Stanford professor Fei-Fei Li has been nicknamed the "godmother of AI." She recently wrote a memoir about her childhood in China, struggles as an immigrant in New Jersey, and the curiosity and relationships that propelled her to the top of the AI field. In this episode, I share my takeaways from her memoir, "The Worlds I See."
[00:00:00] In the late 1980s, in the city of Sichuan, China, Fei Fei Li's middle school teacher accused Fei Fei of spending too much time reading random books like The Embarable Lightness of Being and magazines about marine life and UFOs when she should be preparing for her high school entrance exams.
[00:00:16] The teacher thought Fei Fei needed to learn to set aside her personal interests and study what was useful or she would never succeed.
[00:00:24] Fei Fei's mom disagreed.
[00:00:26] Is this what Fei Fei wants? Is this what I want for Fei Fei? She asked defiantly.
[00:00:31] Over and over again, even as they moved to America working minimum wage jobs to scrape by, Fei Fei's mom set aside conventional wisdom and societal pressures.
[00:00:41] She insisted that Fei Fei follow her intellectual curiosity rather than accept any lucrative job that she didn't love.
[00:00:48] By following her tremendous curiosity, Fei Fei has become a tenured professor at Stanford University, a chief scientist at Google and is known as the godmother of AI for her scientific contributions to the AI technology that has brought us chat-qpt, self-driving cars, and more.
[00:01:06] She was even called recently to testify before the US Senate about the societal impact of AI.
[00:01:13] Welcome to Women of Power, a podcast that reflects on what power means and looks like for women past and present in the spotlight and behind the scenes.
[00:01:23] This episode is part of a series of case studies where we examine the story and strategies of one woman who's acutved great power in business, politics, or technology.
[00:01:33] In today's episode, I'm going to tell you my key takeaways from Dr. Fei Fei Li's recent memoir, The World's IC.
[00:01:41] As a disclaimer, I have much less information to draw upon today than I usually do because Fei Fei has shared very little about her personal life with the public, so my only source of information about her personal life is her own memoir.
[00:01:55] That said, I personally find her story really compelling. Obviously everyone these days is talking about how AI is changing the world.
[00:02:03] Fei Fei is one of extremely few women at the very top of the AI field. And despite her memoir being kind of short on details about her personal life, what she did share made me tear up multiple times.
[00:02:16] On a more personal note, I actually took Fei Fei's AI class at Stanford and really liked her. And my parents, who recommended this book to me, immigrated from China to the US around the same time that Fei Fei did.
[00:02:28] So let's get into her story. Why was Fei Fei reading Milan Candera, the Bronte sisters, and all these science magazines as a middle schooler?
[00:02:37] To understand Fei Fei's intellectual curiosity and independence, you need to understand her mother. Fei Fei's mother came from a highly intellectual family who was repressed during Mao's communist regime in China.
[00:02:50] Fei Fei's mom was super smart and curious, but because of her family's political associations, she was consistently discriminated against and prevented from reaching her full potential in school.
[00:03:00] This made her scorn her peers' obsession with conformity and social status, it made her a contrarian, and it made her very determined to ensure that her daughter's potential could fully blossom.
[00:03:12] While Fei Fei's mom insisted on Fei Fei pursuing her intellectual interests, it was Fei Fei's dad who inspired her deep curiosity about physics in particular.
[00:03:22] Fei Fei's dad always had this joy and curiosity about the natural world around him. The day that Fei Fei was born, her dad arrived extremely late to the hospital, not because of any traffic issues, but because he had completely lost track of time while birdwatching in a local park.
[00:03:38] He took great joy in showing Fei Fei his love of the natural world and in engineering numerous gadgets to use around the house. When Fei Fei was 11, her parents decided to immigrate to the United States.
[00:03:50] Her dad left first to go find a job in the US and establish a home for them there, while Fei Fei stayed in China with her mom for the time being.
[00:03:59] Fei Fei missed her father terribly, but she says, quote,
[00:04:02] The more I grieved my father's absence, the more I realized the things I missed most about him were the things physics was trying to teach me.
[00:04:10] The way he naturally saw the world in terms of light, speed, torque, force, weight and tension, the way he improvised devices of gears and pulleys to solve problems around the house,
[00:04:21] physics had always been the hidden foundation of my father's mind, and it was only now when I missed him most that I realized it.
[00:04:28] So in middle and high school, Fei Fei fell in love with physics. She also developed a chip on her shoulder about her academic performance.
