Fei Fei Li (on a human-centered approach to AI)

Summary

Armchair Expert with Dax Shepard – Episode Summary: Fei Fei Li on a Human-Centered Approach to AI

Release Date: December 11, 2024

In this insightful episode of Armchair Expert with Dax Shepard, host Dan Rather and co-host Monica Padman engage in a profound conversation with renowned computer science professor Dr. Fei Fei Li. Dr. Li, known for her expertise in computer vision, machine learning, and cognitive neuroscience, shares her personal journey, professional milestones, and her visionary approach to artificial intelligence (AI).

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1. Introduction and Personal Beginnings (00:00 – 02:00)

Dan Rather opens the episode by highlighting Dr. Fei Fei Li's impressive background, emphasizing her journey from immigrating to the United States and mastering the English language to excelling in the field of AI.

Dan Rather (00:05): "What makes this episode so special is Dr. Fei Fei Li's personal story is so compelling."

2. Early Life and Immigration Challenges (03:02 – 16:04)

Dr. Li delves into her childhood in China, detailing the cultural and personal challenges she faced as an immigrant. She recounts the emotional turmoil of adjusting to a new country, learning a new language, and navigating the complexities of adolescence without her father’s constant presence.

Dr. Fei Fei Li (11:17): "A lot of this is hindsight, because I didn't talk to my mom in this way."

3. The Impact of Mentorship: Mr. Bob Sabella (17:04 – 33:10)

A pivotal moment in Dr. Li's life was meeting her math teacher, Mr. Bob Sabella. Despite his tough demeanor, Mr. Sabella recognized her potential and provided unwavering support, including financially assisting her family to start a dry-cleaning business during her high school years.

Dan Rather (18:02): "How inspirational. How instrumental was he in you finding your way to Princeton?"

Dr. Fei Fei Li (22:51): "He was instrumental... he helped me to be grounded."

4. Academic Pursuits and ImageNet Creation (33:10 – 58:45)

Dr. Li shares her academic journey, from earning a degree in physics from Princeton to pursuing graduate studies at Caltech. She discusses the inception of ImageNet, a groundbreaking large-scale image dataset that revolutionized computer vision and AI. The creation of ImageNet was fraught with challenges, including data curation and the sheer volume of required images, but breakthroughs like Amazon Mechanical Turk facilitated its completion.

Dr. Fei Fei Li (55:53): "We decided that was the missing piece. So we need to create a big data set. How big is big?"

Dan Rather (53:45): "They are years down the path and they're calculating how much further it's going to be."

5. Sabbatical at Google and Observations on AI (59:48 – 78:15)

During her sabbatical at Google (2017-2018), Dr. Li gained firsthand experience of AI's rapid advancements and its profound societal impacts. She witnessed both the promising applications of AI in various industries and the emerging ethical concerns, such as bias in facial recognition and the implications of autonomous technologies.

Dr. Fei Fei Li (64:08): "AI can help discover drugs, AI can help break through fusion, AI can personalize education... AI is much like electronic electricity."

6. Advocating for Human-Centered AI (78:15 – 76:05)

Dr. Li passionately articulates her vision for human-centered AI, emphasizing that AI development should prioritize human dignity, well-being, and societal values. She outlines practical frameworks for integrating ethical considerations into AI research, product development, and legislative policies. Dr. Li advocates for interdisciplinary collaboration and robust regulatory frameworks to ensure AI technologies serve humanity positively.

Dr. Fei Fei Li (65:00): "Any technology, AI or any other technology should be human centered."

Dan Rather (65:30): "They’re neutral. There we go."

7. Global Cooperation and AI Governance (75:06 – 78:15)

Addressing the competitive global landscape, Dr. Li emphasizes the necessity of international cooperation to guide AI development responsibly. She draws parallels with nuclear technology governance, suggesting that while AI offers vast benefits, it also requires collective efforts to mitigate its potential risks.

Dr. Fei Fei Li (73:23): "AI can help discover drugs... how do we keep the dark use of AI at bay?"

Dan Rather (75:06): "This is a moment where we have to really figure out how to cooperate a bit, because everyone will experience the downside if we don't."

8. Conclusion and Final Thoughts (78:08 – 78:15)

Wrapping up the discussion, Dr. Li reiterates the importance of human-centered AI and the role of creators and interdisciplinary voices in shaping AI's future. Her heartfelt gratitude towards mentors and collaborators underscores the collective effort required to advance AI responsibly.

Dr. Fei Fei Li (78:08): "Creators' voices are so important... collaboration with humans is so important."

Dan Rather (78:15): "Thank you for trying with all your might to make this a human-centered development."

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Notable Quotes

• Dr. Fei Fei Li (35:20): "It was an identity. For some reason, this girl who works in dry cleaners just wanted to be a scientist."

• Dr. Fei Fei Li (43:06): "Artificial intelligence software should be able to learn."

• Dr. Fei Fei Li (64:46): "We must center the value of technology's development, deployment, and governance around people."

• Dr. Fei Fei Li (73:23): "AI is much more like electronic electricity that it is like nuclear physics."

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Key Takeaways

• Personal Resilience: Dr. Li's journey from an immigrant child facing cultural and linguistic barriers to a leading AI expert underscores the power of resilience and mentorship.

• ImageNet's Impact: The creation of ImageNet was a monumental achievement that significantly advanced the field of computer vision, demonstrating the critical role of large-scale datasets in AI development.

• Human-Centered AI: Dr. Li advocates for an AI paradigm that prioritizes human values and ethical considerations, ensuring that technological advancements benefit society as a whole.

• Global Cooperation: Addressing AI's challenges requires international collaboration, much like historical efforts in nuclear governance, to balance innovation with ethical responsibility.

• Interdisciplinary Approach: Effective AI governance and development necessitate collaboration across various disciplines, including philosophy, anthropology, and psychology, to fully understand and guide AI's societal impact.

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This episode offers a compelling blend of Dr. Fei Fei Li's personal narrative and her visionary insights into the future of AI. Her emphasis on human-centered development provides a crucial perspective in the ongoing dialogue about AI's role in society.

Transcript

[Dan Rather]

Welcome, welcome, welcome to Armchair Expert Experts on Expert. I'm Dan Rather and I'm joined by Modest Mouse.

[Monica Padman]

Hi.

[Dan Rather]

Hello. I've been talking about this book quite a bit over the last six months. The World's I See Curiosity, Exploration and Discovery at the dawn of AI by Dr. Fei Fei Li. She is an expert on computer vision, machine learning, and cognitive and computational neuroscience. She is a computer science professor at Stanford University and founding director of Stanford Institute for Human Centered Artificial Intelligence. We've had a lot of AI But I'll say that this. What makes this episode so special is Dr. Fei Fei Li's personal story is so compelling. I mean, the fact that people can land in this country, not speaking English, deep into school and fucking pick it all up and then master all these.

[Monica Padman]

Fields, become better than everyone.

[Dan Rather]

Oh my God. It's so impressive.

[Monica Padman]

Yeah.

[Dan Rather]

Oh, I loved her. Please enjoy Dr. Fay Feli. We are presented by Amazon Prime. It's more than just fast free shipping. Whatever you're into, it's on Prime. So keep listening, keep watching, keep on keeping on. To hear more about why we love prime during the fact check.

[Dr. Fei Fei Li]

This is a very, very sinking couch. I know.

[Dan Rather]

We're off to a terrible start. You want to swap? You want to sit in this chair? You can.

[Dr. Fei Fei Li]

I feel like I'm going through therapy very soon.

[Monica Padman]

Yeah, well, that is the goal. We do want people to feel very relaxed. Too comfortable. Really?

[Dan Rather]

Yeah. If the spirit moves you to lay down supine, you're invited to do so.

[Dr. Fei Fei Li]

I might. I woke up so early.

[Dan Rather]

What time did you wake up?

[Dr. Fei Fei Li]

Probably 5:40 something.

[Monica Padman]

That's early.

[Dr. Fei Fei Li]

Yeah.

[Dan Rather]

What time do you normally rise?

[Dr. Fei Fei Li]

Not that much later. My alarm is 6:20.

[Dan Rather]

Okay. Mine's 6:40.

[Dr. Fei Fei Li]

Oh, okay.

[Dan Rather]

I'm aspiring. But you know what's funny? Today it was 6:20.

[Dr. Fei Fei Li]

You have kids?

[Dan Rather]

I have kids.

[Dr. Fei Fei Li]

How old are they?

[Dan Rather]

9 and 11.

[Dr. Fei Fei Li]

Mine is 8 and 12.

[Dan Rather]

You have 8 and 12?

[Dr. Fei Fei Li]

Yeah. So we're in the same stage of life.

[Dan Rather]

Boys or girls?

[Dr. Fei Fei Li]

12 is boy. 8 is that girl. How about you?

[Dan Rather]

Girl? Girl.

[Dr. Fei Fei Li]

Oh, yes.

[Dan Rather]

I'm so lucky. Although 11 about to be 12. I'm starting to get an inkling of what's coming my way.

[Monica Padman]

Yeah. Yeah.

[Dan Rather]

In a house with three ladies. In fact, yesterday was a very emotional day. I'm barely hanging on.

[Monica Padman]

What happened?

[Dan Rather]

I don't know what's going on with my three ladies, but all of them are in some kind of hormonal state. Hormonal turmoil.

[Monica Padman]

Interesting.

[Dan Rather]

And every variety, which is fine. We're always recording.

[Monica Padman]

We call it avr. Always be recording.

[Dr. Fei Fei Li]

Always be recording.

[Dan Rather]

Okay, so you have 12 and eight. And are they close?

[Dr. Fei Fei Li]

They're very close.

[Dan Rather]

He's a good big brother.

[Dr. Fei Fei Li]

He totally is. Sweet.

[Dan Rather]

Silvio's your husband? Yeah.

[Dr. Fei Fei Li]

Yes.

[Dan Rather]

And does one of them have Silvio's personality and one have yours?

[Dr. Fei Fei Li]

Well, I can tell you one of them has Silvio's hair.

[Dan Rather]

Okay.

[Dr. Fei Fei Li]

Which is a lot of curls. Curls.

[Dan Rather]

World of curls.

[Dr. Fei Fei Li]

Yeah, world of curls.

[Dan Rather]

Well, you're here because I read your book. Maybe two months ago. I was having dinner with Ashton Kutcher. Do you know who that actor is?

[Dr. Fei Fei Li]

Yes. He just texted me last night. He's like, you're seeing my friend X. Oh, good. Yeah.

[Dan Rather]

So we were at dinner and we were just chatting about people we thought were really interesting. And then he asked me if I had read your book and I hadn't. I went into it thinking I would get a history lesson on AI, which I did, and a very thorough one. But I would not have invited you for that. Your life story is so interesting and beautiful and the way you write about it, you're in a credible writer.

[Dr. Fei Fei Li]

Thank you. I do want to give credit to my friend Alex who co wrote the book with me.

[Dan Rather]

Okay. That's very big of you. Who's Alex?

[Dr. Fei Fei Li]

Alex is a friend of mine. He does not claim to be a writer, but he's a very talented writer and we've known each other for years and he loves AI and we talk about it. So when I was invited to write this book, I do feel like I want Alex to co create this with me. So we became a creative team. It was a really fun process.

[Dan Rather]

How do you know Alex?

[Dr. Fei Fei Li]

I know alex. Alex through Ted 2015. I gave my first TED talk.

[Dan Rather]

I watched it.

[Dr. Fei Fei Li]

Yeah. Thank you.

[Dan Rather]

Yeah. Yeah. About image. How hard it is for a computer to see.

[Dr. Fei Fei Li]

Yes. And that's how I got to know.

[Dan Rather]

Alex, because he worked with Ted in some capacity. Ok.

[Dr. Fei Fei Li]

So yeah, he was in some kind of partnership with Ted and he was helping me to put together my slides. Since then we become friends and we talk about AI and he's also helped me in some of the Stanford Hai Human Centered AI Institute stuff.

[Dan Rather]

You're kind of creative partners.

[Dr. Fei Fei Li]

Yes. It's very interesting because book writing is very different creation compared to doing science. We wrote almost three years or two and a half of those years. During the day I do my science and some of the evenings I do the creative writing. It's such a different part of the brain.

[Dan Rather]

Yeah. Which one do you find more exhaustive?

[Dr. Fei Fei Li]

Both.

[Dan Rather]

Both in different ways.

[Dr. Fei Fei Li]

No, but they are very different. Of course, I've been a scientist for almost three decades, so I'm more familiar with being a scientist. But the creative writing journey, I loved. When I say I love. It's not necessarily happy love, it's like painful love. Some part of it, but I really loved it.

[Dan Rather]

That's what I want to start with because I'm curious. When you sat down with Alex, I'm sure the historical part, the scientific part, that stuff is probably easy. But had you ever told your life story to anyone in that detail?

[Dr. Fei Fei Li]

No, and I didn't want to, and I still don't.

[Dan Rather]

Yeah. Do you think that's a personal disposition or where you come from culturally? I think of the story of your father, which we'll get to, and how little he told you about his own childhood until the time. And I glean from that, oh, this isn't a culture that is just divulging all this emotional trauma and baggage.

[Dr. Fei Fei Li]

Well, I have to say, I think culture in the case of an individual sometimes is too broad a brushstroke. Yeah, I think it's more individual. I'm a relatively shy person. And Alex and I wrote the first version. It was purely scientific. It was the first year of COVID We talk on the phone almost every night. And one of my best friend is a philosopher at Stanford called John H. Mindy. He's a very revered higher education leader. He was Stanford's provost for 17 years, and he is co director with me at Stanford Human Centered AI Institute. So I was really proud. I wrote this first draft. I showed it to him. It was during COVID After like two weeks, he called me. He said, fei, Fei, you and Alex should come to my backyard. That's how we meet. Because we were social distancing. And then we went and he said, you have to rewrite. I was like, what? That's the last thing I wanted to hear. He said, you're missing an opportunity to tell your story. Tell AI story through your lens. And I was just so rejecting that idea. I was like, who wants to hear my story? I want to write about AI. I call him Edge. Edge said there are many AI scientists who can write a so called, quote, unquote, objective story of AI but your angle would be so meaningful, your voice to. To the young people out there, the young women, immigrants, people of all kind of background. And we were sitting in his backyard with a triangular shape, three chairs. And I looked at Alex. He was almost jumping off his chair with excitement. He said, I told you. He said, I told you so many times. Of course, it only takes edge to tell you that.

