Sometime around 1 am on a warm night last June, Fei-Fei Li was sitting in her pajamas in a Washington, DC, hotel room, practicing a speech she would give in a few hours. Before going to bed, Li cut a full paragraph from her notes to be sure she could reach her most important points in the short time allotted. When she woke up, the 5'3" expert in artificial intelligence put on boots and a black and navy knit dress, a departure from her frequent uniform of a T-shirt and jeans. Then she took an Uber to the Rayburn House Office Building, just south of the US Capitol. Before entering the chambers of the US House Committee on Science, Space, and Technology, she lifted her phone to snap a photo of the oversize wooden doors. (“As a scientist, I feel special about the committee,” she said.) Then she stepped inside the cavernous room and walked to the witness table. The hearing that morning, titled “Artificial Intelligence—With Great Power Comes Great Responsibility,” included Timothy Persons, chief scientist of the Government Accountability Office, and Greg Brockman, cofounder and chief technology officer of the nonprofit ­OpenAI. But only Li, the sole woman at the table, could lay claim to a groundbreaking accomplishment in the field of AI. As the researcher who built ImageNet, a database that helps computers recognize images, she’s 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. View more That June, Li was serving as the chief AI scientist at Google Cloud and was on leave from her position as director of the Stanford Artificial Intelligence Lab. But she was appearing in front of the committee because she was also the cofounder of a nonprofit focused on recruiting women and people of color to become builders of artificial intelligence. It was no surprise that the legislators sought her expertise that day. What was surprising was the content of her talk: the grave dangers brought on by the field she so loved. Axis of Strength The time between an invention and its impact can be short. With the help of artificial intelligence tools like ImageNet, a computer can be taught to learn a specific task and then act far faster than a person ever could. As this technology becomes more sophisticated, it’s being deputized to filter, sort, and analyze data and make decisions of global and social consequence. Though these tools have been around, in some way or another, for more than 60 years, in the past decade we’ve started using them for tasks that change the trajectory of human lives: Today artificial intelligence helps determine which treatments get used on people with illnesses, who qualifies for life insurance, how much prison time a person serves, which job applicants get interviews. Those powers, of course, can be dangerous. Amazon had to ditch AI recruiting software that learned to penalize résumés that included the word “women.” And who can forget Google’s 2015 fiasco, when its photo identification software mislabeled black people as gorillas, or Microsoft’s AI-powered social chatbot that started tweeting racial slurs. But those are problems that can be explained and therefore reversed. In the pretty near future, Li believes, we will hit a moment when it will be impossible to course-correct. That’s because the technology is being adopted so fast, and far and wide. SIGN UP TODAY Get the Backchannel newsletter for the best features and investigations on WIRED. Li was testifying in the Rayburn building that morning because she is adamant her field needs a recalibration. Prominent, powerful, and mostly male tech leaders have been warning about a future in which artificial-intelligence-driven technology becomes an existential threat to humans. But Li thinks those fears are given too much weight and attention. She is focused on a less melodramatic but more consequential question: how AI will affect the way people work and live. It’s bound to alter the human experience—and not necessarily for the better. “We have time,” Li says, “but we have to act now.” If we make fundamental changes to how AI is engineered—and who engineers it—the technology, Li argues, will be a transformative force for good. If not, we are leaving a lot of humanity out of the equation. At the hearing, Li was the last to speak. With no evidence of the nerves that drove her late-night practice, she began. “There’s nothing artificial about AI.” Her voice picked up momentum. “It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.” Around her, faces brightened. The woman who kept attendance agreed audibly, with an “mm-hmm.” Christie Hemm Klok

