What is The Worlds I See by Fei-Fei Li about?

The Worlds I See is a science memoir that chronicles Fei-Fei Li's journey from a teenage immigrant arriving in America with less than $20 to becoming a pioneering figure in artificial intelligence. The book interweaves her personal story with the rapid development of modern AI, including her groundbreaking creation of ImageNet, which revolutionized computer vision and deep learning. Li shares firsthand insights into AI's evolution and its implications for humanity's future.

Who is Fei-Fei Li and why is she called the "Godmother of AI"?

Fei-Fei Li is a Chinese-American computer scientist and Stanford professor known as the "Godmother of AI" for her groundbreaking work in computer vision. She invented ImageNet, a massive database of 15 million images that has been widely regarded as one of three driving forces behind the modern AI revolution. Li currently serves as Co-Director of Stanford's Human-Centered AI Institute and recently founded World Labs, focusing on spatial intelligence AI.

Who should read The Worlds I See by Fei-Fei Li?

The Worlds I See appeals to aspiring scientists, immigrants, technology professionals, and anyone interested in AI's development and ethical implications. Students and first-year professionals will find inspiration in Li's story of overcoming adversity through persistence and education. Policymakers, tech leaders, and general readers curious about how AI revolutionized our world and concerns about responsible technology use will gain valuable insights from her unique perspective at AI's forefront.

Is The Worlds I See by Fei-Fei Li worth reading?

The Worlds I See offers exceptional value as both an inspiring immigrant success story and an insider's account of AI's breakthrough moment. Princeton selected it as their 2024 pre-read program, with President Eisgruber praising how Li "beautifully illuminates the persistence that science demands". The book provides rare firsthand perspective from a central figure in AI development while addressing crucial questions about technology's ethical future, making it essential reading for understanding both AI's history and its human-centered potential.

What is ImageNet and why is it significant in The Worlds I See?

ImageNet is Fei-Fei Li's revolutionary database containing nearly 15 million images across 22,000 categories that transformed artificial intelligence. The 2012 ImageNet Challenge, where Geoffrey Hinton's team achieved breakthrough results, represents AI's defining moment that Li describes in her memoir. This dataset enabled rapid advances in computer vision throughout the 2010s and is widely considered one of three critical driving forces behind the birth of modern deep learning.

How does Fei-Fei Li's immigration story connect to AI development in The Worlds I See?

Li arrived in New Jersey at age 16 without English proficiency, money, or connections beyond her parents, working as a waitress and cleaner while attending high school. Her family rebuilt their lives from nothing, with Li helping run her parents' dry-cleaning business on weekends even while attending Princeton on full scholarship. This immigrant journey of resilience, adaptation, and persistence directly parallels AI's own evolution—both required embracing uncertainty, learning from limited data, and pushing through countless setbacks to achieve breakthrough moments.

What does The Worlds I See reveal about human-centered AI?

The Worlds I See advocates for AI development that prioritizes human impact, ethics, and inclusivity rather than technology for its own sake. Li co-founded AI4ALL in 2017 to increase diversity in AI education and has testified before Congress about responsible technology use. Her vision emphasizes that AI should be developed with diverse perspectives and serve humanity's needs, drawing from her experience advising the White House, serving on the UN Scientific Advisory Board, and leading Stanford's Human-Centered AI Institute.

What role did Princeton play in Fei-Fei Li's journey described in The Worlds I See?

Princeton provided Li with a transformative full scholarship that seemed so improbable she asked two advisors to verify the acceptance letter. She earned her bachelor's degree in physics in 1999 with High Honors while commuting home weekends to help her parents' dry-cleaning business. Princeton later recognized her achievements with Distinguished Alumni honors in 2020 and the prestigious Woodrow Wilson Award in 2024, selecting The Worlds I See as their university-wide pre-read program.

