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

Summary

---

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

Young and Profiting with Hala Taha
Episode Date: November 7, 2025
Guest: Dr. Fei-Fei Li, Stanford Professor, Co-Director of the Human-Centered AI Institute, Pioneer of ImageNet

---

Episode Overview

This episode dives deep into the evolution, capabilities, and potential of artificial intelligence, exploring both technical and philosophical dimensions with Dr. Fei-Fei Li—widely considered the "godmother of AI." Together, Dr. Li and host Hala Taha discuss the future of AI, the importance of a human-centered approach, the social responsibilities of scientists, and the immense promise and peril AI holds for society, business, and individual well-being.

The episode is a masterclass on not just the nature of AI technology, but also on its societal impact, how to align AI’s development with human values, and how entrepreneurs and citizens can think constructively about the future.

---

Key Discussion Points & Insights

1. Understanding What AI Can and Can’t Do (03:40)

• AI’s Reach:
  Dr. Li highlights how AI (and specifically machine learning) already permeates daily life: from online recommendations and navigation apps to special effects in movies.
  “Machine learning and AI is already everywhere.” – Dr. Fei-Fei Li [03:40]

• Current Limitations:
  AI cannot substitute complex human reasoning or creativity that combines logic, emotion, and situational awareness.
  “No machines today can help me to fold my laundry or cook my omelet.” – Dr. Fei-Fei Li [03:40]

• Memorable Analogy:
  “The most advanced computer AI algorithm will still play a good chess move when the room is on fire.” [03:40]

---

2. Demystifying Machine Learning vs. AI (06:34)

• Distinction:
  AI is the broader scientific field aiming to make machines think intelligently, while machine learning is its mathematical toolset grounded in neural network algorithms.
  “Artificial intelligence is a scientific field ... the tools we use ... is dominated by machine learning.” – Dr. Fei-Fei Li [06:34]

---

3. The Mystery of How AI Learns (07:54)

• The "Gray Box" Model:
  AI is neither a total mystery nor fully transparent—there’s still much the field doesn’t mathematically understand about why large models behave the way they do.
  “Depending on your understanding ... it’s either darker gray or lighter gray.” – Dr. Fei-Fei Li [07:54]
  Key challenges include tracking how billions of parameters encode patterns and why models “hallucinate.”

---

4. How AI Models Are Trained (12:08)

• Supervised vs. Self-Supervised Learning:
  • Supervised: learning from labeled data (e.g., “cat,” “dog” in images).
  • Self-Supervised (esp. language): learning by predicting the next word in massive corpora.
• Iterative Improvement:
  Models continually update parameters based on prediction errors, accumulating intelligence over vast datasets.
  “If during training it makes a mistake, it goes back and iterates and updates its parameters.” – Dr. Fei-Fei Li [12:08]

---

5. Why AI Struggles with Math (15:33)

• Pattern Recognition vs. Higher Reasoning:
  Language is based on statistical patterns; mathematics requires logical rules, which large language models don’t fundamentally possess.
  “Math takes a higher level of reasoning than just following statistical patterns.” – Dr. Fei-Fei Li [15:33]

---

6. Dr. Fei-Fei Li’s Book: The Worlds I See (22:02)

• Multiple Dimensions of Experience:
  Explores Dr. Li’s scientific journey, immigrant experience, and personal growth—especially through caregiving for her parents—which shaped her sense of responsibility as a scientist.
  “It really is different worlds that I experience, and it’s blended into the book.” – Dr. Fei-Fei Li [22:13]

---

7. The Social Responsibility of AI Scientists (23:55)

• As AI’s societal impact grows, Dr. Li emphasizes the need for public education, policy engagement, and communication to prevent misconceptions and misuse.
  “There’s just so much public discourse about AI and many of them are ill-informed and that’s dangerous.” – Dr. Fei-Fei Li [23:55]

---

8. Computer Vision – Giving AI Sight (27:41)

• What Is Computer Vision?
  “The specific part of AI that makes computers see and understand what it sees.” – Dr. Fei-Fei Li [27:41]

• Human Inspiration:
  The desire is to replicate the human ability to see meaning, not just shapes and colors.

