Pod Insights Armchair Expert with Dax Shepard Fei Fei Li (on a human-centered approach to AI) About this episode Fei Fei Li (The Worlds I See: Curiosity, Exploration, and Discovery at the Dawn of AI) is a computer scientist, co-director of the Stanford Institute for Human-Centered Artificial Intelligence, and is considered by many to be the godmother of AI. Fei Fei joins the Armchair Expert to discuss her initial reluctance to tell her personal story as a part of her book on AI, starting a laundromat with her parents to support themselves, and the high school teacher that changed the course of her life. Fei Fei and Dax talk about how math is the closest thing there is to magic, why being fearlessly stupid is sometimes the best asset you can have, and the reason her north star is asking the audacious question. Fei Fei explains her perspective on the tech-lash, why there is so much humanness in everything we do in technology, and how essential it is to put dignity into how we both create and govern AI.
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Transcript 19,584 words · 5 speakers
Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Dax Shepard? Welcome, welcome, welcome to Armchair Expert, Experts on Expert. I'm Dan Rather and I'm joined by Modest Mouse.
0:05 Speaker 01 Hi.
0:06 Dax Shepard? 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. So, 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...
0:41 Speaker 01 Compelling.
0:42 Dax Shepard? It means 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 fields.
0:51 Speaker 01 Become better than everyone.
0:52 Dax Shepard? Oh my God, it's so impressive.
0:54 Speaker 01 Yeah.
0:55 Dax Shepard? Oh, I loved her. Please enjoy Dr. Fei-Fei Li. 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.
1:29 Speaker 02 This is a very, very sinking couch.
1:35 Speaker 01 I know.
1:36 Dax Shepard? We're off to a terrible start. You want to swap? You want to sit in this chair? You can.
1:40 Speaker 02 I feel like I'm going through a therapy version.
1:42 Speaker 01 Yeah, well, that is the goal. We do want people to feel very...
1:46 Speaker 02 Relaxed.
1:47 Speaker 01 Too comfortable, really.
1:48 Dax Shepard? Yeah, if the spirit moves you to lay down supine. You're invited to do so.
1:54 Speaker 02 I might. I woke up so early.
1:57 Dax Shepard? What time did you wake up?
1:58 Speaker 02 Probably five forty, something.
2:00 Speaker 01 That's early.
2:01 Speaker 02 Yeah.
2:01 Dax Shepard? What time do you normally rise?
2:03 Speaker 02 Not that much later. My alarm is six twenty.
2:06 Dax Shepard? Okay, mine's six forty.
2:07 Speaker 02 Oh, okay.
2:08 Dax Shepard? I'm aspiring. But you know what's funny? Today it was six twenty.
2:11 Speaker 02 You have kids?
2:12 Dax Shepard? I have kids.
2:12 Speaker 02 How old are they?
2:13 Dax Shepard? Nine and eleven.
2:14 Speaker 02 Mine is eight and twelve.
2:16 Dax Shepard? You have eight and twelve?
2:17 Speaker 02 Yeah, so we're in the same stage of life.
2:20 Dax Shepard? Boys or girls?
2:20 Speaker 02 Twelve is boy, eight is a girl. How about you?
2:23 Dax Shepard? Girl, girl.
2:24 Speaker 02 Oh.
2:25 Dax Shepard? Yes, I'm so lucky. Although, eleven about to be twelve, I'm starting to get an inkling of what's coming my way. Yeah, yeah.
2:34 Dax Shepard? In a house with three ladies.
2:35 Speaker 02 Yeah.
2:36 Dax Shepard? In fact, yesterday was a very emotional day. I'm barely hanging on.
2:40 Speaker 01 Oh, what happened?
2:41 Dax Shepard? I don't know what's going on with my three ladies, but all of them are in some kind of, uh...
2:46 Speaker 01 Hormonal.
2:47 Dax Shepard? Hormonal turmoil.
2:48 Speaker 01 Interesting.
2:50 Dax Shepard? And every variety, which is fun.
2:51 Speaker 02 We started.
2:52 Dax Shepard? Yeah, yeah, yeah, yeah, yeah. We're always recording.
2:54 Speaker 01 We'll call it ABR, always be recording.
2:57 Dax Shepard? Always be recording. Okay, so you have twelve and eight, and are they close?
3:02 Speaker 02 They're very close.
3:04 Dax Shepard? He's a good big brother.
3:05 Speaker 02 He totally is.
3:06 Dax Shepard? Yeah.
3:07 Speaker 01 That's sweet.
3:08 Dax Shepard? Silvio's your husband, yeah?
3:09 Speaker 02 Yes.
3:09 Dax Shepard? And does one of them have Silvio's personality and one have yours?
3:13 Speaker 02 Well, I can tell you one of them has Silvio's hair.
3:16 Dax Shepard? Okay, which is...
3:17 Speaker 02 A lot of curls.
3:18 Dax Shepard? Curls. World of curls.
3:20 Speaker 02 Yeah, world of curls.
3:23 Dax Shepard? 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?
3:30 Speaker 02 Yes, he just texted me last night. He's like, you're seeing my friend, Dex.
3:34 Dax Shepard? Oh, good. Yeah, 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 an incredible writer.
3:58 Speaker 02 Thank you. I do want to give credit to my friend Alex, who co- Wrote the book with me.
4:03 Dax Shepard? OK, that's very big of you. Who's Alex?
4:06 Speaker 02 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.
4:27 Dax Shepard? How do you know Alex?
4:28 Speaker 02 I know Alex through TED. 2015, I gave my first TED Talk.
4:33 Dax Shepard? I watched it.
4:34 Speaker 02 Yeah, thank you.
4:35 Dax Shepard? Yeah, about image, how hard it is for a computer to see.
4:38 Speaker 02 Yes, and that's how I got to know Alex.
4:40 Dax Shepard? Because he worked with TED in some capacity?
4:43 Speaker 02 Yeah, he was in some kind of partnership with TED, and he was helping me to put together my slides. Since then, we've become friends, and we talk about AI. And he's also helped me in some of the Stanford HAI, Human Center AI Institute stuff.
4:58 Dax Shepard? You're kind of creative partners?
5:00 Speaker 02 Yes, it's very interesting because book writing is a 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.
5:18 Dax Shepard? Yeah, which one do you find more exhaustive?
5:20 Speaker 02 Both.
5:21 Dax Shepard? Both in different ways?
5:22 Speaker 02 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 every minute of it. When I say I love, it's not necessarily happy love. It's a painful love, some part of it, but I really loved it.
5:41 Dax Shepard? 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?
5:53 Speaker 02 No, and I didn't want to, and I still don't.
5:55 Dax Shepard? 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 was right. And I glean from that, oh, this isn't a culture that is just divulging all this emotional trauma. Baggage.
6:13 Speaker 02 Well, I have to say, I think culture in the case of an individual sometimes is too broad a brushstroke.
6:20 Dax Shepard? Yeah.
6:20 Speaker 02 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 friends is a philosopher at Stanford called John H. Mindy. He's a very revered higher education leader. He was Stanford's provost for seventeen years. And he is co-director with me at Stanford Human Center 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.
7:06 Speaker 02 I was like, what?
7:09 Speaker 02 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 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.
7:50 Dax Shepard? With excitement.
7:51 Speaker 02 He said, I told you. He said, I told you so many times. Of course, it only takes Edge to tell you that.
8:00 Dax Shepard? 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. 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.
8:34 Speaker 02 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.
8:57 Dax Shepard? Yeah. Well, I think even the notion that you're struggling so hard. I gotta set up your story more. This is the last thing I'll say out of context. Monica's like, not everyone's read the book. But just of course math was appealing because math didn't have a language barrier.
9:11 Speaker 02 Yes, but I do wanna 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.
9:28 Dax Shepard? Yeah, there's more philosophy.
9:30 Speaker 02 Yes.
9:31 Dax Shepard? Okay, so let's start in China in 1976. You're the only child of your parents.
9:36 Speaker 02 Yep.
9:37 Dax Shepard? And talk about your mother because she's very interesting.
9:39 Speaker 02 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 come 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 when she had a dream for college. And that carried her through.
10:11 Dax Shepard? 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 gonna be tilted as well if you stay there. So what's happening? What is she noticing as you start getting educated and show this aptitude?
10:29 Speaker 02 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,
11:10 Speaker 02 without me realizing, was the motivation of many of the changes, the decision of immigration.
11:17 Dax Shepard? Well, what would have been your trajectory had you stayed in China in nineteen eighty-eight, when you're twelve? Am I misremembering that your mom felt like they weren't giving you the attention and encouragement that she was hoping you would get?
11:29 Speaker 02 My mom was not looking for attention for me. My mom 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.
11:46 Speaker 01 What do you mean by quirky?
11:48 Speaker 02 This goes to my dad. I didn't follow rules in an average way. In hindsight, maybe it was just me being immature. 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 will in general be less smart by the time you hit your teenage time.
12:18 Dax Shepard? This is what I'm remembering from the book, that you were explicitly told, you're not as smart as boys.
12:24 Speaker 02 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.
12:35 Dax Shepard? Yeah.
12:36 Speaker 02 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 relativity. I would grab my classmates to talk about that, but that was just not normal.
13:10 Speaker 01 Yeah.
13:11 Dax Shepard? It was exposing you to all that stuff.
13:13 Speaker 02 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 did a meadow with it.
13:57 Speaker 02 I kind of followed it through myself.
13:59 Dax Shepard? 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?
14:10 Speaker 02 Yeah.
14:10 Dax Shepard? Do you remember that three years, missing him terribly? How was that experience?
14:15 Speaker 02 It was tough for me. 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 I was sitting in the room crying or anything.
14:36 Dax Shepard? Yeah. So then you come, your sophomore year.
14:40 Speaker 02 Yes.
14:40 Dax Shepard? You start a public high school in New Jersey.
14:42 Speaker 02 Persippany High School.
14:44 Dax Shepard? 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.
14:52 Speaker 02 Library.
14:53 Dax Shepard? 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.
15:07 Speaker 02 Right.
15:08 Dax Shepard? And what happened?
15:09 Speaker 02 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.
15:33 Dax Shepard? Yeah, you said he got a concussion and a broken nose. And there's two boys kicking him.
15:38 Speaker 02 Yeah.
15:38 Dax Shepard? And that's not even maybe the most traumatic part. It's that after he's gone for a couple of weeks, he comes back and he's just not the same boy.
