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Accenture’s Chair and CEO shares insights on key questions leaders need to be asking to keep pace with fast-evolving A.I. technology, reinvent their enterprises and drive business value.

Fortune Brainstorm A.I. 2021: A.I. in the era of compressed transformation

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let's begin with this stat from our presenting partner center, 84% of C. Suite executives believe they must leverage ai to achieve their growth objectives. But 67%, I'm sorry, 76% acknowledged struggling with exactly how to scale ai across their business. So there's clear uncertainty among leadership about how to broadly implement artificial intelligence. And those uncertainties are also compounded by factors such as a pandemic and shifting economic realities. Let's talk about the ways to overcome these challenges. Please welcome a center Ceo julie, Sweet and Fortune Deputy editor brian O'keefe for a conversation on how to successfully embed ai to result in agile and resilient organizations with sustainable growth. Thank you Kristen and good morning to everybody. Um Thanks julie for being here, kick things off and build on the conversation yesterday and the stats that Kristen just cited um about the disconnect between the appreciation of the opportunity and the confusion about how to advance with that I think gets to what we want to talk about. Um you know that part of the title of of our discussion is the era of compressed transformation and it's something that you wrote about today unfortunate dot com. I encourage everybody out there watching to go read julie's piece. You talk about a sense of urgency. So first of all, just tell us what you mean by era of compressed transformation. Sure. There are three kinds of companies today, in our view there's leaders who are leading in digital, there's leapfrog others who have made the strategic choice to be a leader to go much faster than they were going. Pre pandemic and then there is everyone else. And the sort of one third of companies are engaging in what we call compressed transformation. So it's three characteristics. First of all they're touching two times the amount of processes in a much broader reach across the organization. That's the first. The second is that they are engaging in a cloud first strategy and embracing emerging technology. So in the U. S. For example 55% of the company's we categories as leapfrog ear's have significantly increased their investment in ai our topic today. And then the third piece is that the leaders and the progress are investing more in I. T. In innovation than NBA use. So they're you know changing the way they're doing their operations, their maintenance uh to really put much more money over 50% in innovation. And so uh when you think about the impact for that two thirds of company, you know I always joke that transformation doesn't happen fast. Right? So a lot of people said oh the pandemic you know shrunk transformation. We'll all of us who actually engage in it know that it takes a while but are you know we're predicting in 12 to 18 months that you're gonna start to see the results of these compressed transformation across industries and sort of that two thirds of other companies are going to recognize just how much farther they've fallen behind and you've already gone from sort of leaders being two X. Laggards, the bottom 25% pre pandemic to financial performance at five X. Uh And we think that gap is going to continue to widen as you think about the others. So I want to remind everybody if you have questions get them ready we'll have a chance to draw on those questions in a minute digging into that research a little bit more. What I thought was really interesting. Um If I got this right, you talked about the leap frogger. So the companies that maybe didn't go into the pandemic ready to invest aggressively in technology but then they made a decision to do that and that they've significantly outperformed the laggards I think four X. Um What does that look like? What is it, you know what's a successful leap frogger? Uh look like? What are some examples of how companies have been able to do that? Well. Um So we talked about the characteristics maybe take it to a. I so um you know yesterday we talked a lot about ai not being about the technology about being changed. You know think big start small. The reality is you have leaders and leapfrog us who have made a strategic choice to lead and if there's one phrase to remember from our talk today it's cloud is the enabler Data is the driver but Ai is the differentiator. That's how you win. Three examples. Let's start with accenture we're all about talent. We have 624,000 people. We have a database of automatically populated in the cloud of all of the skills of our client facing people. When the pandemic hit, we used Ai to identify who could be retrained to meet the new demand around cloud and collaboration tools. You remember when everybody shifted home and they were starting to accelerate, right? That's a huge differentiator and it's at scale. Right? Let's take a consumer goods company right there using AI right now in the market to see how in real time their products, performing how to adjust it, anticipate supply chain interruptions and then reach. We um redo their entire supply chain when they see a big issue at scale. Right? So 3rd, 3rd example, uh an automobile manufacturer who real time for customers is on the assembly line using Ai to be able to reimagine the different configurations and then do the manufacturing all of these things have at the heart of them a strategic choice and scale. So they're overcoming the cultural issues, the talent issues and you know what we talked about in the bike lane today is what does it take to go from understanding all of the challenges to actually using Ai to differentiate and it starts with that strategic choice and then the second piece is super important is what you're doing actually at the C suite table, totally different level of education and focus from the C suite. Uh And that's a big characteristic that's different when we