내가 보는 세상

AI의 여명기에 마주하는 호기심, 탐험, 그리고 발견

페이페이 리

4.31(5308 Reviews)

내가 보는 세상 개요

"내가 보는 세상(The Worlds I See)"에서 AI 선구자 페이페이 리(Fei-Fei Li)는 자신의 이민 여정과 획기적인 기술 개발 과정을 엮어냅니다. 오바마의 추천을 받았으며 프린스턴 대학교 2028년 신입생 권장 도서(Pre-read)로 선정된 이 회고록은 이미지넷(ImageNet)의 주역인 과학자가 던지는 질문을 보여줍니다. "AI는 궁극적으로 인간의 존엄성을 존중할 수 있는가?"

전달 방식을 선택하세요

한 권의 책, 다양한 듣기 방식.

내가 보는 세상의 같은 아이디어를 담았습니다. 나에게 맞는 목소리, 깊이, 속도를 고르고 언제든 바꿔 보세요.

1장

From Chengdu to Silicon Valley: A Pioneering Journey in AI

The hotel lobby felt strangely ordinary as I prepared to testify before Congress about artificial intelligence. Despite my exhaustion from a red-eye flight and constant rehearsing, I couldn't help reflecting on my first visit to D.C. as a teenage immigrant when the National Air and Space Museum sparked my love for science. Now, twenty years later, I'd found my way to the science of intelligent machines. In just five years, deep learning had transformed from an academic niche to a global force, bringing both excitement and growing concerns about biased algorithms, job displacement, and surveillance. My recent New York Times op-ed had attempted to balance optimism with legitimate concerns, arguing that AI's impact would ultimately depend on human motivations. As I approached the Rayburn House Office Building, personal worries weighed heavily-I'd left my ailing mother's hospital bedside to attend. But she had insisted I come, seeing her daughter address Congress just two decades after arriving in America. Inside the chamber, seeing my name printed simply as "DR. LI" amid the ornate surroundings, I resolved to share my central belief: that AI's development must explicitly center on human benefit, and that harnessing it safely would require much more than science alone.

2장

A Childhood Shaped by Curiosity and Displacement

Under a leafy canopy during a childhood hiking trip in China, I gazed at stars while our guide pointed out constellations, including the romantic tale of Zhi nu and Niu lang separated by the "river of stars" - our galaxy. Even at ten, I felt deeply drawn to exploring nature's wonders, from plants and insects to distant mountains and celestial bodies. Born in Beijing but raised in Chengdu, my life was marked by displacement. My maternal grandfather, once a promising student forced to abandon education during wartime, passed down an unresolved tension - a feeling that home lay elsewhere. My father, an electrical engineer with an "allergy to seriousness," showed me curiosity in its purest form during our cycling adventures through countryside and city. Even my name "Fei-Fei" came from his bird-watching on the day of my birth, the Mandarin word *fei* meaning "to fly." My mother provided direction to this curiosity. Born to an intellectual lineage with a naturally fierce mind, her academic promise was thwarted during the Cultural Revolution due to her family's association with the Kuomintang. Her frustration manifested as a burning desire to escape, finding refuge in books that became windows to worlds beyond her reach-a passion she eagerly shared with me. Through Chinese translations of Western classics, I developed my own love for imagining alternate realities. Our family lived in a fourth-floor apartment in one of three identical Soviet-influenced towers on Chengdu's outermost ring road. My maternal grandparents, who lived nearby, were central to my childhood with their weekly lavish meals featuring coastal-style cooking that subtly reinforced our outsider identity in Chengdu. They nurtured my imagination with books on diverse topics from marine life to robotics to Chinese mythology, treating me as a person first and a girl second. My schooling in Chengdu was engaging, combining math and science with humanities like geography, poetry, and history. This contentment shattered one afternoon when our teacher dismissed the girls but kept the boys behind. Hiding nearby, I overheard her admonishing the boys for underperforming, stating they were "biologically smarter than girls" and assuring them that girls would "naturally grow stupider" as teenagers. These words from a female teacher ignited an unfamiliar anger in me-not discouragement but indignation. I began noticing other signs that my family's inclusivity wasn't universal. Rather than dissuading me, these experiences strengthened my resolve to reach further. In magnet middle school, I embraced being a "tomboy" as a personal mission-cutting my hair short, refusing dresses, and pursuing interests that defied expectations like aerospace science and hypersonic aircraft design.

