Dr. Fei-Fei Li: Turn AI Into Humanity's Greatest Ally, Not Its Biggest Threat | Artificial Intelligence | YAPLive

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Speaker 2 Even as an AI scientist, I feel that I can hardly catch up with the progress of AI.

Speaker 2 There's a quote from 1970s about AI. The most advanced computer AI algorithm will still play a good chess move when the room is on fire.

Speaker 1 Dr. Fei Fei Lee is a professor of computer science at Stanford University as well as the co-director of the Stanford Institute for Human-Centered AI.

Speaker 1 We're going to discuss how she's creating eyes for AI with computer visioning.

Speaker 2 There's just so much public discourse about AI and many of them are ill-informed and that's dangerous.

Speaker 1 Everything that has consciousness has eyes. If AI starts to have eyes, wouldn't it just be that they're living and sentient at that point?

Speaker 2 AI as a technology can be be used by the badness. So from that point of view, I do have fear.
It can go very wrong. If you don't know anything about AI, it is important to educate yourself because.

Speaker 1 What's up, Yap Gang? Welcome back to another episode of our AI Vault series. Joining me today is none other than the godmother of AI, Dr.
Fei Fei Lee.

Speaker 1 She's a Stanford professor, co-director of the Human-Centered AI Institute, and pioneering scientist behind ImageNet. Dr.

Speaker 1 Lee believes that AI is a powerful tool to help us solve important problems, and she believes that AI should empower and enhance our human well-being.

Speaker 1 In this conversation, we'll talk about how computer vision models are trained, what they can and cannot do, and why ethics in AI isn't optional. It's essential.

Speaker 1 You'll hear stories of how AI is already saving lives, spotting disease, and even helping in rescue missions, but also where we face risks and what guardrails we need if AI is going to work for people and not against them.

Speaker 1 So grab your coffee, coffee, grab your notebook, settle in, and join me for this incredible conversation with the godmother of AI, Dr. Fei Fei Lee herself.

Speaker 2 Thank you, Hala. I'm very excited to join the show.

Speaker 1 Likewise, I'm so honored to talk to somebody like you, given all your credentials.

Speaker 1 In fact, Wired named you one of a tiny group of scientists, perhaps small enough to fit around a kitchen table, who's responsible for AI's recent remarkable advances.

Speaker 1 So it feels like AI is changing every day. There's new developments all the time.
So my first question to you is, can you walk us through the development of AI? Like what can it currently do now?

Speaker 1 And what can't it do right now?

Speaker 2 Yeah, great question. It's true.
Even as an AI scientist, I feel that I can hardly catch up with the progress of AI, right?

Speaker 2 So

Speaker 2 it is a young field of around 70 years old, but it's progressing really, really fast. So what can it do right now? First of all, it's already everywhere.

Speaker 2 It's around us. Another name for AI that is a little less of a hype name is machine learning.
It's really just mathematical models

Speaker 2 built by computer programs so that the program can iterate and learn to make the model you know, predict or decide on data better. So it's fundamentally machine learning.

Speaker 2 For example, if we shop on Amazon app, app, the kind of recommendations we get is through machine learning or AI.

Speaker 2 If you go from place A to place B,

Speaker 2 the algorithm that gets you the road, you know, to map out the path is machine learning. If you

Speaker 2 go to Netflix, you know, there is recommendation that's machine learning.

Speaker 2 If you watch a movie, there is a lot of, you know, machine learning, computer vision, computer graphics to make special effects, to make animations. That's machine learning.

Speaker 2 So machine learning and AI is already everywhere. What cannot do, well, no machines today can help me to fold my laundry or cook my omelette.

Speaker 2 It cannot take away, it cannot take away complex human reasoning. It cannot create in a way humans create in the combination of both reasoning, logic, but also beauty, emotion.

Speaker 2 There is a quote from 1970s about AI, and I think that quote still is true today. It says that the most advanced computer AI algorithm will still play a good chess move when the room is on fire.

Speaker 2 It's a quote to show that machines are programmed to do tasks, but it's unlike humans. We have a much more fluid, organic, contextual, situational awareness of

Speaker 2 our own thinking, our own emotion, as well as the surrounding. And that is not what AI is today.

Speaker 1 So insightful. And I love that you said that it's sort of like an evolution of machine learning because I always wondered like, well, what's the difference between machine learning and AI?