look at the company's who are leaders in the progress versus the others today. Yeah. I mean I was going to ask you about that. What are the questions like that you're getting now when you talk to clients as opposed to two years ago when we were going into this, is it across the board, you're seeing like much more of a sort of understanding of how the technology can work and sort of the basic challenges and like a deeper level of what are the nuts and bolts of making that strategic shift. Well, one of things that's really interesting is that Ceos go pretty deep, right? So pre pandemic. They were not going deep to really understand the technology itself but I would tell you in the last three months there's been a significant shift in the tone of the conversation. And if you go back to clouds, the enabler uh data is the driver. The Ceos that I'm talking to now are really uh uh frustrated because if they started behind and they need to get to the cloud, they understand they've got to get to the cloud as fast as possible and they were accelerating before but they're trying to get to the prize of how do I use the data and ai to do something different. Uh And so that's very interesting to see that shift. And so things like culture is really important. The conversation I'm having with many ceos is there are ways to actually make the cultural change go faster and that you can't sort of embrace that culture takes a long time. Let me give you a simple example. You have office 365 which most of us have right. Use my analytics which we do at accenture but you can actually tell are the people that need to be uh collaborating differently in order to utilize the cloud et cetera. Are they actually doing that? And not in a creepy way? Right. But you can actually look at that right? Why is that important? Because cultural change requires you to work differently to collaborate across the organization in order to use the cloud data and AI and this is a way of having a leading indicator right? That would allow you to say wait a minute that's not happening. And I will tell you the you know Ceos are embracing the idea that cultural change can happen faster. And partially because the pandemic demonstrated how when when you need to the organization can change. And I think that's really important in the context of Ai to not use as an excuse the need of cultural change but really to embrace the challenge of achieving that change and bringing your people along as quickly as possible. I feel like after we get off stage I got to go use the cloud so I look forward looking but we have a question back here for julie if you could just identify yourself really quickly. Yeah. Hi abdication allowed Chief Ai officer of steadily black and Decker. Oh I love your statement about cloud. Is there the ability to the dry well at aI is the differentiator, Are you seeing qualitative shift in the strategic use cases of Ai or are you seeing it all pervasive what what I'm seeing is that the companies are leading don't confuse necessity with a strategic choice. And in other words um a lot of companies started using AI because of the pandemic like to use contact lists, manufacturing and pick up and you know chatbots to answer questions that I would call by necessity. The leaders are saying okay that's necessity. Now we're gonna make a strategic choice and their qualifying that strategic choice by making decisions to make it material and that's where um you know yesterday I think someone said think big start small. However you know, starting small pre pandemic took a long time and it's the strategic the qualitative decision to pick areas where you can move much faster have poc. That scale must much faster most of time doing that with partners. We just did something with a big consumer goods company and one of the ecosystem partners to go across the manufacturing plants. Pre pandemic would never move faster in this case it was like 12 weeks, it was using Ai digital twins and now scale and from the beginning it was let's let's figure this out together, let's get in a room let's get on the shop floor together. That's different. So it's both selection but also execution. Uh And if you know I ask a simple question that ceos who say they want to move faster I ask what have you changed and I think all of you can help uh you know yourselves your teams and the C street across the companies are saying what are we really doing differently to take advantage of the opportunities and the technology ahead of us? We have a question in the back and then we'll come back over here. So hi I'm rocket johann from Mastercard. I'd like to disagree with the statement that you just made So great. I love this agreement. We like push back so um you said data is the enabler and Ai is the differentiator is the data the driver clouds the enabler data as the driver and Ai is the differentiator and I and I take a position that the data is the differentiator and Ai is the enabler because in long and a long haul the only thing that will differentiate a company will be the data and not a I because I had already been commoditized. Every Ai algorithm is available in the open source so it is the data that brings it to life and hence data is the differentiator and not not the I think it's a great a great point because um you know right now every company is trying to struggle to get their data and that's why data and cloud is converging. However, I'd like you to think about what's also happening today which is the accessibility to third party data. And so while your own unique data can you know, you can say hey that's a differentiator, the leading companies are taking their data and they're figuring out how to bring in the third party data. And so I would say not not this moment, but think about 2-3 years from now where companies are able to access third party data mix it with their own data is your unique data truly the differentiator or how you use that data right? Uh to to build goods and services to personalize, which requires the Ai Now at the end of the day it's all in how you use it, right? So you know, its technology was the same for everyone. You'd never