• Are Eyes = Consciousness?
  Dr. Li explores whether giving AI "eyes" leads to consciousness:
  “Just seeing itself doesn’t mean it has consciousness.” – Dr. Fei-Fei Li [29:24]

---

9. Biological Inspiration in AI (31:48)

• Neural Networks:
  Early neural network models were inspired by research on mammalian vision systems, though human and AI brains work differently.
• Functional Benefit:
  Computer vision can aid the visually impaired, power home robotics, or enable rescue robots in hazardous environments.
  “It would be amazing if robots can do that. And that needs seeing.” – Dr. Fei-Fei Li [33:54]

---

10. Defining Human-Centered AI (35:06)

• Focuses on developing and applying AI technologies guided by human values, dignity, and societal benefit.
• “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.” – Dr. Fei-Fei Li [35:06]
• Inspired by Dr. Li’s experiences in industry and her leadership at Stanford.

---

11. AI and the Future of Jobs (43:02)

• Jobs as Human Dignity:
  Employment is critical not just for income but also for purpose and respect.
• Augmentation, Not Replacement:
  Tasks that humans prefer not to do (e.g., cleaning toilets) are good targets for automation, while emotionally meaningful or dignity-centered work should be preserved.
  “We focus on those tasks that humans prefer robotic help rather than those tasks that humans care and want to do themselves.” – Dr. Fei-Fei Li [43:02]
• Policy Is Essential:
  Technologists must work with policymakers and economists to ensure positive outcomes.

---

12. Three Pillars of Human-Centered AI (48:21)

1. Interdisciplinarity:
   Integrating policy, social science, law, business, and ethics into AI research and education.
2. Augmenting Humans:
   Enhance human capabilities, well-being and dignity—not just automation.
3. Inspired by Human Intelligence:
   Creating more efficient and emotionally resonant systems; current AI is energy-intensive whereas the human brain is vastly more efficient.
   “Our brain works around 20 watts ... we can do so many things.” – Dr. Fei-Fei Li [48:21]

---

13. The Threat of AI: Apex Intelligence & Power Concentration (50:42)

• Fears grounded in human nature:
  “AI as a technology can be used by the badness. So from that point of view, I do have fear.” – Dr. Fei-Fei Li [50:42]

• The Real Danger:
  Today’s risks are not from sentient AI, but from misuse and concentrated power.
  “If AI is concentrated in only a few powerful people's hands, it can go very wrong.” – Dr. Fei-Fei Li [50:42]

---

14. Can We Coexist With AI? (54:05)

• AI ≠ Nature:
  Dr. Li pushes back on the analogy that AI is like uncontrollable nature; unlike nature, AI is programmable and can have collective intent (for good or for ill).
• Human Agency:
  It’s up to humans to design systems that promote good and prevent harm. “When we create machines that resemble our intelligence, we should prevent it to do similar harms to us, to each other, and try to bring out the better part of ourselves.” – Dr. Fei-Fei Li [55:14]

---

15. Advice to Entrepreneurs (55:51)

• Find Your North Star:
  “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. And that is the core of what entrepreneurship is about.” – Dr. Fei-Fei Li [55:51]
• AI as Essential Knowledge:
  “It’s possible that AI will play either in your favor or in your competitor’s favor. So knowing that is important.” – Dr. Fei-Fei Li [55:51]

---

16. Future Scenarios: 2034 With and Without Human-Centered AI (57:33)

• Best case:
  A thriving democracy, empowered individuals, scientific breakthroughs, driverless cars, personalized education, cures for diseases, sustainable agriculture, and climate solutions.
• Worst case:
  AI used to undermine democracy, sow disinformation, concentrate power, and erode civil society.
  “Concentrated power using powerful technology is not a recipe for good.” – Dr. Fei-Fei Li [57:33]

