们很粗鲁。
They're forgetting the past.
他们正在忘记过去。
But if you look at arc of history of humanity, by and large, we advance for the better.
但如果你看看人类的历史轨迹，总的来说，我们会朝着更好的方向前进。
Now, I'm not denying the atrocities.
现在，我并不否认这些暴行。
I'm not denying the setbacks.
我并不否认挫折。
I'm not denying this.
我并不否认这一点。
But fundamentally, I'm an optimist in humanity.
但从根本上来说，我是一个人性乐观主义者。
So that's where I come from.
这就是我来自的地方。
So if you're a total pessimist, maybe we're already on the wrong footing.
因此，如果你是一个彻底的悲观主义者，也许我们已经立足错误了。
But I look at kids, they're curious.
但我看着孩子们，他们很好奇。
That's why they're kids.
这就是为什么他们还是孩子。
They're curious.
他们很好奇。
Of course, they get massively entertained by this technology, but they also are starting to use it.
当然，他们从这项技术中获得了巨大的乐趣，但他们也开始使用它。
What I worry about are teachers and some parents because I think our society today, and especially Silicon Valley, are not doing them a service.
我担心的是老师和一些家长，因为我认为当今的社会，尤其是硅谷，并没有为他们提供服务。
Andrew Huberman:
安德鲁·休伯曼：
Mm.
嗯。
Dr. Fei-Fei Li:
李飞飞博士：
We're forgetting about them.
我们正在忘记他们。
We are lecturing them.
我们正在给他们讲课。
We are berating them.
我们正在斥责他们。
We are looking down at them.
我们正在俯视他们。
They are the most important people in our society.
他们是我们社会中最重要的人。
We should be talking to them.
我们应该和他们谈谈。
We should be uplifting them.
我们应该提升他们。
We should be supporting them.
我们应该支持他们。
We should be providing resources to them.
我们应该为他们提供资源。
K-12 teacher or K-16 teachers, they share the most important, critical burden of our society.
K-12 教师或 K-16 教师，他们承担着我们社会最重要、最关键的负担。
I'll tell you a real story.
我给你讲一个真实的故事。
November 2022, ChatGPT came out.
2022年11月，ChatGPT问世。
Obviously, I'm an insider in terms of technology, but the first thing I did was emailing the principal of the elementary school my kid was in and said, "I would like to come and guest lecture for your students and teachers." It's not because I'm so special.
显然，我是技术方面的内部人士，但我做的第一件事就是给我孩子所在小学的校长发电子邮件说：“我想来为你们的学生和老师做客座演讲。”这并不是因为我有多么特别。
It's because I want them in real-time to know what's happening because nobody in Silicon Valley, no investors, multi-billion dollar investment firms, or multi-million dollar, multi-trillion dollar companies, when ChatGPT came out, the first thing is, "What about our teachers in the neighborhood?" Nobody thinks like that.
这是因为我希望他们实时了解正在发生的事情，因为硅谷没有人，没有投资者，没有数十亿美元的投资公司，或者数百万美元、数万亿美元的公司，当 ChatGPT 出现时，第一件事是，“我们附近的老师呢？”没有人这么想。
But we need to be talking to teachers.
但我们需要和老师谈谈。
We need to show teachers.
我们需要向老师展示。
Of course, they're going to ask the question about what if kids cheat.
当然，他们会问如果孩子作弊怎么办。
It's okay they ask those questions.
他们问这些问题没关系。
Let's just show them, let's work with them, and empower them to come up with ways to deal with that.
让我们向他们展示，与他们合作，并授权他们想出解决这个问题的方法。
They are smart too.
他们也很聪明。
They are eager to change.
他们渴望改变。
They're just forgotten.
他们只是被遗忘了。
So I have hope for kids, but in order not to have a blind hope, I think we should all remember our teachers and help our teachers and parents so that we can help our kids.