15:48 Speaker 02 Yeah, I mean, nobody would be the same after going through something like that. Definitely, it's a huge impact. It was an experience that was definitely pretty intense for all ESL students. Nobody felt safe for a long while.
16:04 Dax Shepard? Yeah, I think it changes your worldview on a dime, which is, ooh, this new place I'm in 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.
16:20 Speaker 02 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 multi-dimensional. 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.
17:04 Dax Shepard? Everything's happening. A lot of opportunity is happening as promised. And then a lot of xenophobia and violence is happening.
17:11 Speaker 02 Right.
17:11 Speaker 01 Yeah. Did you feel like you had to after that sort of like keep your head down?
17:15 Speaker 02 Maybe it's just my own personality. I always felt I had to keep my head down.
17:19 Speaker 01 Right.
17:20 Speaker 02 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 they also taught me you have to stand up for yourself. It did open different insights to me.
17:46 Dax Shepard? I don't know if you would rank these things in your life of like serendipitous things happening, but meeting Mr. Sibella has to be minimally in the top 10 and I would hope in the top three.
17:56 Speaker 02 Minimally, yeah, it's possibly in the top three for sure. Meeting Mr. Sibella was so lucky for me.
18:02 Dax Shepard? 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. Sibella in his office.
18:30 Speaker 01 That's your teacher?
18:31 Speaker 02 Yes, Mr. Sibella was my math teacher. I got into calculus and then Presley High School doesn't have calculus BC. We only had 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.
18:55 Dax Shepard? That they had gotten at a garage sale. Her father loves garage sales. It was his favorite thing in the world.
19:00 Speaker 02 I know.
19:00 Dax Shepard? Every weekend they'd go to. He still does.
19:03 Speaker 02 I love garage sale. I don't have time to go to garage sale, 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.
19:22 Dax Shepard? He didn't pity you.
19:23 Speaker 02 No, not at all. I for one quarter of semester, I got eighty nine point four something. I still remember that. I was like, oh, God, point six. I would get to at least an A and he would not give me that A.
19:37 Dax Shepard? You asked about extra credit and he was like, get real. How about good to good grade on the test?
19:41 Speaker 02 Yeah, he would say there are many smart kids in the class. You just have to work hard.
19:45 Dax Shepard? 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.
20:16 Speaker 02 Yes.
20:17 Dax Shepard? And he is like shook. He's like, you've read Jules Verne?
20:20 Speaker 02 Yes.
20:20 Dax Shepard? And then you go on to say, yes, and you've read Hemingway and you've read everything.
20:25 Speaker 02 Well, I've read everything my mom gave me, which is a lot.
20:29 Dax Shepard? Which was extensive.
20:29 Speaker 02 Yeah.
20:30 Dax Shepard? If I were him and this young girl from China comes in and she has read most of the classics, that's so 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.
20:45 Speaker 02 I think he saw a person he can befriend 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 ESL kid at that point. And he's also a shy person himself. Later, his wife Jing and Bob, later I call him Bob, we became such good friends.
21:31 Dax Shepard? Many, many years, way longer than maybe.
21:33 Speaker 02 Jean said, he's such a bookworm, even during his family parties, he'll be by himself reading.
21:38 Dax Shepard? Yeah.
21:39 Speaker 02 So he's totally an introvert in a way that we just had chemistry.
21:44 Dax Shepard? Yeah.
21:44 Speaker 02 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 diary, 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?
22:05 Dax Shepard? Yeah.
22:06 Speaker 02 So years later, before he passed away, we didn't know he was gonna pass away. I told Bob, I said, Bob, you've gotta turn this into a book. Of course, we can anonymitize. But this is an American teacher's story of so many students, many of them are immigrant students. Because they lack the support, they lack the family, some of them are in high school by themselves, family is 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.
22:51 Dax Shepard? Yeah, I think he's struggling with the same issue you're struggling with.
22:54 Speaker 02 Probably.
22:54 Dax Shepard? You don't feel entitled to tell this story.
22:57 Speaker 02 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.
23:41 Dax Shepard? Yeah, they're very unsung here. They're not tenured professors at elite universities.
23:45 Speaker 02 They're totally unsung heroes.
23:47 Dax Shepard? And they're the ones that get the people to those destinations. Yeah, it's a really beautiful story. How instrumental was he in you finding your way to Princeton?
23:55 Speaker 02 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, ESL kid, you land in a country without speaking the language and going through so many things, it feels so unstable.
24:13 Dax Shepard? 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.
24:21 Speaker 02 To learn English.
24:22 Dax Shepard? 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 that land at their most elite university.
24:43 Speaker 02 Moscow State University.
24:46 Dax Shepard? It's not gonna happen. It's not gonna happen for 99.999% 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?
25:11 Speaker 02 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 continued 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.
25:32 Dax Shepard? Garage sales, free.
25:33 Speaker 02 Exactly.
25:34 Dax Shepard? Museums, free.
25:35 Speaker 02 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, trying to Fresh from walking dogs.
25:54 Dax Shepard? You're working a ton of hours.
25:56 Speaker 02 Yeah, exactly. I didn't forget about physics. I was taking physics class in school. But I forget about the sense of love.
26:03 Dax Shepard? Romanticism.
26:05 Speaker 02 It really is that first love and it kind of got me back to that, rekindled something.
26:10 Dax Shepard? Well, don't you think it left an imaginary word where this person existed and it put it in your own three dimensional reality?
26:17 Speaker 02 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?
26:28 Dax Shepard? Yeah, yeah.
26:29 Speaker 02 That felt very different.
26:31 Dax Shepard? And he lived there for what thirty some years or maybe more? I think that would be a special moment as well.
26:37 Speaker 02 I'm sure you watched the movie Oppenheimer.
26:39 Dax Shepard? Yeah, yeah.
26:40 Speaker 02 Do you remember the opening scene was I standing in front of that little pond?
26:44 Dax Shepard? Yep, yep, talking with Oppenheimer.
26:46 Speaker 02 Right, he was first there by himself.
26:48 Dax Shepard? Yeah.
26:49 Speaker 02 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.
27:02 Dax Shepard? Yeah.
27:03 Speaker 02 So like when the thing came out, Sylvia was sitting next to him. I'm like, Sylvia, this is my pond.
27:11 Speaker 01 Yes, such a full circle.
27:12 Dax Shepard? Yeah, 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?
27:23 Speaker 02 No.
27:24 Dax Shepard? Or have you read The Maniac? Either of those?
27:26 Speaker 02 No.
27:27 Dax Shepard? Oh, The Maniac's all about Janusz von Neumann.
27:31 Speaker 02 I'm reading a different bio of him, but not The Maniac.
27:34 Dax Shepard? Which one?
27:35 Speaker 02 Oh, it's in my phone.
27:36 Dax Shepard? Yeah, same. Don't worry about it. This one's fun because it has the perspective of a million different people in his life. Like a student, he was friends with the school, one of his wives, people who worked with him. And you get this really comprehensive view.
27:47 Speaker 02 Another Princeton guy.
27:49 Dax Shepard? 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?
28:03 Dax Shepard? Guy in a nine day, one hundred and six degree fever, writing down the matrices and not understanding the maths 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. Mystical. Yeah.
28:24 Speaker 02 Physics is absolutely the discipline that pushes you to think so audaciously that you have to transcend the immediate reality.
28:34 Dax Shepard? Yes.
28:35 Speaker 02 That's what I loved. I loved about, I loved about modern physics, even Newton, classic physics, you have to think so beyond the immediate reality.
28:46 Dax Shepard? 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?
29:08 Speaker 02 There is actually even beyond math.
29:11 Dax Shepard? Right. And then there's a realm beyond math.
29:13 Speaker 02 Yes.
29:14 Dax Shepard? 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.
29:21 Speaker 02 I love that you call it magic. It is also the furthest thing we have to AI is that humanity in us, that magic, that creativity, that intuition, that almost ungraspable way of intelligence.
29:36 Dax Shepard? Yes.
29:37 Speaker 02 We should keep that in mind.
29:39 Dax Shepard? So you're at Princeton. You're also working a ton, right?
29:41 Speaker 02 Yes.
29:42 Dax Shepard? When do your parents start the dry cleaners?
29:44 Speaker 02 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.
29:57 Dax Shepard? I know Newark, yeah.
29:58 Speaker 02 From Persipony 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 them. Doctor was worried. We finally decided if we could. Can do a local thing in persimmony. 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.
30:42 Speaker 02 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.
31:04 Dax Shepard? Because your mother had had a reoccurring fever as a child and it greatly degenerated some of her heart valves. So she was really struggling with heart issues.
31:12 Speaker 02 Yes, she carried that illness with her all her life.
31:15 Dax Shepard? And there's no money in the dry cleaning. There's only money in the seamstress scene, whatever we call it.
31:20 Speaker 02 The tailoring.
31:21 Dax Shepard? The tailoring.
31:22 Speaker 02 I mean, there's no money in any of these.
31:24 Dax Shepard? Yeah, yeah, yeah.
31:25 Speaker 02 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.
31:37 Dax Shepard? She should have done what you did.
31:38 Speaker 02 Right.
31:38 Dax Shepard? Yeah.
31:39 Speaker 02 I don't think she would love physics, but.
31:41 Dax Shepard? But you know what I mean. She should have probably been an academic.
31:43 Speaker 02 Yes, 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.
31:55 Dax Shepard? I think a chimp can do that.
31:58 Speaker 02 Thank you.
32:00 Speaker 02 Yeah, exactly.
32:01 Dax Shepard? I say that because I know how to remove stitches from garments. And I don't have more skills than a chimp.
32:05 Speaker 02 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.
32:32 Dax Shepard? You said you didn't go to a single party.
32:34 Speaker 02 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 30 years later.
32:47 Dax Shepard? Instead of the freshman 10, you gained the 30 10.
32:50 Speaker 02 Yeah, exactly.
32:51 Dax Shepard? The faculty 10.
32:52 Speaker 02 So I felt so good. I finally got my Sunday brunch.
32:56 Dax Shepard? And I think it's worth mentioning, when you guys were trying to open that dry cleaners, you were trying to raise a hundred thousand dollars and you were 20,000 dollars short. And again, Mr.
33:07 Speaker 02 Sabella.
33:08 Dax Shepard? Monica.
33:09 Speaker 01 He gave the money.
33:10 Speaker 02 Yeah, it was a total shock. To this day, actually, as a 19 year old, as much as I appreciated Jing and Bob, I did not realize the extent. We're talking about late 1990s. There are two public school teachers with two kids about to go to college.
