Fei-Fei Li, Co-director, Stanford Institute for Human-Centered AI; Professor of Computer Science, Stanford University
Interviewer: Jeremy Kahn, Senior Writer, FORTUNE; Co-chair, Fortune Brainstorm A.I.

Brainstorm A.I. 2022 - Building The Future We Want: A.I’s Human Factor

Conferences

Fei-Fei Li, Co-director, Stanford Institute for Human-Centered AI; Professor of Computer Science, Stanford University
Jeremy Kahn, Senior Writer, FORTUNE

Building The Future We Want: A.I’s Human Factor

Fei-Fei Li, Co-director, Stanford Institute for Human-Centered AI; Professor of Computer Science, Stanford University
Jeremy Kahn, Senior Writer, FORTUNE; Co-chair, Fortune Brainstorm A.I.

Hi, everybody. Welcome to Fortune Brainstorm A I 2022. Um I'm Mihara, I'm editor at large at Fortune and co-chair of the A I Summit. Um It is great to be with all of you here in San Francisco in my own backyard. Um especially during this really exciting period in the development of A I, what could be better than intelligent human beings such as yourselves, gathering to share insights and expertise on the simulation of human intelligence, right? Artificial intelligence is transforming business around the world as we all know, often working quietly behind the scenes, but make no mistake about its growing impact, which some say could be as great as electricity or maybe even more. Who knows over the course of the next few days, we're going to separate AI hype from reality. While AI adoption is on the rise, there have been some headwinds as we know talent shortages, the inability of companies to share data efficiently and even a lack of data. Above all the industry is realizing that for A I to reach its full potential, it will need to solve growing ethical and safety concerns. I'm sure that'll come up repeatedly over these conversations. Even with these issues AI is already having some really impressive impact and delivering on some of its promises from speed speeding up new drug discoveries to increasing the efficiency of cloud computing. There are still so many outstanding questions. What does the expanding adoption of A I mean for business and for employees, how are firms changing, how they build and share values? Should we have an A I bill of rights? What does governance look like? We're going to discuss all of this and more. We're going to talk to some of A I's top leaders including Andrew Ang Kevin, Scott Reed Hoffman, as well as get an inside look at the A I Strategies of Apple, Microsoft and Google. And because seeing is sometimes believing we'll also enjoy demos from open A I and Stability A I. So get ready for those. Uh These conversations are going to be led by members of Fortunes editorial team and by my fellow brainstorm A I coaches who I'd like to take just a quick moment to recognize Fortune, senior writer, Jeremy Kahn, fortune, senior editor, Vern Coo and deputy CEO of smart eye doctor Rana El Kabi. And now is a great time to also thank our fantastic event partners for their work in bringing us all together here today. Our presenting partner, Accenture and our partner C three A I and Intel. We greatly appreciate your work in making these conversations come to life. And now before we tick off just a few light housekeeping reminders, what said here on stage and then breakout sessions is going to be on the record. Um What's said over the dinner table on a break on a walk between two people is off the record also at some of our private lunches unless mutually agreed otherwise. Ok. And with that, all those rules, let's get started as the pace of A I innovation and breakthroughs accelerates. So has the conviction that its application should be safe, ethical and inclusive in our theme setting conversation. An Ai Luminary makes the case for a Human Centered approach, a future where people and machines are collaborators, not competitive. We'll ask Dr Fei Fei lie about her work with ambient intelligence in health care and robots and explore the crossover of visual and artificial intelligence that enable computers to see what us intelligent humans see as well as what they don't see. Please welcome to the stage, Dr Fay Fay, co director of the Stanford Institute for Human Centered and A I and professor of Computer Science at Stanford University for a conversation with Fortune brings from A I CO chair, Jeremy Kahn. Great. And thank you all for being here. Um We are going to try to take some questions from the audience so please have your questions ready. There's a couple of people with mics, they'll come around and if you want to ask a question, you can raise your hand, they'll get to you. Um So I want to start out by asking a lot of people are saying A I is now reaching its industrialization phase. The first question I wanted to ask you is just, do you agree with that? Do you think that's sort of where we are in the development of this technology right now? Yeah. Well, first of all, Jeremy and uh everybody, thank you for inviting me here. I absolutely do agree with it. I think A I is a very nice field of all sciences and technology is one of the younger field. We are a little more than half a century old. Uh But um if you look at the history of A I and look at where A I is now, I think this technology has absolutely reached an inflection point where um it's being used, um you know, worldwide in a daily basis in many applications and its potential is just still gonna be on the rise and quite