[00:04:36] At the very end of elementary school, she had overheard her female teacher giving the boys in their class a lecture about how as boys they were biologically smarter than girls,
[00:04:46] especially in math and science, and that the boys therefore ought to be ashamed that their average score on the recent exams had been lower than the average girls score.
[00:04:55] Upon hearing this, Fei Fei recalls feeling a quote heavy jagged new feeling. She says,
[00:05:02] I didn't feel discouraged or even offended. I was angry. It was an anger I wasn't familiar with a quiet heat and indignation.
[00:05:11] Fei Fei cut her hair short, refused to wear dresses and grew deadly determined to succeed in school. Though she doesn't say this explicitly,
[00:05:20] I imagine that this incident must have also contributed to her later ambition to become a great scientist.
[00:05:26] When Fei Fei was 15, she and her mom received visas to go to the US and they boarded a plane to join her father in New Jersey.
[00:05:34] Life in New Jersey was tough. Fei Fei describes that every class was like an English class.
[00:05:40] She had previously excelled at math, but now in the US she couldn't understand what the word problems were even asking her to do.
[00:05:48] In those pre-Google Translate days, Fei Fei recalls that quote,
[00:05:52] Even the simplest homework assignments took hours.
[00:05:55] Nearly every step was saddled with a discouraging appeal to one of two enormous dictionaries,
[00:06:00] one for translating Chinese to English and the other for the reverse.
[00:06:05] Still though, Fei Fei's academic ambition remained. She recalls that the quote threshold between A's and all other grades had taken religious gravity for her.
[00:06:15] Then of course there was the poverty. Fei Fei and her parents shared a tiny one bedroom apartment where Fei Fei slept in a narrow gap
[00:06:23] between the kitchen and the dining table. To pay rent, her parents worked long hours as a cashier and camera repairman.
[00:06:31] Fei Fei herself waited tables at a Chinese restaurant on evenings and weekends.
[00:06:35] Sometimes during breaks she would sit down in the back of the restaurant and start reading a book,
[00:06:40] but her boss would tell her to stop wasting time that could be used to clean the toilets.
[00:06:45] Then there was the racism and bullying.
[00:06:48] One day, her fellow immigrant classmate accidentally bumped arms with a white student on his way out of the library.
[00:06:55] Fei Fei could do nothing but watch in horror as a group of white students beat up the immigrant boy and blood poured from his face onto the pavement.
[00:07:04] But not all her interactions with Americans were negative.
[00:07:07] Partly as a result of her newfound fear of the library where the kid got beat up,
[00:07:12] Fei Fei started spending more time in her math teacher Mr. Sibel's office.
[00:07:17] He would ask Mr. Sibel for extra math help, and because there were so many English concepts that he didn't know,
[00:07:24] even a simple question would lead them to a longer back-and-forth conversation.
[00:07:29] One day, Fei Fei asked Mr. Sibel another seemingly simple question.
[00:07:33] Can you recommend some books to me to help me improve my English?
[00:07:37] Mr. Sibel's eyes lit up.
[00:07:40] Turns out, Mr. Sibel loved reading just like Fei Fei.
[00:07:44] Fei Fei eagerly shared the titles of the Western classics that she'd read Chinese translations of back in China.
[00:07:50] As she brought up Jules Verne, Dickens and Hemingway,
[00:07:54] he recalls that, quote, Mr. Sibel fell back into his care, cuckling in pure stunned delight.
[00:08:00] I believed it was the first moment an American had ever seen me as more than a Chinese speaking immigrant.
[00:08:07] Mr. Sibel saw her potential and wanted to foster it.
[00:08:10] Their high school didn't have an advanced calculus class, so he came up with a curriculum and taught Fei Fei during lunch.
[00:08:17] He invited her over for dinners with his family, where Fei Fei tasted homemade American foods like brownies for the first time.
[00:08:25] Over time, the Sibel family became a second family to Fei Fei.
[00:08:29] She considers Mr. Sibel her third parent, he's referred to her as his daughter,
[00:08:34] and they've had weekly phone calls for decades after high school.
[00:08:38] Thanks to Fei Fei's hard work and Mr. Sibel's mentorship,
[00:08:42] Fei Fei was accepted to Princeton University with a full scholarship in 1995.
[00:08:47] At Princeton, Fei Fei continued to love physics,
[00:08:51] but in her first semester, her mom got terribly sick and needed heart surgery.