Fei-Fei Li grew up in Chengdu, an industrial city in southern China. She was a lonely, brainy kid, as well as an avid reader. Her family was always a bit unusual: In a culture that didn’t prize pets, her father brought her a puppy. Her mother, who had come from an intellectual family, encouraged her to read Jane Eyre. (“Emily is my favorite Brontë,” Li says. “Wuthering Heights.”) When Li was 12, her father emigrated to Parsippany, New Jersey, and she and her mother didn’t see him for several years. They joined him when she was 16. On her second day in America, Li’s father took her to a gas station and asked her to tell the mechanic to fix his car. She spoke little English, but through gestures Li figured out how to explain the problem. Within two years, Li had learned enough of the language to serve as a translator, interpreter, and advocate for her mother and father, who had learned only the most basic English. “I had to become the mouth and ears of my parents,” she says. She was also doing very well in school. Her father, who loved to scour garage sales, found her a scientific calculator, which she used in math class until a teacher, sizing up her mistaken calculations, figured out that it had a broken function key. Li credits another high school math instructor, Bob Sabella, for helping her navigate her academic life and her new American identity. Parsippany High School didn’t have an advanced calculus class, so he concocted an ad hoc version and taught Li during lunch breaks. Sabella and his wife also included her in their family, bringing her on a Disney vacation and lending her $20,000 to open a dry-cleaning business for her parents to run. In 1995, she earned a scholarship to study at Prince­ton. While there, she traveled home nearly every weekend to help run the family business. At college, Li’s interests were expansive. She majored in physics and studied computer science and engineering. In 2000, she began her doctorate at Caltech in Pasadena, working at the intersection of neuroscience and computer science. Her ability to see and foster connections between seemingly dissimilar fields is what led Li to think up ImageNet. Her computer-vision peers were working on models to help computers perceive and decode images, but those models were limited in scope: A researcher might write one algorithm to identify dogs and another to identify cats. Li began to wonder if the problem wasn’t the model but the data. She thought that, if a child learns to see by experiencing the visual world­—by observing countless objects and scenes in her early years—maybe a computer can learn in a similar way, by analyzing a wide variety of images and the relationships between them. The realization was a big one for Li. “It was a way to organize the whole visual concept of the world,” she says. But she had trouble convincing her colleagues that it was rational to undertake the gargantuan task of tagging every possible picture of every object in one gigantic database. What’s more, Li had decided that for the idea to work, the labels would need to range from the general (“mammal”) to the highly specific (“star-nosed mole”). When Li, who had moved back to Princeton to take a job as an assistant professor in 2007, talked up her idea for ImageNet, she had a hard time getting faculty members to help out. Finally, a professor who specialized in computer architecture agreed to join her as a collaborator. Her next challenge was to get the giant thing built. That meant a lot of people would have to spend a lot of hours doing the tedious work of tagging photos. Li tried paying Princeton students $10 an hour, but progress was slow going. Then a student asked her if she’d heard of Amazon Mechanical Turk. Suddenly she could corral many workers, at a fraction of the cost. But expanding a workforce from a handful of Princeton students to tens of thousands of invisible Turkers had its own challenges. Li had to factor in the workers’ likely biases. “Online workers, their goal is to make money the easiest way, right?” she says. “If you ask them to select panda bears from 100 images, what stops them from just clicking everything?” So she embedded and tracked certain images—such as pictures of golden retrievers that had already been correctly identified as dogs—to serve as a control group. If the Turks labeled these images properly, they were working honestly. In 2009, Li’s team felt that the massive set—3.2 million images—was comprehensive enough to use, and they published a paper on it, along with the database. (It later grew to 15 million.) At first the project got little attention. But then the team had an idea: They reached out to the organizers of a computer-vision competition taking place the following year in Europe and asked them to allow competitors to use the Image­Net database to train their algorithms. This became the ImageNet Large Scale Visual Recognition Challenge. Around the same time, Li joined Stanford as an assistant professor. She was, by then, married to Silvio Savarese, a roboticist. But he had a job at the University of Michigan, and the distance was tough. “We knew Silicon Valley would be easier for us to solve our two-body problem,” Li says. (Savarese joined Stanford’s faculty in 2013.) “Also, Stanford is special because it’s one of the birthplaces of AI.” The A.I. Issue The A.I. Issue Business How to Teach Artificial Intelligence Some Common Sense We’ve spent years feeding neural nets vast amounts of data, teaching them to think like human brains. They’re crazy-smart, but they have absolutely no common sense. What if we’ve been doing it all wrong? The A.I. Issue Business The DIY Tinkerers Harnessing the Power of Artificial Intelligence The age