---

Notable Quotes

| Timestamp | Quote | Speaker | |-----------|----------------------------------------------------------------------------------------|------------------| | 03:40 | “Machine learning and AI is already everywhere.” | Dr. Fei-Fei Li | | 07:54 | “It’s neither a white box nor black box. I would call it gray box... darker or lighter.”| Dr. Fei-Fei Li | | 12:08 | “If during training it makes a mistake, it goes back and iterates and updates...” | Dr. Fei-Fei Li | | 15:33 | “Math takes a higher level of reasoning than just following statistical patterns.” | Dr. Fei-Fei Li | | 23:55 | “There’s just so much public discourse about AI and many of them are ill-informed...” | Dr. Fei-Fei Li | | 35:06 | “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.” | Dr. Fei-Fei Li | | 43:02 | "We focus on those tasks that humans prefer robotic help rather than those tasks that humans care and want to do themselves." | Dr. Fei-Fei Li | | 48:21 | "Our brain works around 20 watts ... we can do so many things." | Dr. Fei-Fei Li | | 50:42 | “AI as a technology can be used by the badness. So from that point of view, I do have fear.” | Dr. Fei-Fei Li | | 55:51 | “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. And that is the core of what entrepreneurship is about.” | Dr. Fei-Fei Li | | 57:33 | “Concentrated power using powerful technology is not a recipe for good.” | Dr. Fei-Fei Li |

---

Key Timestamps for Important Segments

• [03:40] – State of AI: Present strengths and real-world penetration.
• [07:54] – How neural networks “learn” (and what remains unknown).
• [12:08] – The process of AI model training.
• [22:02] – The story and theme of “The Worlds I See.”
• [27:41] – Explaining computer vision and its inspiration from biology.
• [35:06] – The concept of human-centered AI.
• [43:02] – How AI impacts jobs and how to ensure it augments, not replaces.
• [48:21] – Three pillars of human-centered AI.
• [50:42] – Risks of AI misuse and concentration of power.
• [55:51] – Advice for entrepreneurs in the AI age.
• [57:33] – Visioning 2034: Human-centered vs. dystopian AI futures.

---

Conclusion

Dr. Fei-Fei Li’s perspective blends extraordinary technical insight with humility, responsibility, and an urgent call for thoughtful engagement. Her message: AI’s future is fundamentally about human choices, values, and collective will. Both risk and reward are immense, but through education, policy, cross-disciplinary dialogue, and an unwavering focus on human flourishing, we can steer AI to become humanity's greatest ally.

Learn more:

• Dr. Li's book: The Worlds I See
• Stanford Human-Centered AI Institute newsletter and website

---

Host: Hala Taha
Guest: Dr. Fei-Fei Li

(For full context and detail, listen to the episode at the provided timestamps.)

Transcript

[A]

Hello my young and profiters. I know most of us, if not all, have been in a situation where you open up your closet and you suddenly feel like you've got nothing to wear. That stress is real, especially if I've got a big speaking engagement or a major event and I need an outfit that makes me feel confident and great about myself. That's why I love Revolve. It's my go to for every occasion. From weddings to work events to going out at night. I I always wear revolve with over 1200 brands and 100,000 styles. They've got everything from elevated basics to statement pieces. Plus they drop new arrivals daily and the curated edits make finding outfits easy and fun. Whether it's a weekend away, a big.

[B]

Night out, or just a little style.

[A]

Refresh, your dream wardrobe is just one click away. Head to Revolve.com profiting shop my edit and take 15% off your first order with code profiting fast two day shipping, easy returns. Sometimes I do overnight delivery when I need an outfit. In a pinch, it's literally the only place you need to shop from. That's Revolve.com profiting to get my favorites and get 15% off your first order with code profiting. Offer ends November 9th. So happy shopping.

[C]

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

[A]

Dr. Fei Fei Li is a professor of Computer Science at Stanford University, as.

[B]

Well as the co Director of the.

[A]

Stanford Institute for Human Centered AI.

[B]

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

[C]

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

[B]

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?

[C]

AI 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.

[A]

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. In this conversation, we'll talk about how computer vision models are trained, what they can and cannot do, and 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.

[C]

Thank you, Hala. I'm very excited to join this show.