[00:08:56] Even after the surgery, her mom's heart was extremely fragile.
[00:09:00] The doctor said that she could never work again, even part-time work could put her life in serious danger.
[00:09:07] This was quite the dilemma because the family simply could not pay their bills without her mom's income.
[00:09:13] So, at this point, with her mom's life literally on the line,
[00:09:16] Fei Fei considered dropping out to get a job or at least switch her major from physics to something with more earning potential.
[00:09:23] But her mom insisted that Fei Fei continued to study what she loved
[00:09:27] and went back to full-time work just two weeks after her heart surgery.
[00:09:31] To try to increase their family income, the family started a dry cleaning business.
[00:09:36] Mr. Sibel very kindly lent them $20,000 to open the business,
[00:09:41] and every weekend instead of going to parties, Fei Fei went home to help with the dry cleaning
[00:09:47] and translate questions from American customers.
[00:09:50] Slowly they started to make money from the dry cleaning business
[00:09:54] so that Fei Fei could continue to study what she loved.
[00:09:57] But what wasn't exactly that she loved?
[00:10:00] Around sophomore year, Fei Fei started wondering
[00:10:03] if it was really physics per se that so inspired her
[00:10:07] or simply the spirit that motivated physics,
[00:10:10] the courage that spurred some of history's brightest minds
[00:10:13] to ask such brazen questions about our world.
[00:10:17] This part is really interesting to me.
[00:10:20] I think a lot of people are generally curious about the world,
[00:10:23] but this passage starts to highlight where Fei Fei's curiosity
[00:10:27] takes on a grander scale and magnitude than those of ordinary people.
[00:10:31] She is no longer content to just ask questions that have known answers.
[00:10:35] She says she, quote,
[00:10:37] wanted desperately to follow in the footsteps of these great scientists
[00:10:41] to help review some unknown truth.
[00:10:44] Fei Fei started studying the biographies of great scientists
[00:10:48] like Einstein, Feynman, and Bohr,
[00:10:50] presumably because he had the ambition to become a great scientist herself.
[00:10:55] He noticed a common pattern in the lives of great physicists.
[00:10:59] Later in their careers, they all seemed to develop an interest
[00:11:03] in, quote, the mystery of life itself
[00:11:05] and even formally shifted to studying biology.
[00:11:09] So Fei Fei started wondering what it was about biology
[00:11:12] that so fascinated these great minds.
[00:11:14] She herself became particularly fascinated with how the mind works,
[00:11:19] how to, quote, make sense of intelligence itself.
[00:11:22] She was drawn to the, quote, humanistic thread of this line of reasoning,
[00:11:26] how it was applying the rigor of the scientific method
[00:11:29] to understanding how humans think and perceive the world.
[00:11:33] Which makes sense because Fei Fei had a scientist's brain
[00:11:36] but also loved classic works of literature
[00:11:39] that try to make sense of the human experience.
[00:11:42] Fei Fei also was drawn to the question of how intelligence worked
[00:11:45] because she felt that it was one of the biggest,
[00:11:48] most audacious questions that she could be asking at that time.
[00:11:52] So to explore how the brain works,
[00:11:54] Fei Fei interned at a neuroscience research lab that summer.
[00:11:58] They hooked electrodes to the brain of a cat,
[00:12:01] then showed the cat an image,
[00:12:03] and then using the electric signals from the cat's brain
[00:12:06] tried to recreate the image that the cat was seeing.
[00:12:09] Pretty cool, right?
[00:12:11] He was hooked.
[00:12:12] Despite the gruelling hours,
[00:12:14] he felt so energized and content every single day.
[00:12:17] She says, quote,
[00:12:19] research triggered the same feeling I got as a child,
[00:12:22] exploring the mountains with my father
[00:12:24] when we'd spot a butterfly we'd never seen before
[00:12:27] or happened upon a new variety of stick insect.
[00:12:30] By her senior year,
[00:12:32] Fei Fei knew that she wanted to be a scientist
[00:12:35] and to keep making these exciting discoveries.
[00:12:38] But what about the money?
[00:12:40] Academic researchers don't get paid much
[00:12:42] and her mom was still endangering her health
[00:12:44] every single day, working long hours at the family laundromat.
[00:12:48] So Fei Fei contemplated a career in finance.
[00:12:51] We could put an end to all her family's financial insecurities.
