Fei-Fei Li, Professor at Stanford University & Chief Technologist at Google Cloud
Fei-Fei Li came to the U.S. from China at 16 with a love for science and she never looked back. Educated at Princeton and Caltech, her early work in robotics revolutionized machine learning and AI. Her focus on inclusion in tech careers and diversity in what we teach machines suggests that tomorrow’s robots won’t be sexist.
Professor at Stanford University & Chief Technologist at Google Cloud
Fei-Fei Li is making sure that the future of AI is female, too.
Fei-Fei Li, Professor at Stanford University & Chief Technologist at Google Cloud
Why She’s a MAKER: She’s a tech rock star who stood up to the AI community to develop a project about machine learning she was sure would push the field further—and it did. In 2006, she launched the ImageNet project to teach machines how to “see” objects. “We got scathing reviews. I didn’t spend too much time thinking ‘should I do it or not’ because I knew in my mind this will change how we think about machine learning.” Today, her work is recognized as a key turning point in AI development.
The Wonder of Science: Li was born in Cheng Du, China, where cloudy skies were the norm. This sparked an interest in the power of nature that grew into a passion for understanding the world through STEM. “As I entered school, the sheer beauty of math and science attracted me.”
Coming to America: In 1992, 16-year-old Li and her family emigrated to the U.S., facing a world of change. “I pretty much had to learn English from scratch. I had to carry all these volumetric dictionaries to survive my day.” Just two years later, she’d earned a near full-ride to Princeton University and went on to earn a Ph.D. from Caltech.
Bringing Diversity to Technology: Her goal is to ensure tomorrow’s brave new world will have feminism built in. But right now women earn only less than 20% of the computer science degrees. Li sees this imbalance as a threat to how technology helps society and advocates for change. “We need to be mindful that human values define machine values. If our training data misses a big population of our world, that would have grave consequences.”
FEI-FEI LI: My role is to be the thought leader of AI and machine learning. One of the most important things for me is not only to advance AI, but also to democratize AI.
My childhood was spent in the southwest of China in villages at the outskirts of the city called Chengdu. So it's a very cloudy city. You don't see starry nights too often, which actually made me really long for those few nights that have clear sky. So I always had that very early sense of wonder of what nature is, who we are. Then as I entered school, just the sheer beauty of math and science just always attracted me.
The transition from China to America was quite a shocker. Typical immigrant story that you have to start from ground zero. And I pretty much learned English from scratch here. One big difference of American school is the books are so much heavier. I had to carry all these, you know, volumetric dictionaries to survive my day.
There was one thing about Princeton that was absolutely my dream is I've always been the nerdy kid. So you know, I would never be so popular because I'm not part of any sports teams. But that intense intellectual environment-- I was like a fish in the water, suddenly.
Visual intelligence is the primary sensory system for humans to use to survive, to work, to communicate. Solving the core fundamental problems of visual intelligence is solving intelligence. If we want to ever make robots do tasks for us or with us, robots need to recognize objects.
Most people were skeptical. So we had pretty scathing reviews for grants to support this project. I didn't spend too much time thinking, oh, my god, these people don't like it. Should I do it or not? Because I know in my mind this will change how we think about machine learning. It was staggering for a while. We ended up employing tens of thousands of online workers across more than 150 countries in the world to help us assemble this data set.
The field of AI, as well as the greater field of STEM, is massively lacking diversity. We need to be mindful that human values define machine values. If our training data misses a big population of our world, that would have grave consequences.
When we have a diverse group of technologists, it's more likely that the technology will reflect our collective values. How do we encourage the future generation of technologists? If we communicate the humanistic value and how it will make our world better, we can hope to encourage more diverse groups of students to feel passionate about AI, then become tomorrow's technology leaders.
FEI-FEI LI: So life has, in general, improved because of the advances in science and technology, but we also can use technology and scientific discoveries in the wrong way. And humanity has plenty of examples through conflicts, wars, weapons, and all this of using technology in ways we don't want, so it is very important we think about the responsible way of developing and deploying our technology and how to make it more benevolent.
FEI-FEI LI: The most exciting thing as a researcher and professor in the Stanford AI lab is the intense passion and ambition and the best and most innovative research. And that is the core of who we are, is that this is a group of people who are passionate about AI research and making a difference in the world. And the most special thing about that is the best students come to Stanford. And working with these brilliant young minds is-- is the most rewarding experience.