Speaker 1 It sounds pretty similar. So machine learning was almost like the basics of AI.

Speaker 2 The tool of AI. AI is, you know, it's a little bit, think about physics, right? Physics in Newtonian time, the most important tool of physics was calculus.
And yet we call the field physics.

Speaker 2 So artificial intelligence is a scientific field that is researching and developing technology to make machines think like humans.

Speaker 2 But the tools we use, the mathematical computer science tool is dominated by machine learning, especially neural network algorithms.

Speaker 1 So good. So AI is actually fresh on my mind because two days ago, I interviewed Dr.
Stephen Wolfram. I don't know if you know him.

Speaker 2 Mathematica?

Speaker 1 Yeah, he did the Wolfram project at computer language Wolfram. Yeah.
So I just interviewed him and we talked about ChatGPT and how ChatGPT works.

Speaker 1 And he was explaining to me that when they were developing ChatGPT, what was surprising is that they found out that these like simple rules would create all this complexity, that they could give ChatGPT simple rules and then it could write like a human.

Speaker 1 And it turns out that we actually still don't really understand how AI learns, which to me is like mind-boggling. How did we create something and yet we don't even know how it really works?

Speaker 1 Can you elaborate on that a bit?

Speaker 2 Yeah, it really at the end of the day,

Speaker 2 there are things we understand, there are things we don't.

Speaker 2 So it's not like completely we don't so it's neither a white box nor black box i i would call it a gray box and and depending on your understanding of the ai technology it's either darker gray or or or lighter gray uh so the things we know is that it is

Speaker 2 uh it's neural net neural network algorithm that is behind say a chat gpt model or a large language model. Of course, you hear the names of transformer models, sequence to sequence and all that.

Speaker 2 At the end of the day, these models take data, like document data, and it learns how the words and sometimes even subwords, right, parts of words are connected with each other.

Speaker 2 There are patterns to see, right? If you see the word how,

Speaker 2 it tends to be followed by are, and then it tends to be followed by you. So how are you you is a frequently occurring sequence.
So that pattern is learned.

Speaker 2 And once you learn enough in a big, huge neural network, your ability to predict the next word when you are given a word is really, really quite amazing, amazingly high to the point that it can converse like more or less like a human and because in the training data it has so much knowledge whether it's chemistry or movie reviews or you know

Speaker 2 geopolitical facts it has memorized all of them and so it can give out very very good answers so those are the things we know we know how the algorithm works we know it needs training we know that

Speaker 2 it's learning and predicting pattern. What we don't know is that

Speaker 2 because these models are huge, there are billions and billions, hundreds of billions of parameters. And then inside these models, there are these little

Speaker 2 nodes. Each one of them have a little mathematical function that connects to each other.

Speaker 2 So how do we know exactly how these billions and billions of parameters learn the pattern and where is the pattern stored?

Speaker 2 And why sometimes it hallucinates a pattern versus it gives out a correct answer? There is no not yet precise mathematical explanation.

Speaker 2 We don't know at the level of, there's no equation that can tell us, oh, I know exactly why at this moment the chat GPT gives you the word,

Speaker 2 how are you? versus how is he, you know, so that that's where the grayness comes from.

Speaker 2 These are large models with behaviors that are not precisely explained mathematically.

Speaker 1 So from my understanding, these neural networks are made to sort of replicate how the human brain works, basically.

Speaker 2 I would not use the word replicate. They're inspired.

Speaker 2 The human brain is,

Speaker 2 it has resemblance. For example, they're made by small neuron, neural nodes.
They're connected in hierarchies. But human brains fundamentally work in a chemical-electrical way.

Speaker 2 The way the neuron-neuron

Speaker 2 communication is very complex. Sometimes it's through spike, sometimes the spike also releases chemicals.

Speaker 2 You know, like there is just these kind of nuanced functions and also the connectivity, how one area of the brain is connected to others are not the same as neural networks.

Speaker 2 So we're inspired, but not replicating.

Speaker 1 That's a really, really helpful distinction right there. Yes.
So talk to us about how AI models are trained. Like how does AI learn typically?

Speaker 2 So typically AI model is given a vast amount of data. And then some of the data are labeled with human supervision.

Speaker 2 Like if I give AI models millions and millions of images, some are labeled cats, dogs, microwaves, chairs, and all that. And they learn to associate the pattern with the labels.

