tv Charlie Rose Bloomberg December 5, 2016 10:00pm-11:01pm EST
♪ announcer: from our studios in new york city, this is "charlie rose." mark: good evening, charlie is away tonight, traveling on assignment. i'm mark halperin of bloomberg politics. cardinal peter turkson is one of pope francis's closest advisers and a man you are likely to hear
more about. he will be running the new vatican office, charged with issues facing migrants, the poor and all of those in need. he is also the expert on laudato si, the pope's encyclical on the ++++++++++++++++dangers of capitalism in an era of climate change and global poverty. he spoke friday morning at the fortune/time global forum in rome. here is the conversation. charlie: i have the great
pleasure of introducing to you -- i said to him, cardinal turkson, what do i call you? he said how about peter? i will not use peter, i will use cardinal turkson. he is at the forefront of the ideas we have been discussing this morning
in the few minutes we have gotten started as we have laid out the imperative of this conference. he has some familiarity with the united states. he went to school there. he went back to ghana, became an archbishop and then a cardinal, first nominated by pope john paul ii. pope benedict brought him here to the vatican and pope francis has given him this new
responsibility. here is what pope francis said to him when handed this responsibility which begins in january 2017. "peter turkson will be competent in issues involving migrants, those in need, the sick, excluded and marginalized, as well as victims of armed conflict, natural disasters and all forms of slavery and torture." you have your hands full. [laughter] cardinal turkson: that is quite a case. good morning to all of you. very glad to be in your midst this morning to share these thoughts with you. if the governments had to deal with this, i don't know how many ministries they would have. pope francis, as part of his reform, decided to bring the different offices. there used to be in office that dealt with migrants, humanitarian assistance and health care and sanitation. i suppose it has been a very good arrowhead for
effectiveness. he decided to bring them all together under one head. that is what he is preparing for. from the beginning, we decided this should not be a marriage of offices. we decided to formulate a new vision of pope francis for the involvement of the social arena. in the process of formulating this -- when we were done, we saw what offices we need to carry out this vision. charlie: you had a lot to do with the pope's encyclical of the environment. connecting what happened with the environment and poverty. i want to pick up on this theme. tell me what you think of business and what you think might come out of this
conference in terms of what business can do and what the church can do in a very concrete way when attacking questions of poverty and inequality? cardinal turkson: concretely now, i came to the vatican in 2010. this was the middle of the financial crisis. we thought it was our responsibility to provide a way of looking at this financial situation for business because of banking and all of that. one of the first things we did was help and encourage the church to stop pointing, accusing the world of business. we produced a small booklet called the invocation of the business leader. we invited business leaders to come and tell us what they do. vocation for us, when people want to become priests, brothers or nuns. it is a invitation to businesses for the resources available as cocreators, partners with god. making the resources of nature set of rules of humanity.
we say that god created the tree, not furniture. we have business transform trees into furniture and minerals. that is what we think a business is. it is a partner with god in bringing the resources of nature to the contribution of humanity. i think business -- not only those who enable business, who go to work, so the investors, but also those who need to benefit from business. we encouraged business to do that. sometimes we have to encourage
people to recognize the reasons to ensure that what it sells is dignified. to ensure the wealth is also good wealth. and to ensure that the customer, the relationship is good. when this is observed, businesses are very dignified in the role of society. charlie: how does business vow their responsibility to stakeholders and the public? cardinal turkson: they invest to bring in business deals. the character of business depends on more than who bring in the money. if we are dealing with a mining company, for example, there is a place to go to. a lot of people lived on the
terrain. people's lives will be transformed by the mining activity. involved in this just more than for the investors. we encourage a holistic view of business. by the end of the day, bottom line for us is that everything that happens should serve the world's well-being. the human person is the only thing god created for its own sake. everything else was created for the well-being of the human person. the human person was created not to serve anything else. when therefore the exercise of business or any other human activity of engagement tends to make man set another goal and they suffer distortion.