explosive. I guess if that's the case, if we are sort of in the industrialization phase of this technology in the past, industrialization has often kind of had a connotation with, with some dehumanization, you know, things machines have replaced people or people have been made to act in a more mechanized or machine like way. How can we avoid this kind of dehumanization with industrialized ai how can we make sure that people remain at the center of what we're trying to do with this technology. Yeah, thank you Jeremy for asking this. This has been a question that my colleagues and I at Stanford have been asking for the past three plus years. It's the core question to this new wave of technological transformation. So first of all, let's just recognize this throughout the entire history of human civilization. Tools are always double edged sword. Every time we invent and innovate a new tool from fire to steam to electricity to biotech. We recognize that there is the pros and cons of the tools. But at the very heart of tool, innovation and tool usage are the humans. Humans are the inventors. Humans are the other users. Humans are at the receiving end as well as at the innovation end of this this technology. So at Stafford's Human Center A I Institute, we feel very privileged and lucky to be in a place where uh we are inventors of this technology. Stafford has a proud history of being at the front. Um A frontier of uh many of the A I technologies that we're we're seeing today. But we are also we get to work with multidisciplinary thinkers and researchers and students to ask this very fundamental question of what it means to be human centered. And just in short, I think it's really important that we recognize um the the impact of A I, the Human Center impact of A I is at individual level at community level as well as at societal level. So in every part of our design, from the problem that we're trying to use A I to solve, to the data we curate to the algorithm we write to the system we build and to the to the to the applications we deploy that entire pipeline, we think hard about its individual impact, community impact and societal impact, right? And I know H A I is very multidisciplinary in the way it's been set up. Do you think there's a sort of lesson there for how companies should also think about building the teams that are creating A I technology? Absolutely, I I think our whole society right now at large is having this conversation of stakeholder economy in addition to shareholder economy. And I think that's part of the same conversation because uh um in this process of creating and deploying A I, it will impact all of our lives. And it is really important that we include the, the stakeholders of, of the, of this technology in the design process. My colleagues at Stanford, some of my esteemed, you know, ethicists and political philosophers always reminds me who is a technologist that let's design ethics in the get go. Not, not think about it as the aftermath. There are a lot of human issues that we can be mindful of in the design and deployment of A I. Great. I want to make sure we have time for audience questions. So I will go and see if there is. Does anyone have a question for? If not, I will carry on with my questions, but I just want to make sure we have time to audience questions. They want to have a, have a question already for a uh I'm not seeing any right now, but I will, I'll put this one there. Oh, yes. Um I need to wait, wait, hang on, hang on one second, wait till Mike gets here. There we go. There we go. All right. And if you could identify who you are also, Dan, we're in accenture. How do you design ethics into software? That's a great question. We need to break this down a little bit. There are many aspects depending on the applications as well. So there are some major known issues, for example, bias of the system privacy remaining applications as well as other other factors including, you know, human rights and other issues. And then for example, bias a lot of us are, are starting to be quite aware of this issue, right? Where did the data come from? Where, what's the representation of this data and what is the downstream impact? Um I work a lot in health care. It's very obvious that if our data comes from certain populations or, or, or socioeconomic class, it will have a pretty profound downstream um downstream impact. So at Stanford, for example, uh I think I believe Stanford University um and A I is the first university that pioneered an ethics and so society, society review process for our A I uh internal A I research projects because we don't have adequate um um um process uh yet today to review that and then we ask our research to, to think how to describe their um what their technology might impact at a human society. At the beginning, we really thought this is going to be a little bit of a burden for our students and faculty. It turned out this process was so welcome. We got so much feedback from faculty and students themselves who are innovating A I and say we thank you for this process because it gets us to think about how to design the fairness, design, privacy awareness and design human well being human dignity into our uh technology. Great other questions from the audience. If we can get a, a mic down here just down in the frontier and if you could identify where you're from. Hi, I'm from better brain. Um My question is at what point