[B]

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

[C]

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. So what can I do right 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 recommendation, that's machine learning. If you watch a movie, there is a lot of machine learning. Computer vision, computer graphics to make special effects, 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, it cannot take away complex human reasoning. It cannot create in a way humans create in the combination of both reasoning, logic, but also, you know, beauty, emotion, it. There is, you know, there's 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. 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 our own thinking, our own emotion, as well as the surrounding. And that is not what AI is today.

[A]

So insightful.

[B]

And I love that you said that it's sort of like an evolution of machine learning. Because I always wondered, well, what's the difference between machine learning and AI? It sounds pretty similar. So machine learning was almost like the basics, the tool of AI.

[C]

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. 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.

[B]

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.

[C]

Mathematica.

[B]

Yeah, he did the Wolfram project. And your language.

[A]

Wolfram. Yeah.

[B]

So I just interviewed him and we talked about ChatGPT and how ChatGPT works. 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, would create all complexity, that they could give ChatGPT 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?

[C]

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. So it's neither a white box nor black box. I would call it 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 ChatGPT 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, right. Parts of words are connected with each other. There are patterns to see, right? If you see the word how, it tends to be followed by are, and then it tends to be followed by you. So 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 are given a word is really, really quite amazing. Amazingly high, to the point that it can converse 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, geopolitical fact, 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 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 ChatGPT gives you the word how are you versus how is he? So that's where the grayness come from. These are large models with behaviors that are not precisely explained mathematically.

[B]

So from my understanding, these neural networks are made to sort of replicate the how the human brain works. Basically, there's.

[C]

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

[A]

It's inspired.

[C]

Human brain is, it has, 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 the neuron, neuron communication are very complex. Sometimes it's through spike, sometimes the spike also releases chemicals. There is just these kind of nuanced function. And 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.

[B]

That's a really, really helpful distinction right there.

[C]

Yes.

[B]

So talk to us about how AI models are trained. Like, how does AI learn typically?

[C]

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. And they learn to associate the pattern with the labels. Sometimes in recent, especially in language domain, what we call self supervision, you give it millions and millions trillions of documents and it just keeps learning to predict 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 is self supervised. It starts with data. Now 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 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. 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. It has some mathematical rules or learning rules to update. And then it just keeps doing that till it, you know, humans ask it to stop or it no longer updates, you know, whatever, stop criteria. And then you're left with a ginormous neural network that's already trained by 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 parameter it has learned, it predicts what I should say given the new sentence, like hello, Hala, how is your breakfast today? And it would predict I had a great breakfast today or whatever. So, so that's how it's gonna be used.

[A]

So, so interesting.

[B]

Like, basically like ChatGPT, 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 ChatGPT 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?

[C]

It'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 the word R and you typically follow. But sometimes it doesn't. So I have to learn these patterns. But if I say the word one plus it's not like five typically follows or two typically follows. There is actually a deeper rule of one plus two equals three. 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 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.

[A]

Yeah fam One of the best parts of my job is that it takes me everywhere. I've traveled for interviews, speaking gigs and podcasting events, and this fall I'll be heading to Nashville and then LA for some exciting podcast interviews. I love that traveling gives me a chance to try new things, meet incredible people, and experience different cultures firsthand. Along the way, I've booked some unforgettable homes on Airbnb that felt warm and welcoming the moment that I arrived. These experiences really inspired me to start thinking about hosting my own place while I'm away for travel. But here's the truth. I could really use some support to manage some of the details of hosting. That's why I'm excited about Airbnb's Co Host Network. A co host is a vetted local expert who partners with you to make sure guests are always having an amazing stay. They handle guest communication, booking supplies, and even add thoughtful touches to elevate your space. You don't need to be on site or hands on to host like a pro. Just team up with a co host and make it happen. Find yourself a co [e-mail] host hey app fam. We talk a lot about protecting our businesses, but let's talk about protecting ourselves and our families for once. These days we share so much information online and most people don't realize that data brokers collect and sell this personal information. Your phone number, your home address, even your family details can be listed out for anybody to buy. That's how risks like stalking or identity theft happen. That's why I Trust and recommend DeleteMe. Delete Me is something that I personally use to remove my data online. They help remove private data from hundreds of data broker websites and their privacy experts. Keep an eye out on those sites and take care of my removals from me all year long. So I don't even have to think about it anymore. After I signed up, I got my first privacy report within a week and I saw dozens and dozens of sites that they took my information off of and it was completely eye opening. I feel so much safer being a creator entrepreneur with my face out there.

