James Manyika, Senior Vice President, Technology and Society, Google
Interviewer: Alan Murray, Chief Executive Officer, FORTUNE

Brainstorm A.I. 2022 - Can A.I. Help Solve The World’s Biggest Problems

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Can A.I. Help Solve The World’s Biggest Problems?

Brainstorm A.I. 2022 - Can A.I. Help Solve The World’s Biggest Problems?

last september, our next guest announced Google's $25 million initiative to help organizations use AI in support of the U. N. Sustainable development goals, reporting straight to the Ceo James Manyika is tasked with assessing the impact of Google's tech on society. He also advises the White House on A. I policy in his role as vice chair of the US National Ai advisory Committee. We'll sit down with this season tech executive and techno optimist to explore how AI can be leveraged to address the world's most critical problems. Please welcome James Manyika, senior vice president of technology and society at Google for a conversation with fortune. Media ceo Allen Murray. Thank you James. Great to be with you. Good to be with you the last time we did this you had a different job. That's true. That was what five years ago it was pre pandemic. Well, uh congratulations, fascinating position you're in. Google has such huge ambitions in a I mean in the last couple of months I've heard talk about predicting natural disasters, dealing with climate change, healthcare, uh broad remit and healthcare, uh writing code et cetera. What is google's Ai mission? Well, first of all, I mean I think you have to go to the to the founding of the company. I mean at the end of the day it's a computer science information company. And I think if you look across all the things that Google is trying to do. I think they fall into a couple categories. # one a huge focus on actually advancing the field of AI so we can talk about the extraordinary number of breakthrough research and results. In fact a lot of the excitement we're all having now about generative models can be go back down to a particular set of research papers that came out of google, the classic attentions, all you need that opened up the space around transformer architectures that have led to these large language models. So the advancing the field itself is one of those goals. I think there's then there's also the practical goal about how do we actually bring these technologies to everybody. So through products, tools, infrastructure services, yeah, basically, you know bring this to the world and in the case of google is doing that in several ways, one is through google's products. So hopefully you've all noticed that search go better uh you know, google photos is much better etcetera, but also new products, but think about also the other companies if you like that google's birthing that would not have been possible. But for Ai so think Waymo think more fake, which is now a new drug discovery companies only made possible by the breakthroughs in AI So that's one said, I should also mention though Alan that I think google are also quite committed to this idea of how do we help others make use of these technologies, whether it's in on societal issues, whether it's climate change, flood forecasting science and discovery is another part of I think what google is trying to do and with ai embedded that deeply in the entire company. What's your job like how does that work? You report directly to the ceo you have responsible for a I had a responsibility for ai what does that mean? Well, so you know this is a dream job for me, alan I did my PhD in and robotics a very long time ago and to see the field come before you became a consultant, you're being very careful not to mention your former employer because even though Mackenzie, so if you look at some of the workouts doing at Mckinsey Global Institute looking a lot of the impacts of these technologies and air on business and the economy, I'm sure we'll get some of the business implications. But I think so my role is to connect the ideas and the research across all of google and alphabet and work with the team's leading that work to advance if you like. Our most ambitious research and innovations which include a lot of ai a lot of quantum computing and all these big computing infrastructure initiatives to make sure that they're being useful to people to society and that we're doing it responsibly and tie all the pieces together all the pieces together. So so you said when we were talking earlier, you said the last year and a half have been phenomenal year and a half for a I explain why well air is already making progress, but if you look at what's happened in the last couple of years I mentioned the transformer paper which has now led to this foundational uh, models that we're now talking about faith was talking about them. Look at what's happened with, for example, innovations such as uh, you know, uh, if Like diffusion models that have led to all these generative capabilities, I think the reason the last year has been interesting that I think we've, we've come to realize a few things. Alan one is these, these technologies have now become multi model so you can use them from text to images, texts too. Uh, code text, all these rights of application areas and similar. We've also discovered that in fact when you build, when you scale them up, you start to get properties and performance that goes far beyond what was possible. Before I'll give you a very good example. Uh, if you take translation, everyone loves Google translation, it works pretty well. And if you notice it says, you know historically cover covers about 230 ISH languages, it does probably 40 or 50 of them extraordinarily well before this last year and a half, that was kind of the story. Now we know that with these very large models, you can actually take languages that were historically called low resource languages where we didn't have the equivalent datasets to train them and you can translate those two. So this year, for example, because of these Large models, we added another 25 languages spoken by 300 million people like Bengali or Uganda, which people thought you couldn't really do those. And now we've just announced in fact we're gonna aim for 1000 languages. Why not? Yeah. When we were talking earlier about that, you said the science implications of this are particularly big. Can you give some examples? Yeah, I think the science arena fascinating things. You've seen what's happened in biology, protein folding. Our team at Deepmind basically went from, you know, this is a 50 year grand challenge where, you know, computational biologists didn't know how you can go from amino acid sequences to understand the structural proteins and every thought this was in theory Possible. So we went from zero between in the 18 months between, you know, the summer of 2020 from showing that you can do this with a I techniques to earlier this year and June predicting the protein structures of all 200 million proteins known to science. And we actually open That data set. So now you now have like, I think close to 600,000 biologists who are accessing these protein structure data set. So that science that's biology, you can go to physics. Uh if you if you care about fusion for example, it turns out one of the biggest challenges your infusion, high energy physics is how do you do plasma containment? Uh if you go to the Topamax