have differentiation, right? You still have to have the human ingenuity uh to sort of figure out what do the clients need, You know, what do the companies, what are your customers, B two B or B two C need. Uh And that will truly be you know what's gonna differentiate but that human ingenuity maybe 20% And the you know the cloud, the access to the data in the AI 80%. Right? So and I think trying to get that balance right will be interesting. They're all key. Let's get one more question in from our audience down here. Hi julie and formal. I lead the data science and analytics team at gap. Your team's doing some really cool work for that thing. One of the things we're doing um is uh, you know, acquiring a lot of AI companies and analytics companies to fill specific gaps in our capability. No pun intended. And I know that's one of the things that santa is also doing. You guys have acquired companies, What can we learn from you on, how do we integrate or not integrate or when do we integrate those companies were acquiring? It's a great question. I start with um how do you make, you know, what do you want from that company besides the talent. Like for example, sometimes we hire companies and they're really great at recruiting and early in the early days we've been managed to actually kill their recruiting because we imposed our things. So I think understanding what the capabilities they're bringing and therefore how to um make them, you know, thrive in your organization. Sometimes it's almost a reverse merger. We bought a organization for federal business where they were much bigger and more advanced than we integrated into them. Great in other cases we keep it separate because it's such unique skill set. So for example, think about some of the cyber stuff. We do, you know, the community, the type of people, it doesn't make sense to integrate it so but it starts with what is unique about them and then really clear eyed and what, you know what my organization might kill, what is unique, you know, or be enhanced by what is unique. And we start the discussion like we're really good at integrating. Um and uh you know, we, this is sort of a core competency of ours, but we have the very first integration discussion on the moment. Someone raises the acquisition and then we co create it with the people that were buying from the beginning. We have that discussion and we use, you know, they learn about us, we learn about them and we have very open and transparent discussions from day one to many companies wait on integration until they quote unquote decided and I will tell you that it's actually an advantage because when you're talking to target and we're already talking about integration, they learn more about us and they feel like well I maybe have a real home. So it's actually an advantage in recruiting companies because at the end of the day they're making choices particularly in the space that you're buying julie. Thank you so much for bringing your clear eyed perspective to where we are with AI now and where we're going and thanks for joining us today. Thanks everyone

Dr. Andrew Ng, Founder and CEO, Landing A.I. and DeepLearning.AI
Interviewer: Brian O’Keefe, FORTUNE

Fortune Brainstorm A.I. 2021: Big breakthroughs

the development and diffusion of artificial intelligence is transforming businesses and impacting multiple facets of our everyday lives. I mean the conversation so far has been proof of that one of the world's foremost computer scientists and AI pioneers highlights the importance of closing the gap from proof of concept to production in order for a i to to deliver on its full potential outside the consumer internet industry. He may also share the next big breakthrough what the next big breakthrough in AI is likely to be dr Andrew ng is founder and ceo of Landing AI, an adjunct professor of computer science at Stanford University. He joins us remotely in a conversation with fortunes brian O'keefe. Thank you rana And there's Andrew. Hello Andrew. Hi, hi brian. So great that you can join us. This is, we can hear you, great, we can hear you. Great. Can you hear me? Yes and today it's great to be here virtually. And I'm so sorry, I couldn't tell you in person I was excited to come in person to boston but with a big announcement today which is why I had to stay close to home but I'm glad to see you all virtually. Yeah, well you're giving me my segue but we're very happy to have you joining us virtually because it shows that we're very cool. 2021 conference, we can bring you in but let's let's go right to that news that you mentioned today. Um uh big news Landing ai your company announced today, $57 million in venture funding series a funding at an undisclosed evaluation. We assume it's very, very big. Um It was led by MacC Rock Capital intel Capital invested Samsung catalyst fund. So some really big time investors, first of all, congratulations. And I'd like to know what was your pitch to the investors? Um, I couldn't even give my pitch to all of you because it affect many of you watching this as well, which is that whereas we figured out how to get a I to work for consumer software, Internet other industries like manufacturing around focus for other industries as well. Needed different recipes. So then the AI has been building data centers and the attitudes, but to to make it fast and easy for other companies focused on manufacturing to build and deploy successfully. So how did you get this idea? I mean, you're so well known for working in the big consumer tech industry. How did you get the idea to apply AI and computer vision to manufacturing? What what was the spark? Yeah. So my previous engagement is something that the google brain team also ran the idea by do and that whole, respectively, to great internet companies embrace modern AI and deep learning. When I stepped away from my road leading those teams, I decided I've done this to consumers internet companies, someone should keep doing that. But I don't need to do it anymore. And I went to work to take me out to other industries and frankly, it's somewhat naively when I started on the a I I did not understand, I