[00:12:55] After interviewing with some law-scrape firms,
[00:12:57] Fei Fei told her mom about the high salaries and perks.
[00:13:01] Her mom replied immediately,
[00:13:03] Fei Fei, is it what you want?
[00:13:05] Fei Fei replied, I know what I want, mom.
[00:13:08] I want to be a scientist.
[00:13:10] Her mom said, so what are we even talking about?
[00:13:13] So Fei Fei stopped talking to the finance firms
[00:13:16] and did what she really wanted,
[00:13:18] which was grad school, but in what subject?
[00:13:21] After her summer researching the cat brain,
[00:13:24] Fei Fei realized that he wanted to study how intelligence worked
[00:13:28] and that the first step to this
[00:13:30] was to understand how vision worked.
[00:13:32] How is it that as light enters our eyes
[00:13:35] and fills them with blobs of color,
[00:13:37] we are able to translate these blobs instantly
[00:13:40] into understanding and knowledge about the world?
[00:13:43] Like when I look around my room right now,
[00:13:45] how exactly is my brain so instantaneously labeling
[00:13:49] and making sense of the shapes around me?
[00:13:51] Like that blob is a chair, that's a lamp,
[00:13:54] that's a golden retriever walking past my window
[00:13:56] and it looks friendly.
[00:13:58] Fei Fei wanted to understand how the brain processes all this.
[00:14:02] So how do you go about doing that?
[00:14:04] One method is to keep putting test subjects
[00:14:07] into various scenarios,
[00:14:09] then measure their brain waves or whatever
[00:14:11] and try to deduce what might be going on inside their brain,
[00:14:15] which Fei Fei did for a few years.
[00:14:17] The other method though is to essentially try to recreate
[00:14:21] an artificial brain.
[00:14:23] And in the process you might get to some understanding
[00:14:26] of how the real human brain works.
[00:14:28] Increasingly Fei Fei got sucked into this line of exploration
[00:14:32] of trying to create a computer system
[00:14:34] that's able to see and recognize shapes and images
[00:14:37] the way that the human brain does.
[00:14:39] So she ended up doing a dual PhD in neuroscience
[00:14:43] and computer vision at Caltech.
[00:14:45] She started her PhD in the year 2000
[00:14:48] when the field of computer vision was very new
[00:14:51] and not very popular.
[00:14:53] The core problem she focused on was,
[00:14:55] okay, let's say you give a computer a picture of a cat.
[00:14:58] How do you make the computer capable of recognizing
[00:15:01] that that is a cat?
[00:15:03] At the time, the few researchers in the field
[00:15:06] were mostly focused on developing complex sets of roles
[00:15:09] or algorithms to explain to the computer
[00:15:12] what a cat looks like.
[00:15:14] Like if there are two triangles on top of a circle,
[00:15:17] it might be a cat.
[00:15:18] But Fei Fei was like, okay,
[00:15:20] but how does a human child learn to recognize a cat?
[00:15:24] The human baby looks around the world
[00:15:26] and sees tons of different images.
[00:15:29] And the baby's parent points to the things in these images
[00:15:32] and tells them, look, that's a cat.
[00:15:34] That's a dog.
[00:15:35] That's a care.
[00:15:37] Over time, after seeing enough examples
[00:15:39] of these different objects,
[00:15:41] somehow the baby's brain is able to connect the dots,
[00:15:45] learn, and recognize cats and dogs and chairs.
[00:15:49] So Fei Fei was like,
[00:15:50] what if I use a machine learning approach,
[00:15:52] which had been invented by then
[00:15:54] but was not very popular.
[00:15:56] Where I compile a set of images,
[00:15:58] I label them with what objects are in the image
[00:16:01] and then try to get a computer system
[00:16:03] to learn to recognize the object
[00:16:05] in the way that a human child learns.
[00:16:08] So Fei Fei said about compiling a set of images
[00:16:11] that a computer could use to learn from.
[00:16:13] This requires a lot of manual labor
[00:16:15] because you need to compile a diverse set of images
[00:16:18] of things in the world
[00:16:20] and then label them with the names of the objects in them.
[00:16:24] But once the manual labor was done
[00:16:26] and the images were compiled,
[00:16:28] the early results were very promising.
[00:16:31] Fei Fei was able to train a system
[00:16:33] that set the world standard for image recognition,
[00:16:36] which is incredibly exciting.
[00:16:38] Imagine that of everyone in the world to date,
[00:16:41] you as a 20-something year old researcher
[00:16:44] have built the computer system
[00:16:46] that is the most successful at recognizing images.
