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[ music ] [ applause ] kelly: so, let me begin by...with a littlebit of a word of caution or a way to think about this, and i urge you to try tokeep this in mind throughout the day. it's very easy as we get into areas ofcognitive computing and artificial intelligence to rapidly drop down and start to talkabout deep machine learning and algorithms and some really exciting technologies. and for those of you that know me, you know,nobody loves technology more than i do. and it's a fascinating, fascinating area,and lots of that will be talked about today.

but i think the thing that we mustkeep in mind is towards what end? towards what end? what is it that we're really trying to do here? and i would argue that it'sall about the outcomes. it's all about changing the world andchanging industries and seeing things and getting insights into things that we neverhave been able to get our arms around before. and so, i would urge you throughout the day aswe, again we talk about the various technologies and opportunities to keepthinking about what applications, what can we do with this technology?

how can we impact society and the human statein ways that we've never been able to do before? so, i sort of put it in the flip context, ifyou will, what is the price of not knowing? we had a discussion lastnight at the churchill club and someone asked me, john,what is watson worth? what is the market value of watson? and i said, i don't know. it's really...i mean, i couldcalculate it based on ibm's business but i think it's much, much broader than that. you know, what is the price ofnot knowing the cure for a cancer?

what is the price, the downstream costof that patient towards end of life? what is the cost of not discovering alternateenergy, not drilling into the proper areas? all i know, as i said last night,it's in the billions and trillions. you know, take healthcare as an example. it's a seven or eight trilliondollar industry worldwide; three and a half trillion in the united states. and every estimate says that 30or 40 percent of that is waste and inefficiency or bad outcomes in the system. so, just healthcare in the unitedstates is a trillion dollar waste --

or, cost of not knowing. so, huge opportunity to applythese new technologies. now what are we really after hereand what is sort of fueling this? we all know all these things about big data, the amounts of data that'sbeing generated, et cetera. another comment or question from lastnight -- and a caution flag, again -- is it's very tempting also in addition totalking about algorithms, it's very tempting to talk about what we're trying to do hereas replicating what the human brain does. that is not at all what this is about.

when we started out in the early days-- as guru will know with watson -- we were not trying to do what previous airesearchers had done to mimic the brain. we were doing something very simple. we were trying to build a system that coulddeal with this massive amount of data, because our human intelligence wasnot scaling the way data is scaling. in a sense, there's a new moore's law andthat new moore's law is in the data space. and much of that data is dark orinvisible to our current computer systems. and that was the aha! moment or the awakening for us in ibm research.

we said, we need an entirely new typeof computer system to deal with this. today, it's estimated that 80 percentof the world's data is dark -- meaning that we as humans in our currentcomputer systems cannot make sense of that data. it's either noisy or in formatsthat can't be read. and furthermore, that by 2020,that's going to exceed 90 percent. very interesting numbers because if you...forthose of you what are...know anything about physics or astrophysics, there's sucha thing called dark matter in the universe. there's a set of matter and energy in theuniverse that has not been observed directly but we observe the effects of itin terms of gravity and things.

our optical telescopes and other ourtelescopes cannot see that matter. this is the equivalent in the data space. ninety percent of it we can'tget our arms around. think about the solutions that are in thatdata if we can get to it because we're in a sense just seeing a little slice ofthe world when beyond that little slice, there's great opportunity andprobably great danger for humanity if we can't get our armsaround the rest of that space. so, in my mind, this is not a journeyto reproduce what the human mind does. yes, we'll be inspired by what the humanmind can do, but that is not the objective.

the objective is to analyze and garnerinsights from that massive amounts of data. if we don't, that 93 percent will just keepgrowing and we'll be getting such a minute view of what's going on in the world that we'llreally be in a very, very rough place. so, think about the industriesthat could be effected by this. and i don't make these things up. we are working with every one of theseindustries that i'm going to show you. oil and gas. huge opportunity. the industry spends billionsof dollar per oil rig.