Speaker 2 Sometimes Sometimes in recent, especially in language domain, what we call self-supervision,

Speaker 2 you give it millions and millions, trillions of documents, and it just keeps learning to predict the next

Speaker 2 syllabus, the next word, because all the training data is showing you all these sequences of words. And there, you don't have to give additional label.
You just give

Speaker 2 the documents. And that's called self-supervised learning.
So, whether it's supervised with additional labels or

Speaker 2 supervised without additional label, it's self-supervised, it starts with data. Now, data goes into the algorithm, and the algorithm has to have an objective to learn.

Speaker 2 Typically, in the language model, the objective is to predict

Speaker 2 the next syllabus as accurately as the training data shows you. In the case of

Speaker 2 images with cat labels, for example, is to predict an image that has a cat with the right label cat instead of the wrong label microwave. And then because it has this objective,

Speaker 2 if during training, if it makes a mistake, you know, if I didn't predict the next word right or if I labeled the cat wrong, it goes back and iterates and updates its parameters based on the mistake.

Speaker 2 It has some mathematical rules or learning rules to update. And then it just keeps doing that till it, you know, when humans ask it to stop, or it no longer updates, you know, whatever stop criteria.

Speaker 2 And then you're left with a ginormous neural network that's already trained by a ginormous amount of data.

Speaker 2 And in that neural network, it has all the the parameters, the mathematical parameters that's already learned. Now,

Speaker 2 you can take this,

Speaker 2 and now you have a new sentence come in. And then it goes through this model, because it has all the parameters it has learned, it predicts

Speaker 2 what I should say given a new sentence, like, hello, Hala, how is your breakfast today? And it would predict, I had a great breakfast today or whatever. So that's how it's gonna be used.

Speaker 1 So, so interesting.

Speaker 1 Like, basically, like Chat GBT, it's just predicting the next word and the next word and the next word and the next word based on all the different patterns and trying to figure out what makes sense to come next.

Speaker 1 So, that's super clear. What I don't understand with something like Chat GBT is that it's so good at writing human language, but it's known to make like simple math mistakes, right?

Speaker 1 How is it possible that it's good at doing human language, but then on math, for example, it's known to make like stupid mistakes.

Speaker 2 It's because math,

Speaker 2 the way we do math in the human mind is different from the way we do language. Language has a very clear pattern of sequence to sequence.
Like I say the word how,

Speaker 2 you know, the word are and you typically follow, but sometimes it doesn't, right? So I have to learn these patterns. But if I say the word one plus,

Speaker 2 it's not like

Speaker 2 five typically follows or two typically follows, right? Like there is actually a deeper rule of one plus two equals three.

Speaker 2 Of course, when it has seen enough of that, it should do, it should predict three for today's language model. And actually it does.
This is too simple an example. But the point is that

Speaker 2 math takes a higher level of reasoning than just following statistical patterns. And large language model, by and large follows statistical patterns.
So some of the mathematical reasoning is lacking.

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Speaker 1 Hiring, Indeed, is all you need. So you've got a new book.
It's called The Worlds I See. And you say that the worlds you see are in different dimensions.

Speaker 1 So can you talk to us about why you titled the book this way?

Speaker 2 Yeah, this title came about after I finished writing the book. And I realized the journey of writing the book is really peeling into different experiences.

Speaker 2 There is the world of AI that I, you know, experience as a scientist. The book is the coming of age of a young scientist.
So I experience the world of science in different stages.

Speaker 2 But there is also the world as an immigrant, right? Like I go through life in different parts of the world, and how do I handle or go through that?

Speaker 2 And then there is like more subtle but profound world like

Speaker 2 learning to be a human. I know this sounds silly, but especially in the context of an AI scientist, it's really important.

Speaker 2 Part of the book is exploring my journey of living and taking care of ailing parents and how that experience, you know, build my own character, how we help each other, support each other, and

Speaker 2 towards the end of the book, how that experience made me see my science in a different light compared to maybe other scientists who haven't had this human, very profound human experience.

Speaker 2 So it really is different worlds that I experience, and it's blended into the book.

Speaker 1 I love that. And I love how you call it a science memoir.
And so you say that you're involved in the science of AI, but you're also involved in the social aspect of AI.

Speaker 1 So what do you mean by the social aspect exactly?

Speaker 2 I started in AI

Speaker 2 as a very personal journey. It's just a young science nerd loves an obscure niche,

Speaker 2 you know, like nobody knows field, but I'm just fascinated in a private way that how do we make machines think? How do we make machines see?