[B]

For the world now that I know.

[A]

That nobody can find my home address, nobody can find my family details. Thanks to Delete me Get 20% off Delete me Consumer plans when you go to JoinDeleteMeMe.com Profiting and use promo code Profiting at checkout, that's P R O F I T I N G at checkout. Again, that's JoinDeleteMe.com profiting use code profiting at checkout to get 20% off your consumer plan. Hey young and profitters, I got a question for you. Are you still running your business on a clunky old phone system? Because that's like competing with one hand tied behind your back. I mean, I've been there. Missing calls, missing deals. But not anymore. Quo formerly OpenPhone, is the modern way to run your business communications. It's rated the number one business phone system for 2025 with over 3,000 reviews. On G2, you can run everything from one app on your phone or computer. Your team can share one number, like a shared inbox, respond faster and keep every customer happy. And when you finally log off, CLOSE AI steps in, taking calls, logging notes, and even qualifying leads after working hours. It's like having a 24. 7 assistant that never sleeps. Quo is offering my listeners 20% off your first six months at quo.com profiting that's Q-U-O.com profiting. You can even keep your existing phone number for free. Quo no missed calls, no missed customers. Hey young and profiters, I got a question for you. Are you still running your business on a clunky old phone system? Because that's like competing with one hand tied behind your back. I mean, I've been there. Missing calls, missing deals. But not anymore. Quo formerly OpenPhone, is the modern way to run your business communications. It's rated the number one business phone system for 2025 with over 3,000 reviews. On G2, you can run everything from one app on your phone or computer. Your team can share one number like a shared inbox, respond faster and keep every customer happy. And when you finally log off, CLOSE AI steps in, taking calls, logging notes, and even qualifying leads after working hours. It's like having a 247 assistant that never sleeps. Quo is offering my listeners 20% off your first six [e-mail] profiting. That's Q U O.com profiting. You can even keep your existing phone number for free. Quo no missed calls, no missed customers. What's up yap gang? If you've ever had to hire, you know the stress of waiting too long to fill a role. Projects slow down, workloads pile up, and frustration sets in. That's why when it comes to hiring, Indeed is all you need. Other job sites make it tough to get noticed, but Indeed Sponsored Jobs helps you stand out and hire fast by putting your post right at the top where relevant candidates can actually see it. And it works. Sponsored jobs get 45% more applications than non sponsored ones. Plus you're not locked into contracts or monthly fees. You only pay for results. And here's how fast Indeed really is in the minute I've been talking to you. 23 hires were made on Indeed Worldwide. There's no need to wait any longer. Speed up your hiring right now with Indeed and listeners of the show will get a $75 sponsored job credit. To get your job's more visibility@ Indeed.com profiting just go to Indeed.com profiting right now and support our show by saying you heard about Indeed on this podcast. Indeed.com profiting terms and conditions apply. Hiring Indeed is all you need.

[B]

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 about why you titled the book this way?

[C]

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 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, 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 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 a AI scientists, it's really important. Part of the book is exploring my journey of living and taking care of ailing parents and how that experience build my own character. How we help each other support each other. And 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. So it really is different worlds that I experience and it's blended into the book.

[B]

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?

[C]

I started in AI as a very personal journey. It's just a young science nerd loves an obscure niche, 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? 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 lab 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, 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 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 respected, and still just focus on the science. 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. 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 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. And that all this is the messiness of, of meeting the real world. And I feel I shouldn't shy away from that. I should take on that responsibility.

[B]

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

[C]

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. And this is very profound. When humans open our eyes and 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's. 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. So 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.

[B]

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?

[C]

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 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 touch on something really profound. This is extremely innate, embedded into our development of our intelligence. 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. For 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 reflex or it has a deeper consciousness? I don't know. 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, visual complexity, at least in humans, it does take consciousness.

[B]

Yeah, it's. 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, doesn't mean that it's actually conscious. It could be, to your point, just all like reflexes.