of homebrew AI may not be all sweetness and light. Nor will it be all darkness and porn. Meet some of the pioneers showing what happens when the masses can teach computers new tricks. The A.I. Issue Science The Genius Neuroscientist Who Might Hold the Key to True AI Karl Friston’s free energy principle might be the most all-encompassing idea since Charles Darwin’s theory of natural selection. But to understand it, you need to peer inside the mind of Friston himself. In 2012, University of Toronto researcher Geoffrey Hinton entered the ImageNet competition, using the database to train a type of AI known as a deep neural network. It turned out to be far more accurate than anything that had come before—and he won. Li hadn’t planned to go see Hinton get his award; she was on maternity leave, and the ceremony was happening in Florence, Italy. But she recognized that history was being made. So she bought a last-minute ticket and crammed herself into a middle seat for an overnight flight. Hinton’s ImageNet-­powered neural network changed everything. By 2017, the final year of the competition, the error rate for computers identifying objects in images had been reduced to less than 3 percent, from 15 percent in 2012. Computers, at least by one measure, had become better at seeing than humans. ImageNet enabled deep learning to go big—it’s at the root of recent advances in self-driving cars, facial recognition, phone cameras that can identify objects (and tell you if they’re for sale). Not long after Hinton accepted his prize, while Li was still on maternity leave, she started to think a lot about how few of her peers were women. At that moment she felt this acutely; she saw how the disparity was increasingly going to be a problem. Most scientists building AI algorithms were men, and often men of a similar background. They had a particular worldview that bled into the projects they pursued and even the dangers they envisioned. Many of AI’s creators had been boys with sci-fi dreams, thinking up scenarios from The Terminator and Blade Runner. There’s nothing wrong with worrying about such things, Li thought. But those ideas betrayed a narrow view of the possible dangers of AI. Deep learning systems are, as Li says, “bias in, bias out.” Li recognized that while the algorithms that drive artificial intelligence may appear to be neutral, the data and applications that shape the outcomes of those algorithms are not. What mattered were the people building it and why they were building it. Without a diverse group of engineers, Li pointed out that day on Capitol Hill, we could have biased algorithms making unfair loan application decisions, or training a neural network only on white faces—creating a model that would perform poorly on black ones. “I think if we wake up 20 years from now and we see the lack of diversity in our tech and leaders and practitioners, that would be my doomsday scenario,” she said. It was critical, Li came to believe, to focus the development of AI on helping the human experience. One of her projects at Stanford was a partnership with the medical school to bring AI to the ICU in an effort to cut down on problems like hospital-­acquired infections. It involved developing a camera system that could monitor a hand-washing station and alert hospital workers if they forgot to scrub properly. This type of interdisciplinary collaboration was unusual. “No one else from computer science reached out to me,” says Arnold Milstein, a professor of medicine who directs Stanford’s Clinical Excellence Research Center. That work gave Li hope for how AI could evolve. It could be built to complement people’s skills rather than simply replace them. If engineers would engage with people in other disciplines (even people in the real world!), they could make tools that expand human capacity, like automating time-­consuming tasks to allow ICU nurses to spend more time with patients, rather than building AI, say, to automate someone’s shopping experience and eliminate a cashier’s job. Considering that AI was developing at warp speed, Li figured her team needed to change the roster—as fast as possible. Christie Hemm Klok

Li has always been drawn to math, so she recognizes that getting women and people of color into computer science requires a colossal effort. According to the National Science Foundation, in 2000, women earned 28 percent of bachelor’s degrees in computer science. In 2015 that figure was 18 percent. Even in her own lab, Li struggles to recruit underrepresented people of color and women. Though historically more diverse than your typical AI lab, it remains predominantly male, she says. “We still do not have enough women, and especially underrepresented minorities, even in the pipeline coming into the lab,” she says. “Students go to an AI conference and they see 90 percent people of the same gender. And they don’t see African Americans nearly as much as white boys.” Olga Russakovsky had almost written off the field when Li became her adviser. Russakovsky was already an accomplished computer scientist—with an undergraduate degree in math and a master’s in computer science, both