33:30 Dax Shepard? It's unimaginable.
33:31 Speaker 02 He said Jing and he decided to do that. I mean, at that moment, I was very, very grateful. But now, after I became a grown up, this is unimaginable.
33:41 Dax Shepard? It's impossible that someone would do that.
33:43 Speaker 02 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. I was like, what?
33:52 Dax Shepard? Of course, you have to give it thinking you'll never get it back. I guarantee he and his wife were like, we're giving this money away.
33:59 Speaker 02 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 Jing and Bob did not expect that.
34:09 Speaker 01 So the money was being raised to help your mom?
34:12 Speaker 02 No, to help my family.
34:14 Dax Shepard? To start the business.
34:16 Speaker 02 Yeah, 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 Jing, it's incredible. I don't even think their kids knew about this till they read my book.
34:37 Dax Shepard? 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.
34:47 Speaker 02 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 an engineering physics minors.
34:59 Dax Shepard? And when you're 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.
35:09 Speaker 02 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.
35:19 Dax Shepard? It was an identity.
35:20 Speaker 02 Yeah, it was an identity. For some reason, this girl who works in dry cleaners just wanted to be a scientist. And 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 inquiry. So I was just reading a lot and what really caught my attention was the physicists I admired so much. Einstein, Schrödinger, Roger Penrose, they actually are curious beyond just atomic world. They were curious about other things, especially life, intelligence, minds. And that was immediately the no point and the eye opener for me. I realized I love that.
36:05 Dax Shepard? Yeah, understanding how this.
36:07 Speaker 02 Brain works, intelligence works.
36:09 Dax Shepard? 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.
36:24 Speaker 02 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.
36:37 Dax Shepard? Right. Okay, so then you choose Caltech to go to graduate school.
36:40 Speaker 02 Yes.
36:41 Dax Shepard? What did you think of California? I mean, my God, what a place, right?
36:45 Speaker 02 I know, we're 15 minutes away from. Caltech here for twenty minutes. So I was choosing among MIT, Stanford and Caltech. And honest God, I almost chose Caltech because of the weather.
36:58 Dax Shepard? Yeah.
36:58 Speaker 01 That's fair.
36:59 Speaker 02 It was so.
37:00 Dax Shepard? And the vibe.
37:01 Speaker 02 Yes, the turtles, the garden-like campus. And of course, I walk into this building. I think it was more building at Caltech. And guess whose photo was there? It was Albert Einstein.
37:13 Dax Shepard? Sure.
37:14 Speaker 02 I was like, what? It turned out he was visiting. And of course, there was Richard Feynman, the Feynman Lectures. 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.
37:45 Speaker 01 Did your parents come with you?
37:46 Speaker 02 Later, they did. In the middle of my grad school, they did.
37:49 Speaker 01 Were you worried about that, leaving?
37:50 Speaker 02 I had to switch from being on site to remotely run the dry cleaning. The dry cleaning was stabilized. 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.
38:09 Speaker 01 Oh, my God.
38:10 Speaker 02 Yeah.
38:13 Speaker 02 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.
38:24 Speaker 01 Yeah.
38:24 Speaker 02 He would still proofread my English for me, all my papers.
38:28 Dax Shepard? Tell me about a North Star and how you discovered yours? Because this happens at Caltech.
38:33 Speaker 02 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. No 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?
39:17 Speaker 02 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.
40:02 Dax Shepard? T-Rex skeleton.
40:03 Speaker 02 Okay, that's behind.
40:04 Dax Shepard? That's behind you. It's about to eat you.
40:05 Speaker 02 Yes, shirts and skirts and boots and TV. So the ability for humans to be able to learn such a complicated world of objects.
40:15 Dax Shepard? Oh, millions and millions of objects.
40:18 Speaker 02 Is so fascinating. And I started to believe, along with my advisors, this is a critical problem for the foundation of intelligence. And that really start to become the north star of my scientific pursuit is how do we crack the problem of object recognition?
40:41 Dax Shepard? 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 in computing up to that point.
40:58 Speaker 02 Right. So that's the parallel story I was writing the book.
41:01 Dax Shepard? Now that I have people hooked into you as an individual, now we can get a little protein in this and learn some.
41:07 Speaker 02 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 US government grant.
41:35 Dax Shepard? DARPA funded it or something?
41:36 Speaker 02 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 born, not born yet. They got together and wrote this memo or this white paper about artificial intelligence. In fact, John McCarthy, one of the group leaders, was responsible for coining the term "artificial intelligence."
42:04 Dax Shepard? I think we could get even more rudimentary, right? So up until that point, a computer was something that could solve a problem, it could do computations.
42:12 Speaker 02 It could calculate.
42:14 Dax Shepard? And this notion of artificial intelligence, what it really meant is, could we ever ask a computer questions 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.
42:36 Speaker 02 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?
43:06 Dax Shepard? Right, without having been programmed that.
43:08 Speaker 02 Right, without directly saying red ball is bigger than the blue ball. So that's a reasoning. So that's one aspect. A very, very intertwined. The aspect of that is learning. A calculator doesn't learn. Whether you have a good ten button or not, it just does what it is.
43:24 Dax Shepard? Yeah, you had a bad one.
43:25 Speaker 02 Once 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 one, two, three. Right. So that's learning. But even before the Dartmouth workshop, there were early inclins. Like Alan Turing's daring question to humanity. Can you make a machine that can converse with people? Q A 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.
44:10 Speaker 02 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 computerized and A I 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.
44:46 Dax Shepard? Right.
44:47 Speaker 02 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 fifties, neuroscientists were asking questions, nothing to do with A I, about how neurons work. And again, my own field vision was the pioneering study about cat mammalian visual system. And Hubel and Wiesel in the nineteen fifties and sixties were sticking electrodes in.
45:27 Speaker 02 To cat's 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.
45:49 Speaker 01 Right.
45:49 Dax Shepard? Okay.
45:49 Speaker 02 And that means information travel in a hierarchical way.
45:53 Dax Shepard? Up these columns.
45:54 Speaker 02 Yes, for example, light hits our retina. Our retina sends neural information back to our primary cortex. Our primary cortex processes, send it up 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.
46:23 Dax Shepard? That's a mouse I'm seeing if I'm a cat.
46:25 Speaker 02 Or this tiger sneaking up on me.
46:27 Dax Shepard? 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.
46:55 Speaker 02 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.
47:19 Dax Shepard? And as you point out in the book, it took us 540 million years of evolution to get this system.
47:25 Speaker 02 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 90 80s because it's such a fundamental discovery. But that inspire 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.
47:55 Dax Shepard? You build one algorithm at the bottom that's maybe generic.
47:58 Speaker 02 No, it's a whole algorithm, but you build mathematical functions that are layered.
48:03 Dax Shepard? Okay.
48:03 Speaker 02 So you can have one small function that process brightness, another that process curvature. I'm 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 Jeff Hinton, along with others who have discovered this, was awarded the Nobel Prize last week.
48:43 Dax Shepard? Okay.
48:44 Speaker 02 For this, but that is the algorithm neural network.
48:48 Dax Shepard? 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 hierarchy.
49:01 Speaker 02 You could actually.
49:01 Dax Shepard? Cause it's just like all this raw 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.
49:10 Speaker 02 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.
49:18 Dax Shepard? 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.
49:30 Speaker 02 For linguistics. So WordNet 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.
49:47 Speaker 02 I was obsessing with it. I was not satisfied because my field was using extremely contrived data sets, like data set of four objects or twenty 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 Beederman number in my book is that I read a psychologist's paper, Erv Beederman, who was up till two years ago, a professor at University of Southern California.
50:32 Speaker 02 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.
50:45 Dax Shepard? Animal, food, furniture.
50:47 Speaker 02 German shepherd.
50:48 Dax Shepard? Transportation.
50:49 Speaker 02 Yeah, sedan, fighter jet and all that.
50:53 Dax Shepard? Yeah.
50:53 Speaker 02 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 Beederman 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 twenty 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 in Princeton.
51:32 Speaker 02 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 that apple should be closer to a pear.
51:51 Speaker 01 Oh, I see.
51:51 Speaker 02 Then appliance. So how do you organize that? How do you regroup concepts? So he created WordNet, which hierarchically organized concept according to meaning and similarity, rather than alphabetical ordering.
52:08 Dax Shepard? Does WordNet not lead to the machine that can read the zip codes?
52:12 Speaker 02 No.
52:12 Dax Shepard? It doesn't. What's that called? That's what I meant to bring up.
52:15 Speaker 02 That was ConvNet, convolutional neural network.
52:18 Dax Shepard? That's happening as you're getting your idea about the images, right? We've trained a machine to read zip codes, basically, handwritten zip codes.
52:26 Speaker 02 So that was Young Akun'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 given enough training example of digits, scientists in Bell Labs were able to read from zero to nine, or the 26 letters. And with that, they created an application to read zip codes, to sort mail.
52:58 Dax Shepard? But its dataset was, I forget, it was like a thousand or something, or wasn't that?
53:03 Speaker 02 It was a lot of handwritten digits.
53:05 Dax Shepard? Yeah, and common mistakes, they would feed it.
53:07 Speaker 02 That dataset was probably tens of thousands of example. But we're talking about just letters and digits.
53:14 Dax Shepard? What they had proved in concept, you're gonna try to do in images. But the lift for images is so exponentially larger than getting the machine to read.
53:25 Speaker 02 Exactly.
53:26 Dax Shepard? By a factor of what? I mean, when you lay out what it's gonna take for you to prove this theory you have, and you figure out how long it's gonna take, it's gonna take like a decade of you feeding them, right? There's some moment where the amount of images you're gonna have to feed this computer to train it can't almost be done by the group of you.
53:45 Speaker 02 So I think what you were referring to was the process of making ImageNet.
53:49 Dax Shepard? Yes.
53:50 Speaker 02 And that process was, once we realized, thanks to the inspiration of WordNet, and also Bitterman's number, and also many other previous inspirations. We realized what computers really need is big data. And that was so common today because everybody talks about big data. You know, OpenAI talks about big data. But back in the two thousand six, two thousand seven, 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 Biderman's number. Why don't we just map out the entire world's visual concept?
54:33 Speaker 01 Oh, my God.
54:36 Speaker 01 Why don't we?
54:37 Dax Shepard? And you wrangled someone in that this wasn't even really their nor star.