[Dan Rather]

Well, let's jump to a really big philosophical question about that. I think when reading your story, you came here, this huge language barrier, such a fish out of water. But your work, if good enough, would speak for itself and it would be a meritocracy. And so it's not surprising to me that someone who got to where they wanted to go with that belief would have a hard time thinking, wait. I was trying to transcend this otherness. This otherness is the thing that would be most interesting and worthy of attention and affection. What a gap.

[Dr. Fei Fei Li]

It's very subtle. You caught that. Because when I go into the world of science, I don't think too much about many other things. I just follow that light, follow the curiosity. And to this day, even when I was writing the book, it's AI that fascinates me. And I wanted to write about AI. So it was very strange that someone wants to read about me.

[Dan Rather]

Yeah. Well, I think even the notion that you're struggling so hard, I got to set up your story more. This is the last thing I'll say out of context. Monica's like, not everyone read the book, but just, of course, math was appealing because math didn't have a language barrier.

[Dr. Fei Fei Li]

Yes. But I do want to be honest. Even when I was a young kid in China, I loved math and physics. I love physics. I would say even more than math itself. I saw math more as a tool. I saw more beauty and fascination in physics.

[Dan Rather]

Yeah. There's more philosophy.

[Dr. Fei Fei Li]

Yes.

[Dan Rather]

Okay, so let's start in China in 1976. You're the only child of your parents.

[Monica Padman]

Yeah.

[Dan Rather]

And talk about your mother, because she's very interesting.

[Dr. Fei Fei Li]

She is very interesting. My mom come from a normal family, but as the book says, her family is in a difficult position because of the history. So she was a very good student. I think the intellectual intensity I have, a large part of it comes from my mom. She was a curious student. She was very intense. But her dream was pretty shattered when she was not able to go to a normal high school. High school, when she had a dream for college and that carried her through.

[Dan Rather]

And then you arrive and you show this great aptitude. And now she has, in a sense, a second chance at this dream. But she starts recognizing pretty soon your path is going to be stilted as well if you stay there. So what's happening? What is she noticing as you start getting educated and show this aptitude?

[Dr. Fei Fei Li]

A lot of this is hindsight, because I Didn't talk to my mom in this way. Right. I think it was a combination that my mom has her own longing to be more free, maybe. And in hindsight, I don't know if she knew how to translate that in the world she was living in. And the opportunity to go to a new world was as appetizing to her as it is for her on my behalf. It's also true she saw me as a bit of a quirky kid. I think that blend of what she was longing for and what she was longing for on my behalf without me realizing was the motivation of many of the changes. The decision of immigration.

[Dan Rather]

Well, what would have been your trajectory had you stayed in China in 1988 when you're 12? Am I misremembering that your mom felt like they weren't giving you the attention and encouragement that she was hoping you would get?

[Dr. Fei Fei Li]

My mom was not looking for attention for me, was looking for freedom for me and for herself. A lot of that is projection. Was looking for a world where I can just be who I am. She wasn't necessarily looking for attention.

[Monica Padman]

What do you mean by quirky?

[Dr. Fei Fei Li]

This goes to my dad. I didn't follow rules in an average way. In hindsight, maybe it was just me being immature.

[Dan Rather]

Sure.

[Dr. Fei Fei Li]

But also, there is a part of me, why should girls not play soccer? Why should girls be told they are biologically less smart than boys? I was told at least more than once, watch out. That girls would in general be less smart by the time you hit your teenage time.

[Dan Rather]

This is what I'm remembering from the book, that you are explicitly told you're not as smart as boys.

[Dr. Fei Fei Li]

I wasn't told in the context of one. One, like, let me sit you down, Fei Fei, and tell you. I was told in the way that teachers will say things to boys or the context. Society had a whole different expectation for boys. I was very lucky. My own family protected me, but they can only protect me so much. As soon as you enter a school system, as soon as you interact with society, all that came through from that point of view. I was not following the normal path. I was reading different books. You know, I was so passionate about flights, UFOs, physics, special realms. I would grab my classmates to talk about that, but that was just not normal.

[Monica Padman]

Yeah.

[Dan Rather]

Who was exposing you to all that stuff?

[Dr. Fei Fei Li]

That's a great question. I was trying to ask myself that question when I was writing the book, and I still don't have a strong answer. I think the early curiosity, the exposure came from both my parents, my dad loved nature. My mom loved books and literature. But how did I fell in love with physics and UFOs and all that? I'm not totally sure it could be my dad. Before he came to New Jersey, he was ordering me some magazines outside of the school reading. And that exposed me to those topics. And because my parents protected my curiosity. When I say protected, it really just meant they left it alone. They didn't meddle with it. I kind of followed it through myself.

[Dan Rather]

So your dad leaves when you're 12. He goes to New Jersey. He's there for three years on his own, and he is setting up a landing for you and your mother. Yeah.

[Dr. Fei Fei Li]

Yeah.

[Dan Rather]

Do you remember that? Three years. Missing him terribly. How was that experience?

[Dr. Fei Fei Li]

It was tough. I mean, it was early teenagehood. There was no Internet. Phone calls are extremely expensive to the point of being prohibitively expensive. So it was mostly letters every couple of months. But then I was a teenager, so I had my own world to explore as well. So it wasn't like. Like I was sitting in the room crying or anything.

[Dan Rather]

So then you come your sophomore year.

[Dr. Fei Fei Li]

Yes.

[Dan Rather]

You start a public high school in New Jersey.

[Dr. Fei Fei Li]

Perseprene High school.

[Dan Rather]

One of the experiences you had, I came in and told Monica immediately about. And you were in a class, some kind of a study hall or something.

[Dr. Fei Fei Li]

Library.

[Dan Rather]

Library. And you were with a group of other ESL kids, English as a Second Language kids. And you saw a very. Well, no, I want to say how insignificant this first interaction was. Like benign brushing up against a kid's backpack or something.

[Dr. Fei Fei Li]

Right.

[Dan Rather]

And what happened?

[Dr. Fei Fei Li]

A group of ESL students were in the library. And then the bell rang or something. We have to file out of the library door. And I remembered it was crowded. I honestly did not see what happened to that boy. But all I knew was my ESL friend was on the floor. By the time I realized there was some commotion, being kicked and punched. I think there was nose bleeding and he was holding his head.

[Dan Rather]

Yeah. He said he got a concussion and a broken nose and there's two boys kicking him.

[Dr. Fei Fei Li]

Yeah.

[Dan Rather]

And that's not even maybe the most traumatic part. It's that after he's gone for a couple weeks, he comes back and he's just not the same boy.

[Dr. Fei Fei Li]

Yeah. I mean, nobody would be the same after going through something like that. Definitely. It's huge impact. It was an experience that was definitely pretty intense for all ESL students. Nobody felt safe for a long while.

[Dan Rather]

Yeah. I think it changes your worldview on a dime, which is, ooh, this new Place I'm. And can get pretty violent and a little out of control. And if you're other this could happen. I have to imagine. Yeah. It's an incredibly scary recognition of where you're at.

[Dr. Fei Fei Li]

Yes. But also, I do want to give more colors. Right. I love that your show focuses on the messiness of being human. Being messy is being multidimensional. But it was also an environment where there was so much support, there was so much friendliness, and there was also so much opportunity. So it was very confusing. I'm not trying to say that experience itself is not heavy. I don't feel lucky about that experience. And, I mean, there was anger and all that, but in the meantime, the fuller context of that community was also quite a supportive community. So it was very confusing. It gave me the multidimensionality of the new country I landed in.

[Dan Rather]

Everything's happening. A lot of opportunity is happening as promised, and then a lot of xenophobia and violence is happening.

[Dr. Fei Fei Li]

Right. Yeah.

[Monica Padman]

Did you feel like you had to after that? Sort of like, keep your head down?

[Dr. Fei Fei Li]

Maybe it's just my own personality. I always felt I had to keep my head down.

[Monica Padman]

Right.

[Dr. Fei Fei Li]

Especially as an immigrant. Sometimes I feel that way even now, especially given the AI world we live in, I feel I need to keep my head down to do work. Of course, that particular event probably added a layer of complication, at least for a while, but it also taught you have to stand up for yourself. It did open different insights to me.

[Dan Rather]

I don't know if you would rank these things in your life of, like, serendipitous things happening, but meeting Mistress Sibella has to be minimally in the top 10, and I would hope in the top three.

[Dr. Fei Fei Li]

Yeah, it's possibly the top three for sure. Meeting Mr. Sibella was so lucky for me.

[Dan Rather]

Yeah. I find this to be one of the sweetest stories I've ever read about and kind of makes me hopeful for people, how generous they can be. But in a nutshell, minimally, you're thinking, I'm going to do good at math. I don't have to go to my dictionary back and forth like I do in every other class. And you're in math and you're getting problems wrong, and you yourself cannot identify any pattern in this. You don't know what's going on. And you go to see Mr. Sabella in his office.

[Monica Padman]

That's your teacher?

[Dr. Fei Fei Li]

Yes, Mr. Sabella was my math teacher. I got into calculus and then percipity. High school doesn't have calculus BC we ap Calculus for ab. So he had to teach me during his lunch hour for bc. But this story you're talking about was earlier. It was during some pre calculus stuff, and it turned out I was using a broken calculator that they had gotten.

[Dan Rather]

At a garage sale. Her father loves garage sales. It was his favorite thing in the world.

[Dr. Fei Fei Li]

I know every weekend they'd go to.

[Dan Rather]

He still does.

[Dr. Fei Fei Li]

I love garage sale. I don't have time to go, but I love it. Mr. Sabella was tough. He is a tough love kind of teacher. So even though I was esl, I was this mousy girl. He didn't think I needed any extra.

[Dan Rather]

He didn't pity you?

[Dr. Fei Fei Li]

No, not at all. I. For one quarter or semester, I got 89.4 or something. I still remember that. I was like, oh, God, 0.6. I would get to at least an A, and he would not give me that A.

[Dan Rather]

You asked about extra credit and he was like, get real. How about go to good grade on the test?

[Dr. Fei Fei Li]

Yeah. He would say, there are many smart kids in the class. You just have to work hard.

[Dan Rather]

But it sounds in the retelling like the breakthrough. And I think this scene would be in a movie if I were writing the movie. You're there. He discovers the tan symbol on your calculator is malfunctioning. He helps her figure this out because he, too, can't figure out the pattern of all these errors. And then somehow you guys start talking about books. And he asks if you've read a certain science fiction writer. You try to tell him you haven't read that one, but you really love A Million Kilometers under the Sea. You can't translate it and you can't pronounce Jules Verne. But he figures out you've read Jules Verne.

[Dr. Fei Fei Li]

Yes.

[Dan Rather]

And he is, like, shook. He's like, you've read Jules Verne?

[Dr. Fei Fei Li]

Yes.

[Dan Rather]

And then you go on to say, yes, and you've read Hemingway and you've read everything.

[Dr. Fei Fei Li]

Well, I've read everything my mom gave me, which is.

[Dan Rather]

Which was extensive.

[Dr. Fei Fei Li]

Yeah.

[Dan Rather]

If I were him, and this young girl from China comes in and she has read most of the classics. That's a real. Like, what am I dealing with here? I gotta imagine for him, at least, that was a moment where he's like, okay, I'm betting on this horse.

[Dr. Fei Fei Li]

I think he saw a person he could be friend with, just the way I saw in him. Later on, I realized, again, this is hindsight, that he does that to so many students. And he used this way of opening up in different ways. Not necessarily science fiction or classic literature. To really get to be so helpful for him and me beyond math and calculator. When we started talking about science fiction and the English classics, he realized that he was seeing me more than an ESL kid at that point. And he's also a shy person himself. Later, his wife Jean, and Bob. Later I call him Bob. We became such good friends.

[Dan Rather]

Many, many years.

[Dr. Fei Fei Li]

Way longer than maybe Jing said. He's such a bookworm. Even during his family parties, he'll be by himself reading. So he's totally an introvert in a way that we just had chemistry. But this is one thing I was not able to fit into the book is that for years, he would keep a diary. And his diary talks about just his teaching life. And I know in this DIA there are so many stories about different students he helped with. Not in the sense of bragging. It's just. He's a writer, right? So years later, before he passed away, we didn't know he was gonna pass away. I told Bob, I said, bob, you gotta turn this into a book. Of course we can anonymize, but this is an American teacher story of so many students. Many of them are immigrant students because they lack the support, they lack the family. Some of them in high school by themselves, families overseas. Many of them are like me. Parents are so busy that the students don't have that emotional support. And he supported so many students. I can sit here and tell you endless story. And then he wanted to translate that into a book, but somehow he just couldn't bring himself to do it. Maybe he's too shy. Maybe he's too humble.

[Dan Rather]

Yeah, I think he's struggling with the same issue you're struggling with. Probably you don't feel entitled to tell this story.

[Dr. Fei Fei Li]

Right. I feel so strongly he needed to write this book. I almost felt like one day I would write it for him. But of course, he passed away so suddenly because of the brain tumor. So when I was writing my book, I realized, let me tell the Bob Sabella story. Let me tell the story on behalf of so many American public school teachers. They don't have much of a voice. Nobody knows their name, but they work above and beyond every day for the students in their community. They don't care which part of the world they come from, which kind of family background they come from, but they invested so much in these students, and they changed lives.

[Dan Rather]

Yeah, they're very unsung heroes. They're not tenured professors at elite universities.

[Dr. Fei Fei Li]

They're totally unsung heroes.

[Dan Rather]

And they're the ones that get the people to those destinations. Yeah, It's a really beautiful story. How inspirational. How instrumental was he in you finding your way to Princeton?

[Dr. Fei Fei Li]

He was instrumental more than Princeton because he was instrumental. As the second dad, he helped me to be grounded. When you're an immigrant kid, an ESL kid, you land in the country without speaking the language and going through so many things, it feels so unstable.

[Dan Rather]

I think you're underplaying your story. If you came here in seventh grade and ended up at Princeton, that's one story. You had two years to get yourself ready to learn English to start Princeton, and you didn't speak any English, you're very much under, which is fine. I think so would your teacher. Yeah. You feel maybe that's self indulgent or something, but that's really bonkers. Again, AI aside to land and go, okay, if you dropped me in Russia and told me I have two years to land at their most elite university.

[Dr. Fei Fei Li]

Moscow State University, it's not going to happen.