FEI-FEI LI: Brace yourself. Being a working parent is hard. I think if there's a single piece of advice I would give to young women thinking about starting a family and a pursue her passion and career is to find the best partner.
FEI-FEI LI: I was passionate about science. I majored in physics. Princeton was, still is a mecca in physics. Einstein was there most of his life after Europe. So I just love the intellectualism. But it's also a very different environment. The students are very international. They come from all kind of backgrounds. Very different from small town Parsippany, New Jersey. And that exposed me to a whole different set of lives, values, ideas.
FEI-FEI LI: If you open your eye and you see the world, yes you see objects, you can name the objects, but that's not the only thing you do. You see the relationships of objects, you see the interactions, you see the social context. And all this is part of visual intelligence that we have yet to have a great handle on it. AI as a field has not solved some of the fundamental questions of learning, of visual intelligence, of language and speech, of manipulation and navigation. So there's just a really big field-- playground still yet to be discovered and exploited.
FEI-FEI LI: I see three very important reasons to have more diverse and inclusive people working in AI. The first was sheer economic and labor reasons, because as the AI and CS field expands, we are lacking the labor force that can do AI and computer science. The second reason is for creativity and innovation. There are so many studies that tell us that when a group of people are more diverse and have different backgrounds and opinions, they come together to work on something, to solve something, that the solutions and outcomes tend to be a lot more innovative and creative. And the third reason is for fairness and ethics and values, because technology at the end serves people. And it should serve as many people as possible in the most fair way.
FEI-FEI LI: I think, if there is one singular influence my parents had on me-- especially, my mom-- in early years is the joy and passion for reading. So she read a lot of books.
It was a period of time that nobody is very affluent in China so we didn't have much in terms of materials, but we had lots of books in the house. And that was-- now, that in hindsight-- was a strong influence.
HOST: Ladies and gentlemen, Fei-Fei Lee.
FEI-FEI LI: Good morning. Good morning, Makers. Good morning, everyone. Such an honor to be here. Let me start my talk. A few years ago, as you saw in the video, I started a summer outreach program at Stanford University to encourage high school girls from diverse backgrounds to participate and get involved in artificial intelligence.
I have one vivid memory of delivering this opening lecture. I was in a room full of ninth grade girls, most of whom have never even set foot on a university campus before. It was a complex lesson. I was really geeking out with them. And they were eager to learn. There was excitement in the air, but a little bit of nervousness too.
As we finished this really long technical discussion, I wanted to inspire them more. So I described how what we have learned. This computer vision technology can help doctors and nurses better track their hand hygiene practice in the hospital, reducing hospital-born infection that kills almost 90,000 patients per year in the United States, several times more than car accidents. I'll never forget what I saw at that moment. Across the room, these young faces just lit up. I saw passion, amazement, and even some relief, as this incredibly technical field that they just heard about suddenly took on a human form.
And this is the story that I want to share with you today, the deeply human side of artificial intelligence. In fact, I hope to convince you that there's nothing artificial about it at all, especially at this very moment. AI is about to transform our world in ways we can barely imagine.
I want to start with the story about a breakthrough moment in science. And it goes back to 1959. Researchers, Hubel and Wiesel, used electrodes to connect the visual cortex of an anesthetized cat to a loudspeaker, and then projected patterns of light for the cat to see. This allowed them to literally hear the cat's visual perception at work, and showed for the first time that the brain is organized by neurons stacked in a hierarchical fashion with each layer responding to increasingly complex visual pattern. And this work got them to win Nobel Prize a couple of decades later.
But more than 40 years after their work, I had an opportunity to be a summer research intern student at Berkeley to replicate this experiment in a neuroscience lab. Hearing the neurons responding to patterns of light in the darkness was a mesmerizing experience. No words can describe the sense of magic I felt at that moment, realizing that this rich and beautiful visual world we see all begins with such tiny neurons in our brain that get excited by simple patterns of light.
So I began to wonder, what if one day we build computers that can see like us. It turned out I wasn't the only one asking this question. Computer vision was already a growing field with thousands of researchers worldwide by the time I started my PhD study in 2000 right here in LA, Pasadena, not very far. Progress was slow but steady. And the amazing technology we now enjoy is possible because thousands of researchers dedicated their careers to establishing the science.
But teaching computers to see is easier said than done. A modern camera easily registers millions of color pixels when taking a picture. But deriving meaning from all that data is an enormous challenge. It's no surprise it takes Mother Nature 540 million years to get this solved right. A human can understand staggering amounts of details about an image with only a split second of glance, and then describe it in language, also very unique to humans.