54:40 Speaker 02 OK, 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 Dan. And Jia was just a deer in the headlights. 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. You know, I don't know what you're talking about, but let's get started.
55:15 Dax Shepard? Yeah.
55:15 Speaker 02 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 just a solution that got us through.
55:26 Dax Shepard? 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 image.
55:51 Speaker 02 That's an interesting observation.
55:53 Dax Shepard? 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.
56:01 Speaker 02 I do tell my kids, ideas are cheap.
56:03 Dax Shepard? Exactly, Hollywood. Someone's like, that was my idea. Oh, really? Did you write the script? Did you execute it? Did you cast it correctly? Did you motivate everyone? Your idea is 1% of the equation of a great movie.
56:14 Speaker 02 Yeah.
56:14 Dax Shepard? Because when I'm reading your thing and the data is 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 gonna be harder than you originally anticipated. It just kept getting worse and worse and worse and worse for years.
56:29 Speaker 02 It was pretty bad.
56:31 Dax Shepard? When I'm reading it, I'm like, I would have quit a ca-trillion 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.
56:39 Speaker 01 How do you even start something like that? Do you literally just look around the room and you're like, okay?
56:44 Dax Shepard? Here we go.
56:45 Speaker 01 Yeah, I'll start with this room and write everything.
56:48 Speaker 02 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 ImageNet, 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 the word to get the source images. I had to curate it. Every step of the way, like Axie were saying, we were just way too optimistic at the beginning.
57:27 Dax Shepard? Naivete is the best asset you can have.
57:30 Speaker 02 I was just fearlessly stupid.
57:32 Dax Shepard? Yeah, it's a great gift.
57:34 Speaker 02 And then we start to hit all these walls. Ja and I and other students, but Ja 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.
58:12 Dax Shepard? A marketplace for that computer labor.
58:15 Speaker 02 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 thing, that was really, really quite a moment for me.
58:34 Dax Shepard? Yeah, that cut this process down by eighty percent or something.
58:38 Speaker 02 Yeah, ten X. That was one of the technical breakthrough that really carried this whole project.
58:45 Dax Shepard? 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 until this moment.
58:52 Speaker 02 Not only years and years, the budget, hiring undergrads or whatever just doesn't cut it. The budget was not gonna cut it. My tenure was on the line. It was a dicey few moments.
59:05 Dax Shepard? 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.
59:48 Speaker 02 Yes.
59:48 Dax Shepard? 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 conscience 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.
1:00:06 Speaker 02 Yes, I did go rogue a little bit.
1:00:09 Speaker 02 So it's very important to call out the year. My sabbatical at Google was twenty seventeen and twenty eighteen. 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 Silicon 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.
1:00:53 Speaker 02 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, 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 clash. Twenty seventeen was right after Cambridge Analytica.
1:01:33 Dax Shepard? Uh huh. Let's remind people, so Cambridge Analytica figured out how to maximize Facebook politically. And people were very upset by that.
1:01:42 Speaker 02 Yeah, social media's algorithmic impact 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.
1:02:10 Dax Shepard? But I do want to point out, because I heard you pointed out, which is in the early advancements, they had all these peaks and valleys AI. And there was a moment in the seventies 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 jumped to it in the seventies. It's worth pointing out.
1:02:30 Speaker 02 That's true. Hollywood is always ahead of the curve on that.
1:02:34 Dax Shepard? Well, we sell fear and excitement, so.
1:02:37 Speaker 02 So it was a tech clash that came at us very fast. And Google has had its own share. I was actually also witnessing the struggle that Google was coming to terms with defense.
1:02:50 Dax Shepard? Yeah, they had taken a contract to develop some drone face recognition stuff. And the people at Google were told that they were only working on nonprofit stuff. There was a bit of a revolt. You were there during all that.
1:03:02 Speaker 02 Yes, in hindsight, it was a mixture of many things. It wasn't a single event. I remember it was summer of twenty eighteen 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 seven hundred 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. You know, Google is really a machine of talents. 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.
1:03:47 Speaker 02 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 multi-dimensional than equations.
1:04:09 Dax Shepard? Yeah, they're all benign. It's how we implement all that. Neutral, there we go.
1:04:13 Speaker 02 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.
1:04:29 Dax Shepard? And is this the first time you articulate that you want a human-centered development of AI?
1:04:36 Speaker 02 Yeah, it was around that time, twenty eighteen, March, I published the New York Times op-ed. I laid out my vision for human-centered AI.
1:04:46 Dax Shepard? So what'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?
1:05:00 Speaker 02 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 deeply human.
1:05:30 Dax Shepard? So what are the practical things we do? What are the legislative things? What does that mean? How do we do that?
1:05:36 Speaker 02 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 company 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 AI.
1:06:21 Speaker 02 So concrete examples, let me start from the very basic size 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 a 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.
1:07:02 Speaker 02 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?
1:07:17 Dax Shepard? And are you bringing in philosophers and anthropologists and psychologists? This is the interdisciplinary aspect.
1:07:23 Speaker 02 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?
1:07:45 Dax Shepard? 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's 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.
1:07:59 Speaker 02 Exactly. What about the scale of the face? And also the algorithm that detects melanoma is it trained on?
1:08:08 Dax Shepard? Just white folks?
1:08:08 Speaker 02 Right, exactly. Narrow type of skin or all skins? What's the impact of that algorithm?
1:08:15 Dax Shepard? Will it disproportionately help some group and alienate another?
1:08:17 Speaker 01 And do you have to pay? Because if you pay, you'll probably get a certain group more than you'll get another group.
1:08:22 Speaker 02 Right, so all this are messy human elements. And then you ask about legislation. Then we come to government. 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 juvenile 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.
1:09:02 Dax Shepard? Yeah, universities can't really compete at the level that Meta, Google.
1:09:07 Speaker 02 My own lab at Stanford has zero Nvidia H100 chips.
1:09:13 Dax Shepard? 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.
1:09:23 Speaker 02 And it's not just competition is that the problems we work on are curiosity driven and sometimes they are really public good. For example, my own lab were 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 kind of work. I have been working really hard in the past five years with HAI, with Washington DC, with congresspeople,
1:10:04 Speaker 02 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 individual lives and dignity and well-being are protected, but the ecosystem is not harmed?
1:10:29 Dax Shepard? 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 in 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
1:11:14 Dax Shepard? humans. No one else does. And those fake humans. 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?
1:11:24 Speaker 02 I was also chatting with Yuval. Did he give the C minus grade to humanity? Did he say?
1:11:31 Dax Shepard? I didn't get the C minus out of him.
1:11:33 Speaker 02 He said that humanity has gotten a 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. 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.
1:12:16 Speaker 02 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.
1:12:31 Dax Shepard? 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 Neumann-y 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.
1:13:13 Speaker 02 Here's the difference between AI and nuclear technology is AI is so much more an enabler of so many good things.
1:13:23 Dax Shepard? True.
1:13:23 Speaker 02 So that's very different from nuclear. Of course, nuclear can be a energy.
1:13:27 Dax Shepard? We're coming back around to it, yeah.
1:13:28 Speaker 02 Right, but AI can help discover drugs. AI can help breakthrough infusion. AI can personalize education. AI can help farmers. AI can map out biodiversity. So AI is much more like electricity than 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?
1:14:12 Speaker 02 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.
1:14:24 Dax Shepard? So I accept that the nuclear analogy falls short in that there's so many benefits of 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 downside if we don't.
1:15:05 Speaker 01 Yeah, climate too would be the other more recent thing. There's the Paris Accord and there is things that globally people have come together.
1:15:12 Dax Shepard? 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.
1:15:35 Speaker 01 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.
1:15:53 Dax Shepard? Yeah, I just hope that we recognize this is a moment to be making friendships a lot better and not doubling down.
1:16:00 Speaker 02 I do think we must always recognize cooperation is one of the solutions.
1:16:05 Dax Shepard? Do you get to the guardrail point in the conversation with the legislators? Do you have certain guardrails that you believe should be like, I like you've 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.
1:16:21 Speaker 02 I do think we should pay a lot of 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. 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
1:17:04 Speaker 02 the outcome. If the result of the MRI can be so accurate.
1:17:08 Dax Shepard? Seventy-eight percent of AI or a human does it at forty. It's a no-brainer.
1:17:12 Speaker 02 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 is an AI product that goes into that MRI, I would like it to be subject to the regulatory framework.
1:17:38 Dax Shepard? There we go. Yeah, yeah.
1:17:39 Speaker 02 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.
1:18:08 Dax Shepard? Yeah, yeah, yeah, yeah.
1:18:09 Speaker 02 I believe the downstream application is where we should put our guardrail attention at.
1:18:15 Dax Shepard? 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.
1:18:33 Speaker 02 Thank you.
1:18:34 Dax Shepard? I mean, what ground you've covered. Do you give yourself any moments where you go, goddamn, girl, we got here?
1:18:42 Speaker 02 That's very sweet. That's the problem of always chasing after North Star. 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, Jean Sabella, the advisors, the students, the colleagues. That I feel very, very lucky.
1:19:03 Dax Shepard? Yeah, there's a lot of sweet people in the world still.
1:19:06 Speaker 01 Yeah, it's good.
1:19:07 Dax Shepard? Yeah.
1:19:07 Speaker 01 It's hopeful.
1:19:09 Dax Shepard? Oh, well, Fei Fei, this has been a delight. I hope everyone gets your book, The Worlds I See: Curiosity, Exploration, and Discovery at the Dawn of AI. And boy, those lucky people that get to have you as a
1:19:21 Speaker 01 Oh, man, so jealous.
1:19:23 Dax Shepard? I also love the narrator of your book. Have you listened to it on tape?
1:19:25 Speaker 02 A little bit. I didn't finish the audio.
1:19:28 Dax Shepard? You didn't finish?
1:19:29 Speaker 02 Right.
1:19:29 Speaker 01 Yeah, it's hard to listen to your own stuff.
1:19:31 Dax Shepard? Well, it's not her.
1:19:32 Speaker 01 I know, but your own stuff.
1:19:33 Dax Shepard? Yeah, yeah, yeah.
1:19:33 Speaker 01 You spent so much time writing it.
1:19:35 Speaker 02 Right. I'm like, do I have time? I should finish my Norman book.
1:19:39 Dax Shepard? Yeah, yeah. And you should read Maniac.
1:19:41 Speaker 01 Yeah, you got a couple new books to read.
1:19:43 Speaker 02 I'm so grateful you like the book.
