The Worlds I See. Curiosity, Exploration, and the Discovery at the Dawn of AI—Fei-Fei Li (New York, NY, USA: Flatiron Books, 2023, 322 pp.)
Fei-Fei Li is known for leading the development of ImageNet, which helped catalyze machine-learning approaches to vision recognition, and for being an essential voice shaping the science behind Artificial Intelligence (AI) today. She offers an extended, thoughtful, and heartfelt memoir in this book. Beautifully written and grounded in many rich, thought-provoking observations, the book describes her journey from being a child immigrant from China to her present position as a Stanford Professor.
Li’s book draws on interconnected vignettes. These vignettes, particularly the deeply personal ones, shed light on family crises and career choices, pointedly when neither could wait. They also offer profound insights into AI science’s antecedents and progress, providing a unique perspective on recent history.
Crucially, she does not spoon-feed the reader. The book anticipates an educated reader who understands the broad context. For example, Li never provides a layman’s overview or timeline to summarize the broad history of AI – its origins, fads, and winters. That should work well for most technically savvy readers.
While the book is easy to devour, I found it challenging to digest. That is a compliment. Li’s point of view is clear, consistent, and thoughtful. Li offers many insightful, broad observations, but not all help the reader interpret every vignette. An engaged reader will think about those observations after putting the book down.
Unlikely Origins
After the introduction, the book follows a chronological narrative. These early chapters contain little about AI, describing instead the Li family’s journey from China to the US and the massive adjustments required. The family settled in New Jersey, living an economically precarious life.
Li possessed an inherent interest in science from a young age, which her parents encouraged. While she had encountered resistance to girls pursuing physics in China, she experienced the social roughness of being an outsider in a US public high school.
Such a crucible at a young age would require much mettle from anyone. Amazingly, Li escapes the situation by following her science north star, which is inspiring. Alternatively, give the first half of this book to a whiny student. Then say, “If you think you had it hard, read this.”
Though Li’s high school was not a likely place to spawn a future academic star, her high school math teacher was the difference-maker. He exemplifies how a thoughtful and empathetic individual can change a student’s life. Though he starts as a science mentor, he eventually becomes more like a benevolent uncle.
Li’s parents’ financial situation overshadowed her ability to pursue her dreams. Her high school teacher gives Li her first financial break; he lends the family money to buy a cleaning business. Then came more successes: a full scholarship to Princeton, funding to be a summer research assistant in an experiment digitizing vision in Berkeley, and a scholarship for a Ph.D. at Cal Tech.
The funding mattered. Li recalls how close she came to taking the money of a standard American job in consulting or finance. She admits that she would not have stayed on the scientific path without her mother’s pointed questions and resolute willingness to sacrifice comfort for her daughter’s dreams. This funding was just enough to keep her on this path.
Funding also opens opportunities to the best of the American academy, starting with Li’s experience as an undergraduate. In graduate school, two professors mentored her on the science of sight, computational linguistics, and an eclectic mix of academic areas. In other words, many saw something in her, made a bet on her future, and then she produced path-breaking research that justified all that investment.
What is the lesson? Computer scientists do not come to breakthroughs quickly. Like other scientists, computer scientists follow a fragile path to the frontier. To be sure, the individual must be extraordinarily intelligent and resourceful. But science also requires hard work, perseverance, and curiosity married to an openness to insight from unexpected sources. A pro-social outlook from many mentors at many stages helps immensely. Li’s account raises the question of how many future Einsteins never made it down the path due to a lack of funds or support.
The atmosphere of science
It is often said that nobody should go into academic research if they crave immediate gratification. Li’s journey shows the extreme side of that platitude. The audacious ideas for ImageNet drew on multiple influences from her training as a graduate student. Yet, nothing paid off until years into her career as a professor.
In graduate school, she began to hypothesize the need to assemble and tag many pictures of various objects. Then, over time, she began to appreciate the order of magnitude required. Yet, the costs initially overwhelmed the aspirations.
