Dr. Fei-Fei Li: Turn AI Into Humanity's Greatest Ally, Not Its Biggest Threat | Artificial Intelligence | AI Vault - Young and Profiting (YAP) with Hala Taha: Entrepreneurship and Self-Improvement Podcast: Summary & Transcript

In short

Podcast Episode Summary: Young and Profiting with Hala Taha

Episode Title

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

Episode Description

In this episode of the AI Vault series, Hala Taha interviews Dr. Fei-Fei Li, a prominent Stanford AI scientist, who discusses the ethical, social, and economic implications of artificial intelligence (AI). Dr. Li emphasizes the importance of developing AI technologies that prioritize human values and dignity. Together, they explore how AI can be harnessed responsibly to benefit society.

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Key Discussion Points

Introduction
Overview of Dr. Fei-Fei Li's background and her contributions to AI and computer vision.
Discussion of AI's rapid advancement and its impact on society.

The Evolution and Limits of Artificial Intelligence (02:33)
AI, particularly through machine learning, is integrated into everyday tasks (e.g., recommendations on platforms like Netflix and Amazon).
Discussion on the limitations of AI: it cannot replicate complex human reasoning or create art in the same way humans do.

How AI Models Like ChatGPT Are Trained (09:56)
AI models learn patterns from vast datasets, which can be either supervised or self-supervised learning.
The conversation highlights the complexity of understanding how these models operate internally (referred to as a "gray box").

Dr. Fei-Fei’s Journey and Responsibility in AI (14:12)
Dr. Li shares her motivations and responsibilities as an AI scientist, particularly in light of AI's societal impacts.

How Computer Vision Brings AI to Life (19:15)
Computer vision allows machines to interpret and understand visual information, inspired by the human visual system.
Applications discussed include enhancing technologies for visually impaired individuals and rescue missions.

Ethical AI, Human Dignity, and the Future of Work (25:59)
The ethical framework required for AI development emphasizes human dignity and the importance of responsible technology use.
Concerns about job displacement and the need for AI to enhance rather than replace human capabilities.

The Three Pillars of Human-Centered AI (32:57)
Interdisciplinary collaboration is vital to AI's development.
Focus on augmenting human capabilities rather than replacing them.
Continued inspiration from human intelligence for AI development.

Confronting Fears of AI in Action (35:10)
Discussion on societal fears surrounding AI, such as job loss and the potential for misuse.
The role of policymakers and technologists in ensuring AI is used for good.

AI in Business: How Entrepreneurs Can Thrive (39:59)
Entrepreneurs should educate themselves about AI to leverage it for competitive advantage.
The importance of embracing AI technologies to enhance business operations and customer experiences.

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Key Takeaways

Human-Centered AI: Development of AI should prioritize human values and well-being, ensuring technology serves humanity.
Education and Awareness: Entrepreneurs must understand AI to utilize it effectively in their business strategies.
Interdisciplinary Approach: Collaboration between technologists, policymakers, and society is essential for responsible AI use.
Ethical Considerations: AI poses risks but also offers tremendous opportunities to improve lives and society when developed thoughtfully.

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Resources Mentioned

Dr. Fei-Fei Li's Book: [The World's I See](http://bit.ly/WorldsISee)
Stanford Human-Centered AI Institute: [Website](https://hai.stanford.edu/)

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Conclusion Dr. Fei-Fei Li emphasizes that while AI holds immense potential, its future depends on how humans choose to develop and incorporate it into society. The conversation serves as a call to action for individuals and businesses to engage with AI responsibly and ethically.

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Sponsorships

Indeed: Get a $75 sponsored job credit at [Indeed.com/PROFITING](https://Indeed.com/PROFITING)
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And more...

For full details, listeners are encouraged to explore the show notes and additional resources provided in the episode.

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Transcript

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1:28There'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. Dr. Fei-Fei Li is a professor of computer science at Stanford University, as well as the co-director of the Stanford Institute for Human-Centered AI. We're going to discuss how she's creating eyes for AI with computer visioning. There's just so much public discourse about AI, and many of them are ill-informed. And that's dangerous. 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?

2:11AI as a technology can 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... 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 Li. She's a Stanford professor, co-director of the Human-Centered AI Institute, and pioneering scientist behind ImageNet. Dr. Li 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.

2:52In 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. 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. So grab your coffee, grab your notebook, settle in, and join me for this incredible conversation with the godmother of AI, Dr. Fei-Fei Li herself. Thank you, Hala. I'm very excited to join this show. Likewise, I'm so honored to talk to somebody like you, given all your credentials.