misunderstood that a different recipe than what some friends and I had built up to consumer internet would be needed for these other industries. We re studies like Mackenzie saying $13 trillion of ai value creation in the next year, a handful of years, but casually outside consumer software internet, I'm not seeing this realize much as we realize anywhere yet. So that's what I'm expected to do. So we're gonna just wanna remind people in the room, we're going to have a few minutes for questions in a moment. Andrew. Um but I want to ask you, you know, we've had some some talk earlier today about data. I mean you're looking at a different data pool to solve these specific problems in manufacturing, that's a lot of what you're taking on now. Andrew moore from your old company Google uh google cloud was here earlier today and he was talking about how google has developed a universal defect detector tool, but you know, taking massive amounts of data of every kind of defect, you know, in different types of products and that they could apply that to custom problems. I think you're approaching it a different way. Could you maybe talk about the differences in that approach? Yeah. So, you know, when when I was working in consumer software companies, I once built a computer vision system using 350 million images. And when I gave a talk in the manufacturing conference and asked the audience how much data do you have? The majority of the audience said 50 or fewer images for defense. So 350 million versus 50 or fewer. That's quite a stark contrast. Mhm. And it turns out maybe not surprisingly uh tech bill for 300 million images doesn't work if you have 50 images and that's all the data that exists in the world. You go to smartphone maker, they only made 50 defective phones of that type of defect or go to L. C. D. Panel maker, PCB maker, some content away from maker. You know, automaker, they don't make that many defective parts. So maybe all the data that exists in the universe It's 10 or 20 or 50 or 100 images and you either have to make the tech work for that or not. So I think that maybe I would been pioneering new direction for Ai Data Central Day I we launched the movement but now lots of companies that work on acknowledges that I'm quite proud of the number of countries talking about data century AI which is generating a new version of machine learning to AI to that is increasingly able to work even when the dataset sizes are smaller. And I think that would be critical to break open these applications of Ai into other industries where the data says just on as big as consumer internet industries with a billion, jesus and very large data. So data centric Ai is the key to applying ai to these more specific problems where there's more limited data, it's a new technology for AI and then I can share you one of the barriers I see two ai adoption outside consumer software internet. I need to be useful to many of you watching this as well. Um which is that in consumer software internet, you can build one monolithic web search engine or one Ai to serve ads or one speech recognition system or something of working all of those. But you can build one Ai system to serve, you know tens or hundreds or millions or maybe billions of users and generate massive economic, but in most other industries, the world's much more heather genius. So rather than everyone kind of using one or a small number of West search engines, if you go to manufacturing every manufacturing plant, make something different. And so an Ai system for detecting scratches, the smartphone, well that's not going to find cross lines in the semiconductor wafer and you need something else to find dents in the sheet of steel. So the economics is instead of one Ai model generating a billion dollars worth of value. The manufacturing industry maybe needs just use round numbers. Maybe you need 10,000 AI models, each of which generates 5,000,001 $25 million of value. And the AI world, my community, we figured out how to get dozens or maybe a few 100 engineers to build one monolithic Ai system. And so for many years there were proof of concept that small scale and these other industries, but until recently no one really had a recipe for how to help and industries generate 10,000 AI models issue, which generates a few million dollars worth of value and lets the same mistake. Even though I'm focused on manufacturing, this problem is pervasive. So take health care. Every hospital has kind of their own way of clothing, electronic health records. So I don't think it's possible to build one Ai model to read all healthcare records across our hospitals And the challenges again. Every one of 10,000 hospitals and 10,000 healthcare systems needs their own AI model, how can one company do that without, without someone like me hiring 10,000 machines and engineers. Which is pretty hard even for me. So there there's the pitch, there's the pitch to investors. I think that was a key apartment. So I want to make sure we have time to get a couple questions I think already saw one in the back. If you could just make sure you speak into the mic and identify yourself so Andrew, can hear you and if you can't, I'll make sure you can hear it here. This is the location along from established black ethical. Hello? Uh, so as you have less data at this manufacturing applications. Are you also using this knowledge heavy techniques like causality or other. Knowledge based systems in order to supplement the data. Yeah. So thanks for the questions uh actually uh called out specifically we're not using that but we do a lot of work to make it possible for the domain experts to the subject matter experts to express their knowledge. Um actually one of the standing back executive team members that publicly talked about their work with us. So bring forth of that a partnership. Um but I think that in manufacturing their inspectors with deep domain knowledge about what is the defect? Uh for example what exactly is a defect? And well I guess exactly doesn't love welding or what exactly is a stretch. This scratch so small you can safely ignore it and what is the stretch And this