Name: Brainstorm AI London 2024: The Future Of Work
Uploaded: 2024-04-15
Duration: 17 min 42 s
Description: Lan Guan, Chief AI Officer and Head, Center for Advanced AI, Accenture Jaime Teevan, Chief Scientist and Technical Fellow, Microsoft Moderator: Alan Murray, FORTUNE

 Lan Guan, Chief AI Officer and Head, Center for Advanced AI, Accenture      Jaime Teevan, Chief Scientist and Technical Fellow, Microsoft Moderator: Alan Murray, FORTUNE

Brainstorm AI London 2024: The Future Of Work

Photography by Joe Maher / Fortune Brainstorm AI

Conferences

Thank you. I thank you, Jamie II. I wanna start with the big question and we can get that out of the way and move on. Uh uh It is remarkable what these technologies can do and it conjures up the image of fully automated robots who can do pretty much anything you want them to do. Do either of you think there's any realistic possibility in our lifetimes that A I will reduce the number of aggregate aggregate jobs out there. Are we really talking about eliminating people's jobs? You want to go first one? Yeah, sure. Uh That's a great question there. A short answer to that is uh I think in the short term there was some job impact. Uh But it's my belief that in the long run, this is a positive sum game. Um So I really want to pivot the conversation to uh you know, around. OK. How do we understand the, you know how A I would substitute or compliment, right? What humans are doing every day so that we can get our people ready for that? Do you? So it's the wrong conversation. It's a mislead. Do you agree with that Jamie? Um, yeah, you know, there's, there's, I don't like the hesitation our jobs are gonna change for sure. Of course, that's not the big disruption. And there are pretty much two ways that companies deal with disruptions. One is to drive efficiencies, get really good at what they're doing and the other is innovation to figure out how to do new things. And I would say we essentially, we're seeing both of those and, and like, the efficiency thing is like, easier when I think about how I use a, I, I'm like, oh, I can write my email. So you start with efficiencies. And so, and that's sort of when we're not very creative, it's where mind goes. But like, innovation is a real opportunity. But we, and we've seen this with every wave of technology that we've had before. People thought, you know, 100 100 years ago, people thought, what are all these people who work on farms gonna do? That's not an issue anymore? So, well, I, I'm just curious with the group. Uh Can we get a quick show of hands if you think that 2030 40 years out, we're actually gonna have a problem creating jobs. Would you raise your hand? Ok. There are a few people back there. I'm gonna come back to you later on, but it's not many and, and I, I would, I would tend to agree, agree with that. So if we start with that as the premise, then the question is how are jobs gonna change? What, what, what activities are you no longer going to be doing and what activities do you need to learn to? Do you want to start with? Yeah. Sure. Uh So I, I think that goes back to my point earlier, how do we get our workforce ready? Right. What kind of skills do they need to grow more? And uh how, how do we get the people ready? Uh you know, for this kind of transformation? I it's my personal belief that, you know, the skills like uh what human is really good at, right? Things like communication, skill being, empathetic, uh you know, doing analytical thinking, structure thinking, these are the cognitive skills that A I, you know, it is not doing quite well yet, right? So uh I think we need to focus on uh having people uh humans focus on applying this kind of skills in our daily job. Um So let me give you one uh uh a frame of reference. Uh So I'm a big fan of uh uh you know, this uh Nobel prize winner and the Princeton scholar Daniel Kman, um who just passed away a couple of weeks ago, right? So his famous theory of system, one system thinking system, one system two thinking, I think it's a perfect uh you know, theoretical ground of what I was describing, right? System one thinking basically is fast in instinctive kind of thinking and oftentimes introduce human bias system. Two thinking is a slow analytical thinking and think about if you know in the future uh stage how A I and humans are working together A I is focusing on fast, you know, this kind of action taking and then the humans are focusing on things that we're really good at being empathetically. Uh you know, doing the structure thinking, being analytical, I think the you know uh you know, like for example, in context, in this case, we are you see that is happening, right? A I is helping our context and agents to you know, do this kind of system one thinking so that they can actually listen to the culture script, right? They can actually be summarizing all the, you know, agent notes for agents. So the agent can actually focus on having meaningful conversations with the customers. So there are many, many examples like that such a dramatic change from where we were just a few years ago, I mean, in in any kind of a conversation about advice to young people and what they should do in their career. In fact, it was a cover of Business Week magazine. Learn to code. You all need to go out and learn to code. Well, in fact, the the the machine is doing a pretty good job taking care of the coding. And what you're saying is we need to be better people, we need to learn to empathize we need better judgment. We need to understand how to motivate people. It's a completely different set of skills. It's a completely different set of skills, Jamie. I know you do a lot of research on this. This is, this is your job. What are you early days? But what are you learning about the skills people need and the skills that are being taken care of? Yeah, I mean, I actually one of the interesting emergent properties of this current generation of language models is actually that they're pushing on some of these meta cognitive skills as well. So in many ways, we're like, oh A I is gonna do all the drudge work so we can have the deep conversations. Um but