1:19:45 Dax Shepard? 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 gonna find out what the real voice is. Oh, God.
1:19:58 Speaker 02 I felt a little self-conscious because of my accent.
1:20:02 Dax Shepard? Oh, really?
1:20:03 Speaker 02 Because I consider if I should narrate my own book, but I feel like my accent is probably too strong for that.
1:20:09 Dax Shepard? 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've heard some writers narrate their own book. You gotta be a performer.
1:20:20 Speaker 02 Right.
1:20:20 Dax Shepard? Forget your accent. There's like a performance to be done.
1:20:23 Speaker 02 Right, and that's how many hundred pages?
1:20:25 Dax Shepard? Yeah.
1:20:27 Speaker 01 You also probably need to put your time there. You have a lot of other stuff.
1:20:30 Dax Shepard? Yeah, don't waste your time doing that. 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.
1:20:42 Speaker 02 Thank you. It's so important. And I do think creators and creators' voices are so 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.
1:21:06 Dax Shepard? Yeah. All right, well, be well, and thanks for coming.
1:21:08 Speaker 02 Thank you.
1:21:12 Dax Shepard? Hi there. This is Hermium Hermium. If you like that, you're gonna love the fact check with Miss Monica. Hi, Moni.
1:21:20 Speaker 01 Hi.
1:21:21 Dax Shepard? We had so much fun yesterday, didn't we?
1:21:24 Speaker 01 We did.
1:21:25 Dax Shepard? I did.
1:21:25 Speaker 01 I did. We shot a commercial.
1:21:28 Dax Shepard? So much fun. So much fun.
1:21:31 Speaker 01 Yeah. I had a really fun full-circle moment.
1:21:35 Dax Shepard? OK, yes, please tell.
1:21:37 Speaker 01 Because I got out of the car. You know, I haven't acted in a while.
1:21:41 Dax Shepard? Sure, we're all rusty.
1:21:43 Speaker 01 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.
1:21:55 Dax Shepard? Sure, one of the many, many thousands of commercials you had done.
1:21:58 Speaker 01 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.
1:22:09 Dax Shepard? Where they asked you to be in it.
1:22:11 Speaker 01 Yeah, and we're doing it together.
1:22:13 Dax Shepard? Yeah.
1:22:14 Speaker 01 Not for this podcast, but
1:22:16 Dax Shepard? But because of this podcast.
1:22:17 Speaker 01 Yeah. And it was something really cool about it.
1:22:21 Dax Shepard? I agree.
1:22:21 Speaker 01 I liked it and it, um, I think it's because my ring is fixed.
1:22:25 Dax Shepard? I have some housekeeping.
1:22:26 Speaker 01 Okay, great.
1:22:27 Dax Shepard? You know, I read the comments and so, uh, and this is so embarrassing and I read it a couple of times. I'm like, these people are crazy. That's, I, I didn't say, so people were like,
1:22:39 Dax Shepard? 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 I 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. It was another three name actor with an Edward in it.
1:23:01 Speaker 01 I see. Yeah, that's hard.
1:23:03 Dax Shepard? So I fucked that up and my apologies. Oh, and then the other thing was they had coitus interruptus because we were chatting and I was gonna say I had, I was gonna give a Danny Ricardo update because I had ridden motorcycles with them.
1:23:14 Speaker 01 Oh.
1:23:15 Dax Shepard? 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.
1:23:27 Speaker 01 Yeah, he's doing great.
1:23:29 Dax Shepard? He's so, so happy. We were riding motorcycles for all day long and we chatted a bunch and, and he said, he's just, he's just very happy.
1:23:37 Speaker 01 I'm glad.
1:23:38 Dax Shepard? Yeah, he's, he's just doing really, really good. So people should rest assured that Danny Rick is thriving.
1:23:44 Speaker 01 Yay.
1:23:44 Dax Shepard? Yay.
1:23:45 Speaker 01 Love to hear that.
1:23:46 Dax Shepard? Yeah.
1:23:47 Speaker 01 Do you want to tell people what Toto texted you?
1:23:51 Speaker 01 It was so funny.
1:23:54 Dax Shepard? Is I had text him to say, hey, um, people really love the episode and me in particular, I really loved it. Thanks for doing it. And he said, uh, How were the numbers? You know I am a lap time guy.
1:24:08 Speaker 01 Oh, so playful.
1:24:10 Dax Shepard? Oh, God.
1:24:11 Speaker 01 God.
1:24:12 Dax Shepard? 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-sized 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.
1:24:44 Speaker 01 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 Tay.
1:24:55 Dax Shepard? Liquid Death. I'm pointing to objects. Monkey with huge balls.
1:24:59 Speaker 01 No, we already had Machine Gun Kelly.
1:25:01 Dax Shepard? We did, we did. Okay, there's another fun update, but this, I'm starting, I'm getting worried that people are gonna be afraid to text me. I guess these people should know, I run it through my analysis.
1:25:10 Speaker 01 Okay.
1:25:11 Dax Shepard? And I would never say anything that was in a text that I didn't think was just lovely. You don't say, I get worried about it, don't you? Like, you know, someone's got a private exchange with me.
1:25:22 Speaker 01 Yeah.
1:25:23 Dax Shepard? And then I'm reporting on it. There's an ethical dilemma here.
1:25:26 Speaker 01 Sure.
1:25:27 Dax Shepard? 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.
1:25:39 Speaker 01 Yeah.
1:25:40 Dax Shepard? And then he's,
1:25:44 Dax Shepard? he said, I made up the thing about him being a good dancer. And I said,
1:25:50 Speaker 01 Oh, no.
1:25:51 Dax Shepard? 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.
1:25:58 Speaker 01 Yeah, me too. But he did say, because Toto was like, how, when did he see me dance?
1:26:03 Dax Shepard? I know. But then he just had to, he had to go, well, I don't understand how that happened, but I'm gonna take that.
1:26:09 Speaker 01 He was just.
1:26:10 Dax Shepard? He's being funny.
1:26:11 Speaker 01 He was doing a yes and.
1:26:12 Dax Shepard? He was doing a bit. He was like, you're not gonna believe this. He's also a phenomenal dancer, but I, he's just with him. I believed it.
1:26:17 Speaker 01 Yeah.
1:26:18 Dax Shepard? I think the.
1:26:19 Speaker 01 Who wouldn't believe it?
1:26:20 Dax Shepard? The crux of that story is I'm gullible.
1:26:21 Speaker 01 I think he is a great dancer.
1:26:23 Dax Shepard? Can we talk about Crisma a little bit?
1:26:25 Speaker 01 Sure.
1:26:26 Dax Shepard? 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.
1:26:33 Speaker 01 Yeah.
1:26:33 Dax Shepard? We watched Christmas Vacation already, Home Alone One and Two. Side note, I've never heard Delta laugh harder in my life than the 27-minute set piece in Home Alone Two, where he's hitting the guys with bricks.
1:26:48 Speaker 01 Oh, sure.
1:26:48 Dax Shepard? 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, there's, I got so much joy out of watching her have that big of a laugh at something.
1:27:05 Speaker 01 So cute.
1:27:06 Dax Shepard? Okay, so Home Alone Two, we did Gremlins, another Christmas favorite for us.
1:27:10 Speaker 01 Okay.
1:27:10 Dax Shepard? Last night, we did the Grinch Who Sold Christmas 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.
1:27:23 Speaker 01 How many more Christmas cartoons are there?
1:27:25 Dax Shepard? There's a lot. You've got Rudolph, you've got, you've got the Chuck Brown.
1:27:30 Speaker 01 Oh, yeah.
1:27:31 Dax Shepard? You've got, um, the, there's a bunch.
1:27:35 Speaker 01 Okay.
1:27:38 Dax Shepard? But I'm saying, 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.
1:27:51 Speaker 01 Yeah.
1:27:52 Dax Shepard? 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.
1:27:59 Speaker 01 Of course, I think he is given his due props.
1:28:04 Dax Shepard? Yeah.
1:28:05 Speaker 01 You know, there's a Seuss land.
1:28:07 Dax Shepard? There is?
1:28:08 Speaker 01 Yeah, one of the parks.
1:28:10 Dax Shepard? There is?
1:28:11 Speaker 01 I think.
1:28:12 Speaker 03 Yeah, Seuss landing in Orlando, Florida.
1:28:15 Dax Shepard? In Orlando, Florida, I should go.
1:28:17 Speaker 01 You should go, you should pay your respects.
1:28:18 Dax Shepard? I like when people use the term Sussian. Did you ever hear anyone use that?
1:28:22 Speaker 01 No, but I like it.
1:28:24 Dax Shepard? Yeah, it's cool, right?
1:28:25 Speaker 01 Yeah.
1:28:25 Dax Shepard? Like Newtonian or like it's a, it's a paradigm.
1:28:28 Speaker 01 But it kind of sounds like Sisyphean, which is my favorite word.
1:28:32 Dax Shepard? Which did I tell you?
1:28:33 Speaker 01 It's not my favorite word anymore.
1:28:34 Dax Shepard? You taught me that word.
1:28:35 Speaker 01 Yeah.
1:28:36 Dax Shepard? 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 people interpret that as a story of not wasted effort, but like, you know what I'm saying?
1:28:47 Speaker 01 Yeah, yeah.
1:28:49 Dax Shepard? A fool's errand.
1:28:50 Speaker 01 Yeah.
1:28:51 Dax Shepard? 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.
1:29:00 Speaker 01 Wow.
1:29:00 Dax Shepard? It's a story of suffering.
1:29:01 Speaker 01 It was a huge rock.
1:29:03 Dax Shepard? Well, first of all, he's probably jacked beyond belief.
1:29:05 Speaker 01 So strong.
1:29:06 Dax Shepard? But that's an interesting way to reframe it that like, no, this person, every day of their life had purpose.
1:29:12 Speaker 01 Yeah.
1:29:13 Dax Shepard? Probably very happy.
1:29:14 Speaker 01 That's a lovely way to look at it.
1:29:15 Dax Shepard? Yeah.
1:29:16 Speaker 01 It's actually Sisyphean.
1:29:18 Dax Shepard? Sisyphean?
1:29:19 Speaker 01 Yeah.
1:29:19 Dax Shepard? I like Sisyphean.
1:29:20 Speaker 01 Me too, and I, I maintain it. Yeah. Okay, so you're in Christmas, the Christmas spirit.
1:29:26 Dax Shepard? 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.