often, drilling is in the wrong place. they miss it. often the pumping from the wells ofthe reservoirs is too much, too little, not optimized, a huge opportunity. tens of thousands of sensors on these platforms. more than current analyticcapability can deal with. huge opportunity to impactthe oil and gas industry. retail. we talked a little bitbefore about the massive amounts of data that's coming through social media.

think of the content and the insights thatcan be garnered from that for retailers. as many of you know, we have a partnershipwith twitter where we get the big hose, if you will from twitter of all tweets. incredible insights into buying patterns,preferences, where society is moving -- insights that can be leveraged acrossliterally every form of commerce. the internet of things, again, i thinkis one of the great next frontiers. signal processing. we started with watson in naturallanguage processing which let of course to the infamous jeopardy!

match, and today it's unmatchedin natural language capability. we've moved it to images and vision. but think about signal processing. machine to machine data will dominatethe data scene in just a few years. and that is noisy, unstructuredand really a perfect application for cognitive computing whether it's connectedappliances or an inner city where you're dealing with security issues, where you'redealing with traffic management issues. all of the things that are going on in the city. a perfect environment for a cognitive system.

security, another area that youmight not think of as a natural, but security is no longerabout building firewalls. security now is about deep behavioralanalysis of people and systems -- a perfect application of cognitivesystems to measure and predict behaviors and abnormalities and react to them in realtime. energy and utilities. we already have instrumented many ofthe meters in many, many countries, but the data now again remains dark. very little is being done.

it's a huge opportunity as we try tointegrate renewables in and start the feedback and altered behaviors of consumers. and one of the biggest, ofcourse, is healthcare. as i mentioned before, an enormous industry,ripe for not only digital disruption -- which was sort of what happenedwith electronic medical records -- but cognitive disruption as wereally bring in new forms of insight. and this is one of the industries where weever really doubled down our bets not only through electronic medical records, patientpopulation, healthcare, but also medical images. and i'll talk a little bitmore about that in a minute.

but think about it, a milliongigabytes per person in our lives. immense amount of information, andin that data is really the secret to our own health and well-being. transportation, another internet of things. we started with, you know, processors in cars; we're now talking about self-drivingor assisted cars. these devices will need to be cognitive. they will need to make realtime on-the-flydecisions about the environment based on learning about the environmentand the driver behavior.

so, every single industrynow is being swamped in data. every industry is trying to find a way toget at and access to that 80 to 90 percent of the dark data and getinsights differentiated. and i think that is really what's goingon here in terms of the turning point in our industry and all of these industries. so, think about this in terms of, weare at an incredible inflection point. we are no longer just sort ofincrementally improving our it equipment. the first era of computing wassimply a set of tabulating machines. largely mechanical.

we put data in through punch cards orsome other means of setting switches. we programmed it by telling themachine what to do, and away we went and we automated basic humantasks such as arithmetic. the second era of computing -- which beganin the late forties and early fifties -- was a real turning point wherewe went to programmable systems. and the point in time where this really startedwas where we had enough memory in the computer that we could put the programmingfrom the punch cards into the computer and let the system run itselfwith no external programming. and again, it was because we had enough memoryin the system to put those instruction sets

into the memory; and of course, away we went. and everything since the late fortiesto today has been programmable. but again, as we looked atthis a number of years ago in ibm research, we said, you know what? we're going to run out of programmers. there's no way we are going to be ableto program and keep up with the scale and speed and exponential growth of data. we had better take a different path. and that was what really got us

on this cognitive computingai path in a major, major way. and we believe that we are at one of these veryunique points in time that only occurs every 40 or 50 years in this industry where we arecreating entirely new computer systems that do entirely differentthings than the last era. this era will be more different fromthe programming than programming was from the tabulating era in my belief. think about also what happened withthat, you know, infamous system/360 which was...ibm had done smallnumbers of programmable systems before that 360, but the 360 was a platform.

it was a platform where wemass produced the systems. we separated the hardware and software andwe create a platform that became the platform that transformed banking, airlines,transportation, literally every industry in the world and remains sort of thebackbone of all enterprise transactions. it's important in this new erato also think about a platform. not a discrete tool to address one problem, but a platform that willtransform a number of industries. and that is how we are thinking about thisin ibm and what we're trying to achieve with our cognitive system and with watson.