Speaker 2 And that, I was happy. And I would have been content with that, you know,

Speaker 2 through the rest of my life, honestly. Even if nobody in the world has heard of AI, I would be happily in my lap being a scientist.
But what really changed is around 2017, 2018,

Speaker 2 I felt like me as a scientist and the tech world woke up and realized, oh, wow, this technology has come to a maturation point that is impacting society.

Speaker 2 And because it's AI, it has so much, it's inspired by human thinking, it's inspired by human behavior,

Speaker 2 it has so much human implication at the individual level as well as the societal level. So as a scientist, I feel I was thrusted into

Speaker 2 a messier reality that I never I never really realized. Now I have a choice.
A lot of my fellow scientists

Speaker 2 would just continue to stay in the lab, which I think is very admirable and

Speaker 2 respected,

Speaker 2 and just still just focus on the size.

Speaker 2 But my other choice is to recognize as a scientist, as an educator, as a citizen, I have social responsibility. My responsibility is more

Speaker 2 focused on what I need to educate young people. And

Speaker 2 while I can teach them equations and coding and all that, I also want to share with them what the social implications are of this science because it's my responsibility.

Speaker 2 I also have a responsibility to communicate with the world because even starting quite a few years ago, now it's even worse because of the large language model. There's just so much

Speaker 2 public discourse about AI, and many of them are ill-informed and that's dangerous, right? That's unfair, that's dangerous.

Speaker 2 It tends to harm people who are not in the position of power and I have a responsibility to communicate.

Speaker 2 And then third, I also feel Stanford, especially as one of America's higher institutions, have a responsibility to

Speaker 2 help make the world better, to help our policymakers, to help civil society, to help companies, to help entrepreneurs,

Speaker 2 to educate, to inform, and to give insights. And that, all this

Speaker 2 is the messiness of meeting the real world. And I feel I shouldn't shy away from that.
I should take on that responsibility.

Speaker 1 Yeah, for sure. You're one of the most knowledgeable people about AI.
We need you to tell us what are the

Speaker 1 roadblocks that we need to look out for. And how can we make sure that we use AI for good and not for bad and

Speaker 2 take the steps to do that so let's talk about computer vision next so you are a computer vision AI scientist so what first got you interested in this and what is computer vision AI yeah well in one sentence computer vision AI is part of AI is the the specific part of AI that makes computers see and and understand what it sees.

Speaker 2 And this is very profound. When humans open our eyes, we see the world not only in colors and shades, we see it in meaning, right? Like I'm looking at my messy desk right now.

Speaker 2 It has cell phones, it has

Speaker 2 a cup, it has a monitor, it has

Speaker 2 my allergy medicine, and

Speaker 2 it has a lot of meaning. And more than that, we can also construct,

Speaker 2 especially even if we're not the best artists, we, you know, humans since the dawn of civilization have been drawing about the world, has been sculpting about the world, has been building bridges and

Speaker 2 monuments and has created the visual and

Speaker 2 the world.

Speaker 2 So the ability to see and visually create and understand is so

Speaker 2 innate in humans and wouldn't it be great if computers have that ability And that is what computer vision is.

Speaker 1 So interesting. And, you know, when I think about consciousness, everything that has consciousness has eyes.
And I always, this always like freaked me out.

Speaker 1 Like bugs have eyes, fish have eyes, and the eyes look like our eyes. Like fish eyes look like our eyes.
And that's so like scary, weird, the fact that all these living things have eyes.

Speaker 1 If AI starts to have eyes, wouldn't it just be that they're living and sentient at that point?

Speaker 2 so first of all hala you touched on something really really profound because visual sensing is one of the oldest evolutionarily speaking so 540 million years ago animals just started developing eyes it was a pinghole you know that collects light but it evolved into the kind of eyes the fish the octopus the the the the the elephant the the the eyes we have so you actually touched on something really profound profound.

Speaker 2 This is extremely innate, embedded into our development of our intelligence. And of course, you also ask a philosophically really profound question.
Everything has eyes as consciousness.

Speaker 2 Actually, a neuroscientist or neurophilosopher will probably

Speaker 2 should invite one to debate with you. For example,

Speaker 2 does a tiny shrimp using eyes doing doing things, does it have consciousness or it has just perception? I don't have an answer, honestly. How do you measure consciousness, right?