we're starting to see like one of the things that's really cool is a I is actually quite good at planning. It's quite good at figuring out how to break paths down and, and allocate them. It's quite good at actually connecting in an empathetic way with people and understanding how your message is going to land. And so that really like it increases actually the meta cognitive burden on people. And I think it's um and important for us to think about how to take advantage of it to actually support our meta cognitive abilities. How do we come up with good conversations? How do we think about things critically? Yeah. Well, let's dive into that. I mean, so how, so as organizations, both of you work with companies. How do you, how do you prepare people for that? How do you, you know, you made the point at the beginning about many companies are taking the easy route early on and saying, let's just use technology to replace things we're already doing as opposed to use it to rethink what we do. How do, how do companies get themselves ready to do the right thing here? Yeah, I mean, and that's a huge question. If you think about it all of a sudden, we're giving people this empty prompt books and saying, like, say whatever you want and that's overwhelming. We've only been talking to our computers for a few months, you know, maybe a year if you're an early adopter, um we've been talking to people for a long time but we haven't actually been communicating uh with the machine. Yeah, you, you wrote a great piece for the Harvard Business Review. I'd love for you to share some of what you said there because all of us are experimenting with this stuff. We're writing prompts. Actually, there's an art to writing prompts. So how do we become better prompt writers? What makes a good prompt? I mean, so it's a good question and I'll have, you know, it is like one of the top academic questions as well. So when you look at the academic literature, a lot of the papers around prompt strategies and how you prompt uh a language model and that's because a good prompt strategy will actually give you better outputs than a bigger, more powerful uh model. Um So actually, one of the things that's important is to recognize that a language model isn't a person. And when you're prompting it, you need to prompt it a little differently. So uh when we were chatting backstage, we did a fair amount of grounding. This is that process of like getting to know each other. But because they're humans, I don't have to do the same level of grounding that I have to do with a language model with a language model. You have to be super explicit and say like you are playing this role and this, I want you to produce an output in this format for this audience. So that's very explicit and that's because it's not a human. Um But there's other things that actually you can use to your advantage with the language model not being human. Um You know, I can't be like, oh my gosh, I said something really dumb. Let's just start over and try again. When I'm chatting with these folks with a language model, you can actually just erase all your history and erase all the context and start the conversation again. And so you can actually start doing things like um I don't know if you know the concept of the wisdom of the crowds where you like ask a bunch of people how many jelly beans are in a jar. And if I ask this whole room, we'd on aggregate, get the right number of jelly beans. Uh You can start doing that with the language model. You can ask it to play different roles or produce answers in different contexts and then look at the sum total um of, of what kind of answers you might get it. Yeah. No, I think that's a great question. That's the question. I'm talking to C three clients all day long, right? I think from our perspective, I really think there are two things. One is this is time to get your talent strategy ready, right? So I have a bold prediction. I don't know if, you know, Jamie agree with that or not, right? I think, you know, in the future, basically, you look at all your roles, you know, they fall into two camps, one of the two camps, right? You're either builder of A I, right? Or you are the consumer of A I focusing on consumption side, right? So again, it's time to think, to rethink your talent strategy and bridge the gap. And I think the second thing which is kind of obvious, right? Time to uh you know, really drive the this kind of the concept of a continuous learning, right? So we learn, you know, new skills and you know, the skill definition could be changing. But what we can do is get ourselves ready and, and so how do you do that. I know accenture takes this very seriously. And someone told me once you spend something like a billion dollars a year on training, uh, uh, exercises, how do you, uh, how do you prepare the consumers of the technology? What is the, what's the nature of training that you give people? Yeah. Very good question. This could be our long conversation, but I'm gonna give you the right. That's what I think, Ellen num, I think the number one important thing is to have the top down support, you need to have the, you know, top CEO level support that, you know, basically articulating the importance of this talent transformation because we have 700,000 employees, right? Probably a lot of them are gonna be, you know, consumer for A I. So we actually have a program called TQ, right? TQ stands for technology quotient. So I'm one of the faculty members for TQ, right? And of course, a lot of our latest episodes is about generative A I. So this is kind of a program that we ask our every single employee to take. But that's not possible without the strong support from the leadership team. I I think that's one of the key building blocks. I want to call up uh Jamie uh a somewhat separate issue. Uh If you look at the last 23 decades of technology development, it seems pretty clear to me that it is increased inequality within societies that technology has enabled some people to move much faster and further and accumulate uh wealth and income uh than others. I've seen some early studies. I know it's very early