들리는 모든 단어를 눈으로 확인하세요

내가 보는 세상의 각 단어가 오디오에 맞춰 실시간으로 강조되어 편하게 따라 읽을 수 있습니다.

3장

Revolution and Escape: The Journey to America

In 1989, everything changed. School schedules became erratic with unexplained breaks, and teachers' demeanors shifted with patriotic overtones woven into every subject. At home, my parents spoke in hushed, cryptic tones about something exciting on the horizon. Then suddenly, the cheer vanished, replaced by closed-door conversations from which I was deliberately excluded. Through late-night eavesdropping, I caught fragments mentioning "education," "opportunity," "freedom," and frequently, my name. Finally, my parents revealed that my father would be moving to America-the first step in a larger plan orchestrated primarily by my mother. He would secure work and housing, and we would join him shortly after. Within weeks, he was gone, taking a third of my familiar home life with him. By 1992, when I turned fifteen, our visas finally arrived. Our journey began with a trip to Shanghai, where I insisted we visit the Bund. I was particularly drawn to the Pujiang Hotel where Einstein had once stayed-finding comfort in remembering that he too had been an immigrant. Boarding the plane, I took inventory of what I had: grandparents I loved but would lose, a father I longed to see again despite lingering hurt, and a mother whose confidence was faltering. I didn't yet have a clear identity, but I had physics. The rest would be left to fate.

4장

The Immigrant Experience: Survival and Discovery

Our plane's landing at JFK jolted me back to harsh reality. This wasn't an adventure but the abrupt end of my only familiar life. Our plan to reunite with my father at the gate dissolved into hours of anxious waiting in baggage claim. When he finally burst through the doors, frantic after his car broke down in a tunnel, we were too exhausted for the joyful reunion it should have been. Our cramped one-bedroom apartment in Parsippany, New Jersey, quickly dispelled any illusions of American grandeur. I slept in a narrow gap between kitchen and dining area, while we furnished our space with discarded items found on curbs. Just forty-eight hours after arrival, I faced my next challenge: American high school. My first days at Parsippany High School overwhelmed every sense. Everything was brighter, faster, and noisier than my world in China. Between classes, loud bells unleashed roaring crowds that flooded hallways with teenage energy, unlike the docile atmosphere I knew. At home, even simple homework assignments took hours as I consulted two enormous dictionaries. Meanwhile, my parents worked exhausting jobs-my father repairing cameras and my mother as a cashier in a gift shop, her intellectual talents ignored. Working in a Chinese restaurant kitchen became my first job-twelve hours daily at two dollars per hour, off the books in a sketchy neighborhood requiring nightly rides home. What truly drained me wasn't the long hours but the apparent hopelessness of immigrant life. Everyone around me worked relentlessly without escaping menial labor. I immersed myself in academics, seeking stability amid chaos. During one weekend outing to Princeton University, I encountered Albert Einstein's bronze memorial that stopped me in my tracks. Reading the inscription describing him as "a Nobel Laureate in physics, a philosopher, a humanitarian, an educator, and an immigrant" who had lived nearby until 1955, I felt a fever breaking within me. The monument rekindled my passion for physics that had been reduced to mere survival.