3:29In 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. 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? And what can't it do right now? 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? So it is a young field of around 70 years old, but it's progressing really, really fast.

4:10So what can I do right now? Now, first of all, it's already everywhere. 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 built by computer programs so that the program can iterate and learn to make the model predict or decide on data better. So it's fundamentally machine learning. For example, if we shop on Amazon app, the kind of recommendations we get is through machine learning or AI. If you go from place A to place B, the algorithm that gets you the road to map out the path is machine learning. If you go to Netflix, there is a recommendation that's machine learning.

5:08If you watch a movie, there is a lot of machine learning, computer vision, computer graphics to make special effects, to make animations. That's machine learning. 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 omelet. It cannot take away complex human reasoning. It cannot create in the way humans create in the combination of both reasoning, logic, but also beauty, emotion. 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.

6:08It'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 our own thinking, our own emotion, as well as the surrounding. And that is not what AI is today. 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? It sounds pretty similar. So machine learning was almost like the basics of AI. The tool of AI. AI is, you know, it's a little bit, think about physics, right?

6:51Physics in Newtonian time, the most important tool of physics was calculus. And yet we call the field physics. So artificial intelligence is a scientific field that is researching and developing technology to make machines think like humans. But the tools we use, the mathematical computer science tool is dominated by machine learning, especially neural network algorithms. 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. Mathematica? Yeah, he did the Wolfram project in Computer Language Wolfram. Yeah. So I just interviewed him and we talked about ChatGPT and how ChatGPT works.

7:39And 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 Chachi Bouti simple rules and then it could write like a human. 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? Can you elaborate on that a bit? Yeah, it really, at the end of the day, there are things we understand, there are things we don't. So it's not like completely we don't.

8:16So it's neither a white box nor a black box. I would call it a gray box. And depending on your understanding of the AI technology, it's either darker gray or lighter gray. So the things we know is that it's 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. At the end of the day, these models take data, like document data, and it learns how the words and sometimes even subwords, parts of words, are connected with each other. There are patterns to see. If you see the word how, it tends to be followed by R, and then it tends to be followed by U.

9:12So how are you is a frequently occurring sequence. So that pattern is learned. And once you learn enough in a big, huge neural network, your ability to predict the next word when you're 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 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.

10:01We know that it's learning and predicting pattern. What we don't know is that because these models are huge, there are billions and billions, hundreds of billions of parameters. And then inside these models, there are these little nodes. Each one of them have a little mathematical function that connects to each other. So how do we know exactly how these billions and billions of parameters learn the pattern? and where is the pattern stored and why sometimes it hallucinates a pattern versus it gives out a correct answer. There is no, not yet precise mathematical explanation. 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, how are you versus how is he, you know?

11:06So that's where the grayness come from. These are large models with behaviors that are not precisely explained mathematically. So from my understanding, these neural networks are made to sort of replicate how the human brain works, basically. I would not use the word replicate. They're inspired. It's inspired. Human brain, 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. The way that the neuron-neuron communication are very complex. Sometimes it's through spike. Sometimes the spike also releases chemicals.

11:54You know, like there is just these kind of nuanced function. and then also the connectivity, how one area of the brain is connected to others, are not the same as neural networks. So we're inspired, but not replicating. 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? So typically, AI model is given a vast amount of data. And then some of the data are labeled with human supervision. Like if I give AI models millions and millions of images, some are labeled cats, dogs, microwaves, chairs, and all that.

12:39And they learn to associate the pattern with the labels. Sometimes in recent, especially in language domain, what we call self-supervision. It's, you give it millions and millions, trillions of documents, and it just keeps learning to predict the next, you know, the next 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 the documents, and that's called self-supervised learning. So whether it's supervised with additional labels or supervised without additional label, it's self-supervised, it starts with data.

13:31Now, data goes into the algorithm, and the algorithm has to have an objective to learn. Typically, in the language model, the objective is to predict the next syllabus as accurately as the training data shows you. in the case of, you know, 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, if during training, if it makes a mistake, you know, if I didn't predict the next word right, or if I label the cat wrong, it goes back and iterates and updates its parameters based on the mistake.

14:19It has some mathematical rules or learning rules to update. And then it just keeps doing that until humans ask it to stop or it no longer updates, whatever stop criteria. And then you're left with a ginormous neural network that's already trained by a ginormous amount of data. And in that neural network, it has all the parameters, the mathematical parameters that's already learned. Now, you can take this 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 what I should say given the new sentence. Like, hello, holla, how is your breakfast today?

15:07And it would predict I had a great breakfast today or whatever. So that's how it's going to be used. So it's so interesting. Like, basically, like chatGBT, 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. So that's super clear. What I don't understand with something like ChatGBT is that it's so good at writing human language, but it's known to make like simple math mistakes, right? 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?

