 whom this is her first trip to Davos. So be particularly nice, right, at least in a long time. And what are the people behind us doing? Yeah, what are you doing? So what we're going to do is try and talk about the future of technology, looking at it, perhaps with the trend, the phenomenon that people are most fascinated by, and probably is likely to have the biggest impact on the future of industry, the economy, jobs, and something even beyond that, I would say, our conception of what it means to be human. So all that in one Davos session. And what I'm talking about, of course, is artificial intelligence and the way in which artificial intelligence is being applied. And it stems from something, Ginny, you talked to me about a while ago. I remember when you first became CEO, you had this very powerful observation that it came out of three revolutions technologically that were taking place simultaneously. Explain what those three revolutions are and how they impact on this. Well, certainly the largest is part of the reasons I think I came to Davos this time, right? And what we talked about at the time, that there were three big waves of technology, which make this a different point in time than ever before. So when people say, oh, I've seen this before. This has happened before. Not really true. And you've got the rise of cloud computing, which in this sense, the reason that it's relevant is the ubiquitous nature, that you can get things everywhere. The second thing was the rise of data. And we'll come back. That'll be at the heart of this, the rise of data. And then, of course, you have mobility. But I always have added another one. I always say, but underscored with security, which we will talk a little bit about at some point, because security and privacy are things that will derail one way or another here. But those were the three that came together. And I think when you said, this is my first time back at Davos in a long time. And in part of that, I was sharing with someone yesterday the reason I wanted to come back, the subject is responsible leadership. And of course, what jumps to all our minds are things like health care education and intersecting with those is this topic of artificial intelligence. And we can talk about why we call it cognitive, but artificial intelligence. And I do see it as a solution to much of this. But back to those waves, you have so much information. And this is what got us started on the journey long ago, so much that if you don't do something, your brain, us as humans, cognitively can't deal with all of that, right? A doctor, 8,000 papers a day. Your doctor cannot be current. It's impossible. And so what would you do? And to date, every system ever built has been programmed. I mean, your phones, no matter what you use in technology to date, it's been programmed in some way. These systems, you can't program for all of the different if then what could possibly be. So you need a system that could understand all this data, could reason over it, and could learn. Which means they do become more powerful with time. So that to me, this intersects this idea of what all these big issues are in the world right now. And it is in part a solution to it. So welcome back to Davos. That's why. And so when you describe this, it makes me think one of the comparisons that people sometimes make is that in the 1980s, there were three very powerful supercomputers in the world, the crazy supercomputers. I think US government owned them all. Or maybe IBM owned one of them. Excuse me. And the iPhone 7 is more powerful than a Cray supercomputer. But that doesn't even begin to get at it, because the Cray was not connected. It didn't have a cloud. It didn't have the internet. And so you have this extraordinary level of computing power that everybody has. And now it's beginning to do what you're describing. So explain why, for example, Watson being able to, or IBM's computing power being able to win the Jeopardy competition was actually, in some ways, more important than winning the chess, than deep blue beating the world's chess grandstands. Yeah. So before we talk just a little bit about how all these technologies be applied and then their impact, I think this is an illustration to bring up what has happened here. So if you look at even our history, there's a new movie out right now called Hidden Figures. And it's about John Glenn orbiting the moon and the earth. And what in that movie, though, it's talking about the IBM mainframe. It's the first one. It's the first time it's being used here. And that is this programmable era. And it was with math. The answers were deterministic in what's out there. And so when we did chess, those are deterministic. You can, with a powerful enough system, fast enough, you can calculate every move and decide what to do. Very true with these games. You see people having out on TV and the like playing other supercomputers playing these games, they're mathematically determined. The difference with what we did in Jeopardy is it's open domain. And so as I say, what these systems do, artificial intelligence that understand reason or they can deal in gray areas, but even then it was an open domain and a question and answer. And so if someone asked you a question about, well, where is that chick? OK, is that slang for a young girl or is that a little chicken? And for the system to have context of what it is they're talking about. And so Jeopardy was a game show where the answer was given and you have to determine what the question is. So the ability