내가 보는 세상에서 더 많은 것을 얻으세요

매일 새로운 인사이트를 10만 개 이상의 레슨에서 만나보세요
단어가 강조되어 오디오와 실시간으로 동기화됩니다
내 라이브러리를 저장하고 모든 기기에서 이용하세요

5장

Finding Mentorship and Direction

Mr. Sabella's towering height and curt tone made his reputation for sternness instantly manifest when I approached his desk after receiving an 89.4 on my math test. Though initially frustrated by what seemed like dismissal, I began to understand he was challenging me to stand out rather than blend in. His classroom and office-unofficially named "the Math Lab"-became my refuge. Despite language barriers, our conversations evolved beyond mathematics into literature. Unlike my parents, whose expertise I'd outgrown, Mr. Sabella recognized potential in me that I hadn't seen myself. When the thin envelope from Princeton arrived that December afternoon, I assumed rejection until spotting "Yes!" in bold type. The enclosed financial aid document revealed I'd received not just acceptance but a nearly full scholarship. For my mother, who had spent her life on the wrong side of unbridgeable divides, it offered unprecedented hope. Princeton's beautiful campus often seemed dreamlike. My journey had taken me from China's middle class through American poverty, through language acquisition and green card approval. Despite dark moments like witnessing a fellow student beaten for not fitting in, I'd discovered a wealth of humanity along the way-a community that welcomed immigrants, teachers who encouraged students struggling with English, and an Ivy League education.

6장

The Birth of a Scientific Passion

In a darkened lab in 1997, I watched as electrodes translated a cat's visual cortex activity into sound-what the cat saw, we heard. Though I'd entered Princeton as a sophomore physics undergrad, this encounter with the mysteries of the mind would soon consume me completely. By sophomore year, my relationship with physics began shifting. I wondered if I was drawn to physics itself or the spirit that motivated it-the courage to ask brazen questions about our world. I devoured biographies of Einstein, Feynman, and Bohr, noticing how many physics giants developed late-career interests in biology and life itself. Books like Schrodinger's "What Is Life?" and Hofstadter's "Godel, Escher, Bach" captivated me, suggesting the mind could be understood in discrete, mathematical terms. My first solo trip within the United States took me to California for an eight-week research position at Berkeley. I discovered our project wasn't studying the brain biologically but computationally-extending Hubel and Wiesel's work by analyzing how cats' brains responded to full-motion video. In this understaffed, underfunded but unconstrained lab environment, I took on responsibilities years before I was qualified. The brain's sophistication dwarfs our most powerful supercomputers, operating within cubic inches and powered by mere calories. Despite the relative simplicity of individual neurons, their 100-billion-fold replication with 10^11 connections creates something transcendent-matter becomes mind. Through painstaking work with microelectrodes and signal processing, we successfully reconstructed the movies projected before a cat's eyes using only intercepted brain signals. Berkeley transformed my perspective. While Princeton meant regimented life between classes and helping at my parents' dry-cleaning shop, Berkeley offered a vibrant reality where I felt like a scientist, not an immigrant. The mysteries of intelligence captivated me more than physics ever had-more expansive yet more intimate. After years of not belonging, I'd found my place.

들리는 모든 단어를 눈으로 확인하세요

내가 보는 세상의 각 단어가 오디오에 맞춰 실시간으로 강조되어 편하게 따라 읽을 수 있습니다.