[Dan Rather]

It's not going to happen. For 1999.999% of people. Let's talk for a second about you going to Princeton. This is another fun moment for me in the book because there's something so much more important about Einstein than the theory of special relativity. And I can't really articulate what it is, but I know you have a good dose of it. So what was it like going there and seeing the statue of Albert Einstein and imagining that you would in some way be touching that reality?

[Dr. Fei Fei Li]

So the first time I saw the statue of Albert Einstein before I was applying for college, it was probably early junior year. My dad continue to find things for us to do. That's free. It's very important. It's free. Princeton's Natural History Museum was free, so that's why we went there.

[Dan Rather]

Garage sales, free.

[Dr. Fei Fei Li]

Exactly.

[Dan Rather]

Museums, free.

[Dr. Fei Fei Li]

Yes. Seeing Einstein's statue was kind of symbolic for me that I'm getting back to where really my soul wanted to be at. Because as a teenager, landed in a new country, trying to learn language, deal with all the messiness, you know, Chinese restaurant, walking dogs.

[Dan Rather]

Yeah. You're working a ton of hours.

[Dr. Fei Fei Li]

Yeah, exactly. I didn't forget about physics. I was taking physics class in school. But I forget about the sense of love.

[Dan Rather]

Romanticism.

[Dr. Fei Fei Li]

Yes. It really is that first love. And it kind of got me back to that rekindled something.

[Dan Rather]

Well, don't you think it left an imaginary word where this person existed and it put it in your own Three dimensional reality.

[Dr. Fei Fei Li]

Yes. Suddenly I feel so much closer to that person. And that person symbolizes the entire world of physics. I feel so much closer. I was literally in Princeton. Right. That felt very different.

[Dan Rather]

And he lived there for what, 30 some years? Yeah, maybe more. I think that would be a special moment as well.

[Dr. Fei Fei Li]

I'm sure you watched the movie Oppenheimer.

[Dan Rather]

Yeah, yeah.

[Dr. Fei Fei Li]

Do you remember the opening scene was Einstein in front of that little pond?

[Dan Rather]

Yep, yep. Talking with Oppenheimer.

[Dr. Fei Fei Li]

Right. He was first there by himself.

[Dan Rather]

Yeah.

[Dr. Fei Fei Li]

I call that my pond. That pond literally exists. It was very close to my dorm by the time I got to Princeton. And I would go there a lot because I know that was close to the Advanced Institute where Einstein worked.

[Dan Rather]

Yeah.

[Dr. Fei Fei Li]

So like when the scene came out, Silvio was sitting next to like, Silvio. This is my punk.

[Monica Padman]

You have such a full circle.

[Dan Rather]

Yeah. I'm currently stuck in a rut where I'm learning a lot about physicists, historical physicists, and I'm wondering, have you read When We Cease to Understand the World? Have you read that book?

[Dr. Fei Fei Li]

No.

[Dan Rather]

Or have you read the Maniac? Either of those? No, the Maniac's all about Janusz von Neumann.

[Dr. Fei Fei Li]

I'm reading a different bio of him, but not the Maniac you are.

[Dan Rather]

Which one?

[Dr. Fei Fei Li]

Oh, it's in my phone.

[Dan Rather]

Yeah, same. Don't worry about it. This one's fun. Cause it has the perspective of a million different people in his life. Like a student he was friends with at school, one of his wives, people who worked with him. And you get this really comprehensive view.

[Dr. Fei Fei Li]

Of another Princeton guy.

[Dan Rather]

Yeah, I'm obsessed with all these guys. And then when we cease to understand the world is many of these physicists who were so brilliant at a time, who ultimately became crazy and how many of their breakthroughs in the math of quantum mechanics coming to this guy in a nine day, 106 degree fever, writing down the matrices and not understanding the math when he comes out of it. But it holds. There's a lot of weird magic in this space, I think, where people have these breakthrough thoughts and they touch some understanding and they're in a compromised state mentally. It's just fascinating.

[Monica Padman]

It's mystical.

[Dan Rather]

Yeah.

[Dr. Fei Fei Li]

Physics is absolutely the discipline that pushes you to think so audaciously that you have to transcend the immediate reality. Yes, that's what I loved. I loved about Einstein, I loved about modern physics, even Newton, classic physics. You have to think so beyond the immediate reality.

[Dan Rather]

All those stories of him getting asked a question and then answering it two and a half days later and he hasn't left the chair and the person left, like, he went away for two and a half days and then came back with the answer or just the notion. I think one of the most intriguing parts is, like, you're going to have thoughts that cannot be expressed in language but can only exist in math. That already is, like, what there is.

[Dr. Fei Fei Li]

Actually even beyond math.

[Dan Rather]

Right. And then there's a realm beyond math.

[Dr. Fei Fei Li]

Yes.

[Dan Rather]

It's the closest thing I think we have to magic, where it's, like, completely outside of our grasp, but for a handful of people.

[Dr. Fei Fei Li]

I love that you call it magic. It is also the furthest thing we have to AI Is that humanity, that magic, that creativity, that intuition, that almost ungraspable way of intelligence.

[Dan Rather]

Yes.

[Dr. Fei Fei Li]

We should keep that in mind.

[Dan Rather]

So you're at Princeton. You're also working a ton, right?

[Dr. Fei Fei Li]

Yes.

[Dan Rather]

When do your parents start the dry cleaners?

[Dr. Fei Fei Li]

So we started very quickly, right after my freshman year started because my mom's health was going so badly. They were working in Newark, New Jersey. I don't know if you guys know that. New Jersey from Persephone to Newark, New Jersey, is a very difficult drive. My mom's health was bad, and it was long working hours. I was really worried about. The doctor was worried. We finally decided if we can do a local thing in Persimponi, it'll be better for the family. And it was very important for me that the business is a weakened business, because that way I can do the lion's share of work. But there are pretty much three kind of weakened business for immigration families like us. Open a restaurant, open a grocery store, or open a laundry. And restaurant and grocery require very late working hours for restaurant and grocery is very early. You have to go to Chinatown to get supplies. So neither of these work for my mom's health, whereas dry cleaning was actually perfect because it's a daytime business. It's very long hours during the weekend, but it's at least daytime. And a lot of my mom's work, especially when it comes to alteration, she can sit in front of the sewing machine.

[Dan Rather]

Because your mother had had a reoccurring fever as a child, and it greatly degenerated some of her heart fat. So she was really struggling with heart issues.

[Dr. Fei Fei Li]

Yes, she carried that illness with her all her life.

[Dan Rather]

And there's no money in the dry cleaning. There's only money in the seamstressing, whatever we call it.

[Dr. Fei Fei Li]

The tailoring.

[Dan Rather]

The tailoring.

[Dr. Fei Fei Li]

I mean, there's no money in any of these. But having that tailoring Ability was nice because it helps a little bit. And my mom is incredible. She never learned this. She was a bookworm and she's kind of a brainy.

[Dan Rather]

She should have done what you did, right? Yeah.

[Dr. Fei Fei Li]

I don't think she would love physics.

[Dan Rather]

But you know what I mean, she should have probably been an academic.

[Dr. Fei Fei Li]

Yeah, she would have been an academic. But then she just kind of figured out tailoring by herself. I still don't know. Like, I tried, I could not. The only thing I can do is sit there and unstitch things for her. Sure.

[Dan Rather]

I think a chimp can do that. Yeah.

[Dr. Fei Fei Li]

Thank you. Yeah, exactly.

[Dan Rather]

I say that because I know how to remove stitches from garments and I don't have more skills than a chimp.

[Dr. Fei Fei Li]

Yeah. So we opened a dry cleaner shop during the middle of my freshman year and that became my entire memory of my undergraduate. Here's a fun fact. Princeton is organized by residential dorms. I lived in one of them called Forbes. It turned out Forbes is very famous for its Sunday brunch. I didn't know there was a Sunday brunch because I was home doing dry cleaning.

[Dan Rather]

You said you didn't go to a single party.

[Dr. Fei Fei Li]

Right. But then when I went back to Princeton as a faculty, Forbes was very kind. They made me a faculty fellow and I discovered Sunday brunch that was like.

[Dan Rather]

What you had been missing. Years later, instead of the freshman 10, you gained the 30 10.

[Dr. Fei Fei Li]

Yeah.

[Dan Rather]

The faculty tend.

[Dr. Fei Fei Li]

So I felt so good, I finally got my Sunday brunch.

[Dan Rather]

And I think it's worth mentioning when you guys were trying to open that dry cleaners, you were trying to raise $100,000 and you were $20,000 short. And again, Mr. Sabella, Monica, she gave the money.

[Dr. Fei Fei Li]

Yeah. It was a total shock. To this day, actually, as a 19 year old, as much as I appreciated Gene and Bob, I did not realize the extent we're Talking about late 1990s, they Two public school teachers with two kids about to go to college. Wow.

[Dan Rather]

It's unimaginable.

[Dr. Fei Fei Li]

He said, jean. And he decided to do that. I mean, at that moment I was very, very grateful. But now, after I became a grownup, this is unimaginable.

[Dan Rather]

It's impossible that someone would do that.

[Dr. Fei Fei Li]

Especially he later told me, I think when I was returning the money, he said, I didn't realize you'll be able to return.

[Dan Rather]

I was like, what? Of course you have to give it, thinking you'll never get it back. I guarant he and his wife were like, we're giving this money Away.

[Dr. Fei Fei Li]

I did not know that he did use the word lend. And of course, in my mind, I was like, of course I'm gonna return. Like, I'll do anything to return. But Jean and Bob did not.

[Dan Rather]

They could not have assumed that.

[Monica Padman]

So the money was being raised to help your mom start the dry clean?

[Dr. Fei Fei Li]

No, to help my family to start the business. We, as a family, I still consider myself the CEO of the dry cleaner. I live in Silicon Valley. You have to claim yourself to be a CEO of something. Yeah, exactly. So Bob and Jean, it's incredible. I don't even think their kids knew about this till they read my book.

[Dan Rather]

Wow. Oh, my God, how proud I'd be of my dad. Okay, so you graduate from Princeton and you have a degree in physics as well, some kind of computational.

[Dr. Fei Fei Li]

Yeah. So Princeton is a quirky school. It didn't have minors, so it has these certificates, but they're just minors. I had a computational mathematics as well as engineering physics.

[Dan Rather]

And when you were there, unless I'm misremembering, you had a very singular focus on being a physicist. But while you're there, you start realizing you're maybe open to something different.

[Dr. Fei Fei Li]

It's actually really interesting. I never necessarily thought I would be a physicist, but I wanted to be a scientist. That was almost a sacred calling for me.

[Dan Rather]

It was an identity.

[Dr. Fei Fei Li]

Yeah, it was an identity. For some reason, this girl who works in dry cleaners just wanted to be a scientist.

[Dan Rather]

Yeah.

[Dr. Fei Fei Li]

And then. Then I loved physics, but I loved physics for its audacity and curiosity. I didn't necessarily feel. I'm married to a big telescope in Korea, so I was just reading a lot. And what really caught my attention was the physicists I admired so much. Einstein, Schrodinger, Roger, Penrose. They actually are curious beyond just atomic world. They were curious about other things, especially life, intelligence, minds. And that was immediately no point. And the eye opener for me, I realized. I love that.

[Dan Rather]

Yeah. Understanding how this brain works, intelligence works. It's crazy, the overlap. That has now been proven. But at that time, that's not an obvious. We haven't figured out neural pathways, and we're not going to map that onto computers yet. So these seem, on the surface, very different fields. One's biology and one is, you know.

[Dr. Fei Fei Li]

Right. But for me, it was the science of intelligence. I always believed it's the science of intelligence that will unite our understanding of both the brain and the computers.

[Dan Rather]

Right, okay, so then you choose Caltech to go to graduate school.

[Dr. Fei Fei Li]

Yes.

[Dan Rather]

What did you think of California? I mean, My God, what a place, right?

[Dr. Fei Fei Li]

I know we're 15 minutes away from Caltech here. 20 minutes. So I was choosing among MIT, Stanford and Caltech. And honest to God, I almost chose Caltech because of the weather. Yeah, it was so funny. Yes. The turtles, the garden like campus. And of course I walk into this building, I think it was more building at Caltech. Guess whose photo was there? It was Albert Eisdeck.

[Monica Padman]

I was like, what?

[Dr. Fei Fei Li]

It turned out he was visiting. And of course there was Richard Feynman, the Feynman lecturer. So I just followed these physicists apparently. And New Jersey was cold. And also I really have an issue with cold because my mom's illness is exacerbated by cold. So every winter she suffers a lot. So I have this negative affinity to coldness coming from taking care of my mom. So coming to Southern California, I was like, oh my God, I love this place.

[Monica Padman]

Did your parents leave later?

[Dr. Fei Fei Li]

They did. In the middle of my grad school they did.

[Monica Padman]

Were you worried about that leaving?

[Dr. Fei Fei Li]

I had to switch from being on site to remotely run the dry cleaning. The dry cleaning was stabilized that the customers are all returning customers. So my mom would be able to handle with one part time worker. And Bob Sabella was doing bills for my mom.

[Monica Padman]

Oh my God.

[Dr. Fei Fei Li]

Yeah, he was just helping me. And another thing, he helped me as a young graduate student. I would be entering the world of writing scientific articles. That's pretty intense. He would still proofread my English for me all tell me about North Star.

[Dan Rather]

And how you discovered yours. Because this happens at Caltech.

[Dr. Fei Fei Li]

Yes. The prelude of the North Star was my education from physics is always about asking the right questions. If you go to the Nobel Museum in Stockholm, there is an Einstein quote about much of science is asking the right questions. Once you ask the right questions, solutions follow. You'll find a way for solutions. Some people call it hypothesis driven thinking. I've always been just thinking this way. So as I was studying computational neuroscience as well as artificial intelligence at Caltech, I was always kind of seeking, what is that audacious question I wanted to ask? And of course my co advisor, Pietro Perona and Christoph Koch, they were great mentors guiding me. But many things start to converge. Not just my own work, but the field. People working on visual intelligence from neuroscience, from AI, start to orbit around this idea that the ability to recognize all kinds of objects is so critical for human visual intelligence. When I say all kinds of objects, I really mean all kinds. I'm sitting here in your beautiful room. There's table bottles Couch, pillow. A globe. Books, flower, vase. Plants.

[Dan Rather]

T. Rex skeleton.

[Dr. Fei Fei Li]

Okay, that's behind you, that's behind you.