to not be able to phone home to anyone for Watson to have been fed lots of literature, and so he's read lots of literature, and then to get an answer and then be able to back up and parse into what could have been the question. That's not search. I keep saying this is not keyword search. This is actually understanding context. So that was the first foray into Jeopardy was with and to prove that. But we certainly then moved on, because that was really never just about it. This was a mission for us that started long ago, so we're probably 15 years now into this. And when we said, hey, the world's going to have all this data, you're not going to be able to deal with it, and it's not all text. Things don't all fit in rows and columns. These will be pictures, images, tweets, sounds, sensors, video. So something has to understand all that. That's unstructured data of which the world's 80% of that kind of information. So that embarked us on the journey. We picked this milestone to do a game show, which a year in advance, as our researchers will tell you, they weren't ready a year in advance. We're like, well, OK, this show does air on this day. And we're going to humiliate you if you're going to work hard. If Watson had lost. That would have been humiliating, right? And so but the real point was to say, OK, to show the art of the possible. And to me, this is what's much more important and what has moved on. I think these systems have the opportunity to do for some of what have been the world's most unsolvable problems to find solutions to them, particularly health care. And that was the first big one that we started on. But it's health care, it's education, but it's retail. It's your everyday life. So it is from the everyday to the unsolvable is how I like to think of it. And if I give you two examples on each end of what you can do. So in health care, right or wrong, we started with the hardest thing. We started with oncology. So what we did with Watson, Watson had been fed all textbooks, all journals, about 18 million papers. And then we started training with some of the best cancer centers in the world. Because these systems do have to be trained. You don't just pour all their information in and say, you're a doctor. And by the way, I want to be really clear. And I've been so emphatic. I want to come back to a policy letter I issued to our company yesterday. These are technologies to augment human intelligence. In fact, I actually don't like the word AI, because cognitive is much more than AI. And so AI says replacement of people. It carries some baggage with it. And that is not what we're talking about. By and large, we see a world where this is a partnership between man and machine and that this is in fact going to make us better and allow us to do what the human condition is best able to do. And I have to tell you, we have seen this play out that way. With every profession we've worked through, you watch it become not a thing. It's a very two-way relationship. And it is a tool that helps. And it makes you, as a professional, do a better job. So we started, as I said, sideways there, on augmenting and with the doctors, training on oncology. So I fast forward to where we are today. Watson has learned. We started with breast lung colon, the hard-bodied cancers. By the end of this year, he'll have trained on what causes 80% of cancer in the world, 80% of the cases. And explain how you train. What goes on, as you said, you don't just throw the data into a machine. No. So first, very early on, had to learn the language, which is true in every profession. Think about whatever job you have in the room. Every profession's got its language and what it means. You take it for granted now that you know it. So we have to teach him the language of medicine and then what's associated. And so as you give more information, ask questions, check answers, right and wrong, it begins to learn over time. So fed the information from the journals of textbooks. Then, in fact, the training went on. So tens of thousands of hours with these doctors. Then we took medical records and where you knew the outcomes. And you would give him the input of the EMR. You would give him the x-rays, the diagnostics. Learned, did he get it right? Did he not? So back and forth. And you would improve the accuracy, improve the accuracy. And so you get to a point where you could diagnose and you could offer treatments. And around different kinds of, you might put limiters. If I'm a cancer patient and of a certain age, I still want to have children. There are certain things I don't want to take as medicine and the like. And so you could put different. And what we learned, and this will be true with every profession, people don't want a black box. They do want to understand, this is assisting me. So give me the percents confidence of this diagnosis. Give me the sources of data that fed in primarily to do this. So where we're at now, we are rolling out in hospitals across India, China, Thailand, Finland, Italy. Three more in the way. Of course, in the United States, it started as an oncology advisor. Then we moved on to clinical trial. And just to explain that basically the idea would be correct if I'm wrong, which is that no doctor can possibly know every case that is ever taken. Breast cancer's got 800 therapies around it. And not to even mention clinical trial matches. And so you can't, it is, I think everyone could see that very genuinely. It's impossible. And so, or go to a place like India, one doctor for 1600 cancer