7장

Vision as the Gateway to Intelligence

According to zoologist Andrew Parker's hypothesis, photosensitivity-the foundation of the modern eye-ignited the Cambrian explosion that transformed Earth's ecosystem in just ten million years. Beginning with light-sensitive opsin proteins that could transmit bioelectric signals, vision evolved rapidly: first a shallow divot to discern light direction, then deepening into a pinhole-camera-like aperture, finally developing lenses that increased clarity. As my Princeton graduation approached in 1999, I faced a pivotal decision between scientific ambitions and financial security. Wall Street firms aggressively recruited me with promises of perks and eye-watering salaries that could relieve our family's financial burdens. When I mentioned these opportunities to my mother during a quiet moment at the dry-cleaning shop, she cut through my equivocation with characteristic directness: "Fei-Fei, is it what you want?" When I admitted I wanted to be a scientist, she simply replied, "So what are we even talking about?" Her clarity settled it-I was going to grad school. During my Caltech visit, I encountered professors who challenged me intellectually in ways I'd never experienced. Christof Koch posed profound questions about consciousness and visual experience, while Pietro Perona brought an infectious intellectual curiosity to complex questions. Despite my struggles with English and self-doubt, I found myself drawn to these brash personalities because our shared passion for science created an intellectual equality that transcended language barriers and status differences. The human capacity for vision has long defied automation attempts, but the potential applications of machine vision are enormous-from environmental monitoring to medical diagnostics. The challenge lies in translating pixels into meaningful concepts, a task that reveals vision as not merely an application of intelligence but synonymous with it. Understanding vision means understanding ourselves, a journey to the most foundational layers of our experience.

8장

Breakthrough Moments in Computer Vision

At Caltech, we studied the visual cortex as a pathway to machine intelligence, believing that understanding human perception was essential to creating artificial versions. Anne Treisman's "feature integration theory" provided an early foundation, suggesting vision begins with identifying small details before establishing relationships between them. When Pietro handed me Simon Thorpe's brief three-page article from Nature, "Speed of Processing in the Human Visual System," it upended everything I thought I understood. Using EEG measurements, Thorpe had discovered that humans could identify the contents of photographs flashed for just 27 milliseconds-roughly a honeybee's wingbeat. Even more remarkably, the brain showed recognition just 150 milliseconds after seeing an image-about the time it takes to blink. By my second year at Caltech, I'd reached a powerful conclusion: visual perception relies fundamentally on categorization. Rather than drowning in endless details of light and color, our vision transforms the world into discrete concepts we can understand and react to instantly. This ability-to see a tree not as patterns of green and blue but as an object with known properties-was how our ancestors survived chaos. Testing our algorithm with Pietro in 2003, I watched as it attempted to identify an airplane in a photograph-a task trivial for humans but challenging for machines. What made this thrilling wasn't just that it worked, but how: unlike traditional approaches requiring hundreds of airplane images, our algorithm had seen only one before. We'd shown it hundreds of unrelated images first-jungle cats, motorcycles, human faces-proving that exposure to visual diversity improved specific recognition later. We called this "one-shot learning," mimicking humans' ability to recognize new objects after just a single glimpse. During another lunch at Red Door Cafe, Pietro proposed creating an entirely new, larger dataset from scratch. When I suggested starting with ten image categories (compared to Caltech's existing seven), Pietro pushed for more ambition. Our negotiation escalated until Pietro dramatically declared, "Let's do a hundred, Fei-Fei." Though initially shocked by the workload this would entail, I grew excited at how our model might perform with such a resource.

내가 보는 세상에서 더 많은 것을 얻으세요

매일 새로운 인사이트를 10만 개 이상의 레슨에서 만나보세요
단어가 강조되어 오디오와 실시간으로 동기화됩니다
내 라이브러리를 저장하고 모든 기기에서 이용하세요