is a stretch of the chip. So there are inspectors and in factoring with domain knowledge and it turns out every factory actually tends to define his own defects difference. So I think the right approach, frankly, I think the only approaches and think of is to build tools and we call them data, centric Ai to that makes it fast and easy. But someone will get deep domain knowledge to express the deep knowledge about what exactly is a defect or well or septic Midway for all the component or whatever in terms of labeling data for Ai so they can show a hand for very clear examples of Ai that can then learn to detect these defects reliably even from just a modest number of example. Great! We have another question in the room from back here again, just speak clearly into the mic. Thank you. Andrew this is real hit. Japan from Mastercard. Next few 100 quick question. So once you want all the work that you guys are doing at landing Ai gets into production, does that give us a glimmer of hope of getting manufacturing back in this country? Great question. Thank you for that. Great question. You know, uh living in the United States, I think a lot about national competitiveness and I think that the best way for America or for any nation to build up in manufacturing and to create jobs, preserve a Torrey jobs is to build for the future rather than only trying to preserve the jobs of 30 years ago. So I think american manufacturing is one of those industries. I would love to see take off and deep learning technologies here. It's going to take off whether any of us like it or not. And I would love for american manufacturing. In fact the all nations around the world to embrace this and I think that to create a lot of jobs because it turns out a I is a very good compliment. So high school manufacturers and America's manufacturing raised relatively high and relatively high skill. I think that's a very strong compliment there for accelerating your manufacturing in this country. Great. We have another question on this side of the room. Sure. Hi Andrew Andy Hawk here from cerebral systems. Maybe speak up a little bit because I want to make sure you can hear Hi Andrew Andy Hawk here from cerebral systems. First of all, congratulations on the round. Uh second many of existing state of the art applications for AI were powered by large public open source datasets. So I'm curious how you think about that in the context of this data centric AI with smaller datasets, Do you plan on making data sets for the kinds of applications you're building available in the open source. Thank you. You know, I think that the types of data potentially open source tends to be um things like images of your text because that's a quite common, right? Like a different countries want to recognize, you know, pets and people and things like that. But I find a lot of in this industrial data is very specific. So for example, I've been surprised working two different manufacturers, the two different students actually work with, they have different ways of defining defects. So it's not even obvious to me that data from the manufacturer. Number one, is that useful for steel manufacturer. Uh Number two. And I think that at least for the foreseeable future, most companies treat their um images of products is highly secretive. You know, you don't want to let pictures frankly defective stuff. Right, go out, that's often a few. And these data sets are often small, proprietary, I would love for more companies to open source their data and I think that will help others. Although I think as a practical matter I think it'll be difficult to get any interaction companies to do that. And that's you know, working with one company at the time with proprietary data says small datasets especially seems like it's going to achieve to move this forward, so Andrew, we're getting close to time, but I just wanted to ask you, you know, if you look back at from the time when you found a google brain and the advancements that we've made from then to now, less than a decade. Um what has surprised you about the ways that ai has developed in that time period and what are you, you know, what would you point to as maybe like what would be very surprising or some areas where we would have these big breakthroughs uh in an unexpected place, you know, going forward if you think about the next 57 years. Mhm Yeah, so maybe in the last decade, I think the biggest movement in a I was a shift Deep learning also called neural networks that created a lot of the upside over the last decade. Um looking forward, I think the next big shift that needs to go through for the next decade is a movement to a data center day. I there's a little bit technical but bear with me a last decades a lot of progress on the software, the code and your natural details to build AI systems. But a lot of teams just took some data and shoved into these new networks. So what's missing, there's a more systematic entering discipline of treating good data defeat of AI. So something technically very deep field. So my team but multiple big companies are now working on this data. Century eye movement and I think this is also key to democratizing access to AI because it goes to manufacturing plants or a hospital or these terms and awesome to invent the next generation of map for a I you know, it's not that easy but lets them express the domain knowledge about what is the defect, what is it good electronic health record by entering the data that seems more feasible. And I think that would be highly recipe. So finally taking out from consumer software to net was worked great all of these other industry. Well, I think that's a perfect vision in with democratizing access to AI Andrew. Thank you so much for making time for us and joining us and we'll talk to you again soon. Thank you. Thank you. All right, join me in. Thank you. Andrea yeah.

No company has made bigger investments in cutting-edge A.I. than Google. But those advances have not come without challenges. We’ll hear from one of the company’s key executives on A.I.’s potential impact and how the technology can be made more accessible for all.