everything should help the human person. the human person cannot be reduced. charlie: do you believe that, or does the church believed there is necessary regulation -- it is not so much the market economy itself, but the ideology behind it. the conception of the market which exists in political oversight and regulation. what did the holy father mean? cardinal turkson: you will meet him tomorrow. he were probably tell you what he meant. [laughter] i think there is something from this we can pick up. when i got here, we produced a small booklet. we called it reforming the financial system and went on to say in the life of a global authority, global financial authority. the booklet was well received in
several places. it got to frankfurt to the bundesbank to discuss with businesspeople and all of that. the analysis we made of the crisis was accepted. we identified technological causes. when it came to establishing global authority, to exercising oversight, there we had the greatest resistance. the establishment of any form of authority to regulate this is not easy. that is probably why the book was referred to. the only time we can guarantee -- you know it all so well. it needs to be fixed and there is the problem of who controls it. a certain out of control is necessary to ensure the ethical form of all of these.
charlie: in america, they call it dodd frank. [laughter] the idea of the dignity of work -- you have asked us, the world to consider the dignity of work and whether we ought to be about having machines perform the work of human beings. cardinal turkson: this i think will be a problem that will engage our creativity for quite a bit of time. john paul ii, we were invited not to produce work. its objective creations.
we have been invited to recognize what work does for the human person. we have been invited to recognize the objective and subjective character of work. not reduced to what we produce, but what it does to the worker or person. it is dignity not because of salary or putting bread on the table, but creating a human person that can provide opportunity and wants to exercise its own creativity, put the work his own talents.
work therefore -- the sense of work is not to be limited to the work we produce, but recognize does it improve the subjective nature, character of the person who exercises the work. the dignity of the person himself is what he does. that is how one person resembles god, producing himself out of his own creativity and talent. charlie: the dignity of family. in the washington post, a columnist said the thing about globalization and the rise of populism and all of that -- it said globalized elite are leading participants in a system with removing capital and rapid innovation. during the past 20 years, it has taken one billion people out of extreme poverty around the world.
this is arguably the greatest humanitarian achievement in history. without debating the greatest, that is a remarkable achievement. so, the point here -- i keep coming back to it -- what is necessary to make sure that government, ngo's, business with all the resources, the opportunities, on the human capital businesses have, the best and brightest in many cases -- how do you employ it along lines of morality and profit? cardinal turkson: the statement you just quoted of business, lifting a lot of people out of poverty, it goes on to say why.
charlie: it does. cardinal turkson: lifted a lot of poverty and inequality has been increased. it would be great lifting people out of poverty does not increase inequality in any way. exactly what does it require? i think it is to be commended and trade and commerce helps. it does give opportunity for occasion to people to exercise their own creativity, talents. but, it will be great if in the process -- we are also able to make the threat of business and avenues of work does not socially reduce inequality from this. i don't know how many -- it is best not to go into countries.
in certain cases, it introduced a lot of work but also widened the gap between the rich and the poor. charlie: the question is could it have reduced -- it lifted so many people out of poverty without increasing the gap of inequality and what is the pathway to do that? what can the church do? [laughter] cardinal turkson: i think you do this by asking them not to make profit the main objective of business, investment and activities, but to recognize as a main goal of business the lifting people out of poverty. i know that business requires investment, but profit may not become the objective. as for what the church can do, i think they can give us an outlet for promoting and lifting people out of poverty by directing resources and investment to needy areas. and, we have a case before us now -- we're looking at haiti
and south sudan. these are places with poverty and we need to build a model that enables to get them out of poverty. next year, one of our concerns would be to try to see the paths that have failed and adopt new ones. we have the means to change the orbit in which people live and do that in the case -- housing, work and access to work. so, housing, work and, if you want, what we call land. access to property.
when we have them have roof over their head and work to sustain their lives and ultimately something they can call their own personal capital, we will succeed in transforming the orbit in which people live. charlie: there is a call for action that a colleague of mine said you can do well and do good at the same time. cardinal turkson: we wish we could do well. i think there is a lot of goodwill out there.
since the beginning of the appointment of pope francis, there has been a lot of goodwill to the call to action by pope francis. we have received gifts from fast food chains who said we heard what the pope is singing, what can we do? begin by looking at your supply chain. you will have helped many people. there are some of that say we recognize success of energy is a problem in a lot of countries. they want to help with production and scale so that people do not struggle with smoke and lung cancer. that is something we welcome. just a bit of information -- on account of this, the access to participate in the -- 2017, new
forms of energy for the poor. forms of energy for the poor. the vatican decided to participate. we are learning about new technology in form of energy. trying to tell a story that tells the origin of humanity. again, the hands of humanity. is god good, is god bad? and that emphasize lifting people out of poverty. it is energy within all of us. we identify that as spiritual energy. that leads us to pray, meditate and do good things.