in europe and other places are building. So it turns out you can actually use ai to stabilize uh you know, the plasma structures in physics, you go all the way to quantum computing. You probably saw last week we announced an incredible result in quantum computing, which shows that you can actually even create gravitational wormholes. So you can go in all these fields in science and I didn't mention mathematics. So we're starting to be able to show that AI can actually help you help us make extraordinary breakthroughs in this foundational fields that are going to be incredibly, what's the business model behind all that work in science? Uh Well, if you take an area like drug discovery in biology is straightforward, you hopefully we'll discover something extraordinary therapies and drugs we've now launched. I mean for google though. What? Yeah, I mean you you you talk about this almost like google is a university. Oh no it's not, it's a business. So the business model for us is I said come back to how we're doing it, we have a cloud infrastructure that's now ai enabled google cloud which is making extraordinary I think uniquely distinctive uses of AI. We also have products, core products, search and other things that are now, I think that's better, but also creating new products. So you're gonna see a whole range of new products that are only possible through a I come from, we have such knowledge in this audience and my knowledge is limited. So I really want you all to engage if you have questions for James or comments or something that you're particularly interested in hearing about, raise your hand and we'll get a uh we'll get a microphone to you right away so you can ask but but James, you're as you described your job, you're responsible for monitoring the interaction and the effects on society of all these massive changes in technology faithfully was talking about that a little bit earlier. How do you do that? I mean it's well to be perfectly my job is not so much to monitor but to work with our teams to make sure we are building the most useful technologies and do it responsibly. So to predict it in some ways to see it and to see the problems that may come in advance. Yes, but also make sure being we're building the most useful things now to be perfectly clear to your point, these technologies have come with extraordinary range of risks and challenges, right? We know that sometimes their own work is expected right? The leader to issues of bias and toxicity in their outputs and quite frankly non factuality. So that's a problem. There's also other issues that kick in such as for example the possible use and misuse of these technologies which you can imagine all the kinds of you know miss uses this could be put to. Then you've also got the kinds of things that we alluded to this, where we have to think about the impact in the labor market and the jobs. So there's a whole range of things we have to be thoughtful about. And that's why you probably noticed that in the case of google, google has been I think extraordinarily thoughtful about not rushing to throw these things out into the world without actually doing proper research and fully understanding what they're capable of. And these days, what at the top of your personal list of concerns? I mean when we were talking four years ago there was still a lot of concern about, oh this is going to destroy jobs, take my job away. I'm not gonna be able to work. Well we're 3.7% unemployment. Everybody who wants work can find work somewhere that doesn't seem to be the issue. But what are the issues that are top of mind for you? Well, I'll tell you the ones that I think a lot about. I think these issues to do when when they technology don't work as intended. So the, you know, the toxicity bias and so forth. And I think we're making progress on those things. I'm happy to talk about that. But I also worry about what I think of as the use misuse questions when is it gonna be appropriate to use these for what and quite frankly how we regulate that. I actually think, you know, this may surprise people, I think you'll find that, you know, I actually, you know, many of us embrace regulation because we're gonna have to be thoughtful about when is it appropriate to use these technologies? How do we put them out into the world so that we're not only, you know, not to throttle them. I don't think we should do that, but make sure we're using them in most useful ways. The most appropriate way with sufficient oversight and thought into them and that confidence that there will be somebody to rein in the inappropriate use is absolutely, there's a question uh back there in the back Yes. Mark Anderson Ceo of pattern computer. First of all, I want to say how cool, I think is what you guys are doing. You know, we we are involved in the same world in a tiny way, but google is a bright, shining light and I'm really impressed and I appreciate all you do. So that's a heartfelt thing. I will mention that we now have demonstrated explain ability. So, um I'd love to talk to you about that. We've actually done it. Berkeley Labs has kind of certified it for us. So I think we're good and it makes a huge difference. The I was inspired by the title which I assume you chose for this conversation and it had something to with the world's largest problems and we worked on that pattern. Um we're creating five new novel drugs for treating the top five cancers every month. So um we care a lot about this kind of stuff. There's a question I have a really personal question, I have to say that first. So when you is a little bit like the last question, but I hope more piercing when you personally look for what to work on, I think that there's this world of Ai discoveries can be broken into incremental and Pat Collison thinks it's too incremental or major. We call them pattern discoveries. Do you see that difference and how do you navigate that? Well, I do see the difference that I appreciate the question. I just think that the the kinds of questions in science and biology and in health that you're describing related drug discovery are such huge important issues for society. So think about those, think also about what's happening with climate change and sustainability. One of the things that didn't explain for example is it turns out that in fact you can do a whole range of things in climate sustainability and and the people affected by those, whether it's around flood forecasting and those kinds of solutions that are incredibly useful and important. So I think the big questions are really, really, really important and I think there's an interesting intersection here between businesses like google and others pursuing those opportunities, response and making sure everybody benefits from those solutions, but at the same time being responsible. You know, often this question of AI and responsibilities kind of framed as a either or in my mind, being responsible about AI involves both, capitalizing on all the amazing things that it can do for people and for society while being responsible. It's gotta be both. We have to do both. That's fascinating. Thank you James so much. Forget helping. Uh we didn't get to the business to the business application issues, but we will throughout the conference, you've really given us a great framework to think about what's happening in AI. Thanks for taking the time. Great to see you again.