1:29:40 Speaker 01 Wow.
1:29:41 Dax Shepard? And I want to make a great recommendation to people who are using Spotify and you can make a station.
1:29:46 Speaker 01 Uh huh.
1:29:47 Dax Shepard? 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 had.
1:30:00 Speaker 01 Ooh, that sounds lovely.
1:30:01 Dax Shepard? It's, and it's on all the time. And so, you know, the, the tree is, is over decorated.
1:30:08 Speaker 01 Yeah.
1:30:09 Dax Shepard? You know, we get one tree and Kristen gets a tree in the kitchen.
1:30:12 Speaker 01 Uh huh.
1:30:12 Dax Shepard? And hers is artistic and this year it's wicked.
1:30:15 Speaker 01 Oh, cute.
1:30:16 Dax Shepard? 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 they, I pull the cord and they all fall down. It's, it's a Sisyphean task.
1:30:35 Speaker 01 Wow, ding ding ding.
1:30:35 Dax Shepard? I didn't expect it to come around that quick. I had all these things I had about presents, but I knocked a bunch of presents out the other day.
1:30:40 Speaker 01 Nice. You, you used a little bit of my gift guide.
1:30:44 Dax Shepard? Your gift guide, uh, almost exclusively.
1:30:48 Speaker 01 There were good gifts on there.
1:30:49 Dax Shepard? Complain about your gift guide though, you make things sell out. Your gift guide is moving markets.
1:30:58 Speaker 01 Yeah, well, I pick great items.
1:31:00 Dax Shepard? Yeah.
1:31:00 Speaker 01 I have to say.
1:31:01 Dax Shepard? You have exquisite taste.
1:31:03 Speaker 01 Thank you.
1:31:03 Dax Shepard? Some of your recommendations were so good that I found myself dancing around on the websites.
1:31:07 Speaker 01 Yes, that's the goal.
1:31:09 Dax Shepard? Yeah. Yep. And, um, yeah.
1:31:12 Speaker 01 Fun stuff abound.
1:31:14 Dax Shepard? There's fun stuff, so.
1:31:15 Speaker 01 So, and let's just, so your tree has colored lights, right?
1:31:20 Dax Shepard? So many.
1:31:21 Speaker 01 Yeah.
1:31:22 Dax Shepard? I have four strands, really long strands and four of those bubbly light strands. And the tree's touching the ceiling. It's a Clark Griswold, it's too big.
1:31:33 Speaker 01 Uh huh.
1:31:34 Dax Shepard? And I'd cut a foot off.
1:31:35 Speaker 01 I just want to talk about lights.
1:31:36 Dax Shepard? Oh, okay.
1:31:39 Dax Shepard? My apologies, Miss Monica. Miss 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.
1:31:50 Speaker 01 I wonder how Hermium, do you have a, does he have a delivery service? How does he get his tree?
1:31:57 Dax Shepard? I have a cousin who's not working at the moment and he loves going to department stores and, uh, plazas and shopping malls and strip malls.
1:32:06 Speaker 01 Wow.
1:32:06 Dax Shepard? And, um, I'll call him on the landline. That's what I have, Miss Monica. I pick up the phone and I call his, his name is Bert.
1:32:14 Speaker 01 Oh.
1:32:14 Dax Shepard? Yeah, he's my, did I say my brother-in-law or my cousin?
1:32:17 Speaker 01 You said cousin.
1:32:17 Dax Shepard? Yeah, he's my cousin. I just remembered. Weirdly enough, he's also my brother-in-law, but it's my stepsister.
1:32:23 Speaker 01 Okay, so it's all on the up and up.
1:32:25 Dax Shepard? Everything's on the above board, as they say. And I call up Bert and I say, here's what I need, Bert. Six water weenies, ten 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.
1:32:40 Speaker 01 Sure.
1:32:40 Dax Shepard? And then I have to call him back up and ask him to deliver the presents.
1:32:44 Speaker 01 Wow.
1:32:45 Dax Shepard? That's okay, though. He charges me for that, too.
1:32:47 Speaker 01 Okay, getting a little taken advantage of, but that's, you seem fine with it. Okay, great.
1:32:53 Dax Shepard? Mom?
1:32:55 Speaker 01 No, remember, I'm not your mom.
1:32:56 Dax Shepard? Okay, Miss Monica. Mom Monica.
1:33:02 Dax Shepard? Mrs. Mom.
1:33:05 Dax Shepard? Color lights.
1:33:06 Speaker 01 Yes, the lights, because Chris and I assume on her nice tree has white lights.
1:33:11 Dax Shepard? Yep, yep.
1:33:12 Speaker 01 Yeah, and this is, you know, this is a, a big thing. I don't know if it's a, Rob?
1:33:18 Speaker 03 Yes.
1:33:18 Speaker 01 What color lights do you have?
1:33:20 Dax Shepard? Well, first of all, do you have the lights you want?
1:33:22 Speaker 03 Uh, yes.
1:33:24 Dax Shepard? Okay.
1:33:24 Speaker 03 I do. Um, I like the, like, yellowy.
1:33:29 Speaker 01 White light.
1:33:29 Speaker 03 Warm gold, yeah, white lights.
1:33:32 Speaker 01 That's.
1:33:33 Speaker 03 I mean, there's shades of white, too, like.
1:33:37 Speaker 01 He's trying to, he's trying to walk in the middle and be nice, but really, he has
1:33:42 Dax Shepard? He has white lights.
1:33:43 Speaker 01 White lights and he likes them.
1:33:44 Speaker 03 I have white lights, but they're kind of yellowy.
1:33:46 Speaker 01 Yeah, I know what you mean. There's like a warm and a cool.
1:33:49 Dax Shepard? 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, it would suck.
1:34:00 Speaker 01 Well, yeah, but he's.
1:34:00 Dax Shepard? He disproves my gender, um, stereotypes quite a bit.
1:34:04 Speaker 01 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.
1:34:12 Dax Shepard? That's right, Monica. That's right, Miss Monica.
1:34:15 Speaker 01 So, yes, Rob did not grow up with you and like you.
1:34:19 Dax Shepard? Oh, he's from the big windy.
1:34:21 Speaker 01 So, I don't think it's gender, but I do think some people love. The nostalgic colored lights.
1:34:28 Dax Shepard? Yeah.
1:34:29 Speaker 01 And then other people who care about aesthetics, love the white light.
1:34:33 Dax Shepard? I could really get on my high horse about it. I used to have a really strong stance on it.
1:34:38 Speaker 01 Yeah.
1:34:38 Dax Shepard? And it's all my, um, class warfare stuff.
1:34:41 Speaker 01 Yeah, which is.
1:34:42 Dax Shepard? It's so tired. Is that what you're gonna say?
1:34:44 Speaker 01 No, I wasn't gonna say that. Your life does not match that mentality anymore.
1:34:50 Dax Shepard? But did you see Chris Rock's latest standup? He said, I am rich, but I identify as poor.
1:34:56 Speaker 01 Yeah, that's fine.
1:34:57 Dax Shepard? Okay, for him.
1:34:58 Speaker 01 Yeah.
1:34:58 Dax Shepard? Okay, but for me.
1:35:00 Speaker 01 Yeah, you, you aren't, you're of the highest class in this country.
1:35:06 Dax Shepard? Yeah, well, there's a lot of people with a lot more money than me, but I do have.
1:35:09 Speaker 01 You're of the highest class.
1:35:11 Dax Shepard? Okay, okay.
1:35:11 Speaker 01 And you, um, you also hobnob with the highest class.
1:35:17 Dax Shepard? 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.
1:35:24 Speaker 01 Yeah.
1:35:25 Dax Shepard? Is like, occasionally I'd see that at people's houses who had a, an extra living room that no one went into. And you weren't allowed to go in there. You know, take off all your shoe, your, you know, you'd get in a fucking, uh, intel outfit to go in the room. And all of it seems stuffy and not playful and fun and colorful. This felt very presentational.
1:35:50 Speaker 01 And where's your tree?
1:35:50 Dax Shepard? But I used to be judgmental of that. I don't, I still don't like it, but I'm not as judgmental.
1:35:57 Speaker 01 Cause your, your second tree is in your second living room.
1:36:03 Speaker 01 Okay?
1:36:04 Dax Shepard? Okay.
1:36:04 Speaker 01 You know, I gotta keep you, I gotta, I gotta just remind you.
1:36:08 Dax Shepard? I know, I'm spoiled. I know I'm spoiled.
1:36:11 Speaker 01 Yeah, okay.
1:36:12 Dax Shepard? I'm really spoiled.
1:36:13 Speaker 01 It's just class, to me, the class warfare thing, I would hope you now see.
1:36:19 Dax Shepard? That it wouldn't be fair for a stranger to hate me just because I have money?
1:36:21 Speaker 01 Yeah.
1:36:22 Dax Shepard? Yeah. Yeah, I would feel that way on the other side of it, but um, I wouldn't expect anyone to feel that way, not because I get it.
1:36:31 Speaker 01 Okay, so I have white lights, obviously.
1:36:33 Dax Shepard? Yeah, I know that. I would, I would know that.
1:36:35 Speaker 01 Yeah, everyone would know.
1:36:36 Dax Shepard? You don't need to tell me that. I know that. And I'm not judgmental of you. I'm so glad you're having the Christmas you've always wanted.
1:36:42 Speaker 01 Thank you.
1:36:43 Dax Shepard? Yeah.
1:36:43 Speaker 01 Yeah. Jess and I had pig day and we went to home. We just missed you, I guess, because it seems like the timing.
1:36:50 Dax Shepard? Yeah, because we were there at like 11 a.m. on a Saturday and you were there at 11 a.m. But I gotta say, this is my record of all time. I was so fast and there was no fighting. This is like first year in a few. That day is very triggering for our family.
1:37:03 Speaker 01 I think it's hard for families that have to decide.
1:37:06 Dax Shepard? You gotta compromise.
1:37:07 Speaker 01 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.
1:37:16 Dax Shepard? You call the tree a bald puss?
1:37:18 Speaker 01 Bald pussy. If there's bald spots.
1:37:20 Dax Shepard? Okay, great.
1:37:21 Speaker 01 And we don't like that.
1:37:22 Dax Shepard? Okay.
1:37:22 Speaker 01 Um, and.
1:37:24 Dax Shepard? You like more of a Brazilian tree?
1:37:25 Speaker 01 No, Brazilian is.
1:37:26 Dax Shepard? That's shaped and full.