Speaker 2 Just because the shrimp can see the rock and

Speaker 2 climb around, does it mean it's just a sensory reflection, a reflex, or it has a deeper consciousness? I don't know.

Speaker 2 Just because machines have eyes, does it develop consciousness? It's a topic we can talk about, but I just want to make sure

Speaker 2 that we are at least on the same page, that just seeing itself doesn't mean it has consciousness.

Speaker 2 But the kind of visual intelligence we have, like I just described, to understand, to create, to build,

Speaker 2 to represent a world with such visual

Speaker 2 visual complexity, at least in humans, it does take consciousness.

Speaker 1 Yeah, everything that you're saying is just so interesting. Even that shrimp example, it's true.

Speaker 1 It's like, even though it's like navigating, swimming around rocks and whatever, it doesn't mean that it's actually conscious. It could be, to your point, just all like reflexes.

Speaker 1 And that makes it a little less scary if machines end up having eyes. So, how are you replicating biological processes like vision in computers now?

Speaker 2 Yeah, so

Speaker 2 again, I think a lot of computer vision is biologically inspired and it's inspiring in at least two areas. One is the algorithm itself.
So the whole neural network algorithm.

Speaker 2 In fact, back in the 1950s and 60s, the computer scientists were inspired by vision neuroscientists. When they were studying cat mammalian

Speaker 2 visual system, they discover the kind of hierarchical neurons and it's because of that it inspired computer scientists to build neural network algorithms. So

Speaker 2 the visual, the animal visual structure in the brain is very much the foundational inspiration to today's AI technology. So that's one area.
The second inspiration comes from functionality, right?

Speaker 2 The ability to see. What do we see?

Speaker 2 Humans are not that good at seeing color, for example.

Speaker 2 We see color rich enough, but the truth is there's infinite wavelength that defines infinite colors, but we have only probably dozens of colors.

Speaker 2 So clearly we're not seeing just colors in the same way like if I use a machine to register wavelength. But we, on the other hand, we see meaning, we see emotion, we see all these things.

Speaker 2 And it's just incredibly inspiring that we can

Speaker 2 build this functionality into machines. And that is another part of biological inspiration.
It's the functional inspiration. And with that, I think there is a lot to imagine.
For example,

Speaker 2 you know, first of all, visually impaired patients, if we help them with artificial visual system to understand the world, rich world we see, it will be

Speaker 2 tremendously helpful.

Speaker 2 Machines, right?

Speaker 2 I don't know, do you have a Roomba in your house?

Speaker 2 yeah yeah right so it almost is kind of seeing it's not seeing the same way we are but it's kind of seeing a mapping but one day i hope i not only have a roomba i also have a cleaning robot right like then it needs to see my house in a much more complex way and then

Speaker 2 The most important, right, for example, rescue robots.

Speaker 2 There's so many situations that puts humans in danger or humans are already in danger and you want to rescue humans, but you don't want to put more humans in danger.

Speaker 2 Think about that Fukushima nuclear leak incident.

Speaker 2 People had to really sacrifice to go in there to stop the leak and all that. It would be amazing if robots can do that.
And that needs seeing. It needs visual intelligence in much deeper ways.

Speaker 1 That's so interesting. And it's helpful for you to say that because my first reaction is like, why are we giving robots this much power? Like, we're like losing our power as humans.

Speaker 1 But to your point, it can help humans. And I know that's a whole, like what you talk about is human-centered AI, right?

Speaker 1 So can you define what human-centered AI is in your own words?

Speaker 2 Yeah, human-centered AI is a framework. of developing and using AI.
And that framework puts humans, human values, human dignity,

Speaker 2 in the center so that we're not developing technology that's that's harmful to humans. So it's really

Speaker 2 a way to see technology or use technology in a benevolent way. Now, I'm not naive.

Speaker 2 I know technology is a double-edged sword. I know that double-edged sword can be used intentionally or unintentionally by

Speaker 2 bad,

Speaker 2 you know, in bad ways. So human-centered AI is really trying to underscore that we have a collective responsibility to focus on the good development and good use of AI.

Speaker 2 And it was really inspired by my

Speaker 2 timing industry when I was on sabbatical as a professor is seeing the incredible business opportunities that is already opening the floodgate of AI back in 2018.

Speaker 2 And knowing that when business starts to use AI, it impacts lives of every individual, right?