15:45It's because math, the way we do math in 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, you know, the word R and U typically follow, but sometimes it doesn't, right? So I have to learn these patterns. But if I say the word 1 +, it's not like 5 typically follows or 2 typically follows, right? Like there is actually a deeper rule of 1 plus 2 equals 3. Of course, when it has seen enough of that, it should predict three for today's language model. And actually, it does. This is too simple an example. But the point is that math takes a higher level of reasoning than just following statistical patterns.

16:41And large language model, by and large, follows statistical patterns. So some of the mathematical reasoning is lacking.

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22:56Just go to indeed.com slash profiting right now and support our show by saying you heard about Indeed on this podcast. Indeed.com slash profiting. Terms and conditions apply. 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. So can you talk to us about why you titled the book this way? 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. There is the world of AI that I, you know, experience as a scientist.

23:39The book is the coming of age of a young scientist. So I experience the world of science in different stages. 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 or go through that. And then there is like more subtle but profound world like learning to be a human. I know this sounds silly, but especially in the context of an AI scientist, it's really important. Part of the book is exploring my journey of living and taking care of alien parents and how that experience, you know, build my own character, how we help each other, support each other.

24:26And 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 very profound human experience. So it really is different worlds that I experienced, and it's blended into the book. 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. So what do you mean by the social aspect exactly? I started in AI as a very personal journey. It's just a young science nerd, loves an obscure niche, you know, like nobody knows field.

25:16But I'm just fascinated in a private way, that how do we make machines think? How do we make machines see? And that, I was happy. And I would have been content with that, you know, 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, 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. And because it's AI, it has so much, it's inspired by human thinking, It's inspired by human behavior.

26:09It 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 a messier reality that I never really realized. Now, I have a choice. A lot of my fellow scientists would just continue to stay in the lab, which I think is very admirable and respectful. respect it, and still just focus on the size. But my other choice is to recognize as a scientist, as an educator, as a citizen, I have social responsibility. My responsibility is more focused on what I need to educate young people. And 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 size, because it's my responsibility.

27:12I 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 public discourse about AI, and many of them are ill-informed, And that's dangerous, right? That's unfair. That's dangerous. It tends to harm people who are not in the position of power. And I have a responsibility to communicate. And then third, I also feel Stanford, especially as one of America's higher institutions, have a responsibility to help make the world better, to help our policymakers, to help civil society, to help companies, to help entrepreneurs, to educate, to inform, and to give insights.

28:09And that all this is the messiness of meeting the real world. And I feel I shouldn't shy away from that. I should take on that responsibility. Yeah, for sure. You're one of the most knowledgeable people about AI. We need you to tell us what are the 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 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.

Read the full transcript

28:51It is the specific part of AI that makes computers see and understand what it sees. 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. It has cell phones. It has, you know, a cup. It has, you know, monitor. It has, you know, my allergy medicine. And it has a lot of meaning. And more than that, we can also construct, you know, 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 monuments and has created the visual world.

29:46So the ability to see and visually create and understand is so innate in humans. And wouldn't it be great if computers have that ability? And that is what computer vision is. 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. 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. If AI starts to have eyes, wouldn't it just be that they're living and sentient at that point?

30:29So 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 started developing eyes. It was a pinhole that collects light, but it evolved into the kind of eyes, the fish, the octopus, the elephant, the eyes we have. So you actually touched on something really profound. 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. Actually, a neuroscientist or neurophilosopher will probably, you should invite one to debate with you.

31:23For example, does a tiny shrimp using eyes doing things, does it have consciousness or it has just perception? I don't have an answer, honestly. How do you measure consciousness, right? Just because the shrimp can see the rock and climb around, does it mean it's just a sensory reflection, a reflex, or it has a deeper consciousness? I don't know. So just because machines have eyes, does it develop consciousness? It's a topic we can talk about, but I just want to make sure that we are at least on the same page, that just seeing itself doesn't mean it has consciousness. But the kind of visual intelligence we have, like I just described, to understand, to create, to build, to represent a world with such visual complexity.

32:25At least in humans, it does take consciousness. Yeah, everything that you're saying is just so interesting. Even that shrimp example, it's true. 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. 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? Yeah, so 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.

33:04So the whole neural network algorithm. In fact, back in the 1950s and 60s, the computer scientists were inspired by vision neuroscientists. When they were studying cat mammalian visual system, they discovered the kind of hierarchical neurons. And it's because of that it inspired computer scientists to build neural network algorithms. So 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? The ability to see what do we see. Like, humans are not that good at seeing color, for example.