[Dan Rather]

It's about to eat.

[Dr. Fei Fei Li]

Yes. Shirts and skirts and boots and tv. So the ability for humans to be able to learn such a complicated world.

[Dan Rather]

Of objects, oh, millions and millions of.

[Dr. Fei Fei Li]

Objects, is so fascinating. And I started to believe, along with my advisors, this is a critical for the foundation of intelligence that really start to become the North Star of my scientific pursuit is how do we crack the problem of object recognition.

[Dan Rather]

Okay, so now I think is a great point to just go through a couple of the landmark events that take us to where the technology is at that time. So I guess we could start with Turing, we could start in 1956, give us a couple of things that have happened and computing up to that point.

[Dr. Fei Fei Li]

Right. So that's the parallel story. I was writing the book.

[Dan Rather]

Now that I have people hooked into you as an individual, now we can get a little protein in this and.

[Dr. Fei Fei Li]

Learn some stuff, right? Well, the field of computing, thanks to people like Alan Turing, Van Neumann, was starting during World War II. Time basically, of course, for the world of AI, a very important moment was 1956 when what we now call the founding fathers of AI, like Marvin Minsky, John McCarthy, Claude Shannon, they get together under I believe a U.S. government grant, DARPA funded. DARPA funded to have a summer long workshop at Dartmouth with a group of computer scientists. At that point, the field of AI was barely kind of boring, not boring. Yet they got together and wrote this memo or this white paper about, about artificial intelligence. In fact, John McCarthy, one of the group leaders, was responsible for coining the term artificial intelligence.

[Dan Rather]

I think we could get even more rudimentary, right? So up until that point, a computer was something that could solve a problem. You could do computations, it could calculate. And this notion of artificial intelligence, what it really meant is could we ever ask a computer question that it hadn't been pre programmed to answer? What are the hallmark things that separated at that time artificial intelligence from just computing? Because I think we've just fast forwarded to everyone saying AI and I don't think they really even take a second to think of what that step is between computing and computation and thinking.

[Dr. Fei Fei Li]

Right up to that point you can think. No matter how powerful the computer was, it was used for programmed calculation. So what was the inflection concept? I think two intertwined concepts. One is reasoning. Like you said. If I ask you a question, can you reason with it? Could you deduce if a red ball is bigger than A yellow ball. A yellow ball is bigger than a blue ball. Therefore the red ball must be bigger than the blue ball.

[Dan Rather]

Right. Without having been programmed that way, without.

[Dr. Fei Fei Li]

Directly saying red ball is bigger than the blue ball. So that's a reasoning. So that's one aspect, a very, very intertwined aspect of that is learning. A calculator doesn't learn whether you have a good 10 button or not. It just does what it is.

[Dan Rather]

Yeah, you had a bad one once.

[Dr. Fei Fei Li]

I had a bad one. So artificial intelligence software should be able to learn. That means if I learn to see tiger one, tiger two, tiger three, at some point when someone gives me tiger number five, I should be able to learn, oh, that's a tiger, even though that's not tiger 1, 2, 3. Right. So that's learning. But even before the Dartmouth works, there were early inklings like Alan Turing's daring question to humanity. Can you make a machine that can converse with people, QA with people? Question and answer, so that you don't really know if it's a machine or a person. It's this curtain setup that he conjectured. So it was already there. But I think the founding fathers kind of formalized the field. Of course, what's interesting is for the first few decades, they went straight to reasoning. So they were less about learning, they were more about reasoning. They were more about using logic to deduce the red ball, yellow ball, blue ball question. So that was one branch of computer science and AI that went on during the years predated my birth, but during the years of my formative years without me knowing, I wasn't in there.

[Dan Rather]

Right.

[Dr. Fei Fei Li]

But there was a parallel branch. That branch was messier. It took longer to prove to be right. But as of last week, we had the Nobel Prize awarded to that which was the neural network. So that happened again in a very interesting way. Even in the 50s, neuroscientists were asking questions, nothing to do with AI, about how neural. And again, my own field vision was the pioneering study about cat mammalian visual system. And Hubo and Wiesel in the 1950s and 60s were sticking electrodes into cats visual cortex to learn about how cat neurons work. Details aside, what they have learned and confirmed was a conjecture that our brain, or mammalian brain, is filled with neurons that are organized hierarchically, layered. They're not like thrown into a salad bowl. That means information travel in a hierarchical.

[Dan Rather]

Way up these columns in your brain. Yes.

[Dr. Fei Fei Li]

For example, light hits our retina. Our retina sends neural information back to our primary cortex, our primary cortex process. It sends to say, another layer, and then it keeps going up. And as the information travels, the neurons process this information in somewhat different ways. And that hierarchical processing get you to complex intelligent capabilities.

[Dan Rather]

That's a mouse. I'm seeing if I'm a cat or.

[Dr. Fei Fei Li]

This tiger sneaking up on me.

[Dan Rather]

And I think this could be a bad analogy. But you might be misled to think, oh, well, a camera can take a picture, and then the computer can show the picture. So the computer understands that's a photo, but really, the camera has broken what it's seen into thousands of pixels. They are coded with a numerical sequence. The computer reconstructs those colors. It's a grid. And virtually, that's what our eyes do. Our eyes are just grabbing photons, and they're sending back the ones and zeros, and then back here in the cortex, it's assembling it all.

[Dr. Fei Fei Li]

Yes. And how did evolution assemble us so that we can recognize all this beautiful world? Not only we can recognize, we can reason with it, we can learn from it. Many scientists have used this example is that children don't have to see too many examples of a tiger to recognize a tiger. It's not like you have to show a million tigers to children. So we learn really fast.

[Dan Rather]

And as you point out in the book, it took us 540 million years of evolution to get this system.

[Dr. Fei Fei Li]

Exactly. So just to finish. So the neuroscientists were studying the structure of the mammalian brain and how that visual information was processed. Fast forward. That study got the Nobel prize in the 1980s because it's such a fundamental discovery. But that inspired computer scientists. So there is a separate small group of computer scientists who are starting to build algorithms inspired by this hierarchical information processing architecture.

[Dan Rather]

You build one algorithm at the bottom. That's maybe generic.

[Dr. Fei Fei Li]

No, it's a whole algorithm. But you build mathematical functions that are layered, okay? So you can have one small function that process brightness, another that process curvature. I'm being schematic. And then you process the information. But what was really interesting of this approach is that in the early 80s, this neural network approach found a learning rule. So suddenly it unlocked how to learn this automatically without hand code. It's called backpropagation. And also, Geoff Hinton, along with others who have discovered this, was awarded the Nobel Prize last week for this. But that is the algorithm neural network.

[Dan Rather]

Could you think of it as almost a filtration device, which is like this. Data comes in, we filter out these three key points, that then filters up, and then we come to our conclusion at the top of this virus, it.

[Dr. Fei Fei Li]

Could actually cause it's just like all.

[Dan Rather]

This raw image info at the bottom. And then we kind of recombine it into this layer, and then another process filters. Well, it's not a school bus. It's not this.

[Dr. Fei Fei Li]

You just keep filtering it. Of course, you combine it in mathematically very intricate way, but it is like layers of filtration a little bit.

[Dan Rather]

Right. Okay, great. So, and now also when you find your North Star, another thing that's happening at the same time is wordnet. Right. This is kind of a big breakthrough for early AI for linguistics.

[Dr. Fei Fei Li]

So word that had nothing to do with AI. It had nothing to do with vision. But what happened for my own North Star is that I was obsessed with the problem of making computers recognize millions of objects in the world. While I was obsessing with it, I was not satisfied because my field was using extremely contrived data sets, like dataset of 4 objects or 20 objects. I was really struggling with this discrepancy because my hypothesis was that we need to learn the much more complex world. We need to solve that deeper problem than focusing on a very handful of objects. But I couldn't really wrap my head around that. And then again, Southern California. I remember that Biederman number in my book is that I read a psychologist paper, Irv Biederman, who was, up till two years ago, a professor at University of Southern California. He conjectured that humans can recognize tens of thousands of object categories. So we can recognize millions of objects, but categories are a little more abstract.

[Dan Rather]

Animal food, furniture, German shepherd, transportation.

[Dr. Fei Fei Li]

Yeah, sedan, fighter jet and all that. So he conjectured that, but that conjecture didn't go anywhere. It was just buried in one of his papers. And I dug it out and I was very fascinated. I called it the Biederman number because I thought that number was meaningful. But I don't know how to translate that into anything actionable because as a computer scientist, we're all using data sets of 20 objects. That's it. And then I stumbled upon WordNet. What WordNet was, was a completely independent study from the world of linguistics. It was George Miller, a linguist, prints them. He was trying to organize taxonomy of concepts, and he feels alphabetically organized dictionary was unsatisfactory because in dictionary, an apple and an appliance would be close to each other, but then apple should be closer to a pair.

[Monica Padman]

Oh, I see.

[Dr. Fei Fei Li]

Than appliance. So how do you organize that? How do you regroup concepts? So he created WordNet, which hierarchically organize concept according to meaning. And similarity rather than alphabetical ordering.

[Dan Rather]

Does WordNet not lead to the machine that can read the zip codes? No, it doesn't. What's that called? That's what I meant to bring up.

[Dr. Fei Fei Li]

That was ConvNet. Convolutional Neural Network.

[Dan Rather]

That's happening as you're getting your idea about the images. Right. We've trained a machine to read zip codes, basically handwritten.

[Dr. Fei Fei Li]

So that was Yong LeCun's work in Bell Labs. That was an early application of neural network in the 1980s and 1990s, where that neural network at that time was not very powerful, but giving enough training. Example of digits. Scientists in Bell Labs were able to read from 0 to 9 or the 26 letters. And with that, they created an application to read zip codes to sort mail.

[Dan Rather]

But its data set was. I forget. It was like a thousand or something.

[Dr. Fei Fei Li]

Or.

[Dan Rather]

It wasn't that.

[Dr. Fei Fei Li]

It was a lot of handwritten digits.

[Dan Rather]

Yeah. And common mistakes. They would feed it.

[Dr. Fei Fei Li]

That data set was probably tens of thousands of examples. But we're talking about just letters and digits.

[Dan Rather]

What they had proved in concept, you're going to try to do in images. But the lift for images is so exponentially larger than getting the machine to read.

[Dr. Fei Fei Li]

Exactly.

[Dan Rather]

By a factor of what I mean, when you lay out what it's going to take for you to prove this theory, you have and you figure out how long it's going to take, it's going to take like a decade of you feeding them. Right. There's some moment where the amount of images you're going to have to feed this computer to train, it can't almost be done by the group of you.

[Dr. Fei Fei Li]

So I think what you were referring to was the process of making imagenet.

[Dan Rather]

Yes.

[Dr. Fei Fei Li]

And that process was one. Once we realized, thanks to the inspiration of wordnet and also Biederman's number and also many other previous inspiration, we realized what computers really need is big data. And that was so common today because everybody talks about big data. OpenAI talks about big data. But back in 2006, 2007, that was not a concept, but we decided that was the missing piece. So we need to create a big data set. How big is big? Nobody knows. My conjecture went with Biederman's number. Why don't we just map out the entire world's visual concept?

[Monica Padman]

Oh, my God.

[Dr. Fei Fei Li]

Yeah.

[Monica Padman]

Why don't we?

[Dan Rather]

And you wrangled someone in that. This wasn't even really their North Star.

[Dr. Fei Fei Li]

Okay. So Professor Kylie at Princeton, he was very supportive of me. He was a senior faculty but what was really critical was he recommended his student to join my lab, Jia Den. And Jia was just a deer in the headlight. As a young first year graduate student, he didn't know what's going on. He got this crazy assistant professor me, and told him that we're going to create a data set that map out the whole world's visual concept. He's like, sure, I don't know what, but let's get started.

[Dan Rather]

Yeah.

[Dr. Fei Fei Li]

So he and I went through the journey together. I mean, he's a phenomenal computer scientist and many hoops we jumped through together. It was Joss Solution that got us through.

[Dan Rather]

This level of plotting that you were able to take on is unique to you. And I think it's moving here in 10th grade and looking at that fucking dictionary back and forth and back and forth and back and forth. That kind of really unique dedication and unwavering plotting. A million other scientists could have had your idea, but I think it's that thing right there that makes you capable of creating imagenet.

[Dr. Fei Fei Li]

That's an interesting observation.

[Dan Rather]

Yeah, it's not. I think we like to think of these things very simplistically, like, oh, you had a great idea. Who gives a shit? A lot of people had great ideas in graduate school.

[Dr. Fei Fei Li]

I do tell my kids, ideas are cheap.

[Dan Rather]

Exactly. Hollywood. Someone's like, that was my idea. Oh, really? Did you write the. Did you execute it? Did you cast it correctly? Did you motivate everyone? Your idea is 1% of the equation of a great movie.

[Dr. Fei Fei Li]

Yeah, thank you.

[Dan Rather]

Because when I'm reading your thing and the data's coming in, it feels like. And tell me if I'm mischaracterizing it. The deeper you got into this experience, you were just learning every day it was going to be harder than you originally anticipated. It just kept getting worse and worse and worse and worse.

[Dr. Fei Fei Li]

For years it was pretty bad.

[Dan Rather]

When I'm reading it, I'm like, I would have quit a quadrillion times. I'm like, maybe computing will get to a point where this job will be made easy, but right now it's too hard.

[Monica Padman]

How do you even start something like that? Do you literally just look around the room and you're like, okay, here we go. Yeah, I'll start with this room and write everything.

[Dr. Fei Fei Li]

Well, okay, so first of all, I've had years of training as a scientist. So after you formulate a hypothesis, you do have to come up with a plan. My PhD thesis had a mini version of Image Desk, so I got a little bit of practice but yeah, our idea was to create a dataset and a benchmark to encompass all the visual concepts in the world. So we had to start with WordNet. We had to figure out what is visual. We have to figure out what are the concepts we need and then where to get the source images and how to curate it every step of the way. Like Dax, we were saying we were just way too optimistic at the beginning.

[Dan Rather]

Naivete is the best asset you can have.

[Dr. Fei Fei Li]

I was just fearlessly stupid.

[Dan Rather]

Yeah, it's a great gift.