Speaker 2 So I went back to Stanford and together with my colleagues, we realized as a thought leadership institution, as Americans higher education place to educate the next generation students, we should really have a point of view to develop.

Speaker 2 and stay at the forefront of the development of this technology. This is how we formulated the human-centered AI framework.

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And one of the biggest fears that people have with AI is that AI is going to replace all of our jobs.

Speaker 1 Now, AI is probably going to create a lot of jobs, and I've talked a lot about that with other guests on the podcast.

Speaker 1 But how do you suggest that we make jobs and take consideration into making sure that AI doesn't take all the jobs?

Speaker 2 Yeah, so several things, Hala. First of all,

Speaker 2 why do we have jobs? It's really important to think about it. I think jobs is part of

Speaker 2 human prosperity because we need that to translate into

Speaker 2 financial rewards so that we have the prosperity that our family and we need. It also is part of human dignity.
It's beyond just money is the meaning, for many people, it's the meaning of

Speaker 2 life and self-respect. So from that point of view, I think we have to recognize job shift.
Throughout human history, technology makes and also other factors creates, destroys, morphs, transforms jobs.

Speaker 2 But what doesn't change is the need for human prosperity and human dignity.

Speaker 2 So I think when we think about AI and its impact in jobs, it's important to go to the very core of what jobs are and means and what technology can do.

Speaker 2 So when it comes to say human dignity, for example, I do a lot of healthcare research with AI, and it's so clear to me that many of the jobs

Speaker 2 that our clinicians and healthcare workers do are part of humans caring for humans. And that emotional bond, that dignity, that respect

Speaker 2 can never be replaced. What is also clear to me is that American healthcare workers, especially nurses, are overfatigued, overworked.
And if technology can be a positive force to help them,

Speaker 2 to help them take care of patients better, to reduce their workload, especially some of the repetitive, thankless work like constant charting or walking miles and miles a day to fetch pharmacy medicines and all that, if those parts of the job, the tasks, can be augmented by machines,

Speaker 2 it is really truly intended to protect the human prosperity and dignity, but augment human capabilities.

Speaker 2 So, from that point of view, I think there is a lot of opportunity for AI to play a positive role. But again, it depends on how we truly,

Speaker 2 first of all, it depends on how we design AI. In my lab, we did a very interesting research.
We were trying to create a a big

Speaker 2 robotics project to do

Speaker 2 a thousand human everyday tasks.

Speaker 2 But at the beginning of this project,

Speaker 2 it was very important to us that we are

Speaker 2 creating robots to do these tasks that humans want help. For example, buying a wedding ring.

Speaker 2 I don't think, even if you have the best robot in the world, who wants a robot to choose a wedding ring or opening Christmas gift?

Speaker 2 It's not that hard to open a box, but the human emotion, the joy, the family bond, the moment is not about opening a silly box.

Speaker 2 So we actually ask people to rank for us thousands and thousands of tasks and tell us which tasks they want robots help. For example, like cleaning toilet.
Everybody wants robots help.

Speaker 2 So we focus on those tasks that humans prefer robotic help.

Speaker 2 rather than those tasks that humans care and want to do themselves. And that is a way of thinking about human-centered AI.

Speaker 2 How do we create technology that is beneficial, welcomed by humans, rather than I just go in and tell you I'm using robot to replace everything you care about.

Speaker 2 Another layer, just to finish this topic, is policy layer, right? Like economic, social

Speaker 2 well-being is so important and technologists don't know it all and we shouldn't feel we know it all. We should be collaborating with civil society, legal world, policy world,

Speaker 2 economists to try to understand the nuance and the profoundness of jobs and tasks and AI's impact. And this is also why our Human Center AI Institute at Stanford has a digital economy lab.

Speaker 2 We work with policymakers and thinking about these issues. We try to inform them and provide information information and

Speaker 2 to help move these topics forward in a positive way.

Speaker 1 I feel like you're touching on a lot of, you have three aspects to your human-centered AI framework, right? So AI is interdisciplinary.

Speaker 1 AI needs to be, you know, trying to make sure that we have human dignity and,

Speaker 1 you know, using it for human good. And then there's also one about intelligence.
Can you break down your

Speaker 1 three pillars of your human-centered AI framework?

Speaker 2 Yeah, the three pillars of the human-centered AI framework is really about thought leadership in AI and focusing on what higher education institutes like Stanford can do.