[A]

And that makes it a little less.

[B]

Scary if machines end up having eyes. So how are you replicating biological processes like vision in computers now?

[C]

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. So that 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 discover the hierarchical neurons. And it's because of that, it inspire computer scientists to build neural network algorithms. 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, the ability to see. What do we see? Humans are not that good at seeing color. For example, 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. 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 rich world we see, it will be tremendously helpful. Machines, I don't know. Do you have a Roomba in your house?

[B]

Yeah.

[C]

Right. So it almost is kind of seeing. It's not seeing the same way we are, but it's kind of seeing and mapping. But one day I hope I not only have a room, but 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'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. Think about that Fukushima nuclear leak incident. 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.

[B]

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, you 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?

[C]

Yeah. Human centered AI is a framework of developing and using AI. And that framework puts humans, 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 time in 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 and knowing that when business start to use AI it impacts lives of every individual. 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, to develop and stay at the forefront of the development of this technology. This is how we formulated the human centered AI framework.

[A]

What's up Yap fam? I think we can all agree when your skin looks good, you feel your best. But sometimes it feels like beauty. Brands want us to own everything. Honestly, more isn't better. Especially for busy people like us. And the older I get the more that I like to have classics when it comes to my makeup and I love that. Merit is a minimalist beauty brand that makes elevated makeup and skincare designed to help you look put together in just minutes. Lately I've been loving Merit's great skin serum. Even on a total no makeup day, it's all you need. It instantly hydrates and plumps you for a fresh, dewy look, giving you that natural, healthy glow. Now of course I always get glammed up from my interviews, but on a day to day when I'm just going to the gym or running errands, I use Merit Beauty. If you need a little polish, their bestseller, the Minimalist acts as both foundation and concealer, so it gives you quick coverage without layering on a bunch of products. It's time for your skin care to meet the reality of your daily routine with Merit Beauty. Right now, Merit Beauty is offering our listeners their signature makeup bag with your first order at merit beauty.com that's me. R I T beauty.com to get your free signature makeup bag with your first order. Merit beauty.com Yap gang if you run a small business, you know there's nothing small about it. As a business owner, I get it. My business has always been all consuming. Every decision feels huge and the stakes feel even bigger. What helped me the most when things get overwhelming was finding the right platform with all the tools I need to succeed. That's why I trust Shopify. Because they get it. They started small too. Shopify powers millions of businesses around the world, from huge brands like Mattel and Gymshark to brands just getting started. You can handle everything in one place. Inventory, payments, analytics, marketing and even global selling in over 150 countries. And with 99.99% uptime and the best converting checkout on the planet, you'll never miss a sale again. Get all the big stuff for your small business right with Shopify. Sign up for your $1 per month trial period and start selling today at shopify.com profiting go to shopify.com profiting again, that's shopify.com profiting yap gang as entrepreneurs, we know one of the biggest obstacles to scaling is finding the right team fast. I know firsthand how agonizing it can be when you're ready to hire, but the perfect person takes forever to find. In fact, I was recently texting with my girl chat of entrepreneurs and one of the girls was saying, don't you guys feel like hiring is the worst part of entrepreneurship? That's because it is. And that's where Indeed comes in. Because when it comes to hiring, Indeed is all you need. Their sponsored jobs help you stand out so your listing reaches the right people quicker and it really makes a difference. Sponsored jobs get 45% more applications than non sponsored ones. I love that Indeed doesn't lock you into contracts or subscriptions. You only pay for your results. And get this, 23 hires are made every minute on Indeed, according to Indeed Data Worldwide. There's no need to wait any longer. Speed up your hiring right now with Indeed and listeners of the show will get a $75 sponsored job credit. To get your jobs more visibility at indeed.comprofiting just go to indeed.comprofiting right now and support our show by saying you heard about Indeed on this podcast and indeed.com profiting terms and conditions apply. Hiring Indeed is all you need. Yeah fam One of the best parts of my job is that it takes me everywhere. I've traveled for interviews, speaking gigs and podcasting events, and this fall I'll be heading to Nashville and then LA for some exciting podcast interviews. I love that traveling gives me a chance to try new things, meet incredible people, and experience different cultures firsthand. Along the way, I've booked some unforgettable homes on Airbnb that felt warm and welcoming the moment that I arrived. These experiences really inspired me to start thinking about hosting my own place while I'm away for travel. But here's the truth. I could really use some support to manage some of the details of hosting. That's why I'm excited about Airbnb's Co Host Network. A co host is a vetted local expert who partners with you to make sure guests are always having an amazing stay. They handle guest communication, booking supplies, and even add thoughtful touches to elevate your space. You don't need to be on site or hands on to host like a pro. Just team up with a co host and make it happen. Find yourself a co [e-mail] host Yap Gang the origins of this podcast were once just a dream. That dream became a podcast you're listening to today, which has since grown into a thriving media business. Taking your business to the next level is a dream lots of us share, but too often it just remains a dream. A we hold ourselves back thinking, what if I don't have the skills? What if I can't do it alone? I want you to turn those what ifs into why nots and help your business soar with Shopify. Shopify is the commerce platform behind over 10% of US E commerce helping millions of businesses from startups to household names like Gymshark and Mattel. They handle everything from website design to inventory management and global shipping so you can focus on your vision. Need to find new customers. Their built in marketing tools have you covered. Want to sell globally they help you sell in over 150 countries. Plus their checkout is the best converting on the planet so you'll never miss a sale. Turn those what ifs and keep giving those big dreams their best shot with Shopify. Sign up for your $1 per month trial and start selling today at shopify.com profiting go to shopify.com profiting again that's shopify.com profiting and one of the biggest.