the infamous match almost five years ago. watson is much more than that. watson is the beginning of a new era of computing. you think about computing which has almost a century of technology -- mechanical switches and then we moved to programmable systems that we tell what to do. watson is the first of the next generation of computing, built for big data and extracting and understanding massive amounts of data to help humans make decisions. charlie: tell me about watson's intelligence. john: watson gains its intelligence from the data it gathers or given. it has no inherent intelligence as it starts. it is essentially a child.
as it is given data and outcomes, it learns which is dramatically different from all computing systems in the past which really learned nothing. the more it interacts with data, the smarter it gets. charlie: what is its potential? john: i think it is unlimited. data keeps growing every day. troubles every year. the more data, they continue to work on this. as it interacts with humans, it becomes smarter and never forgets. charlie: it is almost like watching something grow up. you have seen it passed the test, get smarter, assimilate more. you are watching adolescence. john: that is a great analogy. on the "jeopardy!" game five years ago, when we put the computer system on television, we had no control of it. i often feel as though as i was
putting my child on the school bus and i had no control over it. charlie: it was reacting to something it did not know. john: it had no idea the questions it would get. i could not touch it any longer and it has learned ever since. fast-forward five years later, we are in cancer now. we worked with the best people in the field, the best computer scientists. more importantly, we work with the best oncologists. they helped us teach watson. they told us what data to feed watson. they told us the best human knowledge. watson learned over that time. charlie: what watson does is helps find information? john: no, it helps us make better decisions. the analogy i would use is the search we use on the internet helps us find information. charlie: watson does what? john: watson goes through the information but understands the information. it reasons on that information. it builds a model for what is
being said. it understands relationships. it is not just looking for keywords and it will come back to an answer for question or in observation we cannot see. charlie: it is only as good as the information you put in it. john: correct. the more it learns, it consumes more information. it has dates on every clinical trial, drug discovery. charlie: every medical document, every clinical trial, everything of importance to making decisions in the medical arena. that data is already there. john: yes, it is updated every day. charlie: how does watson
analyze? john: watson takes it in and has a series of computer learning engines that tries to make sense of what it is seeing. it has already built a model of its world. as it gets new information, it assimilates that knowledge and tries to put it into categories like we do. charlie: how much control do you give it? john: we control through the data we feed it. data that is relevant it will learn on. charlie: do people say to themselves when they hear about artificial intelligence and here watson, named after one of the founders, and they say how smart can it be and can it be as smart as human beings? john: it can be -- it depends on your definition of smart. it can be smart on finding information, reasoning information and getting insights on why is it too large? when i speak to doctors or lawyers, they always tell me i'm in cognitive overload.
i cannot keep up with this information. i need a system to support me and give me reasons. charlie: and watson becomes their best friend. john: in the health care industry, it is referred to as their learned colleague. charlie: how do you give watson voice and image? john: on the day of the original "jeopardy!" game, we created the voice that is infamous with watson. it is computer-generated. charlie: how did you make the decision on what it would sound like? john: we could have given it any voice. we did a lot of market research and we wanted a voice that was fairly difficult. it have to sound something like a machine. charlie: why was that important? john: we wanted to represent what technology can now do. we were not trying to re-create a human being or human knowledge. charlie: putting hair on it.
john: it is still a machine. charlie: does it have personality? john: today, it does not have a personality but you can understand your personality and can take your language and understand the words in the pattern of your speech. based on that and its knowledge of psychology, it can build -- charlie: it can make a profile of me. john: based on the words you use and the patterns of your speech, it will develop a psychological profile. it is. as we get into neurological diseases and diseases of the as we get into neurological diseases and diseases of the brain, we are finding that watson can pick out patterns of speech which often are early indicators of neurological problems. charlie: that is crucial, isn't it? john: it is crucial. charlie: it is early detection. john: that's right. it opens up a whole new set of possible therapies.
charlie: how does it do that? john: it listens very carefully to the words you use and the patterns. it says it will affect speech. actually your speech is a tremendous window into your mind. often, neurological diseases manifest themselves in speech patterns first. charlie: people who have a.l.s. have said to me they first noticed it in voice. john: and we can clearly see speech patterns in pete who have schizophrenia and other things. charlie: there is knowledge, and then there is ethics and morals? john: it only knows what we teach it. charlie: so you can teach it ethics? john: it can only learn what our ethics are.