Dr. Andrew Moore, Vice President and General Manager, Cloud A.I. and Industry Solutions, Google Cloud; Former Dean, School of Computer Science, Carnegie Mellon University
Interviewer: Jeremy Kahn, Senior Writer, FORTUNE; Co-chair, Fortune Brainstorm A.I.

Fortune Brainstorm A.I. 2021: A.I. for all

Alright. Hi everybody and welcome to the first ever fortune brainstorm AI summit. All right, thanks so much for joining us here in boston. Uh This has been, we were supposed to be here right at the beginning of the pandemic and we had a little bit of a curve ball you might have heard about that but we're so excited that this was able to come together and we really thank you for joining us and and making this a great event. What is sure to be a great event and also to all of the people who are joining us Online. We have a big group of virtual attendees this year which is, we're doing a conference in 2021 so that's what it's all about. Um So what could be better than gathering a group of highly intelligent humans that's all of you to talk about and share insights and expertise on the increasingly robust simulation of human intelligence that we call artificial intelligence as you know. Now more than ever, artificial intelligence is being woven into our daily lives and becoming central to business processes and strategies, not just on the tech side of the organization either as I've heard a lot of conversations already, but throughout a company's entire ecosystem and that's why we're here to delve into that. Over the course of these next two days, we're going to go in depth on how artificial intelligence is unfolding and how ai is shaping all of us both, the business world and society overall. We're going to look at how it's transforming companies and industries and disrupting traditional models. We'll delve into how it's being used for R and D. Precision medicine, transformation, transportation, its transformation and every aspect talent recruitment, supply chain operations and manufacturing methods and we'll talk about the ethical boundaries and legal challenges of ai. These conversations will be led by members of Fortune's editorial team and by my fellow brainstorm AI co chair. So I'd like to take just a quick moment to acknowledge them. Fortune senior writer Jeremy Kahn who will be hearing from in a moment. Fortune senior editor VErne Kopytoff, Fortune technology writer Jonathan van Ian and Deputy ceo of Smart I dr rana El Halabi who's joining us right here in the front row boston native hosting us here are not boston native boston resident, sorry, I'd also like to take a moment right now to thank our fantastic event partners for their work in bringing us all together and it's really been a journey and we really appreciate the collaboration and partnership we've had, I'll start with our presenting partner accenture and I'd like to acknowledge Sanjeev Arora who joins us here at brainstorm Ai he's the senior Managing Director and global lead of applied Intelligence at accenture. We'd also like to thank our event partners intel and Salesforce dot org. We greatly appreciate their work and making these conversations possible for all of us. Okay before we get started with the actual interviews and discussions a little more housekeeping to go through what's said on stage up here, we're in the breakout sessions is on the record. But what set over the table during a meal or you know, just huddling out there in the networking space that is off the record unless it's agreed upon by all parties involved. We'd also like to mention the ever present covid safety protocols which are here for all of our safety. Be sure to keep your mask on except when eating or drinking. We'll try to model good behavior ourselves and avoid shaking hands, which I've already violated that I'm so excited to be in person meeting people. I've shaken hands with several people, don't tell anybody, but I am sanitizing my hands constantly. Speaking of which utilize the hand sanitizer available throughout the facility. And please do not replace any items that you've touched that may be used by others such as cups or utensils. In keeping with that. We want to mention that the microphones that we're using for the Q and A will be switched out and sanitized before each use. So you don't have to have any uncomfortable feelings about using the microphone. We want you to interact and use the microphone and ask questions. We're all here to have a interactive conversation. And for those of you joining us virtually. Uh and I mentioned, we have a great group joining us virtually as well. If you'd like to ask a question, please drop it in the chat function on the right side of your screen and we'll do our best to get our thoughts to the speakers during the course of the conversation. So with that let's get started. Google is almost synonymous at this point with artificial intelligence and the company has made massive advancements and investments in the field across the industry. We're seeing spiraling costs for developing cutting age, uh, cutting edge AI and by some estimates, the computing power required to train Big I models is doubling every 3.5 months. That's a pretty eye popping step requiring new technologies and hardware. One of google's top executives is here to share his view of the challenges, the opportunities and the future impact of the technology and how AI can be made accessible to all. Please welcome dr Andrew moore, Vice president and general Manager of cloud AI and industry solutions at google cloud in conversation with fortunes, jeremy Kahn. Oh Andrew. Thank you so much for being here with us. Um, and while while we're talking, please guys start thinking some questions because we're gonna try to have some time for Q and A. And that goes for those of you who are listening virtually as well. Um Andrew, we had a little discussion before the session and one of the things you told me is you do not want A I to be reserved for a select priesthood. Can you talk to me a little bit of what that means and what is google doing to really kind of democratize this technology. Very good. Yes. So, uh, there was a time 10 years ago when a PhD student could do something remarkable with computer vision or machine