[B]

Fears that people have with AI is that AI is going to replace all of our jobs. 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. But how do you suggest that we make jobs and take consideration into making sure that AI doesn't take all the jobs?

[C]

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 is the meaning for many people. It's the meaning of, you know, life and self respect. So 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. And 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, 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. 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 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 robotics project to do 1000 human everyday tasks. 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? It's not that hard to open, 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 robot to replace everything you care about. Another 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, and to, to help move these topics forward in a positive way.

[B]

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. 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?

[C]

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. 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. The third one is about continue to be inspired by human intelligence and develop technology, AI technology that is compatible with humans. Because human intelligence is very complex, it's very rich. We talk 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. It draws a lot of energy. Humans, our brain works around 20 watts. That is dimmer than the dimmest light bulb in your house. Yet we can do so many things. We can create the pyramid, we can come up with E equals MC squared, we can 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.

[B]

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, it's going to take over the world, it's going to control humans. Do you have any fears around that?

[C]

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, our civilization, our species is always defined by the struggle of dark and light and by the struggle and good and bad. I, I think it's, you know, we have incredible benevolence in our DNA, but we also have incredible, you know, 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. At 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, that 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 only a few powerful people's hands, it can go very wrong. 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 or 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 by and large good. Our car factory workers are business leaders in building cars. Nobody thinks about doing that. We also have laws if someone is trying to do harm. If we have societal constraints, we also try to educate the population towards good things. So all this is hard work. And we need that hard work in AI to ensure it doesn't do bad.

[B]

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 at ease with AI and the fact that it could get really smart. He said, where living in AI, we live in nature. Nature is so complex, we. 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. 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. 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?

[C]

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 cohabitate with it. I don't agree. Nature's 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. But 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 bring out the better part of ourselves.

[B]

As we wrap up this interview. Because I need to get you out on time, I wanted to ask you a couple of questions. First off, to all the young entre, 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?

[C]

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. 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. 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. So knowing that is important.

[B]

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. 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.

[C]

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 individual to pursue our happiness and prosperity and respect each other. 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 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. Disinformation is an incredibly harmful way of harming democracy. And there's 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. We have seen in human history that concentrated power is always bad and concentrated power using powerful technology is not a recipe for good.

[B]

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.

[C]

Thank you Hala. Thank you for promoting my book and please constantly check in with Stanford Human Centered AI Institute newsletter and website.

[B]

Amazing. We'll stick all those things in the show notes. Dr. Lee, thank you for joining us.

[A]

On Young and Profiting Podcast.

[C]

Thank you Hala.