Many vignettes illustrate what is often unheralded today: creating low-cost supply chains for tagging carefully curated digital photos was an enabler for next-generation visual AI. Moreover, it did not arise overnight. In Li’s case, after years of stops and starts, a creative and hard-working graduate advisee suggested using Amazon’s Mechanical Turk (MT) for tagging. MT got ImageNet to the next level.
When a convolutional neural network won the 2012 ImageNet contest, blowing away the next-best entrant by ten percentage points, Li saw it (correctly) as vindication of all her efforts. Most interesting, however, was that she was as surprised as anyone at the solution. In describing why, she studiously avoids retrospective bias. The account explains why this breakthrough was obvious with some hindsight but not until it happened. It is an uncommonly beautiful piece of science writing.
Li offers many vignettes in this part of the book in which she walks a highwire between personal and professional crises. She has some of the most significant moments of her career while helping her parents with the cleaning business or tending to her mother’s chronic near-death illnesses. Eventually, she and her academic spouse managed a “commute marriage” between California and Michigan for many years while pursuing independent career goals, ending up in the same location just before their first child arrived.
She must have unbounded energy and an uncommon ability to juggle different pressures. She was also fortunate to have nurturing parents. In addition, she found a talented, patient spouse who would delight any set of parents. (He is Italian, has a Ph.D., and cooks!) Every reader will root for her.
The future of science
The last parts of the book touch on contemporary themes. Li covers a provocative list of concerns. For example, she worries about what private firms in AI neglect, such as ethical considerations. She identifies why complex neural networks make transparency about ethical issues so challenging to address. She also gives a window into the next frontier, ambient intelligence. She shows how it could benefit healthcare operations but violate privacy in the wrong hands. It is a credit to her thoughtfulness that she does not offer bromides and avoids the self-important posturing and simplistic declarations common in social media.
Still, no book can do everything, and I would also have liked Li to be more self-aware about a few incomplete observations. The reviewer’s job is to notice blind spots, so let me list a couple.
After an extended sabbatical at Google, Li concludes that today’s frontier AI models rely on more resources and talent than most university labs can afford. She explains why in general terms. A few more specifics would have helped me understand where universities specialize, where firms should focus, and where their interests overlap. Li must think about those questions daily, so hearing her views would have been enlightening.
The tensions in the university/industry relationship also needed more scrutiny, especially how it shapes AI today. Li does talk a bit about the administrative challenges of conducting science at universities, but not much about the many dimensions of short-termism at private firms, especially when researchers in either setting pick projects or follow fads.
She also alludes to the symbiotic relationship between private and university interests. Still, she does not analyze how private interests can keep technical secrets or hollow out university personnel. How can technical training occur in universities if private firms’ investment shapes the technical frontier?
Finally, Li talks about her status as an Asian woman in a male-dominated field. She is certainly justified in feeling lonely as a woman in computer science. While women have slowly worked their way into many academic fields in the last few decades, computer science has stubbornly stayed at a low percentage.
In response, she cofounded AI4ALL, a society to help women get into computer science and find mentors. That is a fascinating initiative for a challenging situation. I would have liked to know more. What has this organization done during its history?
I also wanted a half-empty-half-full perspective. The number and percentage of non-white faculty – i.e., particularly South and East Asians – has grown in the last few decades in computer science and related fields. Her perspective on these trends would be interesting to hear, but she says almost nothing.
Conclusion
The book ends with Li returning from her sabbatical at Google to teach a 600-student class on neural nets. It is a fitting end. Teaching energizes Li, and she hopes to see her younger and more curious self among her students.
I bet most undergraduates would be inspired to take a class from her. More pointedly, the book implies that no Ph.D. student could survive her supervision without high aspirations. Having someone like that as an anchor for the AI scientific community is fantastic.
Copyright held by IEEE Micro
First published V44, 3, May/June 2024.
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