Speaker 2 One we talked about is that interdisciplinary

Speaker 2 recognizing the interdisciplinary nature of AI, welcoming the multi-stakeholder studies, research, and education.

Speaker 2 policy outreach to make sure that AI is embedded in the fabric of our society today and tomorrow in a benevolent way.

Speaker 2 The second one is what you said, is focusing on augmenting humans, creating technology that enhances human capability and human well-being and human dignity rather than taking away.

Speaker 2 The third one is about continue to be inspired by human intelligence and develop technology, AI technology that is compatible with humans.

Speaker 2 Because, you know, human intelligence is very complex, it's very rich. We talk a lot about emotion, intention, compassion, and today's AI is lacks most of that.
It's pretty far from that.

Speaker 2 Being inspired by this can help us to create. And also, by the way, there's another thing about today's AI that is far worse than humans.
It draws a lot of energy.

Speaker 2 Humans, our brain works around 20 watts.

Speaker 2 That is like dimmer than the dimmest light bulb in your house. Yet we can do so many things.
We can create the pyramid. We can, you know,

Speaker 2 come up with E equals M C square. We can, you know,

Speaker 2 write beautiful music and all that. AI today is very, very energy consuming.
It's bulky. It's huge.
So there's a lot in human intelligence that can inspire the next generation AI to do better.

Speaker 1 Every time I have an AI episode, I feel like I learned so much that I didn't really realize before.

Speaker 1 And, you know, we've had conversations with other people on the show about how a lot of people are scared of AI getting like apex intelligence, that it's going to be so much smarter than humans, going to take over the world, it's going to control humans.

Speaker 1 Do you have any fears around that?

Speaker 2 I do have fears. I think,

Speaker 2 you know,

Speaker 2 who lives in 2024 and don't have fears, you know, and as a citizen of the world, I think

Speaker 2 our civilization, our species is always defined by the struggle of dark and light and by the struggle and good and bad.

Speaker 2 I think it's, you know, we have incredible benevolence in our DNA, but we also have incredible

Speaker 2 badness in our DNA. And AI as a technology can be used by the badness.
So from that point of view, I do have fear.

Speaker 2 The way I cope with fear is try to be constructively helpful, is try to advocate for the benevolent use of this technology and to use this technology to combat the badness.

Speaker 2 At the end of the day,

Speaker 2 any hope I have for AI is not about AI, it's about humans. You know, the to paraphrase Dr.
King, the arc of history is long,

Speaker 2 but it does bend towards justice and benevolence in general. But to come down from that abstract thinking, I think we have work to do.

Speaker 2 I honestly, because if AI is in the hands of bad actors, if AI is concentrated in only a few powerful people's hand, it can go very wrong, right? We don't need to wait for sanctioned AI.

Speaker 2 Even today's car, imagine there is a bad person who is in charge of building 50% of America's car, and that person just wants to make all the car brakes malfunction.

Speaker 2 Or add a sensor and say, if you see a pedestrian, run it over.

Speaker 2 Actually, today's technology can do that. You don't need sension AI.
But the fact that we don't have that dystopian scenario is first of all human nature is by and large goods.

Speaker 2 You know, our car factory workers, our business leaders in building cars, nobody thinks about doing that, right? Yeah. We also have laws, right? If someone is trying to do harm,

Speaker 2 we have societal constraints. We also try to educate

Speaker 2 the population towards

Speaker 2 good things, right? So all this is hard work and we need that hard work in AI to ensure it doesn't do bad.

Speaker 1 Yeah. So I just want to give an example that when I was talking to Stephen Wolfram, because the interview is fresh in my head, and he said something that made me feel a little bit about

Speaker 1 at ease with AI and the fact that it could get really smart. He said, we're living in AI.
We live in nature. Nature is so complex.

Speaker 1 We can't control it. It has simple processes that are really, really complex.
We can predict it all we want, but we can't, like, we'll never really know what nature is going to do.

Speaker 1 And already we live in a world where we're interacting with nature every day and we have to just deal with the fact that we don't control it and it's smarter than us to a degree.

Speaker 1 And he's like, that's what maybe AI will be like in the future. It will be there.
It will be its own system.

Speaker 1 What are your thoughts on that?

Speaker 2 That's a very interesting way to put it.

Speaker 2 Okay, first time I heard that.