patients. You will, a great statistic in the world, in the United States, very developed country, 15% of people with cancer will be treated at a real cancer center. The other 85% are treated at whatever their local hospital is. Now just, you know, you can magnify that in another country. And so that is the real issue. And that's true with many diseases by the way. Or just, I even, this idea of what is really credible information, right? Over the weekend, my husband burnt his hand. And do you know, what are you supposed to do with a burned hand, do you know? I was, of course, working, he was cooking. So what, Put cold water on it. Okay, you know. So he walks in with ice cubes all over it, right? And I said, you know, I don't think you're supposed to do that. And he's like, well, he's a guy, he's a, nah, nah, it's fine. And I said, no, no. So I get online, I do a search. And of course you get 3.5 million answers to that. And I'm like, well, now which one do I trust here? And of course I go to like a Cleveland clinic, a Mayo clinic, because I, all the rest before it, forget it. You have to, and of course it is, run it for 20 minutes, by the way, cool water. And it's all the reasons why ice is bad. But then it goes on to say, you know, all these other things all the way up to calling them, you know, for an ambulance, right? And so this is true with everything is like that. The search is no good for the, for these kinds of, for many of the problems you and I deal with in a profession or in anything that I think you're making important decisions. So. Did you find resistance from doctors? No, so that was a really. I can do a unique diagnosis. I have developed these skills. I went to Harvard Medical School. What do you tell, you know, some machine is going to tell me what I should do? A little bit. I'll tell you the bigger thing that I've learned with working with professionals, all of us are professionals in one sense or another in our work. At first we did do that, except I tell you, there have been so many compelling examples. They quickly did see this is a help. So as an example, University of North Carolina took their tumor board, 1,000 cases. And these are the best doctors and ran the same cases through, if you've ever heard of a tumor board of someone, unfortunately, if you know anyone that had cancer, and they'll often all look at the information at the same time to help with the diagnosis. 1,000 cases, toughest cases, and the good news is, and virtually almost 100%, Watson came up with the same thing the doctors did. Excellent. 30% of the cases he found more. And that is the difference of life and death. And so that to me, and that's, that to me is, I kind of almost say, case done closed. You know, that proves it. And so what I found then is, so they understand that, right? 8,000 papers a day, this is ridiculous. In fact, Toby Cosgrove is here from Cleveland Clinic. Toby will say, medical data is gonna double every 60 days now. What are you going to do? Every 60 days. Every 60 now, right? I remember when it was every 18 months, and that was not long ago. So this, what I have found more important though, is that it has to come in your workflow. So all of you, again, you have a job. What is more important is that you find a way to insert these abilities to help in someone's workflow. And that's what I think is the real opportunity. So I say medicine, so we went on to clinical trial matching now, so anyone with breast cancer at Mayo Clinic, clinical trial matching, which is very difficult to do otherwise. And then to genomic sequencing and really progressing precision medicine. So Watson's been taught by the top 20 genomic centers. And if you are a late stage cancer, particularly you have your whole genome sequenced. Now with Illumina and with Quest Diagnostics, they run it through Watson and you get the different sets of possible, both diagnoses and treatments that come out. So that's the other side. Well, yeah, the other side is just as exciting, but it is different, right? So whether you are talking about staples, a retailer, whether you are Macy's a retailer using artificial intelligence, it's really matching what it is you walk in the store helping guide you through the store to what it is that you want as an example of what's being used there or whether you are selling just even a financial services product. And if you go online, some of the big insurers, everything you're filling out, it's actually Watson behind it answering this. But something else to touch everyone's daily lives is education. And another one, go out, do a search and look for third grade math lessons. I checked it again the other night. 3.7 million, okay? Now, if you have a child that isn't good at math or even good, which is a good math lesson, right? And teachers will tell you, it's what their friends tell them or what they've had and they've used before. So we have been working with teachers. So these systems will be built by, they'll be taught by their professionals. And first what's rolling out is math. Then we'll go on to other subjects. And it is about matching a child's learning to what the right lesson plan is. And Sesame Street, we're doing the work where videos for your kids and how they, what is the right video clip for how your child learns, matching them up. And so these will be things you'll feel, the weather. We do the weather, the weather company. It's the most of you that have weather on your phone. That is IBM. And we bought it last year. We bought it last year. They're great acquisition for