1:37:28 Speaker 01 Brazilian?
1:37:29 Dax Shepard? Yeah. Isn't a Brazilian like you have a landing strip?
1:37:32 Speaker 01 I thought Brazilian is.
1:37:33 Dax Shepard? Clean?
1:37:34 Speaker 01 Clean.
1:37:35 Dax Shepard? Rob?
1:37:36 Speaker 03 And do I, you want me to Google?
1:37:38 Dax Shepard? Yes, I do.
1:37:38 Speaker 01 Just definition of Brazilian wax.
1:37:41 Speaker 03 And pictures?
1:37:42 Speaker 01 Yeah, pictures. You can do that on your own time.
1:37:48 Speaker 03 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.
1:37:55 Speaker 01 Yeah.
1:37:56 Dax Shepard? Okay.
1:37:57 Speaker 01 I'm glad I.
1:37:57 Dax Shepard? What's the landing? The landing strip's just the landing strip?
1:37:59 Speaker 01 Yeah, there's like, you can just get different kinds, but Brazilian generally means all hair.
1:38:06 Dax Shepard? Do you think any dudes get a landing strip? I was just thinking, I want to go do that just as a bit.
1:38:10 Speaker 03 I've done that as a joke.
1:38:12 Dax Shepard? You have?
1:38:12 Speaker 03 For Natalie.
1:38:14 Speaker 01 Oh, my God. That's so funny. Did she like?
1:38:20 Dax Shepard? Did it make her horny?
1:38:21 Speaker 03 No, no. It was, it was.
1:38:24 Speaker 01 That's really funny.
1:38:27 Speaker 04 Stay tuned for more Armchair Expert if you dare.
1:38:33 Dax Shepard? 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 on Prime.
1:38:47 Speaker 01 This is very exciting. It's holiday party season.
1:38:50 Dax Shepard? Yes, it is. That time of year.
1:38:52 Speaker 01 Work parties, family parties, parties with friends.
1:38:56 Dax Shepard? Party parties.
1:38:57 Speaker 01 Parties with your animals.
1:39:00 Dax Shepard? If you're as popular as Monica, you're hitting the party circuit.
1:39:04 Speaker 01 It's a great reason to shop for new clothes or accessories and really like spice up your wardrobe. Make it fancy.
1:39:11 Dax Shepard? Prime 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.
1:39:33 Speaker 01 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.
1:39:51 Dax Shepard? Absolutely. And you can get all those things on Prime.
1:39:55 Speaker 01 Oh, and one other thing, Amazon Music is here to help with the playlist. Curating the party playlist, it's an art.
1:40:02 Dax Shepard? 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.com/prime to get more out of whatever you're into.
1:40:37 Speaker 01 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, he knew his.
1:40:50 Dax Shepard? Like Christmas Vacation, there was a beam of light shining down on it.
1:40:53 Speaker 01 Yes, and he knew his daughter.
1:40:56 Dax Shepard? Yeah.
1:40:57 Speaker 01 It was the one.
1:40:58 Dax Shepard? Are you his daughter? Cause I think you view more of his mom.
1:41:00 Speaker 01 No, the tree is our daughter.
1:41:02 Dax Shepard? Okay, okay, that makes sense.
1:41:04 Speaker 01 We have, um, we co-parent.
1:41:06 Dax Shepard? Okay.
1:41:07 Speaker 01 But she lives at my house.
1:41:08 Dax Shepard? Yeah, so I got you.
1:41:09 Speaker 01 Um, 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, cause last year, Archery was a boy and he was a model. Like, he was.
1:41:24 Dax Shepard? Striking?
1:41:25 Speaker 01 He's striking and, and very similar, like perfect.
1:41:29 Dax Shepard? Angular.
1:41:29 Speaker 01 Exactly. Very angular. Not round feet. Just, this girl is, um, she's not a model, but she's a star.
1:41:38 Dax Shepard? Oh, yeah. That's my, that's the kind I like.
1:41:42 Speaker 01 Exactly. And, um, I've been trying some different hats on her, toppers.
1:41:48 Dax Shepard? Oh, okay. Hats?
1:41:50 Speaker 01 I haven't decided yet.
1:41:52 Dax Shepard? 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 tree. Charlie Brown did a bad job and they hated it. They're yelling at Charlie because of the tree, but then they decided to love it and it's a good little tree.
1:42:10 Speaker 01 It's a sweet story.
1:42:11 Dax Shepard? And I always am drawn to the shitty tree there. Cause 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.
1:42:20 Speaker 01 Wow.
1:42:21 Dax Shepard? Yeah, I wanna like rescue the shitty tree.
1:42:23 Speaker 01 Oh my God, you're, the way you feel about the trees is like how Kristen feels about the dogs.
1:42:27 Dax Shepard? That's right. That's right. And all because of Charlie Brown, I think.
1:42:30 Speaker 01 Wow.
1:42:30 Dax Shepard? So 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.
1:42:41 Speaker 01 So for her, trees are not dogs. She wants a pretty one.
1:42:45 Dax Shepard? Yeah.
1:42:45 Speaker 01 Yeah.
1:42:45 Dax Shepard? She's got something in her mind she's looking for.
1:42:47 Speaker 01 Yeah.
1:42:48 Dax Shepard? 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 will have gotten the tree. So my only objective is to get the trees in time by the time I'm pulling the truck. No, in previous years, they go in and I wait in the car. This year I went to.
1:43:20 Speaker 01 So what'd you do with the truck?
1:43:21 Dax Shepard? I just parked it and I'm like, I'm gonna run in, I'm gonna see if I find a tree. It's not gonna move up that fast. They gotta load a tree. I didn't hold anyone up. 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.
1:43:35 Speaker 01 Yeah, I don't feel bad for the, for the.
1:43:37 Dax Shepard? You don't?
1:43:38 Speaker 01 I don't.
1:43:38 Dax Shepard? How could you not? A tree that no one wants, Monica?
1:43:44 Speaker 01 It's already dead. Tax is already dead. It got chopped down.
1:43:48 Dax Shepard? I always get a tree that has a little bit of personality. And by personality, I mean missing parts.
1:43:55 Speaker 01 Bald puss?
1:43:56 Dax Shepard? Miss Monica, I don't know what you just said, but please don't say it again.
1:44:05 Dax Shepard? You wanna talk about that? But I'm here to buy a big old Christmas tree. Tell me about your tree. Does it have a Brazilian?
1:44:13 Speaker 01 Ew!
1:44:14 Dax Shepard? What?
1:44:15 Speaker 01 Stop!
1:44:16 Dax Shepard? What do you put on?
1:44:17 Speaker 01 Make him go away. Make him go away. I can only take, I can't, I kind of forgot.
1:44:23 Dax Shepard? It 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 puss. I don't even want to say it either.
1:44:32 Dax Shepard? My God. And you're saying it's your daughter? This is twisted. Certainly don't want Jess talking about his daughter in that fashion.
1:44:39 Speaker 01 No!
1:44:40 Dax Shepard? Last update. It was time for a crop, a harvest. Everyone already knows that. I feel like people are gonna have a bunch of judgment about this. I guess fuck them. Delta's like, I wanna shave my legs. Will you shave my legs?
1:44:53 Speaker 01 Yeah.
1:44:54 Dax Shepard? I'm like, okay, people are gonna be like, you shouldn't shave your kid's legs. I can already feel that coming. But I don't give a fuck. She wants me to shave her legs.
1:45:01 Speaker 01 Yeah, why not?
1:45:01 Dax Shepard? She feels left out. I did it. Monica? Her leg hair is also cashmere.
1:45:09 Speaker 01 It is.
1:45:10 Dax Shepard? So we now have two fields in rotation. And so I want you to see what an enormous.
1:45:16 Speaker 01 Are you combining?
1:45:17 Dax Shepard? Yes. It's now father, daughter, cashmere. And I want you to, you remember how much we had just.
1:45:24 Speaker 01 Yeah, not practically none.
1:45:27 Dax Shepard? Look at the amount of cashmere we now have.
1:45:31 Speaker 01 Wow, it's, it's like quadrupled in size.
1:45:34 Dax Shepard? I was making a joke that we might get a mitten or a scarf in 10 years, but I actually think that's a real possibility now. Look at the amount in there now.
1:45:42 Speaker 01 This is really, you know.
1:45:43 Dax Shepard? Do you want to feel her?
1:45:44 Speaker 01 I do, I want to touch it, but also last time we touched it.
1:45:47 Dax Shepard? Some of it disappeared.
1:45:48 Speaker 01 Yeah.
1:45:49 Dax Shepard? That's okay. Now we got two growers.
1:45:50 Speaker 01 Wow, there is so much.
1:45:52 Dax Shepard? Yeah.
1:45:54 Speaker 01 Now you have two growers.
1:45:57 Dax Shepard? We got basically a mink farm.
1:45:58 Speaker 01 Are they separated?
1:46:00 Dax Shepard? There's no real, yeah, it's just, I think it's separated on.
1:46:06 Speaker 01 Wow, it's so soft.
1:46:09 Dax Shepard? Yeah, I think hers might even be softer than my back hair.
1:46:12 Speaker 01 Oh, my God.
1:46:13 Dax Shepard? But that's got a time limit. Her leg hair will turn into shitty hair like our leg hair.
1:46:17 Speaker 01 Exactly.
1:46:18 Dax Shepard? But currently she is growing cashmere.
1:46:21 Speaker 01 Oh, my God.
1:46:22 Dax Shepard? You think I need to get a work permit for her? Cause she is now kind of actively.
1:46:26 Speaker 01 Illegal, it's like, yeah, it's illegal.
1:46:30 Dax Shepard? I don't, I don't want to out her because she did such a great job. Lincoln shaved my back, did a great job.
1:46:35 Speaker 01 Yeah.
1:46:35 Dax Shepard? But she was, she, she thought she had some cashmere on the razor and she emptied a little bit into our pouch. And then I discovered, no, that, that, some of that was beard hair. So I had to actually go in and pull it. I don't know, 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.
1:46:54 Speaker 01 Did you use tweezers?
1:46:55 Dax Shepard? No, I just, I could feel and I'd pull that out. I probably lost a lot of really good products.
1:47:00 Speaker 01 That's okay.
1:47:00 Dax Shepard? Yeah, we live and learn.
1:47:01 Speaker 01 This is an R and D situation.
1:47:03 Dax Shepard? Second harvest, so.
1:47:04 Speaker 01 Wow.