[00:16:49] Obviously she did this with the help
[00:16:51] of her advisor and colleagues and such,
[00:16:53] but still super exciting.
[00:16:55] So he decided to assemble bigger and bigger datasets
[00:16:58] for the computers to learn from
[00:17:00] to further improve their performance.
[00:17:02] This problem of assembling a bigger and bigger database
[00:17:05] to train a more and more powerful machine
[00:17:08] consumed the next couple years of her life.
[00:17:11] It also opened up semi-philosophical questions
[00:17:13] like how many different categories of objects
[00:17:16] exist in the world.
[00:17:18] But a few years into the project,
[00:17:20] her mom had to undergo another terrible heart surgery.
[00:17:23] In the wake of the surgery,
[00:17:25] Fei Fei again contemplated abandoning her research career.
[00:17:28] She interviewed and got an offer to work
[00:17:30] as a management consultant at McKinsey,
[00:17:33] which would set her up for a super lucrative, prestigious career
[00:17:36] and allow her mom to retire.
[00:17:39] But again, her mom forbade her from taking the McKinsey job
[00:17:42] and forced her to stay in grad school
[00:17:44] to keep pursuing her passion.
[00:17:46] To accommodate her mom's health,
[00:17:48] her parents moved into Fei Fei's dorm room at Caltech
[00:17:51] and subsisted off her grad student stipend
[00:17:54] while Fei Fei continued her research.
[00:17:56] Around the same time, Fei Fei met Sovio,
[00:17:59] a fellow PhD student from Italy.
[00:18:02] My God, what a nerd this guy is, he thought to herself.
[00:18:05] But he's the same kind of nerd as me.
[00:18:08] They bonded over their intense
[00:18:10] and similar curiosity about the world,
[00:18:12] fell in love and got married.
[00:18:15] Fei Fei has described Sovio as
[00:18:17] a confident, without equal, my best friend,
[00:18:20] my fellow AI scientist and my soulmate.
[00:18:23] A few years later, Fei Fei got her PhD.
[00:18:26] She became a professor at Princeton
[00:18:28] and continued working on building a client dataset
[00:18:31] of images to use to train computers.
[00:18:33] The early days were pretty rough.
[00:18:36] Nowadays, big data is such a cliche term
[00:18:39] and everyone recognizes its tremendous value.
[00:18:42] But back then, Fei Fei was kind of alone
[00:18:45] and she was living in its power.
[00:18:47] Everyone thought she was wasting her time
[00:18:49] overfixating on gathering training data
[00:18:52] rather than improving the underlying machine learning
[00:18:54] algorithms that would learn from this data.
[00:18:57] At that time, 2006, quote,
[00:18:59] algorithms were the center of our universe
[00:19:02] and data just wasn't a particularly interesting topic.
[00:19:06] After repeated knows,
[00:19:08] she finally attracted one PhD student
[00:19:11] who is willing to work with her on her project.
[00:19:14] She also called the Foundation
[00:19:16] and asked Fei Fei to translate the approach
[00:19:19] of the data in the project.
[00:19:21] Fei Fei was already a big investment
[00:19:23] and she had to make some huge approaches
[00:19:25] to assembling the dataset of images and labels.
[00:19:28] But the initial estimate was that it would take 19 years
[00:19:31] to assemble a dataset that was as big as what they wanted.
[00:19:34] Getting funding for the project became so tough
[00:19:37] that Fei Fei considered going back
[00:19:39] into the laundry business to raise money for the project.
[00:19:41] Fei-Fei's team was then able to compile by far the largest data set of labeled images in the world in far less than 19 years.
[00:19:49] The completed data set had, quote, 15 million images spread across 22,000 distinct categories,
[00:19:57] annotated by more than 48,000 contributors, healing from 167 countries.
[00:20:03] Fei-Fei's team released this huge data set to the public and tried really hard to market it to the AI research community
[00:20:10] to get people to train their computer models with this data.
[00:20:14] While it was tough, ultimately Fei-Fei did do a decent job of marketing both the data set and herself,
[00:20:20] because first she ended up moving to Stanford as a professor,
[00:20:24] and second through the course of a couple years,
[00:20:27] a bunch of researchers tried training various types of machine learning models on Fei-Fei's giant data set,
[00:20:33] and the process revealed some really counterintuitive insights about what kinds of machine learning models and techniques worked best.