internet of things as well and voted and scientifically proved the most accurate weather forecast out there. And but what we're adding is Watson to it. And we're also adding the ability if you do advertising, that it be a cognitive advertising that you interact with it. If it's for flonies, for your flu, I have a four-year-old child, should I be using this? And so you can interact, because these technologies, you will interact in natural language. So we're gonna face a world where what makes this great is they have domain knowledge, which hopefully I've convinced you. You can have domain, they have to be taught, underwriting, math, whatever it is. They have domain knowledge. It's much more than just artificial intelligence. And I'll come, many of you in the room have businesses. You do though, have to be clear about what the business model is. And we come to a world where we've architected Watson in a way where we'll bring data, because by the way, we do bring in healthcare. We have a health cloud. We have a financial services cloud. We bring anonymized data. Client brings data. But the insights belong to the client. And that's critical going forward. When you talk about things like retail, I think about what people say, the scale of these three revolutions coming together, particularly of cloud computing and big data, is that you can essentially predict people's behavior, perhaps to an extent that they don't even recognize it themselves. That computers can essentially, because they're looking at five million cases, not just me, they will notice that if I listen to certain songs on Spotify and if I buy certain things on Amazon, I'm likely to vote for candidate X in the election. And my next purchase is likely to be Y. Is that, if A is that true and B, isn't that slightly scary that a computer can predict what you're going to do? Well, perhaps yes and yes, right? Because Watson today, and I'll use Watson as our example. It's all our experience. We were the first one. What I call the Watson is the AI platform for business. We will touch a billion people this year, not through us directly. People using Watson are touching a billion people. And so we have a lot of lessons and learnings on this topic. Now, if you go out and you all can, because it's an open platform, people are building anything on this. Things I don't even know, right? We see them because you go out. So one of them is the personality insights in the tone analyzer. And with, David can tell me how many words on personality, and it's not very many words that you're about 150. I think that's equivalent to a tweet, 140. About 150 words, characters, 150 words. You can get a pretty decent, we can do you. We probably should have done you ahead of time, actually. I got to do that for the next time I see you. Oh no, I can't wait. So I can't believe I didn't think of this. And I got plenty of his writings to put in there too. So that's not gonna be a problem. But you get personality insight in your tone, right? In fact, I'll get sometimes notes from folks at work and they'll put their notes through the tone analyzer first before they get it. I'm like, which tone were you trying to write that in? Yeah, so that is true, you can do that. And I think these are choices we're going to make about trading off some, in some cases, you don't know you're trading it, which I think you have to be aware of when you're trading off information and how it's used. And you'll make some of those trade-offs for convenience. But I do think we're gonna enter a world that has to be in your hands, whether or not you want to live and work that way. Some people will trade that off for convenience. They do today. You trade off your location because you want to know where a restaurant is and this and that are directions. So you're making a conscious decision. I don't want to share my location, so I don't do that, right? So, but you'll do that. And that is a world you can do those things, but I don't necessarily find that bad. And I do think it will enhance a lot. So you said, could I make something better? I can't remember if you read, but last time I'd seen you or not, we had done this. We did some work with a music producer called Alex DeKid. So unless, if you're into current contemporary kind of music like this. And what we did, he took and we worked with him, but it just shows you how it can improve the creative, take that, but to improve what humans can do creatively. He's a world-renowned record producer, having done people like Rihanna, Beyonce, a contemporary artist of our time. And took all of the, we took all of the famous top sort of hundred songs of every year for a great number of years, Fetamin. We almost analyzed all the conversation of the world because we took all of the Twitter sphere, everything for the last five years, analyzed it. He got a lot of information about mood, what people react to, the music, whether they like. And to make a long story short, about two months ago, he put out a song and it went to number one on Spotify and on iTunes in the shortest period anything has ever moved. And so, did he get lucky or was he, was this the combination of these two things? When we've seen it, you and I talked about a movie trailer. We've done it to enhance that and you've seen the same thing. In fact, in that case, the funny story was the movie trailer was more famous than more watched than the movie actually. They should've used it with the movie. And the reason this is interesting is movie trailers was always regarded as a great editing