days, but I've seen some early studies that suggest that generative A I may be different in that the biggest productivity gains often seem to be with the least skilled experienced workers. Do you think that's true? Um It's, it's a really important question that you're asking. Um And it, and it's a really interesting like generative A I feels different to me as a technology that's distributing and that it's distributing at a global scale so fast. So it, it is, it is different to be bringing this technology to every country and every location at the same time. It brings up all sorts of interesting challenges about how do you do that? How do you manage all the different regulations and reactions that are happening? Um uh It's actually very interesting because one of the emergent properties of the language models to spend their ability to understand multiple different languages. So we should have co pilot, for example, in all of our tier one languages out the gate um in terms of the research that you were referencing. So it's been a very consistent finding across these um sorts of tasks that are um the ones we were talking about that are the, the efficiency kinds of tasks that people are doing them a lot faster. And a a lot more efficiently and then particularly the people who are less skilled at those tasks are so like, I, if I'm not a great coder, I can actually generate code. That's quite good where I'm an amazing coder, it'll provide me some efficiency and benefit, but it won't be as, as significant. Um I do think this innovation question comes into play though because it starts becoming like, how are we figuring out the new ways to use the model and, and that's gonna be a place where I think it's actually our responsibility to make sure we distribute the technology and figure that out and distribute our learnings in a way that builds equity and like benefits the whole world because that's gonna be what benefits all of us the most is how we do it, do it, how we do it matters. Exactly. Let, let me open it up. See if there are any, there's a, a question Olivier and then a question right over here. Uh Right there. Yes. And then uh right, right here. I apologize to people in the back. The lights are very bright and it's hard to see beyond the first couple of tables. But Olivia, go ahead. Olivia, neuroscientist and entrepreneur in 96 Gary Kasparov wins against A I in 97 he loses, creates freestyle chess where humans can use technology in order to compete. And in 2005 amateurs beat grandmasters because they had a better process. Don't you think that the one thing we need to teach people if possible is process plus, I mean, I think that, that to me connects with this notion of meta cognition and like, how are you thinking about using things? And, and that feels like the real opportunity and actually the, the things I get really excited about with the model when we sort of talking about this innovation frontier, um you know, our uncreative selves are sort of thinking about like, how do we get A I to obey us? And I think our creative selves are like, how do we get A I to challenge us and get to think about things in new ways. Um So that's what I get excited about right there, please. Yes. Um Parliament from University of Birmingham here in the UK. Um And I wanted to go back to the point of uh OK. Jobs are going to change in ways that are somewhat unpredictable and my job as an educator as well as a researcher. Yeah, you're gone. That was kind of my question. I didn't want to project my existential crisis in public. But as you can tell, we, you know, we have been disrupted as a higher education institution. I'm actually professor of computer science, particularly impact us. So my, my, my question is really, what is the role, uh how is the role of higher education changing? It's not just the pedagogical approach which is changing because of A I. But what are your expectation as basically downstream from higher education in regards of how we best, how can we best prepare the next generation to basically be ready to move forward? And you understand that Microsoft is already making the tool that's going to replace you. Yes, we have a separate chat and I and I, I wanna so as a computer scientist, this is, there's a metaphor that I use actually. And, and partly my role, Sata uh created my role about five years ago to help the company deal with disruption and think about how, how things are going to change. And I would say that we're all right now responsible for building something new. We're figuring out an entire and that's not gonna slow down, that disruption is coming. And I actually think the whole world needs to start leading like scientists. So I think your science and what you teach, you know, and that means like experiment and develop hypotheses and try things out. But that's not the only part of the scientific process that means learning and building on the state of the art, you know, reading broadly and like writing that related work section and figuring out what you're doing, it means disseminating knowledge. So so that others can build on it. But even more importantly for validation because that's the way that people will challenge you look at things differently, you know, and it means thinking about the externalities and sort of the long term effects. So I actually think that scientific framework is amazing, you're going to give him superpowers. Yeah. Yeah, I mean, you're bringing superpowers to the technology. Land final word. Um So it's definitely exciting time, right? Uh Ellen and Jamie, I think to me A I is a team sport A I is not just, you know, as what we've been talking about, it's not just being done by technical experts or, you know, uh uh limited to ac three conversation boardroom conversation. I think this is a time that everybody has a role to play, right? The democratization of A I and everybody needs to play their own role, right? So, um this is a time that nobody wants to get left behind and uh happy to work with all of you on this. Uh Jamie. Thank you so much. I think that was encouraging. I feel a little better than I did. Thanks so much. Thank you.