所以我对孩子们抱有希望，但为了不盲目希望，我想我们都应该记住我们的老师，帮助我们的老师和家长，这样我们才能帮助我们的孩子。
Andrew Huberman:
安德鲁·休伯曼：
I absolutely love that answer, and I know that sentiment is shared by many, many people listening.
我非常喜欢这个答案，而且我知道很多很多听的人都有同样的感受。
God bless the teachers, and they need help, support, and information.
上帝保佑老师们，他们需要帮助、支持和信息。
Dr. Fei-Fei Li:
李飞飞博士：
Because now they turn on, not yours, but most podcasts, they're just scared.
因为现在他们打开了，不是你的，而是大多数播客，他们只是害怕。
They're so scared.
他们很害怕。
They hear these doomerism.
他们听到了这些厄运论。
They hear the doomsayer, or they say, "Oh, don't worry, it's utopian." Neither of these messages can help our teachers.
他们听到了末日预言者的声音，或者他们说，“哦，别担心，这是乌托邦。”这些信息都无法帮助我们的老师。
And if they're not helped, our kids are not helped.
如果他们得不到帮助，我们的孩子也得不到帮助。
Andrew Huberman:
安德鲁·休伯曼：
Couldn't agree more.
完全同意。
Dr. Fei-Fei Li:
李飞飞博士：
Yeah.
是的。
Andrew Huberman:
安德鲁·休伯曼：
I couldn't agree more.
我完全同意。
Fei-Fei, thank you so much for taking the time out of your incredibly busy schedule.
飞飞，非常感谢您在百忙之中抽出时间。
I'm so glad to hear your father's okay, and that is also part of your schedule, taking care of your parents, kids, and all the rest, to come educate us on this thing that's not just important, it's a major wedge of where we're at and where we're headed.
我很高兴听到你父亲没事，这也是你日程安排的一部分，照顾你的父母、孩子和其他人，来教育我们这件事，这不仅重要，而且是我们现在和前进方向的一个主要障碍。
And I share great optimism with caution, even more so on the basis of what you shared today.
我非常乐观，但也持谨慎态度，根据你今天分享的内容更是如此。
And also, thank you for teaching us more neuroscience- ...
另外，感谢您教给我们更多的神经科学知识 - ...
as we went along, because these machines are informed by the brain, and the brain is informed by these machines, and this is the world we're living in.
随着我们的进展，因为这些机器由大脑通知，而大脑又由这些机器通知，这就是我们生活的世界。
And I have great optimism, in no small part, thanks to the fact that you exist in this world.
我非常乐观，这在很大程度上要归功于你存在于这个世界上。
And thank you for taking the time to come here to share.
感谢您抽出时间来这里分享。
I know many people are very grateful, so thank you.
我知道很多人都很感激，所以谢谢你们。
Dr. Fei-Fei Li:
李飞飞博士：
Thank you, Andrew, and I really appreciated this conversation.
谢谢你，安德鲁，我真的很欣赏这次谈话。
It's a civilizational moment.
这是一个文明的时刻。
Andrew Huberman:
安德鲁·休伯曼：
Thank you for joining me for today's discussion with Dr. Fei-Fei Li.
感谢您参加今天与李飞飞博士的讨论。
To learn more about her work, please see the links in the show note caption.
要了解有关她的作品的更多信息，请参阅演出注释标题中的链接。
If you're learning from and/or enjoying this podcast, please subscribe to our YouTube channel.
如果您正在学习和/或喜欢此播客，请订阅我们的 YouTube 频道。
That's a terrific zero-cost way to support us.
这是支持我们的绝佳零成本方式。
In addition, please follow the podcast by clicking the follow button on both Spotify and Apple.
此外，请通过单击 Spotify 和 Apple 上的关注按钮来关注播客。
And on both Spotify and Apple, you can leave us up to a five-star review.
在 Spotify 和 Apple 上，您都可以给我们五星级的评价。
And you can now leave us comments at both Spotify and Apple.