[Dr. Fei Fei Li]

Yeah. And then we started to hit all these walls and Jia and I and other students, but Zhao was the main student. We had to just deal with every obstacle that came. Now, science is a funny thing, right? Sometimes serendipity makes a world of difference. What was really critical was the Amazon Mechanical Turk, the crowdsourcing platform. Amazon. Nothing to do with us. We're like, oh, we have all these servers sitting in our data centers and we have nothing better to do. Let's make an online worker platform so people can just trade little tasks.

[Dan Rather]

A marketplace for that computer labor.

[Dr. Fei Fei Li]

Exactly. Which I didn't know it exists. I was in New Jersey, Princeton, and trying to pull my hair out. And then some student who did his master at Stanford came to Princeton and just mentioned it casually and said, do you know this? That was really, really quite a moment for me.

[Dan Rather]

Yeah. That cut this process down by 80% or something.

[Dr. Fei Fei Li]

Yeah. 10x. That was one of the technical breakthrough that really carried this whole project.

[Dan Rather]

They're years down the path and they're calculating how much further it's going to be. And they know they have years and years and years ahead.

[Monica Padman]

Until this moment.

[Dr. Fei Fei Li]

Not only years and years, the budget, hiring undergrads or whatever just doesn't cut it. The budget was not going to cut it. My tenure was on the line. It was a dice few moments. Scary.

[Dan Rather]

So to fast forward to the end, you create imagenet and you can feed in a picture of a boy petting a elephant and the computer knows that's a boy and that's an elephant. Might be a different size than the other elephant I saw, but I know that's an elephant. And this is huge. This earns you the title of godmother of AI. I know you don't have to comment. I know you don't want that. And I want to fast forward now. You've accomplished this incredible thing. You teach at Princeton for a while, as you say, and then you take up a teaching position at Stanford where you still currently are. You become one of these People that undergrads would then study about, which is fascinating. And you go to work for Google during a sabbatical for like a year and a half.

[Dr. Fei Fei Li]

Yes.

[Dan Rather]

And there's a moment where part of your job is to go meet with the new recruits that are going to start their employment at Google. Is it fair to say this is one of your. I don't want to call it a crisis of conscious, because that would be too strong, but how would you say it? You have an opportunity to talk to those people and it sounds to me like you went rogue a little bit.

[Dr. Fei Fei Li]

Yes, I did go rogue a little bit.

[Dan Rather]

Yeah.

[Dr. Fei Fei Li]

So it's very important to call out the year. My sabbatical at Google was 2017 and 2018. That was my first sabbatical. I finally had a sabbatical and it was a conscious decision for me to go to Google because this is right after AlphaGo. So AI was having its first hype wave, at least public moment. And Silica Valley of course, was ahead of the curve and new AI was coming. So I had multiple choices. But I really wanted to go to a place for two reasons. One is to learn the most advanced industry, AI and Google was by far the best. But also to go to a place where I can see how AI will impact the world. And Google cloud was a perfect place because cloud business is serving all businesses. So at cloud, being the chief scientist, I was able to see the technology translating to product and product impacting healthcare, hospitals, financial services, insurance companies, oil and gas companies into entertainment, agriculture, governments and all that. But in the meantime, it was confirming my hypothesis that this technology has come of age and will impact everyone. It was the First Tech Lash. 2017 was right after Cambridge Analytica, let's remind people.

[Dan Rather]

So Cambridge Analytica figured out how to maximize Facebook politically and people were very upset by that.

[Dr. Fei Fei Li]

Yeah, social media's algorithm can drive societal changes. It was also around the time face recognition bias was being publicized for the good reasons of calling out bias. It was also around the time that self driving car accidents start to happen. So before that, tech was a darling. The media doesn't report tech as a force of badness.

[Dan Rather]

But I do want to point out, because I heard you point it out, which is in the early advancements it had all these peaks and valleys, AI and there was a moment in the 70s where it looked promising and immediately people went to robots were going to take over the world. So we also do have this immediate sense. We do jump to that. They jump to it in the 70s. It's worth pointing out that's true.

[Dr. Fei Fei Li]

Hollywood is always ahead of the curve on that.

[Dan Rather]

Well, we sell fear and excitement.

[Dr. Fei Fei Li]

So it will was a tech clash that came at us very fast. Google has had its own share. I was actually also witnessing the struggle that Google was coming to terms with. Defense.

[Dan Rather]

Yeah, they had taken a contract to develop some drone phase recognition stuff. And the people at Google were told that they were only working on nonprofit stuff. There was a bit of a revolt. And you were there during all that?

[Dr. Fei Fei Li]

Yes. In hindsight, it was a mixture of many things. It wasn't a single event. I remember. I remember it was summer of 2018 and we were just coming off this turmoil. In hindsight, they're small, but at that point. And I was just like, I'm about to speak to. Maybe my memory is wrong, but I thought it was 700 interns from worldwide who worked at Google that summer. And they're the brightest from the whole world. And they were hand selected by Google. Google is really a machine of talent. And what do they want to hear from me? Of course I can talk about, come work at Google. That's my job as someone who was working at Google. But I felt there was more I should share, really coming from the bottom of my heart at that point. Something that you will appreciate is that the math behind technology is clean, but the human impact is messy. Technology is so much more multidimensional dimensional than equations.

[Dan Rather]

Yeah, they're all benign. It's how we implement.

[Dr. Fei Fei Li]

They're neutral.

[Dan Rather]

Neutral. There we go.

[Dr. Fei Fei Li]

But once they start to interface with the world, the impact is not necessarily neutral at all. And there is so much humanness in everything we do in technology and how do we connect that I decided to talk about that with the interns.

[Dan Rather]

And is this the first time you articulate that you want a human centered development of AI?

[Dr. Fei Fei Li]

Yeah, it was around that time. 2018, March, I published the New York Times op ed. I laid out my vision for human centered AI.

[Dan Rather]

So let's parallel your speech to the interns and then also getting to go in front of Congress. So what is your overarching sense of how we keep this technology going in a direction that does serve humans?

[Dr. Fei Fei Li]

My overarching thesis is that we must center the value of technologies development, deployment and governance around people. Any technology, AI or any other technology should be human centered. As I always say, that there's no independent machine values. Machine values are human values. Or there's nothing artificial about artificial intelligence. So it's the.

[Dan Rather]

So what are the practical things we do? What are the Legislative things. What does that mean? How do we do that?

[Dr. Fei Fei Li]

So human centered AI should be a framework. And that framework could be applied in fundamental research and education. That's what Stanford does. Or creating business and products, that's Google and many other companies do. Or in the legislation and governance of AI, which is what governments do. So that framework can be translated into multiple ways fundamentally is to put humans dignity, humans well being and the value that a society care about into both how you create AI or how you create AI products and services or how you govern a. So concrete examples. Let me start from the very basic side. Upstream at Stanford we created this human centered AI Institute. We try to encourage cross pollinating, interdisciplinary study and research and teaching about different aspects of AI like AI for drug discovery, AI for developmental studies, or AI for economics and all that. But we also need to keep in mind we need to do this with the kind of norm that reflect our values. So we have actually a review process of our grants. We call it ethics and society review process. Where even when researchers are proposing a research idea to receive funding from hai, they have to go through a study or a review about what is the social implication, what is the ethical framework?

[Dan Rather]

And are you bringing in philosophers and anthropologists and psychologists? This is the interdisciplinary aspect.

[Dr. Fei Fei Li]

That's the very fundamental research example. Now translate to a company when we think about an AI product. Let's say I would love for AI to detect skin condition for diseases. That's a great idea. But starting from your data, where do you curate data? How do you ensure data fairness?

[Dan Rather]

So if I play out that experiment, it's like, yes, I would love to take my phone, scan my face and know if I have a melanoma. That all sounds great. Where does the results of that get stored? Does my insurance provider have access to that? What all happens? It's not just me that's going to find out I have this melanoma.

[Dr. Fei Fei Li]

Exactly. What about the scale of the face and also the algorithm that detects melanoma, Is it trained on just white folks? Right? Exactly. Narrow type of skin or all skins? What's the impact of that algorithm?

[Dan Rather]

Will it disproportionately help some group in alienate another?

[Monica Padman]

You have to pay, because if you pay, you'll probably get a certain group more than you'll get another group.

[Dr. Fei Fei Li]

Right. So all those are messy human elements. And then you ask about legislation, then we come to government. Now of course there is always a tension between how much regulation, how do you regulate? Is good policy only about regulating? For example, I firmly Believe we actually should have good policy to juvenate our AI ecosystem, to make our AI ecosystem really healthy. For example, right now the gravitational pull is that all the resources, the data, the computation and the talents are all concentrated in a small number of large companies. It's all for commerce.

[Dan Rather]

Yeah, universities can't really compete.

[Dr. Fei Fei Li]

Not at all.

[Dan Rather]

Meta Google.

[Dr. Fei Fei Li]

My own lab at Stanford has zero Nvidia H100 chips.

[Dan Rather]

There you go. Yeah, like that's always been the good corrective mechanism we've had societally is the world of academia and it competed pretty robustly with any private sector.

[Dr. Fei Fei Li]

And it's not just competition, it's that the problems we work on are curiosity driven and sometimes they are really public good. For example, my own lab work collaborating with hospitals to prevent seniors from falling. That is not necessarily a commercially lucrative technology, but it's humanistically important. Universities do all kinds of work like that. Now our universities in the age of AI is so under resourced that we cannot do this. I have been working really hard in the past five years with HAI, with Washington D.C. with congresspeople, senators, White House agencies to try to encourage the resourcing of AI through national AI research, cloud and data. And then we have legislation and regulation. How do you thoughtfully put guardrails so that in individual lives and dignity and wellbeing are protected but the ecosystem is not harmed.

[Dan Rather]

So all of this I'm always on board with. I love it. I'm so grateful there's people like you pushing us in that direction. But we just had Yuval Harari on to talk about his take on it. And what I ultimately get so discouraged and defeated by is we're not doing this on an island, we're doing this while many other countries do this simultaneously. So how do you see us dealing with the competitive nature of these AI technologies emerging and us maybe proposing we're going to do it in this way, but being realistic and saying, well, Russia might not have those guidelines and China might not have those guidelines and if they have a product that people like, we can't compete now with it. So do you believe there could be cooperation? We could outlaw faking humans. Okay, so the US has outlawed faking humans, no one else does. And those fake humans are really convincing and entertaining and all these things and then that industry takes off somewhere else. Like how do we do this in a world that there are no barriers of this technology?

[Dr. Fei Fei Li]

I was also chatting with Yovel. Did he give the C minus grade to humanity? Did he say that I didn't get.

[Dan Rather]

The C minus out of him.

[Dr. Fei Fei Li]

He said that humanity has gotten C minus. And I was like, yuval, you know, I'm a teacher and a mom. When a kid comes home with C minus, you don't throw the kid out. We help the kid to get better. So, first of all, you're right. We're not living in a vacuum. And AI also is not living in a vacuum. AI is just one technology that's among many. So I absolutely do believe that there can be cooperation. How exactly we cooperate. Who we cooperate with and what are the parameters of cooperation is much, much more complicated. Look at humanity. We have gone through this so many times. I mean, Yuval is right. We have many messy chapters, even nuclear technology. But we have gotten to a state that there is a fine balance at this point of nuclear powers. I'm not saying that's necessarily culpable.

[Dan Rather]

I think it is. And then I think what's really important. And I only know this because I'm on my second von Neumann book. But von Neumann was employed in the wake of the Manhattan Project to deal with how this proliferation was going to work. And he was so analytical and so realistic that he said mutually assured annihilation is the solution. He knew that was the only outcome. It felt sociopathic to say it and to commit to it, but he's like, look, I'm modeling this out. This is the only way it works is mutually assured annihilation. And that's what we ended up with. And so I'm having a little Van Neumanny feelings about, like, no, I think it's a race to who can win until everything gets neutralized. I don't know another comp other than the nuclear arms race.

[Dr. Fei Fei Li]

Here's the difference between AI and nuclear technology is AI is so much more an enabler of so many good things.

[Dan Rather]

True.

[Dr. Fei Fei Li]

So that's very different from nuclear. Of course, nuclear can be an energy.

[Dan Rather]

We're coming back around to it. Yes.

[Dr. Fei Fei Li]

Right. But AI can help discover drugs, AI can help break through infusion, AI can personalize education, AI can help farmers, AI can map out biodiversity. So AI is much more like electronic electricity that it is like nuclear physics. So that's the difference. So from that point of view, the viewing angle of AI at least, I do not think it has to, only from the competitive lens, because it should be also through the enabling lens, the enabling of our world of so many good things that can happen. And that's the challenge is how do we keep the dark use of AI at bay? How do we create that kind of balance somehow? But in the meantime, encourage the development of AI so that we can do so many things that's good for people.

[Dan Rather]

So I accept that the nuclear analogy falls short in that there's so many benefits to this. Totally agree. But I will say again to parallel nuclear arms race in this moment in time, I think it would be only the second time where international cooperation is at its peak, where it's most needed. We have got to recognize this as a moment where we have to be getting closer to all these places and not further away our competitors, our geopolitical adversaries, that if ever there were a time where everyone stands to gain other than the nuclear arms race, this is the time where it's like we got to really figure out how to cooperate a bit, because everyone will experience the down if we don't.

[Monica Padman]

Climate, too, would be the other more recent thing. There's a Paris Accord and there is things that globally people have come together.

[Dan Rather]

I agree with you. But I will just say that climate, to me, is a little dicier simply because you have all of these burgeoning industrial economies that we would be slapping rules on. It's easy for us to adopt a lot of things that it's not for Sri Lanka, it's not totally fair. There actually should be areas of the world where they are allowed to pollute more as they pull themselves, you know, like.

[Monica Padman]

Yeah, I mean, I think that's part of it. It's just an acknowledgement globally that we're all gonna have to do something, and especially the superpowers do need to take more on than others. But it's just getting on the same page that I think we've done okay at, and at least there's some consensus there. So there could be some consensus here, potentially.

[Dan Rather]

Yeah. I just hope that we recognize this is a moment to be making friendships alone lot better and not doubling down.

[Dr. Fei Fei Li]

I do think we must always recognize cooperation is one of the solutions.

[Dan Rather]

Do you get to the guardrail point in the conversation with the legislators? Do you have some guardrails that you believe should be like, I like you all, you've all said we shouldn't ever be able to fake humans. And I also think there should be a disclaimer on all AI generated things that you at least know it came from that source.