art that you had to sort of, at a great editor had to look at the movie and figure out what were the most, you know, the moments that would most likely make people wanna watch the movie. And Watson did it essentially better. So, but all of that makes one. But he didn't do it alone. And we're gonna talk about jobs. That's the other reason I'm here. It does make everyone wonder, if the computer can do all this, what do human beings do? Yeah, so this is what our thousands and thousands of lessons and experiences taught us though. And it is a really important part of being here that it's why it's augmented intelligence. And in all those cases, the person played a very important part in things that the system could not recreate. And so I view, this is an era, this will play out over decades to come in front of us, but there are some things that are really important. And if I might, I want to share the maintenance of a policy letter I sent to my IBMers yesterday. So I have almost 400,000 IBMers. And the last time a policy letter was written was a policy letter that said, if you had genomic testing, we would not use that for any form of information or discrimination. They are very rare. I wrote one yesterday on the principles for the cognitive era, or think of it as the principles for AI if that's better. And it gets to the heart of this. And I said there are three things because history has taught us many things and us at 105 years old, that when you introduce powerful technologies into this world, you have a responsibility that they are introduced in the right way. And you can guide their adoption and you can guide how they are used. I go back to the 1960s when IBM first came out with, it was really that big, massive programmable system. To this day, reinvented many times. But that gave rise, our role was to teach the world about how that could be used. And you might say that automated many of the back offices of the world. That gave rise and we played a big role in education to computer science. There was no computer science at the time taught in universities. That was really, I go back and look at pictures from that time. They're all pictures of classrooms and teaching. And so I fast forward now, what are the three principles that I shared with the IBMers to guide our work, what we do, what we believe the world should do, the industry should do, what we will devote ourselves to. So that's very important and powerful. The first one is these technologies, we will be clear of their purpose. And our belief is these purpose is in service of mankind. They are in service of humans. They are here to extend what you and I can do and to extend the human capability. And we debated these. We do not believe either in principle or actually even in the state of science today that these will be self-aware and conscious. And that's not what we're advocating for. And so that if you put simply, the human is in ultimate control here of what happens. So that's the first principle. And we really, David Kenney's on my team or we debated these. Number one is purpose is to extend what humans do in service of. Second big principle is the word transparency. I think time has taught us this. So we need to be transparent with everyone when, and we believe others should, when are you using artificial intelligence? Tell people when is that answer come with this, when? The second most important is how are these systems trained? Who trained them and what data was used to train them? So would you, if a doctor knew that the top 20 best cancer institutes train that, what's the likelihood he's gonna listen more than, well, this came from somewhere. This came from scraping the web. I mean, no, that's not how an underwriter works. It's not how a doctor works, a teacher works. So you need to know where and who taught it. That's what we're doing financial risk systems now. We bought the world most renowned company for doing financial risk and they're doing the training. So you need to be clear. And the other part of transparency is on business model. This is quite, this to me is a wedge issue and quite concerning meaning all data and algorithms should not be concentrated in one company. This is not a good thing. So with a business model, as a company as an example, those of us in the room of companies, you've got accumulated decades of knowledge. Do not turn it over. You should know what you turn over. And when these algorithms are trained, that that insight belongs to you that you are not training someone's data to help your competitor. And that's how some of the other systems work out there today. So transparency is a second big principle and the third principle is around skills which may end that topic of the importance of guiding and building the skills in the world to effectively use this technology, safely use this technology, put it in the right service and be sure the right jobs are created and that you re-skill where that's required. So those three principles of purpose, transparency and skills are something I am gonna adopt in my company and I hope people globally do. It's something I think whether it's government, company, academia, it's really important because we're just at the beginnings of this era. I think we have to close on that but it's a fascinating note to close on and fascinating to remember that this is a 105 year old company that is now reinventing itself once again around these technologies of the future. Thank you, Ginny. Thank you for you. Thank you. Thank you. My friend is all over the world.