does A I start to take away jobs rather than augment teams? Yeah. Great question A I and labor market disruption and transformation is a major topic. You, you'll have more speakers on that too. At A I, we actually have founded a digital economy lab with experts across computer science all the way to economics, to study this. It's quite a nuanced question because the very definition of jobs is actually a collection of tasks. It's, it's most jobs is hardly one single task. So when we're talking about the disruption and transformation of jobs, we need to break it down to the impact of the technology on different tasks. And um you know, we don't have time to explore deeper into this right now that the huge large language models are definitely changing the way that text based and, and uh uh tasks are, are, are, are done. We, we're seeing software engineering assistance and all that. So the, the the 30,000 ft um comment I can make is, is absolutely gonna gonna transform jobs. How is, is it pure simple replacement versus transformation? And who are the people um who are going to be impacted the most? And how do we um um how do we guide our society through that transformation that is less painful or cruel? Are all important questions that we have to study forecast and, and, and really frankly work with policymakers? Great. Uh Another question from the audience, if we have one, they're just here at this table here. Uh The woman in the leather jacket, I think Rebecca Finley uh from the partnership on A I. Good to see you again. Um When we think about human A I collaboration and integrating A I tools into the workplace, one of the key challenges is around explainable and all the different dimensions of explainable depending upon the context. How would you rate sort of the current state of the art with regard to the ability of A I in the workplace setting or even in, in the research context? Great question, Rebecca. Um So for those of you who are not um um totally fully aware, explain is kind of a general word to um that encompasses just the understanding of the what the algorithm do. And in fact itself has different dimensions. Sometimes it's the mechanistic and algorithm uh explain explain of exactly how we get this inference results. Sometimes it's a causal explain that doesn't necessarily explain mechanism. For example, my favorite example is Tylenol to this day, we don't know exactly how that works, but we have causal ability of this drug and FDA approval. So that's another aspect of explain. Sometimes explain is just a trustworthy um trustworthy communication with the the human users that doesn't necessarily give you the mechanistic mechanistic details, but it induces enough trust. So um all three fronts A I technology is still nascent. We're missing a lot of ability on all three fronts and the applications of this is evolving and developing really fast. And this begs um industries uh academia as well as society at large for, for, for working this, for example, Stanford A I last week just rolled out the first benchmark of large language models. We're seeing a lot of that, you know, I'm sure chat GP P is the talk of the conference today. And how do we um how do we know how these uh large company, big tech uh large language model work against? What benchmark, what, where are the pitfalls and biases and Stanford as a open neutral academic platform that doesn't have another agenda is actually at the helm of uh trying to explain this by the way that the whole system is called helm. So no pun in terms. Um So you mentioned large language models, a lot of excitement about these. There's a lot of talk about generative ai you know, you have a great overview of the whole field. What what sort of most excites you about what's happening now? Are there particular innovations that you look at and say, oh, this is something that I think is really significant? Honestly, I just feel so privileged and and still feel surreal to be born into this time of history and be in the middle of this technology because it's, it's like, you know, every few days now or sometimes weeks, months, you see this incredible breakthroughs. That's just so exciting as a technology nerd, I wake up and just feeling, feeling so fortunate, for example, large foundation models are truly exciting me because it it will be it will be an empowering technology to many, many sectors, not just text based work, but also image based structure database and, and all that. So that's one area generative model is also, you know, especially as a vision is incredibly exciting. I'm just thinking about uh not only uh goofy arts we can do but thinking about its usage in, in medicine, in education, in sustainability. There, there is far more human centered applications in in this area and then some applications that are very exciting. Like you said, I work in health. But I think there is more, how do we empower our teachers, how do we empower our doctors and nurses? How do we empower actually um uh help uh governance and to help, to help people? These are all great areas. But you know, there we should not be completely um delusional about the negative impact and, and, and the human aspect of this. This is why it's a mixture of excitement and sense of responsibility. Excellent. Well, thank you so much, Doctor Lee. We're out of time, but thank you very much for coming and sharing those thoughts to open up this conference. It's fantastic.