it doesn't develop its own ethics. it can look at our patterns of conclusions and mimic that. charlie: is there any reason for it to be an ethical or moral machine? john: there is no reason for it to be one way or another. charlie: what do you wish it had? john: when we did the original jeopardy match, it did one thing and did it well. it understood language and open domain questions. but it had no ability to understand images. if you feed is a digital image, it had no idea what it was. since that time we have taught watson basically how to see and how to analyze images. so much of the world's information now is images. 2/3 or 3/4 of the data in health care now is in images. charlie: because it is about the brain. john: that is right. it is about the brain, not the eye. what watson does is it looks at images. we tell it, this is it and this is that, and it learns on its on
to then go forward and understand images. all we need to do is tell it what is what to begin with, and it will extract its own understanding and machine learning. charlie: but is that the way you teach it, just by the information you give it? john: yes. that is what is so different about this era of computing. in the past we would have to go in with programmers and tell the machine to do something differently. in this case you don't reprogram watson. you give it new information. it develops and reasons in new ways and gets new insights on it own in working with humans. charlie: when did you make the decision that that was the way to go? john: in early 2000's were looking at what at the time was artificial intelligence. everyone before this effort had tried to build systems that directly mimicked human understanding, human learning.
they were developing all sorts of rules. they were trying to program systems to be like the brain. there were some tremendous break-throughs in i.b.m. research that said we are not going to do it that way. we are going to do it differently, purely statistical. we are going to put learning engines in, and we are not going to tell the system. we are going to give it the ability to learn. that was a tremendous break-through in technology and will be a tremendous break-through in history. charlie: and that was the history you based the company on? you basically said to i.m. b. this is our future right here? john: that's right. we have fundamentally made the decision this was about helping humans make better decisions. charlie: rather than try to imitate or duplicate? john: tats right. charlie: why is that the better way to go? john: we have proven time and
time again that man plus machine will beat man or machine. time again that man plus machine charlie: any doubts about that decision? have you been confirmed in that decision by everything that happened since you made it? john: yes. and we see cases in every industry and every domain with that combination of things, an expert in a field make a better decision in a timely matter. a doctor only has a few minutes to prepare for a patient. bring that capability together. now the doctor can make a better decision with the patient. charlie: what has surprised you in this? john: there were many times in the early days when we were bringing up the watson system that it would answer a question, charlie, and i was stunned.
it was how did it do that? we would go back and look at all the computer traces to figure out how it did it. i thought i was shocked and surprised at that time. but when i look at what watson has done in areas like health care in the last three years, it has basically gone to medical school, through its residency and has become an expert in some forms of cancer in three or three and a half years. charlie: so your child has gone to medical school and is now an expert. john: it is true. charlie: do you feel a certain fraternity? john: i am proud of the team. i am proud of the i.b.m. company who made the investments in this. it is now bigger. there is a whole new field that has resurrected in the i.t. industry, and i think it is going to transform not just the i.t. industry but health care and all the other important questions for society. charlie: the other thing you have been witness to is the cultural shift in terms of
artificial spell janssen. everybody was instantly attracted to the idea. then there was the lull. now every company that i know that is a tech company in the forefront of their business is thinking about an artificial intelligence component of their business. john: that's right. charlie: you watch this happen in a short time. john: a very short time. people studied artificial intelligence in the 40's and 50's. the best universities and companies failed at it, but they were trying approaches that wouldn't work. the break through here is to try an entirely different approach. i think the watson has stunned people, and now it has attracted tremendous attention. charlie: talk me through the jeopardy challenge, what it was, what it meant and how you handle it had? john: i think it was a tremendous inflection point. charlie: because you had done
chess already and learned something from that. john: that's right. these games are often viewed as man versus machine, but we are trying to focus our technologist on something that was a milestone. the goal here wasn't the jeopardy match. that was just a demonstration of its ability. charlie: i want to know what you learned from chess? i didn't know they could beat kasparov. john: we built a very large computer that could make all possibly moves faster than garry kasparov. that but was take tackle a deep search problem. watson is aimed at an entirely different problem.