[Dr. Fei Fei Li]

I do think we should pay a lot of attention attention on where rubber meets the road, because AI can sound very fancy, but at the end of the day, it impacts people. So if you use AI through medicine, then there is a regulatory framework. For example, my mom Again, does imaging all the time because the doctors have to use mri, ultrasound, you name it, to monitor her auto. Honestly, do I care if that MRI is fully automatic or is it operated by humans or it's a mixture. As a patient family, I probably care more about the outcome. If the result of the MRI can be so accurate.

[Dan Rather]

78% at an AI or a human does it at 40. It's a no brainer.

[Dr. Fei Fei Li]

Exactly. But all I care are two things. One is it is the best result my mom can get. Second is it's safe. Right. I don't care if it's that kind of mixture. So that regulatory framework is there. I'm not saying FDA is perfect, but it is a functioning regulatory framework. So if there's an AI product that goes into that mri, I would like it to be subject to the regulatory framework.

[Dan Rather]

There we go. Yeah, yeah.

[Dr. Fei Fei Li]

Right. So that's where rubber meets the road. The same as finance, environment, transportation, you name it. That's a very pragmatic approach. It's also urgent because as we have AI products that's entering our customers market and it takes away from, in my opinion, the science fiction rhetoric about existential crisis machine overlord. That can stay with Hollywood.

[Dan Rather]

Yeah, yeah, yeah, yeah.

[Dr. Fei Fei Li]

I believe the downstream application is where we should put our guardrail attention at.

[Dan Rather]

Right. I really want to encourage people, even if people have only a cursory or no interest in AI. I really think your book is one of my favorites I've read. It's just your personal story. As reluctant as you are to embrace it or talk about it, is a really special story.

[Dr. Fei Fei Li]

Thank you.

[Dan Rather]

I mean, what ground you've covered? Do you give yourself any moments where you go, God damn, girl, we got here?

[Dr. Fei Fei Li]

That's very sweet. That's the problem of always chasing after Northstar. I try to like look forward. One thing I do reflect back is how grateful I am. I'm not here by myself. I'm here because of the Bob Sabella, Gene Sabella, the advisors, the students, the colleagues that I feel very, very lucky.

[Dan Rather]

Yeah, there's a lot of sweet people in the world still.

[Monica Padman]

Yeah, that's good.

[Dr. Fei Fei Li]

Yeah, it's hopeful.

[Dan Rather]

Oh, well. Fei, Fei, this has been a delight. I hope everyone get your book. The world's I see curiosity, exploration and discovery at the dawn of AI and boy, those lucky people that get to have you as a teacher. I also love the narrator of your book. Have you listened to it on tape?

[Dr. Fei Fei Li]

A little bit. I didn't finish the audio. You didn't finish, right?

[Monica Padman]

Yeah, it's hard to listen to your own stuff.

[Dr. Fei Fei Li]

Well, it's not her.

[Monica Padman]

I know, but your own stuff. You spent so much time writing it.

[Dr. Fei Fei Li]

Right. I'm like, do I have time? I should finish my V. Norman book.

[Dan Rather]

Yeah, yeah. And you should read Maniac. Yeah.

[Monica Padman]

You got a couple new books to read.

[Dr. Fei Fei Li]

I'm so grateful you like the book.

[Dan Rather]

Oh, I love it. It's just really beautiful. I love the narrator, but I was having the moment where I was like, I was only introduced to you through this book. I was completely ignorant about you. And then there's a narrator when I was doing research on you. I'm like, oh, we're going to find out what the real voice is. No, I had that.

[Dr. Fei Fei Li]

I felt a little self conscious because of my accent.

[Dan Rather]

Oh, really?

[Dr. Fei Fei Li]

Because I consider if I should narrate my own book, but I feel like my accent is probably too strong for that.

[Dan Rather]

That wouldn't be the reason. I'd advise you not to do it. I think it's way, way harder than people think. And there's a lot more acting involved. I heard some writers narrate their own book. You gotta be a performer.

[Dr. Fei Fei Li]

Right.

[Dan Rather]

Forget your accent. There's like a performance to be done.

[Dr. Fei Fei Li]

Right. And that's how many hundred pages.

[Dan Rather]

Yeah.

[Monica Padman]

You also probably need to put your time there. You have a lot of other stuff going on.

[Dan Rather]

Yeah. Don't waste your time. Well, I hope you come back and see us again sometime. And I'll be following everything you do. And thank you for trying with all your might to make this a human centered development.

[Dr. Fei Fei Li]

Thank you. It's so important, and I do think creators and creators voices are so important. Important because we started this conversation with what's different from human intelligence, AI and that creativity. The insight is a huge part of it. And now that we have the generative AI trying to create things, I think the collaboration with humans is so important.

[Dan Rather]

Yeah. All right, well, be well and thanks for coming.

[Dr. Fei Fei Li]

Thank you.

[Dan Rather]

Hi there. This is hermium Permian. If you like that, you're gonna love the fact. Miss Monica. Hi, Monty.

[Monica Padman]

Hi.

[Dan Rather]

We had so much fun yesterday, didn't we?

[Monica Padman]

We did.

[Dan Rather]

I did.

[Monica Padman]

I did. We shot a commercial.

[Dan Rather]

So much fun. So much fun.

[Monica Padman]

Yeah, I had a really fun full circle moment.

[Dan Rather]

Okay. Yes. Please tell.

[Monica Padman]

Because I got out of the car, you know, I haven't acted in a while.

[Dan Rather]

Sure. We're ball rusty.

[Monica Padman]

Yeah. I got out of the car and I started recognizing some of the people on set and I realized I had worked with a lot of that crew on some Previous commercials in my day.

[Dan Rather]

Sure. One of the many, many thousands of commercials you had done.

[Monica Padman]

It felt so nice and cool. Like, you know, I had done these commercials as just this actor auditioning and doing this thing. And now we're doing a commercial commercial.

[Dan Rather]

Where they asked you to be in it.

[Monica Padman]

Yeah. And we're doing it together. Not for this podcast, but because of this podcast.

[Dan Rather]

Yeah.

[Monica Padman]

And there was something really cool about it.

[Dan Rather]

I agree.

[Monica Padman]

I liked it. And it. I think it's cuz my ring is fixed.

[Dan Rather]

I have some housekeeping.

[Monica Padman]

Okay, great.

[Dan Rather]

You know, I read the comments and so. And this is so embarrassing. And I read it a couple times. I'm like, these people are crazy. That's. I, I didn't say so people were like, you said the wrong voice of Darth Vader in the Morgan Freeman intro. And I thought they were saying I had said Morgan Freeman was the voice of Darth Vader. And I'm like, I know he didn't say that because I know he's not. And James Earl Jones was the voice of Darth Vader and I said Edward James almost. So I did say it wrong. Is another three name actor with an Edward in it.

[Monica Padman]

I see. Yeah, that's hard.

[Dan Rather]

So I fucked that up and my apologies. Oh, and then the other thing was they had coitus interrupt us because we were chatting and I was going to say I had, I was going to give a Danny Ricardo update because I had ridden motorcycles with him.

[Monica Padman]

Oh.

[Dan Rather]

But I guess then we got sidetracked and I never did. So all these people who are rightly concerned about our sweetheart Danny Ricardo, how's he doing? Were left hanging. And I'm here to report that he's so happy.

[Monica Padman]

Yeah, he's doing great.

[Dan Rather]

He's so, so happy. We were riding motorcycles all day long and we chatted a bunch and he said he's just very happy.

[Monica Padman]

I'm glad.

[Dan Rather]

Yeah. He's just doing really, really good. So people should rest assure that Danny Rick is thriving.

[Dr. Fei Fei Li]

Yay.

[Dan Rather]

Yay.

[Monica Padman]

Love to hear that.

[Dan Rather]

Yeah.

[Monica Padman]

Do you want to tell people what Toto texted you? It was so funny.

[Dan Rather]

I had text him to say, hey, people really love the episode and me in particular. I really loved it. Thanks for doing it. And he said, how are the numbers? You know, I am a lap time guy.

[Monica Padman]

Oh. So playful.

[Dan Rather]

Oh, God.

[Monica Padman]

God.

[Dan Rather]

I gotta say, I want to say out loud that really put a lot of wind in my sails. That made me so happy to have that episode come out. It really right size my perspective as I vocalize on here. It's been a challenging transition. I've been really stressed. There's been bad news and challenges. And this came out and I was like, oh, right, dumbass. You get to meet people that you are obsessed and in love with. Holy lottery. Yeah, I just was. I was. I was beaming all day Wednesday from it.

[Monica Padman]

Yeah. It was a great episode. And so. Yeah, just so cool. We get to talk to anyone we want to talk to. Not anyone. I still have a list. We still got 10k.

[Dan Rather]

Liquid death. I'm just pointing to objects. Monkey with huge balls.

[Monica Padman]

No, we already had Machine Gun Kelly.

[Dan Rather]

We did. We did. Okay, there's another fun update, but this I'm starting. I'm getting worried that people are going to be afraid to text me. I guess these people should know. I run it through my analysis.

[Monica Padman]

Okay.

[Dan Rather]

And I would never say anything that was in a text that I didn't think was just lovely. You know what I'm saying? I get worried about it. Don't you? Like, you know, someone's got a private exchange with me and then I'm reporting on it. There's an ethical dilemma here.

[Monica Padman]

Sure.

[Dan Rather]

But sometimes they're so funny, and I think the person would like it. Anyways, so I sent Pitt the clip of Toto talking about me telling him that Pitt said he was a good dancer. And then Toto talking about him coming to dinner.

[Monica Padman]

Yeah.

[Dan Rather]

And then he's. He said, I made up the thing about him being a good dancer.

[Monica Padman]

And I said, oh, no.

[Dan Rather]

I said, I can't believe you made that up. In fact, I don't believe you made that up. I still believe he's a great dancer.

[Monica Padman]

Yeah, me too. But he did say. Because Toto was like, how? When did he see me dance?

[Dan Rather]

I know. But then he just had to. He had to go, well, I don't understand how that happened, but I'm going to take that.

[Monica Padman]

He was just.

[Dan Rather]

He's being funny.

[Monica Padman]

He was doing a. Yes.

[Dan Rather]

And he was doing a bit. He was like, you're not going to believe this. He's also a phenomenal dancer, but he's just. With him, I believed it.

[Monica Padman]

Yeah. I think the who wouldn't believe the.

[Dan Rather]

Crux of that story is I'm gullible.

[Monica Padman]

I think he is a great dancer.

[Dan Rather]

Can we talk about Christma a little bit?

[Monica Padman]

Sure.

[Dan Rather]

I got the fever as much as I've ever had it. As hard as I've ever had it. Let me tell you what's happening. So. So far from our homework.

[Monica Padman]

Yeah.

[Dan Rather]

We watched Christmas Vacation already Home Alone 1 and 2. Side note, I've never heard Delta laugh harder in my life than the 27 minute set piece in Home Alone 2 where he's hitting the guys with bricks.

[Dr. Fei Fei Li]

Oh, sure.

[Dan Rather]

She was laughing uncontrollably for like 27 minutes. She said at one point, it doesn't get old. Like they threw a fifth brick or whatever. And she's like, it doesn't get old. And I was. There's. I got so much joy out of watching her have that big of a.

[Monica Padman]

Laugh at something so cute.

[Dan Rather]

Okay, so Home Alone 2, we did Gremlins, another Christmas favorite for us.

[Monica Padman]

Okay.

[Dan Rather]

Last night we did the Grinch who Sold Christmas, originally original cartoon. And I want to go out and say for the record, it's the number one Christmas cartoon to ever be made. It is the most creative. We all watched it.

[Monica Padman]

And how many more Christmas cartoons are there?

[Dan Rather]

There's a lot. You've got Rudolph, you've got. You got the Chuck Brown.

[Monica Padman]

Oh, yeah.

[Dan Rather]

You've got the. There's a bunch.

[Monica Padman]

Okay.

[Dan Rather]

But I'm saying I'm maybe even Christmas anything. It ends. And I said, you know, Dr. Seuss should really be regarded as like Salvador Dali. He had such a unique.

[Monica Padman]

Yeah.

[Dan Rather]

Imaginative world he created in the words and the set pieces. And I mean, that's one of the most creative people to ever live.

[Monica Padman]

Of course, I think he is given his due. Props.

[Dan Rather]

Yeah.

[Monica Padman]

You know, there's a Seuss land there.

[Dan Rather]

There is, yeah.

[Monica Padman]

At one of the parks. There is, I think.

[Dr. Fei Fei Li]

Yeah. Seuss landing in Orlando, Florida.

[Dan Rather]

In Orlando, Florida. I should go.

[Monica Padman]

You should go. You should pay your respects.

[Dan Rather]

I like when people use the term susian. Did you ever hear anyone use that?

[Monica Padman]

No, but I like it.

[Dan Rather]

Yeah. It's cool, right?

[Monica Padman]

Yeah, yeah.

[Dan Rather]

Like Newtonian or like, it's a. It's a paradigm, but it kind of.

[Monica Padman]

Sounds like Sisyphusian, which is my favorite.

[Dan Rather]

Word, which I tell you.

[Monica Padman]

I heard word anymore.

[Dan Rather]

You taught me that word.

[Monica Padman]

Yeah.

[Dan Rather]

And I thank you for that. To remind people, Sisyphus pushed the rock up the hill every day. There's a Buddhist. Take that. Like, that's what's. People interpret that as a story of not wasted effort, but like, you know what I'm saying?

[Monica Padman]

Yeah, yeah.

[Dan Rather]

A fool's errand.

[Monica Padman]

Yeah.

[Dan Rather]

But there's a Buddhist way of looking at it, which is like, this person had purpose every single day, all day long, and was not suffering. Probably. Probably. Well, it's a story of suffering. Well, first of all, he's probably jabbed to be. Believe. Yeah.

[Monica Padman]

So strong.

[Dan Rather]

But that's an interesting way to reframe it. That like. No. This person, every day of their life had purpose.

[Monica Padman]

Yeah.

[Dan Rather]

Probably very happy.

[Monica Padman]

That's a lovely way to look at it.

[Dan Rather]

Yeah.

[Monica Padman]

It's actually Sisyphean.

[Dan Rather]

Sisyphean.

[Monica Padman]

Yeah.

[Dan Rather]

I like Sisyphusian.

[Monica Padman]

Me too. And I maintain it. Yeah. Okay. So you're in the Christmas spirit.

[Dan Rather]

Yes. And I wake the girls up every morning. I wake up about 20 minutes before the girls to meditate. And so now I'm. They wake up to me playing from my phone over the Sonos Christmas music.