learning and natural language. But as we've all seen, and I hope it's a major theme of this conference. Ai is not primarily about the technology, it's about change and how you're gonna change the way something in the world works in a way that's good for the human condition. The people who can do those changes are not the ones who have been to MIT or Carnegie Mellon for seven years for a PhD. They are the folks out there actually living the business. And so I think it's fair to say that us internet companies were racing now, not just to have the most advanced technology, but have the most accessible, Right? Yeah. Andrew Google has invested a tremendous amount in cutting edge ai for its own business, but also to build out these tools that can offer to its customers, including its cloud service customers. But there are a lot of companies that are wary of this. They're worried about getting locked into one cloud provider for instance, and they're also a little bit concerned about tools that they have not trained themselves on their own data. I'm curious to google, how are you addressing those concerns? And do you think that's a legitimate worry for these companies have. I think it is by far the most common thing now for companies to go with a multi cloud approach uh and speaking both for myself as a academic practitioner and for google, I think that's incredibly healthy. So uh we don't like to see it when there's a system where every part of the stack depends on uh every other part of the stack which requires this kind of lock in, what What I have found. And this has been a remarkable thing for me, just over the last 18 months, is how difficult it is to ask a customer over a customer internally to suddenly produce a petabyte of data to train a machine learning model for that customer. So uh we and google because of this, we've been working so hard on the question of how to reduce data requirements for users of ai and if you don't mind, I want to give an example of this yet. So uh visual inspection, Very important thing in manufacturing is a non-0 sum improvements to have fewer defects in parts and finished products. Uh But if every time you want to train up an ai to spot floors, you have to spend months sort of showing it positive and negative examples, that really reduces the speed of uh usage of this kind of thing. So uh some of my more uh cantankerous engineers got fed up with this uh and really pushed hard on the question of what is a generic notion of a fault, is that faulty nous as a general concept and just trained up a eyes to across the board, get an idea of whether something is faulty or not without having been trained in a particular discipline. And to my surprise and horror because they really were cantankerous about it, they were right, we actually can do this kind of thing. And so now, uh we are not in a position where the first thing we have to talk to with the customer is persuade them to give us like a million examples of a good product and 1000 examples of a bad product. It's more like 10 of each and that's been a huge Ben. Well, that's interesting. I mean, we've seen this with computer vision, as you say, obviously that's happening with language where you have these sort of foundational models. Um, but and and google is using these, it's got Bert, it's got mum the planes, the search results, but as you know, there's been a tremendous concern about, well, what if there's bias kind of baked into these foundational models? What if in learning that embedding of what is a flaw look like there's some bias that you're not aware of and then you're going to use that to train, you know, uh, with a few shot learning, you know, for your specific use case, what what happens if you kind of ingest that bias? This has been kind of controversial, you know, within google itself, What is google doing to kind of address that concern about bias? So this is a genuinely hard problem and I would love to be able to tell you that there's a great google approach at the moment. I would say we are extremely conservative but our approach is very people intensive. So if you take uh an implementation of an AI pipeline, maybe something for loan approvals or job applications or uh research, health research study, uh eligibility and so forth. These are all extremely dangerous ones where it's perfectly possible for the training data to inherit some of the biases in the world. Each of these cases has to have folks looking not just at the technology piece but at the change whatever change management, remember I said ai is change change management is involved in it. To ask what are the ways in which this could make things even worse or make things better for our sort of attempts to have a inclusive world. So of course, we have statistical tests to do various kind of stratified analyses to to give a warning light if there's an obvious relationship unexplainable correlation between the protected attributes and an outcome. But that's definitely not enough right now. And I will push back very hard if someone says there's going to be an automated engine for this. One of the other big concern that's been raised about these models. Some of them are very, very large. Um is the environmental impact of these models again, what do you how do you view that concern? And do you think, you know, there's a solution for that? Yeah, that one really uh is a sad one, especially if some of our models are being used to reduce energy in places like our data centers. It kind of doesn't work if you actually consume that much energy to learn the model. So, this one, it turns out is real, it's absolutely a problem. And if, say, for example, we were able to give the best 20 0 ai researchers in the world the power to build the kinds of models you see in google or open ai bills in language. Uh that would be an absolute crisis. There would be the need for many more large power stations to support it. So what can we do? The first thing is lower power compute. So people are working at the hardware level, the I come from a more mathematics software background and she's still scared of getting involved with hardware. Uh but at the mathematical background level there's so much you can do and focus with google