[00:20:41] In particular, it turns out that a model called a neural network,
[00:20:45] which is structured like the neurons in an actual human brain,
[00:20:48] was extraordinarily good at learning from Fei-Fei's huge image set,
[00:20:52] which is cool first because it tied back to Fei-Fei's neuroscience roots,
[00:20:57] but also because it ended up being super powerful and able to generalize to all sorts of tasks beyond just labeling images.
[00:21:05] This approach that Fei-Fei and collaborators had used of compiling a huge data set,
[00:21:11] then putting a neural network on top of it, proved to be a generalizable formula with formidable and world-changing results.
[00:21:19] It came to be known as deep learning and it now powers everything from chat QPT to self-driving cars.
[00:21:26] Several of Fei-Fei's grad students actually became founding and core members of the teams that built these technologies.
[00:21:33] As the AI revolution spread, Fei-Fei grew in her power and scope.
[00:21:37] A turning point for her was one day in 2014.
[00:21:41] She and her students believed that they had just built the very first system to use AI to generate sentences to describe images.
[00:21:48] They had just submitted their findings to an upcoming conference when Fei-Fei got a call from the New York Times.
[00:21:55] Apparently, another team had produced the exact same findings and that team had come from Google.
[00:22:02] Her research was officially so impactful and commercially useful that Google was hiring teams to solve the exact same problems that she was.
[00:22:10] On top of that, the New York Times wanted to report on their findings.
[00:22:15] Later in 2016, she took a sabbatical from Stanford to spend two years as the chief scientist of AI at Google Cloud.
[00:22:23] This was not being a corporate sellout.
[00:22:25] AI had grown so commercially valuable that tech companies had become the best place to do AI research because these companies alone had the money and the massive amounts of data to support the most ambitious projects.
[00:22:38] The day Fei-Fei arrived at Google, she was given a 15 person team of brilliant PhDs along with a ton of powerful computers and data and within 18 months her team had grown to 20 times that size.
[00:22:51] The more the AI continues to progress, the more recognition she's gotten.
[00:22:56] Wired Magazine called her, quote, one of a tiny group of scientists, a group perhaps small enough to fit around a kitchen table who are responsible for AI's recent remarkable advances.
[00:23:07] The impact of her research has been so great that in recent years, Fei-Fei has devoted increasing amounts of her time to studying how AI will shape society.
[00:23:16] At Google, she coauthored a pledge to use AI for social good.
[00:23:21] Back at Stanford, she co-founded the Institute for Human-Centered AI to research what it even means for AI to be used for social good and how to steer it in that direction.
[00:23:32] She also kicked off a research project to use AI to advance healthcare and she co-founded a non-profit AI for all to increase diversity in AI.
[00:23:42] On top of all this, she and Soville now have two kids.
[00:23:46] So what can we learn from Fei-Fei's story?
[00:23:48] The most obvious takeaway is that to achieve large scale outcomes, follow your curiosity and passion instead of optimizing for short term accolades and starting salaries.
[00:23:59] But Fei-Fei's curiosity was no ordinary curiosity.
[00:24:02] It was on a much larger scale and magnitude than most people's.
[00:24:07] Fei-Fei was always wondering, what is the most audacious question that I can be asking that most people are too afraid to ask an earnest because we as a human race have not yet found a complete answer?
[00:24:19] Over and over, she talks about her quest to find a quote North Star question that felt central to advancing the frontier of human knowledge in some field.
[00:24:28] That she could spend years of her life trying obsessively to answer that felt historic in nature.
[00:24:34] I think it's the combination of her great curiosity and ambition that led her to the forefront of the field of AI, a field that's so young and she used so rapidly and has such an outsized impact on the world.
[00:24:47] I think from Fei-Fei, we can learn that in order to succeed, yes you do need a long term commitment to curiosity and passion.
[00:24:54] But equally important is the drive to do the biggest, most audacious version of whatever it is that you're curious or passionate about.
[00:25:03] I really admire the combination of these traits in Fei-Fei Li.
[00:25:07] I hope you enjoyed this episode.
[00:25:09] Again, it's lighter on personal details and analysis than my usual episodes because it's just based on what Fei-Fei chose to share in this one memoir.
[00:25:17] But if you want to check out the memoir, it's called The Worlds I See and I especially recommend it if you want to learn more about AI.
[00:25:25] Thanks for listening and don't forget to subscribe and rate the show.