9장

Personal Struggles and Scientific Perseverance

My academic momentum was abruptly halted when my mother suffered congestive heart failure. After rushing her to a doctor, I faced a devastating reality: we needed to sell the dry-cleaning shop that had been our lifeline for seven years. I decided to move my parents across the country to join me in my tiny Pasadena dorm. Speaking with Mr. Sabella, I questioned whether I could continue my studies while supporting my family. The McKinsey interview went surprisingly well, and the company quickly offered me a permanent position with an attractive salary. I rushed home to tell my mother, conflicted between abandoning my scientific dreams and finally providing financial stability. But my mother was resolute: "We didn't come this far for you to give up now." She reminded me that my scientific journey had always been our family's collective mission since leaving Shanghai. Her words finally resonated-I would never question my path again. While trying to remember the word "beagle" in the lab one day, I had a revelation about our image dataset problem. My English dictionary provided the perfect solution-we could use the illustrated nouns throughout the book as an unbiased selection method for our categories. This immigrant's tool became instrumental in creating Caltech 101, the largest collection of images ever assembled for machine learning at that time: over 9,000 images across 100 categories. Our follow-up paper established a benchmark that researchers worldwide soon aimed to beat. Despite improvements with larger datasets like Caltech 101, I wondered if we needed even more categories. Searching the literature for answers, I rediscovered a paper by Irving Biederman from 1983 that suddenly appeared in a new light-his estimate that the world contained roughly 30,000 unique visual categories, not just 101. This revelation was staggering, suggesting our research had barely scratched the surface of what was needed to model human visual understanding.

10장

The Birth of ImageNet

After my Princeton lecture, I met computational linguist Christiane Fellbaum. Though our fields seemed distantly related, we shared a fundamental interest in how minds conceptualize the world through categories-the intersection between vision and language. When she asked if I'd heard of WordNet, everything changed. Created by psychology legend George Miller, WordNet maps every concept humans articulate through language, organizing words by meaning rather than spelling. Christiane mentioned a related abandoned project called ImageNet that aimed to illustrate each WordNet concept with a single image-a name and concept that immediately resonated with me. The pieces suddenly aligned in my mind: WordNet capturing the world's concepts in a natural hierarchy, and the abandoned ImageNet attempting to match pictures to concepts. What if we created a dataset like Caltech 101 but scaled to WordNet's ambition? Despite seeming logistically impossible, the idea was powerful-not just in size but in diversity that reflects the world's true complexity. Despite my colleagues largely rejecting the concept of ImageNet, questioning the practicality of a dataset with thousands of categories, I persisted. Only Professor Kai Li, a Chinese immigrant like myself who understands exponential thinking, supported me by donating workstations. Together with brilliant student Jia Deng, we trimmed WordNet's 140,000 entries to 22,000 visual, countable objects, aiming for 1,000 photographs per category-a staggering 20 million images. When Amazon Mechanical Turk (AMT) entered the picture, everything changed. This crowdsourcing platform transformed our workforce from a handful of undergraduates to thousands of international contributors. Our completion timeline plummeted from nineteen years to under one year. By June 2009, the inaugural version of ImageNet was complete: fifteen million images across twenty-two thousand categories, annotated by forty-eight thousand contributors from 167 countries. However, when we presented at CVPR 2009 in Miami, the project received little attention, leaving me wondering if the naysayers were right about my hypothesis.

들리는 모든 단어를 눈으로 확인하세요

내가 보는 세상의 각 단어가 오디오에 맞춰 실시간으로 강조되어 편하게 따라 읽을 수 있습니다.

11장

Revolution in Machine Learning

Turning ImageNet into a competition required scientific rigor and careful design. We created extensive documentation explaining evaluation metrics, including a "top-5 error rate" allowing algorithms to provide five possible labels. We distributed a scaled-down subset-1.4 million photos covering a thousand categories-and created new test images with crowdsourced labeling. Despite initial enthusiasm, the results of the first "ImageNet Large Scale Visual Recognition Challenge" in 2010 were underwhelming with only incremental improvements. By 2011, participation dropped dramatically to just 15 entries. Our grand hypothesis that ImageNet would force revolutionary algorithm development seemed to be failing. By August 2012, after giving birth, I received an unexpected late-night call from Jia about the ImageNet Challenge results. An entry using a neural network-an approach many considered "ancient"-had achieved an astonishing 10-percentage-point leap in performance, setting a world record of about 85% accuracy. This breakthrough, called AlexNet after lead researcher Alex Krizhevsky, was steamrolling the competition. AlexNet was a convolutional neural network that mirrored biological vision, with layers of neurons learning their own sensitivities from training data. Though AlexNet built upon Yann LeCun's decades of work with CNNs, its key advantages were massive scale, affordable GPU hardware originally developed for video games, and most importantly, access to ImageNet's vast training data. The network contained 650,000 neurons with 630 million connections, trained continuously for a week until it could recognize a thousand different object categories with unprecedented accuracy. The Florence experience revealed how two different gambles had converged perfectly-my lab's yearslong pursuit of unprecedented data scale and Hinton's commitment to abandoned neural network algorithms. Though vindicated separately, their true power emerged only in combination. Flying home, I sat in contemplative silence, aware that history had just been made with only a handful of witnesses.