the world's data now is so large, and people cannot deal with it and extract knowledge quickly enough. so we are trying to improve decision-making versus do deep searches in a game. charlie: so the jeopardy challenge represented what? john: we felt the jeopardy challenge was interesting because it tackled the natural language problem. natural language is very difficult for a computer system. charlie: what do you mean by natural language? john: our speaking english. very difficult for computers. it wants to be written in computer languages, which is what programming is all about. we wanted to teach this system to understand first english and other natural languages. so much of our knowledge is in natural language, written in books. now it is all digitized. so we felt the first milestone was to really crack the natural
language and to be able to answer any question in any field quickly better than those two human beings. that was quite a challenge. that was a major milestone. we were looking beyond just human language, into images. charlie: how long did it take you to get it ready? john: we started about five years before the match. at that time it was taking watson hours to answer one question, and it never got it right. but i knew about 12 to 18 months before that match that we were going to be head-to-head with those human beings, and it was going to be a very close match at that time. i remember when i introduced the match to the audience that day, i said to the audience, you know this is not a question of if. this is only a question of when a machine will be able to win at this game.
i don't know if we will see it today, but we will see it soon. charlie: there is some story i read somewhere about a bunch of people at an event, and there is a television on in another room, and they all rush in there they all want to watch jeopardy. that taught you what? john: we love grand challenges i.b.m. what is a problem a system has never done before? we had a bunch of researchers in a restaurant, and at the time there was a famous jeopardy match on the tv over the bar. my team got up and ran in there. we said if we can build a machine to do that, that is a big goal. charlie: that is a big day you realize if we can do this, the future is ours? john: that's right. the day we realized if we can do that, the future is ours.
and then the day that the team said we are going to do it this way versus the old traditional base? charlie: was it a sure bet? john: not at all. you know how to access all the orlando's information. john: everyone had failed at this before. for decades. charlie: why had they failed? john: because they had tried to basically, in structured ways, recreate the human mind and recreate human intelligence, which we still have no idea how to do. charlie: they failed because it was man plus machine. john: that's right. our goal was to only help humans make better decisions. ♪
charlie: so tell me about the final jeopardy challenge. i mean your heart was beating faster. maybe you have done all this work for naught? john: yes. but you know, charlie, the day before we would have told watson to don't embarrass i.b.m. or try to play the other two players or play to win no matter what. we told watson play to win. charlie: how do you tell watson to play to win? john: you tell it to bet more money and get more aggressive to try to win.
you can imagine what the outcomes could have been. charlie: it is like a boxing match, and you are telling your fighter go for the knockout, go for the win. john: it was big for i.b.m. it was our 100th anniversary in 2011. more importantly for our industry, where are we going to take technology in the future? it is not just about gadgets that are cell phones and things. it is about how are we going to hit humans make better decisions? how are we going to solve the health care problems, the climate problems, et cetera? while winning that game fell good, to me it was a confirmation that we could go on and change the borland with this technology. charlie: you really believe you can do that, change the world? john: yes. charlie: change every industry? john: that's right. in fundamental ways. not only are we going to take the cost out of the health care system, which is bankrupt can't countries, we are going to
improve the outcomes. they systems are going to help doctors save lives. charlie: you know people are going to doubt you? john: yes. charlie: why do you think they doubt you? john: they doubt because technologies like this have failed in the best. but i think while they doubt, they watch us very closely. recently there has been an explosion of intelligence in the tech industry. this is one you cannot miss. because it is a learning system, the longer you wait to get on the train, you further behind you will be because that machine is learning every day and getting martyr, and smarter and smarter. charlie: smarter today than yesterday but not as smart as it will be tomorrow. but you are saying you have to get on board now? john: yes.
this is not a technology where you let the early adopters do it, wait until it is de-bugged and then get on the train. because the train will be way out of the station, and it is accelerating because we are feeding it more data. you want to be an early adopter, and you want the machine to start learning in your domain. the earlier the better. charlie: in the evolution from jeopardy to fighting cancer, what was necessary to go that distance? john: well, three to five years to get it done. the first thing is watson had to understand the language of health care and the language of cancer, because it is different. charlie: so one thing watson had to do was understand natural language, and then it has to understand the language of cancer? john: of cancer, which is very sophisticated. it then had to ingest all of the information in the medical literature about cancer.