33:54We see color rich enough, but the truth is there's infinite wavelength that defines infinite colors, but we have only probably dozens of colors. So clearly we're not seeing just colors in the same way like if I use a machine to register wavelength. But on the other hand, we see meaning, we see emotion, we see all these things. And it's just incredibly inspiring that we can 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, 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 tremendously helpful.

34:55Machines, right? I don't know. Do you have a Roomba in your house? 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 the most important, right, for example, rescue robots. There are 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. Think about that Fukushima nuclear leak incident.

35:36People 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. 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. 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? Yes. So can you define what human-centered AI is in your own words? Yeah, human-centered AI is a framework of developing and using AI.

36:18And that framework puts human values, human dignity in the center so that we're not developing technology that's harmful to humans. So it's really a way to see technology or use technology in a benevolent way. Now, I'm not naive. I know technology is a double-edged sword. I know that double-edged sword can be used intentionally or unintentionally 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. And it was really inspired by my 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.

37:22And knowing that when business starts to use AI, it impacts lives of every individual, right? So I went back to Stanford and together with my colleagues, we realized as a thought leadership institution, as Americans higher education, a place to educate the next generation students, we should really have a point of view to develop 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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39:19Young and profiter. I know there's so many people tuning in right now that end their workday wondering why certain tasks take forever why they're procrastinating certain things, why they don't feel confident in their work, why they feel drained and frustrated and unfulfilled. But here's the thing you need to know. It's not a character flaw that you're feeling this way. It's actually your natural wiring. And here's the thing. When it comes to burnout, it's really about the type of work that you're doing. Some work gives you energy and some work simply drains you. So it's key to understand your six types of working genius.

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44:57Now, AI is probably gonna create a lot of jobs and I've talked a lot about that with other guests on the podcast. But how do you suggest that we make jobs and take consideration into making sure that AI doesn't take all the jobs? Yeah, so several things, Hala. First of all, why do we have jobs? It's really important to think about it. I think jobs is part of human prosperity because we need that to translate into 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. For many people, it's the meaning of life and self-respect.

45:48So from that point of view, I think we have to recognize jobs shift. Throughout human history, technology makes, and also other factors, creates, destroys, morphs, transforms jobs. But what doesn't change is the need for human prosperity and human dignity. 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. 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 that our clinicians and healthcare workers do are part of humans caring for humans.

46:42And that emotional bond, that dignity, that respect can never be replaced. What is also clear to me is that American healthcare workers, especially nurses, are over-fatigued, overworked. And if technology can be a positive force 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, it is really truly intended to protect the human prosperity and dignity, but augment human capabilities.

47:36So 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, 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 big robotics project to do a thousand human everyday tasks, you know. But at the beginning of this project, it was very important to us that we are creating robots to do these tasks that humans want help. For example, buying a wedding ring. 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?

48:26It'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. 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. So we focus on those tasks that humans prefer robotic help rather than those tasks that humans care and want to do themselves. And that is a way of thinking about human-centered AI. How do we create technology that is beneficial welcomed by humans rather than I just go in and tell you I'm using robots to replace everything you care about.

49:18Another layer, just to finish this topic, is policy layer, right? Like economic, social, 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, 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-Centered AI Institute at Stanford has a digital economy lab. We work with policymakers and thinking about these issues. We try to inform them and provide information and to help move these topics forward in a positive way.

50:08I feel like you're touching on a lot of, you have three aspects to your human-centered AI framework, right? So AI is interdisciplinary. AI needs to be, you know, trying to make sure that we have human dignity and, you know, using it for human good. And then there's also one about intelligence. Can you break down your three pillars of your human-centered AI framework? Yeah, the three pillars of the human-centered AI framework is really about thought leadership in AI and focusing on what higher education institute like Stanford can do. One we talked about is that interdisciplinary, recognizing the interdisciplinary nature of AI, welcoming the multi-stakeholder studies, research, and education policy outreach to make sure that AI is embedded in the fabric of our society today and tomorrow in a benevolent way.

51:04The 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. The third one is about continue to be inspired by human intelligence and develop technology, AI technology that is compatible with humans. Because, you know, human intelligence is very complex. It's very rich. We talked a lot about emotion, intention, compassion. And today's AI lacks most of that. It's pretty far from that. 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.

51:53It draws a lot of energy. Humans, our brain works around 20 watts. 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, come up with E equals MC square. We can, you know, 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. Every time I have an AI episode, I feel like I learned so much that I didn't really realize before. 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.