1:47:05 Dax Shepard? Still learning a lot.
1:47:06 Speaker 01 Exciting. All right. Oh, oh, one more thing, one, one cool thing that happened that I want to put out there in the world. Cause I think it's good for me to manifest this.
1:47:16 Dax Shepard? Okay.
1:47:16 Speaker 01 When Callie and I were shopping, we went into one store and I bought some cute little boxer shorts.
1:47:24 Dax Shepard? Okay.
1:47:25 Speaker 01 As we were leaving, Callie was in front of me and someone had held. The door opened for her to come out and like some woman walked in and then Callie 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.
1:47:50 Dax Shepard? Oh, oh, oh, okay.
1:47:51 Speaker 01 And
1:47:52 Dax Shepard? It looked like, the look on your face was that it was a famous person.
1:47:55 Speaker 01 It was the most
1:47:57 Dax Shepard? Gorgeous.
1:47:58 Speaker 01 Gorgeous person I've ever seen.
1:47:59 Dax Shepard? Really?
1:48:00 Speaker 01 And not, and
1:48:01 Dax Shepard? Male or female?
1:48:02 Speaker 01 Male.
1:48:03 Dax Shepard? Give me age, height, describe.
1:48:05 Speaker 01 He's kind, now he's like, but now he's sort of a haze.
1:48:09 Dax Shepard? Oh.
1:48:10 Speaker 01 Like, I don't, I don't remember.
1:48:13 Dax Shepard? I don't like that part.
1:48:15 Speaker 01 I know, I know, but part of it was I, it, it happened so fast, he took my breath away.
1:48:19 Dax Shepard? Yeah.
1:48:20 Speaker 01 And, and I think it read, you know, it read.
1:48:23 Dax Shepard? Okay, your face betrayed you.
1:48:25 Speaker 01 Yeah, and um, he smiled and I don't remember if he showed teeth or not.
1:48:30 Dax Shepard? Thirting?
1:48:31 Speaker 01 But no, he just like, that's who he is.
1:48:33 Dax Shepard? Okay.
1:48:34 Speaker 01 And I turn, you know, I, I turned and I said, oh my God, that guy was so hot. And she said, I know.
1:48:40 Dax Shepard? Callie was fucked up too.
1:48:41 Speaker 01 Yes, so we, so this is an undeniable situation.
1:48:45 Dax Shepard? You should have gone back inside to talk to the third woman who entered.
1:48:49 Speaker 01 Well, I think they were together.
1:48:50 Dax Shepard? Oh.
1:48:51 Speaker 01 Well, I don't know. There's no way to know.
1:48:53 Dax Shepard? So this is a lost persons report.
1:48:55 Speaker 01 Exactly.
1:48:56 Dax Shepard? If you opened a door for Callie and Monica at the farmer's market.
1:48:59 Speaker 01 Brentwood Country Mart.
1:49:00 Dax Shepard? Brentwood Country Mart.
1:49:01 Speaker 01 Yeah. On Black Friday.
1:49:03 Dax Shepard? On Black Friday.
1:49:04 Speaker 01 Probably around noon.
1:49:05 Dax Shepard? Okay.
1:49:06 Speaker 01 Yeah.
1:49:07 Dax Shepard? Um, contact, I guess, comment in this, I'll read it. I'll read them all.
1:49:12 Speaker 01 Comment or
1:49:13 Dax Shepard? Don't, no catfishes.
1:49:16 Speaker 01 Exactly.
1:49:17 Dax Shepard? I guess you'll be able to see the photo though and you'll know.
1:49:19 Speaker 01 I'll, oh, I'll know.
1:49:20 Dax Shepard? And no one could fake it.
1:49:22 Speaker 01 No, because, you know, when I walk through the world, I'm extremely unobservant. I don't notice people.
1:49:27 Dax Shepard? You're blind, basically.
1:49:28 Speaker 01 I really am.
1:49:29 Dax Shepard? Yeah.
1:49:30 Speaker 01 And speaking of blind, I got some soap in my eye this morning and it was
1:49:34 Dax Shepard? Blinding?
1:49:35 Speaker 01 I thought I, I was, did some permanent damage.
1:49:37 Dax Shepard? Of course.
1:49:38 Speaker 01 Okay, so anyway, I walk around so unobservant and yet this person was strong, he pulled me out. It was shocking.
1:49:46 Dax Shepard? He's like a lifeline.
1:49:47 Speaker 01 He was, he was so attractive.
1:49:50 Dax Shepard? How many more times did you think about him?
1:49:52 Speaker 01 That day, a lot of times.
1:49:54 Dax Shepard? A ton.
1:49:54 Speaker 01 Yeah.
1:49:55 Dax Shepard? Did you like whip up fantasies? I know you're prone to fantasies.
1:49:57 Speaker 01 I am prone to fantasies. I, I didn't actually. I was more just like.
1:50:02 Dax Shepard? Thunderstruck.
1:50:02 Speaker 01 Take, I was just taken.
1:50:04 Dax Shepard? Love at first sight.
1:50:05 Speaker 01 A little bit.
1:50:06 Dax Shepard? Oh my God.
1:50:07 Speaker 01 And I don't even believe in that bit, like maybe. Anyway, that was a big mystery.
1:50:11 Dax Shepard? Yeah. Wow.
1:50:13 Speaker 01 And I wonder.
1:50:14 Dax Shepard? And how often have you thought of him since then?
1:50:17 Speaker 01 Um.
1:50:17 Dax Shepard? Daily or once every few days?
1:50:19 Speaker 01 It's starting to dissipate and I don't remember him as well.
1:50:22 Dax Shepard? I sure hope he reaches out in the comments.
1:50:24 Speaker 01 Me too.
1:50:25 Dax Shepard? Also, no bullshit, no catfishing.
1:50:26 Speaker 01 Yeah, guys, seriously.
1:50:28 Dax Shepard? Stop catfishing, everybody.
1:50:30 Speaker 01 Seriously. Okay, anyway, so that, um, we'll add that to the mystery pile with the guy I met at, uh, in New York, the restaurant guy.
1:50:39 Dax Shepard? Oh, right.
1:50:40 Speaker 01 That mystery is not solved.
1:50:41 Dax Shepard? Catfish is so delicious.
1:50:45 Dax Shepard? Did you ever eat a big catfish sandwich, Monica?
1:50:49 Speaker 01 I don't give him permission to say my name.
1:50:52 Dax Shepard? What do you want him to call you?
1:50:53 Speaker 01 I don't want him.
1:50:54 Dax Shepard? Don't leave it, don't let, let him decide.
1:50:58 Speaker 01 Okay, this is for Fei Fei Lee.
1:51:00 Dax Shepard? Oh, and a ding ding ding, we just, just interviewed someone who knows her intimately.
1:51:07 Speaker 01 Yeah.
1:51:08 Dax Shepard? Not intimately, I mean sexually. He's a colleague. We just interviewed a colleague.
1:51:13 Speaker 01 And he was giving her a lot of props and reverence.
1:51:16 Dax Shepard? Yep.
1:51:17 Speaker 01 That she really deserves. She was, I loved her so much.
1:51:20 Dax Shepard? I loved her so much too.
1:51:22 Speaker 01 She was a delight.
1:51:23 Dax Shepard? Yeah.
1:51:23 Speaker 01 Now, some facts for her. Um, 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 Dukas.
1:51:44 Dax Shepard? His secretary lived with him.
1:51:46 Speaker 01 I guess so.
1:51:47 Dax Shepard? Interesting.
1:51:47 Speaker 01 I bet it's more like an assistant. Nowadays, we call it an assistant.
1:51:52 Dax Shepard? You're probably right. Yeah, I guess secretaries were just assistants.
1:51:56 Speaker 01 Okay. Oh, we talk about Cambridge Analytica.
1:52:00 Speaker 01 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, you're missing, we're all missing a ton of real of information.
1:52:25 Dax Shepard? It's kind of like the Martha Stewart thing. Where we all think she traded her company.
1:52:28 Speaker 01 Exactly.
1:52:29 Dax Shepard? And she didn't. Nor did she even do any insider trading. She still went to prison.
1:52:33 Speaker 01 I know.
1:52:34 Dax Shepard? Yeah. But yeah, the nefarious activity was on the, uh, was on Cambridge Analytica, not Meta.
1:52:40 Speaker 01 Right, but also they.
1:52:41 Dax Shepard? And they were just using existing tools that anyone could have been using.
1:52:44 Speaker 01 But they were using old information from an old quiz. Or the quiz or something that Facebook did a long time ago. And that's what they used. They weren't using current information. Um, and yeah, they, like, Facebook didn't sign off on, they didn't hand over this information.
1:53:03 Dax Shepard? Yeah, yeah, yeah. Everyone should listen to Acquired, just period.
1:53:06 Speaker 01 It's such a good podcast.
1:53:08 Dax Shepard? It is, it is.
1:53:09 Speaker 01 I'm always shocked.
1:53:10 Dax Shepard? Yeah, if you like a deep dive, that's the show for you.
1:53:13 Speaker 01 In the business world, like learning, I mean, you listen, I mean, they're four hours long.
1:53:17 Dax Shepard? Meta's six.
1:53:19 Speaker 01 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.
1:53:31 Dax Shepard? Oh, big time, yeah, yeah, yeah.
1:53:33 Speaker 01 I recommend Acquired. Um, and that's it.
1:53:37 Dax Shepard? That was it?
1:53:37 Speaker 01 Yeah.
1:53:38 Dax Shepard? Okay, I just adore, I wish her the best.
1:53:40 Speaker 01 Me too.
1:53:41 Dax Shepard? I'm grateful for.
1:53:42 Speaker 01 Me too.
1:53:42 Dax Shepard? Yeah.
1:53:43 Speaker 01 That's the line we learned from the Lisa Kudrow fact check that we say to people that, I'm just grateful for you, I'm grateful you exist.
1:53:50 Dax Shepard? Oh, yeah, yeah, yeah, yeah. And I'm grateful for her existence.
1:53:53 Speaker 01 Yeah, me too.
1:53:54 Dax Shepard? Okay, and Toto's a great dancer. Don't listen to anybody else.
1:53:58 Speaker 01 He's great.
1:53:59 Dax Shepard? Love you.
1:54:00 Speaker 01 Love you.
Previous Lisa Kudrow Next Armchair Anonymous: Crazy Neighbors Page updated November 5, 2026 · transcript and summary are machine-generated