[Dr. Fei Fei Li]

Wow. That's.

[Dan Rather]

And I want to make a great recommendation to people who are using Spotify. And you can make a station.

[Monica Padman]

Uhhuh.

[Dan Rather]

Go to the Charlie Brown Christmas album and then go specifically to the song Christmas Time is Here. Make a station out of Christmas Time is Here. And it's the best Christmas mix I've ever.

[Monica Padman]

Ooh, that sounds lovely.

[Dan Rather]

And it's on all the time. And so you know, the tree is over decorated.

[Monica Padman]

Yeah.

[Dan Rather]

You know, we get one tree and Kristen gets a tree in the kitchen and hers is artistic. And this year it's wicked.

[Monica Padman]

Oh, cute.

[Dan Rather]

Yeah. And our tree is a throw up of color. And I have those old fashioned bulbs that the water bubbles up in them. They're almost impossible to get to sit vertical on your tree. I've spent most of my free time positioning all of them and then I pull the cord and they all fall down. It's a Sisyphusian task.

[Dr. Fei Fei Li]

Wow.

[Monica Padman]

Ding, ding, ding.

[Dan Rather]

I didn't expect it to come around that quick. I had all this anxiety about presents, but I knocked a bunch of presents out the other day.

[Monica Padman]

Nice. You used a little bit of my.

[Dan Rather]

I used your gift guide almost exclusively.

[Monica Padman]

There were good gifts on there.

[Dan Rather]

Complain about your gift guide though. You make things sell out. Your gift guide is moving markets.

[Monica Padman]

Yeah. Well, I pick great items.

[Dan Rather]

Yeah. I have to say you have exquisite taste.

[Monica Padman]

Thank you.

[Dan Rather]

Some of your recommendations were so good that I found myself dancing around on the websites.

[Monica Padman]

Yes. That's the goal.

[Dan Rather]

Yeah. Yep. And yeah.

[Monica Padman]

Fun stuff abound.

[Dan Rather]

There's fun stuff. So.

[Monica Padman]

So. And let's just. So your tree has colored lights, right?

[Dan Rather]

So many.

[Monica Padman]

Yeah.

[Dan Rather]

Four strands. Really long straight strands and four of those bubbly light strands. And the trees touching the ceiling. It's a Clark Griswold. It's too big and I'd cut up foot off.

[Monica Padman]

I just want to talk about lights.

[Dan Rather]

Oh, okay. My apologies, Ms. Monica. Ms. Monica. I'm sorry. I get so carried Away. Sometimes when the spirit moves me, I don't leave my apartment much. So I really enjoy decorating it. Get all those colors. Makes me optimistic.

[Monica Padman]

I wonder how. How hermium. Do you have a. Does he have a delivery service? How does he get his tree?

[Dan Rather]

I have a cousin who's not working at the moment and he loves going to department stores and plazas and shopping malls and strip malls.

[Dr. Fei Fei Li]

Wow.

[Dan Rather]

And I'll call him on the landline. That's what I have, Ms. Monica. I pick up the phone and I call his. His name is Bert.

[Monica Padman]

Oh, yeah.

[Dan Rather]

He's my. Did I say my brother in law or my cousin?

[Monica Padman]

He said cousin.

[Dan Rather]

Yeah, he's my cousin. I just remembered. Weirdly enough, he's also my brother in law, but it's my stepsister.

[Monica Padman]

Okay.

[Dan Rather]

So it's all above board, as they say. I call up Bert and I say, here's what I need, Bert. Six water weenies, 10 spatulas. And Bert. It takes him a while, sometimes four or five days. And then he comes over and he. He does charge me a little more, but that's okay.

[Monica Padman]

Sure.

[Dan Rather]

And then I have to call him back up and ask him to deliver the presents.

[Monica Padman]

Wow.

[Dan Rather]

That's okay, though. He charges me for that, too.

[Dr. Fei Fei Li]

Okay.

[Monica Padman]

Getting a little taken advantage of, but that's. You Fine with it. Okay, great, Mom. Now remember, I'm not your mom.

[Dan Rather]

Okay, Ms. Monica. Mom. Monica. Mrs. Mom. Colored lights.

[Monica Padman]

Yes, the lights. Because Chris and I assume on her nice tree has white lights.

[Dan Rather]

Yep, yep.

[Monica Padman]

Yeah. And this is, you know, this is a big thing. I don't know if it's a. Rob.

[Dr. Fei Fei Li]

Yes.

[Monica Padman]

What color lights do you have?

[Dan Rather]

Well, first of all, do you have the lights you want?

[Dr. Fei Fei Li]

Yes.

[Dan Rather]

Okay, I do.

[Dr. Fei Fei Li]

I like the, like yellowy white light, kind of warm. Gold. Yeah, white lights. That's warm. I mean, there's shades of white, too.

[Dan Rather]

Like, we.

[Monica Padman]

Like he's trying to. He's trying walk in the middle and be nice, but really he has white lights. White lights. And he likes them.

[Dr. Fei Fei Li]

I have white lights, but they're kind of yellowy.

[Monica Padman]

Yeah, I know what you mean. There's like a warm and a cool.

[Dan Rather]

Now listen, sometimes you complain about there being two boys, one girl in this situation, but you have to admit, Rob is a perfect middle ground. Like, if Aaron was here, it would suck.

[Monica Padman]

Well, yeah, but he does.

[Dan Rather]

He disproves my gender stereotypes quite a bit.

[Monica Padman]

Yeah, yeah. Yes, because Aaron grew up exactly like you. So it's not fit. You just assume it's men because you and Aaron believe It.

[Dan Rather]

That's right, Monica. That's right, Ms. Monica.

[Monica Padman]

So, yes, Rob did not grow up with you and like you.

[Dan Rather]

Oh, he's from the Big Windy.

[Monica Padman]

So I don't think it's gender, but I do think some people love the nostalgic colored lights and then other people who care about it aesthetics love the white light.

[Dan Rather]

I could really get on my high horse about it. I used to have a really strong stance on it. And it's all my class warfare stuff.

[Monica Padman]

Yeah. Which is.

[Dan Rather]

It's so tired. Is that what you're gonna say?

[Monica Padman]

No, I wasn't gonna say that. Your life does not match that mentality.

[Dan Rather]

Doesn't at all. But did you see Chris Rock's latest standup? He said, I am rich, but I identify as poor.

[Monica Padman]

Yeah, that's fine.

[Dan Rather]

Good for him.

[Monica Padman]

Yeah.

[Dan Rather]

Okay. For me.

[Monica Padman]

Yeah. You aren't. You're of the highest class in this country.

[Dan Rather]

Yeah, well, there's people with a lot more money than me, but I do have.

[Monica Padman]

You're of the highest class.

[Dan Rather]

Okay. Okay.

[Monica Padman]

And you, you also hobnob with the highest class.

[Dan Rather]

Yeah. But you know what? I act like myself and I have color. Here's what I'll say. The white, all white Christmas tree is like, occasionally I'd see that at people's houses who had an extra living room that no one went into and you weren't allowed to go in there. You take off all your shoe, your. You know, you get in a intel outfit to go in the room and all of it seems stuffy and not playful and fun and colorful. It felt very presentational.

[Monica Padman]

And where's your tree?

[Dan Rather]

But I used to be judgmental of that. I don't. I still don't like it, but I'm not as judgmental.

[Monica Padman]

Cuz your, your second tree is in your second second living room. Okay.

[Dan Rather]

Okay.

[Monica Padman]

You know, I gotta keep you. I gotta. I gotta just remind you.

[Dan Rather]

I know I'm spoiled. I know I'm spoiled.

[Monica Padman]

Yeah. Okay.

[Dan Rather]

I'm really spoiled.

[Monica Padman]

It's just class to me, the class warfare thing. I would hope you now see that.

[Dan Rather]

It wouldn't be fair for a stranger to hate me just because I have money.

[Monica Padman]

Yeah, yeah, yeah.

[Dan Rather]

I would feel that way on the other side of it, but I wouldn't expect anyone to feel that way, not be on the other side of it, because I get it.

[Dr. Fei Fei Li]

Okay.

[Monica Padman]

So I have white light dates, obviously.

[Dan Rather]

Yeah, I know that. I would. I would know that.

[Monica Padman]

Yeah. Everyone would know.

[Dan Rather]

You don't need to tell me that. I know that. Well, and I'm not judgmental of you. I'm so glad you're having the Christmas you've always wanted.

[Monica Padman]

Thank you.

[Dan Rather]

Yeah.

[Monica Padman]

Yeah. Jess and I had pig day and we went to home. We just missed you, I guess because we really like the timing.

[Dan Rather]

Yeah. Because we were there at like 11am on a Saturday and you were there at 11am but I gotta say, this is my record of all time. I was so fast and there was no fighting. This is like the first year and a few. That day is very triggering for our family.

[Monica Padman]

I think it's hard for families that have to decide.

[Dan Rather]

You gotta compromise.

[Monica Padman]

And everyone has their things they care about. And luckily, Jess and I have the same thing. We don't like bald. Call it bald puss.

[Dan Rather]

You call the tree a bald puss?

[Monica Padman]

Bald pussy. Bald pussy if there's bald spots.

[Dan Rather]

Okay, great.

[Monica Padman]

And we don't like that.

[Dan Rather]

Okay. And you like more of a Brazilian tree?

[Monica Padman]

No, Brazilian.

[Dan Rather]

That's shaped and full.

[Monica Padman]

Brazilian.

[Dan Rather]

Yeah. Isn't a Brazilian like you have a landing strip?

[Monica Padman]

I thought Brazilian is clean. Clean, Rob.

[Dr. Fei Fei Li]

Do I. You want me to Google.

[Dan Rather]

Yes, I do.

[Monica Padman]

Just definition of Brazilian wax.

[Dr. Fei Fei Li]

And pictures.

[Monica Padman]

Yeah, pictures. You can do that on your own time.

[Dr. Fei Fei Li]

It removes most of most or all of the hair from the pubic region, including the front, sides, back, and often the area around the anus.

[Monica Padman]

Yeah. Okay, I'm glad.

[Dan Rather]

I. What's the landing? The landing strip's just the landing strip.

[Monica Padman]

Yeah, there's like. You can just get different kinds, but Brazilian generally means all hair.

[Dan Rather]

Do you think any dudes get a landing strip? I was just thinking I want to go do that just as a bit.

[Dr. Fei Fei Li]

I've done that as a joke.

[Dan Rather]

You have?

[Dr. Fei Fei Li]

For Natalie.

[Monica Padman]

Oh, my God, that's so funny. Did she like.

[Dan Rather]

Did it make her horny?

[Dr. Fei Fei Li]

No, no, it was. It was supposed to be.

[Monica Padman]

That's really funny.

[Dan Rather]

Stay tuned for more Armchair Expert if you dare. Okay, let's take a break from the fact check to thank our presenting sponsor, Amazon Prime. Prime has you covered with movies, music, and everything you could possibly need to make the holidays perfect. Whatever you're into, it's all on Prime.

[Monica Padman]

This is very exciting. It's holiday party season.

[Dan Rather]

Yes, it is that time of year.

[Monica Padman]

Work parties, family parties, parties with friends. Party parties, parties with your animals.

[Dan Rather]

If you're as popular as Monica, you're hitting the party circuit.

[Monica Padman]

It's a great reason to shop for new clothes or accessories and really, like, spice up your wardrobe. Make it fancy.

[Dan Rather]

Probably can help with that. Especially if you decide last minute you want to Buy something new. You're set with Prime's fast free shipping. And hey, what you're buying for holiday parties depends on whether you're a guest or a host. If you're hosting, then you're going deep on prime to find everything you need to make your home feel fun and festive and perfectly like you.

[Monica Padman]

Oh, tell me about it. I really like to make my house feel very me. During the holidays, you could be decorating the outside of the house, getting some lights, something for the windows, grab some new holiday towels, some festive hand soap. Oh, I love a good festive hand soap. Candles. You really. You can do it all?

[Dan Rather]

Absolutely. And you can get all those things on Prime.

[Monica Padman]

Oh, and one other thing. Amazon Music is here to help with the playlist. Curating the party playlist. It's an art.

[Dan Rather]

Amazon Music will get the vibe, right? Listen, what we're saying is anything you need for a holiday party is on Prime. Nice sweaters. Goofy sweaters for the ugly sweater party. Holiday decor gifts for the host or fun small stuff for a gift exchange at work. The sky is the limit when Prime's fast free shipping is at your fingertips. From streaming to shopping. It's on Prime. Visit Amazon.comprime to get more out of whatever you're into.

[Monica Padman]

Now. We were also so quick. So quick, in fact, it was almost eerie. We walked in and we were doing just like a quick look and Jess just beelined. He knew his like Christmas vacation.

[Dan Rather]

There was a beam of light shining down on it.

[Monica Padman]

Yes. And he knew his daughter and it was the one.

[Dan Rather]

Were you his daughter? Cause I think you view more of his mom.

[Monica Padman]

No, the tree was our daughter. Okay, okay, that makes sense. Have we co parent?

[Dan Rather]

Okay.

[Monica Padman]

But she lives at my house.

[Dan Rather]

Yeah. So I gotcha.

[Monica Padman]

So he's a little bit of a Debbie dad, but whatever. And she's really pretty. She's so nice. She's. We said cuz last year archery was a boy and he was a model. Oh, he was striking. He was striking and very similar. Like perfect.

[Dan Rather]

Angular.

[Monica Padman]

Exactly. Very angry. Singular, not round features. This girl is. She's not a model, but she's a star.

[Dan Rather]

Oh yeah, that's my. That's the kind I like.

[Monica Padman]

Exactly. And I've been trying some different hats on her toppers.

[Dan Rather]

Oh, okay. Hats.

[Monica Padman]

I haven't decided yet.

[Dan Rather]

Is there no part of you that feels sad like what I really. The softest spot in my heart I have is for Charlie Brown's Christmas when they get that really bad. Charlie Brown did a bad job and they hated they're yelling at Charlie because of the tree, but then they decide to love it. And it's a good little tree.

[Monica Padman]

It's a sweet story.

[Dan Rather]

And I always am drawn to the shitty tree there because I think no one wants this tree. And we'd have a great Christmas with this tree. I have a real. I get emotional about it.

[Monica Padman]

Wow.

[Dan Rather]

Yeah. I want to, like, rescue the shitty tree.