52:50Do you have any fears around that? I do have fears. I think, you know, who lives in 2024 and don't have fears, you know? And as a citizen of the world, I think our civilization, our species is always defined by the struggle of dark and light and by the struggle and good and bad. I think we have incredible benevolence in our DNA, but we also have incredible 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. 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.

53:45At the end of the day, any hope I have for AI is not about AI, it's about humans. You know, to paraphrase Dr. King, the arc of history is long, 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. Honestly, because if AI is in the hands of bad actors, if AI is concentrated in owning a few powerful people's hand, it can go very wrong, right? We don't need to wait for sentient AI. 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.

54:42Or add a sensor and say, if you see a pedestrian, run it over. Actually, today's technology can do that. You don't need sentient AI. But the fact that we don't have that dystopian scenario is, first of all, human nature is buying large goods. 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, we have societal constraints. We also try to educate the population towards good things, right? So all this is hard work and we need that hard work in AI to ensure it doesn't do bad.

55:32Yeah. 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 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. We can't control it. It has simple processes that are really, really complex. We can predict it all we want, but we'll never really know what nature is going to do. 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.

56:06And it's smarter than us to a degree. 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. What are your thoughts on that? That's a very interesting way to put it. Okay, first time I heard that. I like his way of saying that humans in the face of complexity and powerful things, that we still have a way to cohabit with it. 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. It's not like the earth wants to be a bigger earth or bluer earth. So from that point of view, it's very, very different.

56:51But I appreciate the way he says that. And I also think using his analogy, we also live with other humans. 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. 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, it's not just the hope, the work is that when we create machines that resemble our intelligence, we should prevent it to do similar harms to us, to each other, and try to, you know, bring out the better part of ourselves.

57:45As we wrap up this interview, because I need to get you out on time, 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. What's your advice to them about how to embrace this AI world? 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. 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 believing in that against all odds and chase after the North Star.

58:28And 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. 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. It either can empower you or it's an essential part of your core product or it keeps you competitive. So 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 either in your favor or in your competitor's favor.

59:16So knowing that is important. 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. OK, 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. Maybe it got in the bad hands of some folks. The world that's human centered AI, 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 by and large free market capitalism, that we are allowed as individuals to pursue our happiness and prosperity and respect each other.

1:00:18And 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 alleviate our aging population problems, to make agriculture more effective, to find climate solutions. There is so much AI can do in the world that we still have the good foundation. Dystopia world is AI can be used as a bad tool to topple democracy, right? Disinformation is an incredibly harmful way of harming democracy and the civil life we have right now, if it's completely concentrated in power, whether it's state power or individual power, it makes the rest of the society much more subject to the will and possibly wrath of that power, whether it's AI or not.

1:01:40We have seen in human history that concentrated power is always bad. And concentrated power using powerful technology is not a recipe for good. 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. Please let us know where we can learn more about you and everything that you do. Thank you, Hala. Thank you for promoting my book. And please constantly check in with Stanford Human Center AI Institute newsletter and website. Amazing. We'll stick all those things in the show notes.

1:02:18Dr. Lee, thank you for joining us on Young and Profiting Podcast. Thank you, Hala.

From the publisher

Now on Spotify Video! As a Stanford AI scientist, Dr. Fei-Fei Li realized that artificial intelligence had advanced to a point where it was transforming society faster than most people could understand. Confronted with the ethical, social, and economic risks of this rapid growth, she felt a deep responsibility to guide AI toward serving humanity. This inspired her to co-found the Stanford Institute for Human-Centered AI, developing a framework that prioritizes humankind. In this episode of the AI Vault series, Dr. Fei-Fei shares how we can harness AI responsibly and design technology that enhances, not replaces, human potential.

In this episode, Hala and Dr. Fei-Fei will discuss:

(00:00) Introduction

(02:33) The Evolution and Limits of Artificial Intelligence

(09:56) How AI Models Like ChatGPT Are Trained

(14:12) Dr. Fei-Fei’s Journey and Responsibility in AI

(19:15) How Computer Vision Brings AI to Life

(25:59) Ethical AI, Human Dignity, and the Future of Work

(32:57) The Three Pillars of Human-Centered AI

(35:10) Confronting Fears of AI in Action

(39:59) AI in Business: How Entrepreneurs Can Thrive

Dr. Fei-Fei Li is a professor of computer science at Stanford University and co-director of the Stanford Institute for Human-Centered AI. Her groundbreaking work in computer vision AI has shaped how machines see and understand the world. Dr. Fei-Fei is the author of The World's I See, a memoir that weaves together her personal journey with the history and development of artificial intelligence.

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

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More from Young and Profiting (YAP) with Hala Taha: Entrepreneurship and Self-Improvement Podcast