then we had to have top doctors help to train watson to say that this means that, this means that, to basically prime the pump of learning and get watson learning on cancer. charlie: m.d. anderson is there, and a lot of people identify them a the cutting edge. when you came to them with the possibility of this, what was their response? john: interestingly, they came to us. they came to us. the top docs said i have never seen a tool like this. i think we can do this with watson. i think we can go further with watson. i think we can scale our knowledge, the best in the world not just to make the very best better, but to help the rest of
the oncologists who don't have access to those facilities and the leading edge information. this system has been trained by the best, learns all new relevant information every day, and in seconds, any oncologist has access to the world's best language. charlie: it is dependent on what has been fed into it. can it make a missed diagnosis? john: it can. watson is never perfect. it has the ability to mean it is more right than wrong, but it is never perfect. it can be very, very precise and accurate, often better than human beings. charlie: often? john: in cases where watson has had sufficient data and sufficient time to learn, it will be as good or better than the best because it has more data to ingest than any other human being. charlie: success is termed by return on investment? john: it is in business. this may separate i.b.m.
it is the core of our strategy. it is one of the reasons we are 105 years old in addition to the ability to change. but we want to help society. we chose health care as the first place to aim watson. yes, it is big, and digitized and we want to have a big business there. but we felt the impact that we could have on human lives was beyond anything we could have in any other industry. we are started there. charlie: but you have to be careful. you can't over promise? john: that is correct. but the potential of this, i have been in this industry-0 over 3 1/2 effect aids. i have built some of the largest super computers. i was involved in the system that beat kasparov. this is like something i have never seen before.
what is most exciting to me is not that it is a bigger faster computer. it is going to transform health care. >> they are working with artificial intelligence. they are applying it to their own business model. they took on the game of go recently. charlie: and won. that is even a more complex game than jeopardy i would argue. john: no. i would say it is like a chess game. more complicated in the sense there are more moves than a chess game. but you are basically looking at combinations of steps. that is a different problem. charlie: could you have beaten go? john: well, if we had focused on that problem, that was not our goal. we wanted to go after the big
data problem. we wanted to go after industries. we wanted to help humans that were basically in this cognitive overload because of information, and we wanted to help them make better decisions. charlie: is any of this scary to you? john: not at all. charlie: all-powerful artificial intelligence, knows where we are, what we are changing, and some suggest one day may control us. john: it only knows what we feed it. charlie: is it possible for it to be more powerful and the person who fed it? john: who we are and the things we bring to decisions that watson can never do. every new technology, everybody will have some fear of what it could do. locomotive trains. they are going to accelerate forever, and we are going to be in trouble. the way i look at it is what is
the cost of not pursuing there? there is no other technology that is going to help us find better cures for cancer and treat things per than watson. it will always be limited by the information we feed it and the interaction with humans. charlie: i know a lot of other people who have reservations in addition to bill gates and others. john: some people, to be honest, still think of the old idea of artificial intelligence. charlie: a lot of things to keep up with. john: they keep up with technology, but if you really understand how a system like watson learns and what it can't learn without certain information, it can be comfortable. charlie: what tech changes will make watson better today than the next five years?
john: the wonderful technology we have brought to the cloud is an advantage. we can access watson any place on the planet instantaneously through the cloud. that has been a tremendous break through. but still the quantity of deity generated by human beings is surpassing what underlying technology can keep up with. we need real break-throughs in underlying micro-technology. we need new mathematical algorithms, new technologies that can find more subtle information. charlie: so watson has put a demand on everything that is known now, and to go further, they have to get per. >> they will or even watson won't be able to keep up with the amount of data that we are generating. charlie: in that arena, what
excites you the most, the arena of things that watson is demanding you do in order for it to do what it is capable of doing, given, updating, modifying, improving, creating new? john: the merging of data from different sources is producing new insights. we are working with a medical divides company, fascinating, around the disease of diabetes. we have taken the knowledge that watson learned through the medical literature, the millions of things we have access to in dive particulars, and we take machine-generated data, we bring all of that together, and for the first time ever, watson was ability to predict the onset of
a hype guys mick attack hours ahead of it. there is an alarm that goes off when you are almost in critical condition. charlie: almost, but not? john: but not, but you had better get to the emergency room instantaneously. you are going to have severe problems. charlie: if you look at watson today, is it at 10% of its potential, 25% of its potential, 50% of its potential? john: it is only at a few percent of it's potential. i think this is a multi-decade journey that we are on with this kind of technology, and we are only a few years into it, and we are just starting to learn about the capabilities of these systems. we are only a few percent into the ultimate potential of this.
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