Speaker 2 I like his... I like his way of saying that humans in the face of complexity and powerful things, that we still have a way to co-habitat with it.

Speaker 2 I don't agree nature is AI in the sense that nature is not programmable. And I don't think nature has a collective intention.

Speaker 2 It's not like the earth wants to be a bigger earth or bluer earth or, you know, so from that point of view, it's very, very different. But I appreciate the way he

Speaker 2 says that. And I also think using his analogy, we also live with other humans.

Speaker 2 And there are humans who are more stronger than us, smarter than us, do better, whatever than us. But yet, by and large, our world is not everyone killing each other, right? Like by and large.

Speaker 2 Now, this is where we do see the darkness. And this has nothing to do with AI.
Human nature has darkness and we harm each other. And the hope is...

Speaker 2 It's not just the hope, the work is that

Speaker 2 when we create machines that resemble our intelligence, we should prevent it to do similar harms to us, to each other, and try to

Speaker 2 bring out

Speaker 2 the better part of ourselves.

Speaker 1 As we wrap up this interview, because I need to get you out on time,

Speaker 1 I wanted to ask you a couple of questions. So, first off, to all the young entrepreneurs, you're talking to a lot of young entrepreneurs right now and people who want to be entrepreneurs.

Speaker 1 What's your advice to them about how to embrace this AI world?

Speaker 2 So first of all, I hope you read my book, The Worlds I See, because the book is written to young people, for young people.

Speaker 2 It's a coming of age of a scientist, but the true theme of the book is Finding Your North Star, is finding your passion and

Speaker 2 believing in that against all odds. and

Speaker 2 chase after the North Star. And that is the core of what entrepreneurship is about: is that you believe in bringing something to the world and against all odds, you want to make it happen.

Speaker 2 And that should be your North Star. In terms of AI, it's an incredibly powerful tool.
So it depends on what business and products you're making.

Speaker 2 It either can empower you, or it's an essential part of your core product, or it keeps you competitive. So

Speaker 2 it's so horizontal that for most entrepreneurs out there, if you don't know anything about AI, it is important to educate yourself because it's possible that AI will play

Speaker 2 either in your favor or in your competitors' favor. So knowing that is important.

Speaker 1 Yeah. Okay.
And since we're just about time here, I'm just going to ask you one last question. And this is really about visioning.
Okay.

Speaker 1 Let's vision a world 10 years from now, 2034, where there's human-centered AI. And let's also try to visualize a world 10 years from now where maybe it's not human-centered AI.

Speaker 1 Maybe it got in the bad hands of some folks.

Speaker 2 The world that's human-centered AI,

Speaker 2 I think it's not too far from at least the North America world we live in, even though I know we're not perfect, is that we still have a strong democracy. We still believe in individual dignity and,

Speaker 2 you know, by and large, free market capitalism, that we are allowed as individuals to pursue our happiness and prosperity and respect each other.

Speaker 2 And AI helps us to do better scientific discovery, to have self-driving cars to help people who can drive or reduce traffic, to make life easier, to make education more personalized to empower our teachers and healthcare workers to discover a cure for diseases to to alleviate our aging population problems to to make agriculture more effective to to find climate solutions there is so much AI can do in the world that we still

Speaker 2 we still

Speaker 2 have the good foundation.

Speaker 2 Dystopia world is AI can be used as a bad tool to topple democracy, right? Disinformation is an incredibly harmful way of

Speaker 2 harming democracy and

Speaker 2 the civil

Speaker 2 life we have right now. If it's completely concentrated in power, whether it's state power or individual power,

Speaker 2 it makes the rest of the society much more subject

Speaker 2 to the will and possibly wrath of

Speaker 2 that power, whether it's AI or not. We have seen in human history that concentrated power is always bad.

Speaker 2 concentrated power using powerful technology is not a recipe for good.

Speaker 1 Yeah. Well, Dr.
Lee, I'm so happy we have somebody like you who's helping us to navigate the AI world, who's also helping to shape the AI world in a way that hopefully is going to be good for humans.

Speaker 1 Please let us know where we can learn more about you and everything that you do.

Speaker 2 Thank you, Hala. Thank you for promoting my book.
And please constantly check in with Stanford Human Center AI Institute newsletter and website.

Speaker 1 Amazing. We'll stick all those links in the show notes.
Dr. Lee, thank you for joining us on Young and Profiting Podcast.

Speaker 2 Thank you, Hala.