12장

Human-Centered Artificial Intelligence

During a friend's bat mitzvah in summer 2013, I received an urgent call about my mother's health. While sitting with her during recovery, she surprisingly asked what I actually do for work. When my technical explanation about AI research didn't connect, I described practical applications like self-driving cars that could help someone with her condition. Her simple follow-up question-"What else can AI do to help people?"-became a pivotal moment, inspiring me to merge my AI expertise with healthcare needs. Dr. Arnie Milstein, a renowned Stanford medical professor, invited me to observe a demonstration of Philips' "eICU" technology-remote monitoring for intensive care units. During the presentation, I was stunned to learn that approximately 100,000 annual deaths result from preventable medical errors. Our conversation revealed awareness as healthcare's most precious and unscalable resource, connecting directly to my research on visual perception and attention. Following our eICU demonstration, Arnie and I assembled a diverse team to develop "ambient intelligence" systems for healthcare, focusing on monitoring hand hygiene in hospitals, tracking senior activities in care homes, and detecting patient immobility in ICUs. Despite Arnie's impressive ability to secure partnerships with real healthcare facilities, we faced a significant challenge: the scarcity of training data, as patients and clinicians are rarely recorded due to privacy concerns. After my mother's heart surgery, she refused to use her incentive spirometer despite its critical importance in preventing lung infections. When I finally confronted her at home, she revealed a profound truth: "Being a patient is horrible. It's not just the pain. It's the loss of control." This conversation crystallized the deepest lesson from my two years exploring AI in healthcare: human dignity transcends any algorithm or dataset.

내가 보는 세상에서 더 많은 것을 얻으세요

매일 새로운 인사이트를 10만 개 이상의 레슨에서 만나보세요
단어가 강조되어 오디오와 실시간으로 동기화됩니다
내 라이브러리를 저장하고 모든 기기에서 이용하세요

13장

The Future of AI and Personal Legacy

The Stanford Institute for Human-Centered Artificial Intelligence (Stanford HAI) now occupies a refurbished wing of the Gates Computer Science Building, symbolically placing humanistic values at the heart of computer science. HAI has become a hub for cross-disciplinary collaboration, bringing together law professors, political scientists, humanities scholars, and computational neuroscientists. Recent years brought alternatives to the human bottleneck in training models. Advances in model processing, size, parallelization, and "attention" mechanisms made it possible to train on data sets comprising significant portions of the internet itself. The resulting "large language models" (LLMs) demonstrated linguistic competence so fluent it was easy to forget one wasn't reading a human author's words. Scale was the operative word in this seemingly magical technology. While AlexNet debuted with sixty million parameters, transformers now reached hundreds of billions. The engineering challenges were endless, but the science surprisingly elegant-as if these possibilities had been waiting since LeCun's ZIP code reader or even Rosenblatt's perceptron. We just needed to make a simple idea big enough. On a bright afternoon, I watched my parents with their grandchildren in the gazebo's shade. My father, finally in his element as a grandfather, laughed and played with the children. A text from Stanford HAI's policy director announced the national research cloud had passed the senate. Another text from Jean Sabella showed her children excitedly opening Star Wars Lego sets from "Auntie Fei-Fei and Uncle Silvio." The Sabellas' compassion remains my greatest lesson in humanity. I find myself reflecting often these days. My parents' formative years come to mind-my mother locked away during the Cultural Revolution, my father lost in tragedy. I remember her trembling hands as we boarded the plane from China, and the dread at JFK when night fell without my father's arrival. Moving across the world marked me in ways I'm only beginning to understand. As a scientist and immigrant, "home" is never a clear concept. The best work happens at borders, where ideas exist between coming and going, explored by people who are both insiders and outsiders. This perspective gives us power to challenge the status quo. AI's future remains uncertain, with equal reasons for optimism and concern. What matters most is what motivates us as creators. As our field grows more diverse and inclusive, I'm more confident in our chances. In the mind, North Stars are limitless-each new pursuit beckons on its own horizon. My greatest joy comes from knowing this journey will never be complete.