and around the world are working on it. For example, there's a technique we've been using with self driving car company is called neural architecture search, which as it's trying to train for really big machine learning model to recognize surprising obstructions on the street. Part of its training is you're only allowed a certain number of what of energy in your predictions. So don't just optimize for accuracy, optimize for small, sparse models. So I do expect you to see big changes there and I would not be happy if everyone tried to uh learn these big models because it is selfish and bad for the environment. I've got plenty more questions that I'd like to post Andrew, but I want to make sure we go to the audience while we have time. Uh there's someone back there at the question. I want to go, we'll go to you first. Um if you could please state your name when you, when you stand up to ask the question, I'm tom davenport's um I know you've gone back and forth between cmu and google for a while. I wonder if you could talk a bit about the current state of high end ai education and where you think that might be going? Great question, but I want to change a little bit. So high end air education is going very well in the sense that there are plenty of qualified applicants and there's a boom there, the professors who can be their mentors while they're learning how to be wild and crazy researchers as opposed to industry researchers. I'm really, really worried that those professors are dissolving into internet companies at the moment. And part of the problem there isn't actually to do with funding for the professors a lot of it is to do with the cost of funding. So a typical professor mr of me, crazy Of all their free time for research at the moment, they're getting close to 50% of it is trying to chase grant money and only 50% of it is actually spent doing research, so they always feel like the grass is greener. On the other side, interesting, I want to go to anyone on this side of the room, have any questions over here because that gives them a chance to clean the mics. Um Any other questions about this gentleman here has a question, wait for the mic please, and then if you can stand up and state your name, okay. Uh and um uh I'm from Gapping. Um Andrew speaking of academia obviously one of the big challenges in this space is talent always has been, can you talk a little bit about that? How do we, how do we solve the challenge challenge, particularly in partnership with academia? Great question on Thailand one. Uh This is going to sound surprising, but I am not particularly interested in injecting vast amounts of extra money into the big academic institutions where I really want to invest in two year colleges places where you can train folks to use the tools which good clouds, I think of one particular good cloud that I really like have made their systems. Mhm Not easy, but straight forward to build something which once you've built it, you know, it can safely go into production. So it's those kinds of folks using these technologies, which I think are the most important. So in my own sort of education lobbying right now, I see our biggest weak spot and biggest opportunity is two year colleges in the United States. We've got another question from this gentleman here. Yeah, hired roof, bookish the lawless, deadly black attacker. So google has lots of data sets you have about the boast of the world. Are you going to do anything about open sourcing some of those, not just to academia, but to other businesses, especially those who don't complete with you? Yes, it's a great question. So I want it's an opportunity to talk about something really, really important. So, so many companies come to me asking to work with google taking advantage of some of the reported data that we have about users or the state of the economy or other information and we in google cloud, we have a strong role. We never ingest data from the other parts of google's businesses for these purposes. So I'm actually not a good person to come to if you're saying like, please, can you use the results of google searches to predict where I should put my next distribution center or something outside of that. There's still huge amounts of data and a lot of it is sitting there publicly and it's hard to access because there are no shared data models. So there's an important google actually, I think I think this could be one of the most important things that google does over the next few years. A huge project called data commons dot org. Uh It's it's owned by a whole conglomeration of folks, but some of the engineers or in google, they are busy getting hold of public datasets, not republishing them particularly, but making sure they all mapped to common data models so that you can start to do things where you might ask a question about like the economic growth of one part of the country. And it turns out that linking that to uh Ocean Ocean National Oceanographic Institutes, whether models actually helps and you can do the linking right Andrew. We're almost out of time. But before we go, I was hoping you could give the audience just a preview of what you might see next, what they might see next in terms of cutting edge enterprise ai coming out of google. Thank you. So, uh one of the things I'm most excited about is there's we still even when we're able to produce the world's most accurate core center uh Question answering systems or uh excellent retail search results which delight the user and so forth. These are in my mind and in our 10 year roadmaps, minds, all components, what's coming together coming next. So you will see these apparently unrelated things to do a document, understanding speech retail prediction uh question answering for users coming together in places where if a customer is asking about the availability of a particular product, there's the speech part, but there's also access to inventory systems, access to predictions about inventory and these really important models as to what makes people happy. Great. Andrew. We're out of time. Thank you so much for joining us. Uh, it's been a great discussion. Thank you so much. Thank you.

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