One of my first experiments as a PhD student quantified this. And then it becomes the Holy Grail of the field of computer vision, to be able to teach computers to see and talk about what it sees. Luckily for me, I arrived at a very unique time in history. The internet was exploding. And that gave researchers access to more data than ever before. The sheer variety and depth of images available online made me think about the constant visual stimulation that children experience as they grow up.
So I saw a parallel in that. What if we could use the internet to help our algorithms explore the world in a similar way? So as you saw in the video, around 2006, 2007, I began a project with my students and collaborators called ImageNet, intended to organize enough images from the internet to teach computer algorithms what everything in the world looks like. In the end, it added up to 15 million photos across 22,000 categories of objects. It was the largest AI dataset ever publicly released at that time.
But here is the tricky part. In order to actually teach an algorithm and benchmark its progress, every single image must be sorted and labeled correctly. We needed to sort, clean, and label from a pool of billions and billions of images. In the end, we had to rely on crowdsourcing by hiring over 50,000 online workers across 167 countries to do this. So yes, we did get a little crazy. But that's the fun of science.
The hard work did pay off. By combining ImageNet with a class of algorithm known as convolutional neural network, or more popularly known as deep learning, and modern computing hardware, like GPUs, AI was revolutionized and ushered into the modern era of what we know today. By 2015, just a few years after ImageNet was released, computers were recognizing objects better than humans in head to head contest. Algorithms built on ImageNet have advanced the state of the art, state of the computer vision considerably with error raising image recognition steadily decreasing every year.
And my students and I began to make major progress on image captioning, the very problem I could only have dreamed of during my PhD studies. And the photo descriptions you are seeing now behind my back were some of the first ever machine-generated sentences for computers when they see a picture for the first time. But we still have a long way to go.
Today's AI is great at pattern matching in narrow tasks, like object classification, facial recognition, and language translation. But there's so much more to human thoughts and intelligence than simple patterns. AI is now targeting loftier goals, like natural communication and collaboration with richer sense of context and even emotional perception. I call this human-centered AI.
And many of my colleagues are working on projects that exemplify it. For example, examples include applying machine learning to education, understanding satellite imageries to track poverty more precisely, or developing diving robots to explore the deep ocean when divers cannot or it's too dangerous for divers to go. And along with my students and collaborators at Stanford, we're working with senior care facilities on early studies of AI assistance for nurses and family members.
But just like any technology, AI is a tool in the hands of people. In fact, I believe there are no independent machine values. Machine values will come from human values. Without thoughtful guidance, many of the benefits of AI could cause unintended harm as well. This is a complex challenge. And I don't pretend to have all the answers. But I do know we have an obligation to build technology that benefits everyone, not just a privileged few. And the first step is understanding who is developing it. So how well is humanity represented in the development of AI today?
I'll be blunt here. Diversity is sorely lacking in the world of computing. And that includes AI. The National Science Foundation reported in 2016 that fewer than 30% of computer science majors are women. A similar 2016 study showed that fewer then 15% are left by the time they reach their professorship. Similar numbers are found across most of Silicon Valley's tech companies. And the statistics for racial minority groups are even worse.
If this technology is going to change our lives, our society, and perhaps the entire future of humanity, and I actually believe it will, then this lack of representation is an absolute crisis. Outreach programs, like the one I started at Standford, are a powerful first step. I co-founded it four years ago with my former PhD students, Olga Russakovsky, now an AI professor at Princeton, with the goal to inspire girls and under-represented minority students, not just to pursue tech jobs, but also to recognize the human impact that AI has to the world.
The result is AI4ALL, a nonprofit organization focusing on increasing diversity and inclusion in AI through education programs. We specifically target high school students of all walks of life, especially those underprivileged communities. AI4ALL was launched in 2017, seed funded by Melinda Gates's Pivotal Ventures and Jensen and Lori Huang Foundation.
From Stanford, AI4ALL is already partnering with Berkeley, Princeton, Carnegie Mellon University, and Canada's Simon Fraser University to bring our AI education to a diverse group of students. No technology is more reflective of its designers than AI. From the architecture of its algorithms to the applications, it's our responsibility to ensure that everyone can play a role from the beginning.
I've always summed it up like this, we know AI is going to change the world. The real question is, who is going to change AI? I hope many of you in the audience will consider yourself to be part of this answer. We need you. Thank you.
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