★★★★★

내가 보는 세상 완독을 축하합니다

100만 명 이상의 학습자가 사랑합니다

“23일째 매일 사용하고 있어요. 이제 제 일상의 한 부분이 되었습니다.”

jayallen

내가 보는 세상의 핵심 주제

human-centered ai
immigrant scientist journey
computer vision evolution
gender bias in stem
academic pursuit of intelligence

내가 보는 세상의 명언

AI's impact would ultimately depend on human motivations.
My father, an electrical engineer with an 'allergy to seriousness'.
I didn't yet have a clear identity, but I had physics.
I embraced being a 'tomboy' as a personal mission.
I resolved to share my central belief: that AI's development must explicitly center on human benefit.

내가 보는 세상의 등장인물

Fei-Fei LiThe author, an AI scientist and immigrant
Fei-Fei's motherAn intellectual woman who drove the family's move
Fei-Fei's fatherAn electrical engineer with a passion for nature
Fei-Fei's maternal grandfatherA former student who valued education

저자 소개

페이페이 리(Fei-Fei Li) 박사는 선구적인 컴퓨터 과학자이자 AI 연구자이며, 이민자에서 인공지능 분야의 가장 영향력 있는 인물 중 한 명이 되기까지의 여정을 기록한 과학 회고록 "내가 보는 세상: AI의 여명기에 펼쳐진 호기심, 탐험, 발견(The Worlds I See: Curiosity, Exploration and Discovery at the Dawn of AI)"의 저자입니다. 1976년 베이징에서 태어난 리 박사는 스탠퍼드 대학교의 세쿼이아 석좌 교수이자 스탠퍼드 인간 중심 AI 연구소(HAI)의 공동 소장을 맡고 있습니다.

그녀는 컴퓨터 비전 분야에 혁신을 일으키고 딥러닝 혁명의 촉매제가 된 획기적인 데이터셋인 이미지넷(ImageNet)을 만든 것으로 가장 잘 알려져 있습니다. 그녀의 연구 분야는 머신러닝, 로봇 공학, 의료 분야의 AI를 아우르며, 300편 이상의 과학 논문을 발표했습니다.

리 박사는 구글 부사장 및 구글 클라우드의 AI/ML 수석 과학자를 역임했으며, AI 교육의 다양성을 증진하는 비영리 단체인 AI4ALL을 공동 설립했습니다. 현재는 공간 지능 AI를 개발하는 월드 랩스(World Labs)의 공동 설립자이자 CEO로 활동하고 있습니다. 그녀의 2015년 TED 강연은 200만 회 이상의 조회수를 기록했습니다. 그녀는 2023년 타임(Time)지가 선정한 'AI 분야에서 가장 영향력 있는 100인' 중 한 명으로 이름을 올렸습니다.

내가 보는 세상 요약 다운로드

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샌프란시스코에서 컬럼비아 대학교 동문들이 만들었습니다

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Who is Fei-Fei Li and why is she called the "Godmother of AI"?

Who should read The Worlds I See by Fei-Fei Li?

Is The Worlds I See by Fei-Fei Li worth reading?

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