from Stanford—but her dissertation work was dragging. She felt disconnected from her peers as the only woman in her lab. Things changed when Li arrived at Stanford. Li helped Russakovsky learn some skills required for successful research, “but also she helped build up my self-confidence,” says Russakovsky, who is now an assistant professor in computer science at Princeton. Four years ago, as Russakovsky was finishing up her PhD, she asked Li to help her create a summer camp to get girls interested in AI. Li agreed at once, and they pulled volunteers together and posted a call for high school sophomores. Within a month, they had 200 applications for 24 spots. Two years later they expanded the program, launching the nonprofit AI4All to bring underrepresented youth—including girls, people of color, and people from economically disadvantaged backgrounds—to the campuses of Stanford and UC Berkeley. AI4All is on the verge of growing out of its tiny shared office at the Kapor Center in downtown Oakland, California. It now has camps at six college campuses. (Last year there were 900 applications for 20 spots at the newly launched Carnegie Mellon camp.) One AI4All student worked on detecting eye diseases using computer vision. Another used AI to write a program ranking the urgency of 911 calls; her grandmother had died because an ambulance didn’t reach her in time. Confirmation, it would seem, that personal perspective makes a difference for the future of AI tools. Christie Hemm Klok

After three years running the AI Lab at Stanford, Li took a leave in 2016 to join Google as chief scientist for AI of Google Cloud, the company’s enterprise computing business. Li wanted to understand how industry worked and to see if access to customers anxious to deploy new tools would shift the scope of her own cross-­disciplinary research. Companies like Facebook, Google, and Microsoft were throwing money into AI in search of ways to harness the technology for their businesses. And companies often have more and better data than universities. For an AI researcher, data is fuel. Initially the experience was enlivening. She met with companies that had real-world uses for her science. She led the rollout of public-facing AI tools that let anyone create machine learning algorithms without writing a single line of code. She opened a new lab in China and helped to shape AI tools to improve health care. She spoke at the World Economic Forum in Davos, rubbing elbows with heads of state and pop stars. But working in a private company came with new and uncomfortable pressures. Last spring, Li was caught up in Google’s very public drubbing over its Project Maven contract with the Defense Department. The program uses AI to interpret video images that could be used to target drone strikes; according to Google, it was “low-res object identification using AI” and “saving lives was the overarching intent.” Many employees, however, objected to the use of their work in military drones. About 4,000 of them signed a petition demanding “a clear policy stating that neither Google nor its contractors will ever build warfare technology.” Several workers resigned in protest. LEARN MORE The WIRED Guide to Artificial Intelligence Though Li hadn’t been involved directly with the deal, the division that she worked for was charged with administering Maven. And she became a public face of the controversy when emails she wrote that looked as if they were trying to help the company avoid embarrassment were leaked to The New York Times. Publicly this seemed confusing, as she was well known in the field as someone who embodied ethics. In truth, before the public outcry she had considered the technology to be “fairly innocuous”; she hadn’t considered that it could cause an employee revolt. But Li does recognize why the issue blew up: “It wasn’t exactly what the thing is. It’s about the moment—the collective sense of urgency for our responsibility, the emerging power of AI, the dialog that Silicon Valley needs to be in. Maven just became kind of a convergence point,” she says. “Don’t be evil” was no longer a strong enough stance. The controversy subsided when Google announced it wouldn’t renew the Maven contract. A group of Google scientists and executives—including Li—also wrote (public) guidelines pledging that Google would focus its AI research on technology designed for social good, would avoid implementing bias into its tools, and would avoid technology that could end up facilitating harm to people. Li had been preparing to head back to Stanford, but she felt it was critical to see the guidelines through. “I think it’s important to recognize that every organization has to have a set of principles and responsible review processes. You know how Benjamin Franklin said, when the Constitution was rolled out, it might not be perfect but it’s the best we’ve got for now,” she says. “People will still have opinions, and different sides can continue the dialog.” But when the guidelines were published, she says, it was one of her happiest days of the year: “It was so important for me personally to be involved, to contribute.”