[Monica Padman]

Oh, my God, you're. The way you feel about the trees is like how Kristen feels about the dog.

[Dan Rather]

That's right. That's right. And all because of Charlie Brown, I think.

[Monica Padman]

Wow.

[Dr. Fei Fei Li]

So.

[Dan Rather]

Yes. So the girls have one agenda, which is to never like the same tree, I think is their agenda. And then mom has an agenda. Mom's very aesthetic, you know, it's very important to her.

[Monica Padman]

So for her, trees are not dogs. She wants a pretty one.

[Dan Rather]

Yeah. She's got something in her mind she's looking for.

[Monica Padman]

Yeah.

[Dan Rather]

My singular goal is when you pull into Home Depot, you can either park and then go by the tree and then enter the line to pull up where they'll put the tree in. And all I want to just pull into the line and know that they can get that tree fast enough that by the time it inches up to the front, we'll have gotten a tree. So my only objective is to get the trees in time, by the time.

[Monica Padman]

I'm pulling the truck.

[Dan Rather]

No, in previous years, they go in and I wait in the car. This year I went two.

[Monica Padman]

So what did you do with the truck?

[Dan Rather]

I just parked it and I'm like, I'm going to run in. I'm going to see if I find a tree. It's not going to move up that fast. They got to load a tree. I didn't hold anyone one up.

[Monica Padman]

Okay.

[Dan Rather]

And then we got the trees by the time I pulled up. So that was my goal. Mine's way less aesthetic and way more time management.

[Monica Padman]

Yeah. I don't feel bad for the. For the.

[Dan Rather]

You don't?

[Monica Padman]

I don't.

[Dan Rather]

How could you not? Well, a tree that no one wants.

[Monica Padman]

Monica, it's already dead. Dax is already dead. It got chopped down.

[Dan Rather]

I always get a tree that has a little bit of personality. And by personality, I mean missing parts, bald puss. Ms. Monica, I don't know what you just said, but please don't say it again. You want to talk about that? I'm here to buy a big old Christmas tree. Tell me about your tree. Oh, does it have a Brazilian?

[Monica Padman]

Ew.

[Dan Rather]

What?

[Monica Padman]

Stop.

[Dan Rather]

What do you put under your tree?

[Monica Padman]

Make them Go away. Make them go away. I can only take. I can't. I. I for. Kind of forgot.

[Dan Rather]

He sounds, though, like you guys are very Frito esque when you're shopping for this tree. You're just not doing the voice. Talking about bald. I don't even want to say it either. My God. And you're saying it's your daughter. This is twisted. Certainly don't want just talking about his daughter in that fashion.

[Monica Padman]

No.

[Dan Rather]

Last update, it was time for a crop, a harvest. Everyone already knows that. I feel like people are going to have a bunch of judgment about this. I guess them Delta's like, I want to shave my legs. Will you shave my legs?

[Monica Padman]

Yeah.

[Dan Rather]

Like, okay. People are gonna be like, you shouldn't shave your kids legs. I. I can already feel that coming, but I don't give a. She wants me to shave her legs.

[Monica Padman]

Yeah, why not?

[Dan Rather]

She feels left out. I did it.

[Dr. Fei Fei Li]

Okay.

[Dan Rather]

Monica. Her leg hair is also cashmere.

[Monica Padman]

It is.

[Dan Rather]

So we now have two fields in rotation. And so I want you to see what an enormous.

[Monica Padman]

Are you combining.

[Dan Rather]

Yes. It's now father, daughter cashmere. And I want you to. You remember how much we had just.

[Monica Padman]

Yeah. Two days ago.

[Dan Rather]

Look at the amount of cashmere we now have.

[Dr. Fei Fei Li]

Wow.

[Monica Padman]

It's. It's like quadrupled in size.

[Dan Rather]

I was making a joke that we might get a mitten or a scarf in 10 years, but I actually, actually think that's a real possibility now. Look at the amount in there.

[Monica Padman]

Now this is really, you know, what.

[Dan Rather]

Do you want to feel? I do.

[Monica Padman]

I want to touch it. But also I. Last time we touched it, some of it disappeared. Yeah.

[Dan Rather]

That's okay. Now we got two growers.

[Dr. Fei Fei Li]

Wow.

[Monica Padman]

There is so much.

[Dan Rather]

Yeah.

[Monica Padman]

Now you have two growers.

[Dan Rather]

We got basically a mink farm.

[Monica Padman]

Are they separated?

[Dan Rather]

There's no real.

[Monica Padman]

Okay.

[Dan Rather]

Yeah, it's just. I think it's separated.

[Monica Padman]

Wow, it's so soft.

[Dan Rather]

Yeah. I think hers might even be softer than my back hair.

[Monica Padman]

Oh, my God.

[Dan Rather]

But that's got a time limit. Her leg hair will turn into shitty hair. Like our leg hair.

[Monica Padman]

Exactly.

[Dan Rather]

But currently she is growing cashmere.

[Monica Padman]

Oh, my God.

[Dan Rather]

You think I need to get a work permit for her because she is.

[Monica Padman]

Now kind of probably illegal. It's like. Yeah, it's illegal.

[Dan Rather]

I don't want to out her because she did such a great job. Lincoln shaved my back, did a great job. But she was. She thought she had some cashmere on the razor. And she emptied a little bit into our pouch. And then I discovered no Some of that was beard hair, so I had to actually go in and pull. Now I'm getting embarrassed. It sounds like a bit, but then you realize, no, it's not a bit. He's really. That happened.

[Monica Padman]

Did you use tweezers?

[Dan Rather]

No, I just. I could feel, and I'd pull that out. I probably lost a lot of really good products.

[Monica Padman]

No, no, that's okay. Yeah.

[Dan Rather]

We live and learn.

[Monica Padman]

This is an R and D situation.

[Dan Rather]

Only this second harvest, so. Wow. Still learning a lot.

[Monica Padman]

Exciting. All right. Oh, oh, one more thing. One. One cool thing that happened that I want to put out there in the world because I think it's good for me to manifest this.

[Dan Rather]

Okay.

[Monica Padman]

When Callie and I were shopping, we went into one store, and I bought some cute little boxer shorts.

[Dan Rather]

Okay.

[Monica Padman]

As we were leaving, Cali was in front. Front of me, and someone had held the door open for her to come out. And, like, some woman walked in, and then Cali walked out, and then he. This person continued to hold the door for me, and I was like, oh, thank you. Then I kept walking. I. I don't know. He's a mystery man.

[Dan Rather]

Oh, oh, oh, oh. Okay. And it looked like the look on your face was that it was a famous person.

[Monica Padman]

It was the most gorgeous, gorgeous person I've ever seen.

[Dr. Fei Fei Li]

Really?

[Monica Padman]

And not.

[Dan Rather]

And male or female?

[Monica Padman]

Male.

[Dan Rather]

Give me age, Height. Describe.

[Monica Padman]

He's kind. Now he's like. But now he's sort of a haze.

[Dan Rather]

Oh.

[Monica Padman]

Like, I don't. I don't remember.

[Dan Rather]

I don't like that part.

[Monica Padman]

I know. I know. But part of it was. I. It. It happened so fast. He took my breath away.

[Dan Rather]

Yeah. Yeah.

[Monica Padman]

And. And I think it read. You know, it read.

[Dan Rather]

Okay. Your face betrayed you.

[Monica Padman]

Yeah. And he smiled, and I don't remember if he showed teeth or not, but no, he just, like, that's who he is.

[Dan Rather]

Okay.

[Monica Padman]

And I turn, you know, I. I turned and I said, oh, my God, that guy was so hot. And she said, I know.

[Dan Rather]

Kelly was up, too.

[Monica Padman]

Yes. So we. So this is an undeniable situation.

[Dan Rather]

Should have gone back inside to talk to the third woman who entered.

[Monica Padman]

Well, I think they were together. Oh, well, I don't know. There's no way to know.

[Dan Rather]

So this is a lost person's report.

[Monica Padman]

Exactly.

[Dan Rather]

If you open a door for Callie and Monica at the farmers market.

[Monica Padman]

Brentwood Country Mart.

[Dan Rather]

Brentwood Country Mart.

[Monica Padman]

Yeah. On Black Friday, probably around noon.

[Dan Rather]

Okay.

[Monica Padman]

Yeah.

[Dan Rather]

Contact, I guess. Comment in this. I'll read it. I read them all.

[Monica Padman]

Comment or don't.

[Dan Rather]

No catfishes.

[Monica Padman]

Exactly.

[Dan Rather]

I guess you'll be able to see the photo, though, and you'll know.

[Monica Padman]

Oh, I'll know.

[Dan Rather]

And no one could fake it.

[Monica Padman]

No. Because, you know, when I walk through the world, I'm extremely unobservant. I don't notice people. I really am. And speaking of blind, I got some soap in my eye this morning and it was blinding. I thought I did some permanent damage.

[Dan Rather]

Of course.

[Monica Padman]

Okay. So anyway, I walk around, so unobservant, and yet this person was strong. He pulled me out. It was shocking.

[Dan Rather]

He's like a lifeline.

[Monica Padman]

He was. He was so attractive.

[Dan Rather]

How many more times did you think about him later that day?

[Monica Padman]

A lot of times.

[Dan Rather]

A ton.

[Monica Padman]

Yeah.

[Dan Rather]

Did you, like, whip up fantasies? I know you're prone to fantasies.

[Monica Padman]

I am prone to fantasies. I didn't actually. I was more just like, thunderstruck. I was just taken.

[Dan Rather]

Love at first sight?

[Monica Padman]

A little bit.

[Dan Rather]

Oh, my God.

[Monica Padman]

And I don't even believe in that.

[Dr. Fei Fei Li]

But, like, maybe.

[Monica Padman]

Anyway, that was a big mystery.

[Dan Rather]

Yeah. Wow. And I wonder. And how often have you thought of him since then? Daily or once every few years.

[Monica Padman]

It's starting to dissipate and I don't remember him.

[Dan Rather]

I sure hope he reaches out in the comments.

[Monica Padman]

Me too.

[Dan Rather]

Also, no. No catfishing.

[Monica Padman]

Yeah.

[Dan Rather]

Guys, seriously, stop catfishing, everybody.

[Monica Padman]

Seriously. Okay. Anyway, so that we'll add that to the mystery pile with the guy I met at.

[Dr. Fei Fei Li]

In New York.

[Monica Padman]

The restaurant guy.

[Dan Rather]

Oh, right.

[Monica Padman]

That mystery is.

[Dan Rather]

Catfish is so delicious. Did you ever eat a big catfish sandwich, Monica?

[Monica Padman]

I don't give him permission to say my name.

[Dan Rather]

What do you want him to call you?

[Monica Padman]

I don't want him.

[Dan Rather]

Don't. Leave it. Don't. Let. Let him decide.

[Monica Padman]

Okay. This is for Fay Lee.

[Dan Rather]

Oh, and a ding, ding, ding. We just. Just interviewed someone who knows her. Intimately. Yeah, not intimately. I mean sexually. He. The colleague. We just interviewed a colleague.

[Monica Padman]

And he was giving her a lot of props and reverence.

[Dan Rather]

Yep.

[Monica Padman]

That she really deserves. She was. I loved her so much.

[Dan Rather]

I loved her so much too.

[Monica Padman]

She was a delight.

[Dan Rather]

Yeah.

[Monica Padman]

Now some facts for her. How long did Einstein live at Princeton? He lived in Princeton, New Jersey for 22 years, from 1933 until his death in 1955. He purchased a house at 112 Mercer street which became his home until his death. The house was for him, his wife, Elsa, stepdaughter, Margot, and secretary Helen Ducas.

[Dan Rather]

His secretary lived with him?

[Monica Padman]

I guess so.

[Dan Rather]

Interesting.

[Monica Padman]

I bet it's more like an assistant. Nowadays we call it an assistant.

[Dan Rather]

You're probably right. Yeah, I guess Secretaries were just assistant.

[Monica Padman]

Okay. Oh, we talk about Cambridge Analytica, which was the whole thing that happened with Facebook. I encourage people to listen to Acquired, the podcast. Acquired. They do an episode on Meta. Fantastic episode. And they talk about what happens with the Facebook Cambridge Analytica scandal. And a lot of it's very misunderstood. A lot of what the public thinks. We're all missing a ton of real. Of information.

[Dan Rather]

It's kind of like a market. The Stewart thing.

[Monica Padman]

Exactly.

[Dan Rather]

We all think she traded her company.

[Monica Padman]

Exactly. And that's not what it was.

[Dan Rather]

Nor did she even do any insider training. She still went to prison. I know. Yeah. But, yeah, the nefarious activity was on the. Was on Cambridge Analytic, not Meta.

[Monica Padman]

Right. But also they.

[Dan Rather]

And they were just using existing tools that anyone could have been using, but.

[Monica Padman]

They were using old information from an old quiz or quiz or something that Facebook did a lot, long time ago. And that's what they used. They weren't using current information. And yeah, they, like Facebook didn't sign off on. They didn't hand over this information.

[Dan Rather]

Yeah. Everyone should listen to Acquired, just period.

[Monica Padman]

It's such a good podcast.

[Dan Rather]

It is. It is.

[Monica Padman]

I'm always shocked.

[Dan Rather]

Yeah. If you like a deep dive, that's.

[Monica Padman]

The show for you in the business world, like learning. I mean, you listen. I mean, they're four hours long this Meta 6. Yeah. They spend a month researching a company and then they. They just tell you everything about the business and how it came to be and all of it. And you do leave feeling like you went to, like, you took a course in business.

[Dan Rather]

Oh, big time. Yeah, yeah, yeah.

[Monica Padman]

I recommend Acquired. And that's it.

[Dan Rather]

That was it.

[Monica Padman]

Yeah.

[Dan Rather]

Okay. I just adore. I wish her the best.

[Monica Padman]

Me, too.

[Dan Rather]

I'm grateful for her.

[Monica Padman]

Me too.

[Dan Rather]

Yeah.

[Monica Padman]

That's the line we learned from the Lisa K. Kudrow fact check that we say to people that I'm just grateful for you. I'm grateful you exist.

[Dan Rather]

Oh, yeah, yeah, yeah, yeah. And I'm grateful for her existence.

[Monica Padman]

Yeah, me too.

[Dan Rather]

Okay. And Toto's a great dancer. Don't listen to anybody else.

[Monica Padman]

He's great.

[Dan Rather]

Love you. Love.