您现在可以在 Spotify 和 Apple 上给我们留下评论。
Please also check out the sponsors mentioned at the beginning and throughout today's episode.
另请查看今天节目开头和整个节目中提到的赞助商。
That's the best way to support this podcast.
这是支持这个播客的最佳方式。
If you have questions for me or comments about the podcast or guests or topics that you'd like me to consider for the Huberman Lab Podcast, please put those in the comments section on YouTube.
如果您对我有疑问，或者对播客、嘉宾或您希望我考虑在 Huberman 实验室播客中讨论的主题有意见，请将这些意见放入 YouTube 的评论部分。
I do read all the comments.
我确实阅读了所有评论。
For those of you that haven't heard, I have a new book coming out.
对于那些还没有听说过的人，我即将出版一本新书。
It's my very first book.
这是我的第一本书。
It's entitled "Protocols: An Operating Manual for the Human Body." This is a book that I've been working on for more than five years, and that's based on more than 30 years of research and experience.
它的标题是“协议：人体操作手册”。这本书是我花了五年多时间编写的，它基于 30 多年的研究和经验。
And it covers protocols for everything from sleep to exercise to stress control, protocols related to focus and motivation, and of course, I provide the scientific substantiation for the protocols that are included.
它涵盖了从睡眠到运动再到压力控制的所有协议，以及与注意力和动机相关的协议，当然，我为所包含的协议提供了科学依据。
The book is now available by presale at protocolsbook.com.
该书现已在protocolbook.com 上预售。
There you can find links to various vendors.
在那里您可以找到各个供应商的链接。
You can pick the one that you like best.
您可以选择您最喜欢的一个。
Again, the book is called "Protocols: An Operating Manual for the Human Body." And if you're not already following me on social media, I am hubermanlab on all social media platforms.
同样，这本书的名字是“协议：人体操作手册”。如果您还没有在社交媒体上关注我，我是所有社交媒体平台上的 hubermanlab。
So that's Instagram, X, Threads, Facebook, and LinkedIn.
这就是 Instagram、X、Threads、Facebook 和 LinkedIn。
And on all those platforms, I discuss science and science-related tools, some of which overlaps with the content of the Huberman Lab Podcast, but much of which is distinct from the information on the Huberman Lab Podcast.
在所有这些平台上，我讨论科学和与科学相关的工具，其中一些与胡伯曼实验室播客的内容重叠，但其中大部分与胡伯曼实验室播客上的信息不同。
Again, it's hubermanlab on all social media platforms.
再说一遍，所有社交媒体平台上都是 hubermanlab。
And if you haven't already subscribed to our Neural Network Newsletter, the Neural Network Newsletter is a zero-cost monthly newsletter that includes podcast summaries as well as what we call protocols in the form of one to three-page PDFs that cover everything from how to optimize your sleep, how to optimize dopamine, deliberate cold exposure.
如果您还没有订阅我们的神经网络时事通讯，神经网络时事通讯是一份零成本的月度时事通讯，其中包括播客摘要以及我们所说的一到三页 PDF 形式的协议，涵盖从如何优化睡眠、如何优化多巴胺、故意冷暴露等所有内容。
We have a foundational fitness protocol that covers cardiovascular training and resistance training.
我们有一个基本的健身方案，涵盖心血管训练和阻力训练。
All of that is available completely zero cost.
所有这些都是完全零成本可用的。
You simply go to hubermanlab.com, go to the Menu tab in the top right corner, scroll down to Newsletter, and enter your email.
您只需访问 hubermanlab.com，转到右上角的“菜单”选项卡，向下滚动到新闻通讯，然后输入您的电子邮件即可。
And I should emphasize that we do not share your email with anybody.
我要强调的是，我们不会与任何人分享您的电子邮件。
Thank you once again for joining me for today's discussion with Dr. Fei-Fei Li.
再次感谢您参加今天与李飞飞博士的讨论。
And last but certainly not least, thank you for your interest in science.
最后但并非最不重要的一点是，感谢您对科学的兴趣。