so, it all began, it doesn't seempossible, but it was almost five years ago, five years in february that thisinfamous match occurred between watson -- the first really cognitiveplatform -- and human beings. our goal that day was not to just win a gameshow; in fact, it was a pretty close match for those of you that study that game. but we wanted to demonstrate thatwe were going through a transition. we were going through a transition from thisprogrammable era into this cognitive era. and i've been amazed, frankly,what happened since february 2011. this whole field has exploded.

it has stirred the imagination of academia,it stirred the imagination of industry, which i think is fantastic -- because again,this is not about one company or one capability; this is about creating awhole new era of computing. lots of stories i could tell about that match. i'll just tell you one fun one. i talked to ken and brad about,how do you do what you do? i mean, these two human beings are amazing. amazing. i asked them, well,how do you know so much? did you study?

and both of them independently said, no,everything i see and hear, i never forget. i remember it. i said, okay. that's pretty interesting. and then i said, well, whenyou're asked a question, what reasoning process do you go through? because i know how my brain works. you know, i'll start to think aboutalternatives, i'll do lookups in my head. both of them independently said, i don't know.

the answer is just instantly in my head. so, they have complete memory and instantrecall of everything they've ever seen. incredible capability. to beat these two humans, that system hadto be right 85 to 95 percent of time in two and a half seconds or it was lights out. that is a really tough problem in open domain. the reason we won is that we tookan entirely different approach -- not a rules-based approach but an openapproach, machine learning, deep learning and very sophisticated naturallanguage processing.

a completely different approach to the problem. and i think that's what'srequired across the board. so, where do we need to go with thisthen as humans, because we're often...one of the positives of that match was it reallyset the stage for cognitive computing. it captured people's imagination, butit also set up man versus machine, and that was not the intent at all. where do we go? it's not man versus machine. every study has shown that man andmachine will beat either man or machine.

and that i think is a really key point becauseof the different capabilities that we each have. so, we as humans have a number of capabilities which i'm not sure we'll ever beable to really get a machine to do. now, i hesitate to say thatbecause i said that before and we've gone on to get machines to do that. but some of these things relative tointuition, compassion, moral values, unless they can really be quantified, i don'tever see a system being able to do that. on the other hand, these massivesystems and immense capability. they'll have total recall.

they'll be the jenner and rudder of computing. they'll have instant recall ofeverything, source to all knowledge. large scale capability for fact checking; andwith deep learning and the capability to start to reason, we can really get into discovery. so, i think the opportunity,again, here is man and machine. and we are seeing this in every discipline,in every industry that we go into with watson. and the pattern seems to be that we as humanshave sort of a normal distribution of capability for whatever it is we're talking about. it could be simply a call center operatoror literally an oncologist, a cancer doctor.

and the distribution is not surprising totechnical people, a normal distribution. what we're finding in this man plus machineis that we can move that distribution. we can take the best oncologist at memorialsloan-kettering and make them even better. we can take the mean of the distributionand move it and we can take those that are on the tail end of the distribution andmove them up to be as good as anyone else in the world by introducingthis man and machine. and we're seeing this repeatedlyin the financial sector and across other industries of the world. so, the secret is then how do we getthis synergy between man and machine?

now, since that jeopardy! match, this field has lit up. just lit up. and that's of course what bringscolloquium like this together. lots of people are working on this. lots of people are trying to doimage processing and finding pictures of cats on the internet and whatnot. lots of people are tryingto optimize buying behavior. lots of people are trying todo signal processing of voice

or making voice recognition smarter. but each of these is really a point solutionto improve some sort of one-dimensional aspect of a business model or somethingthat they're trying to achieve. and it's wonderful. it's great. but it is "a" tool -- a hammeror a screwdriver from a toolkit. very few, and really, other than ourselvesat ibm, i don't know of anyone that's trying to build a whole toolkit and a wholeplatform equivalent to what we did with the system/360 back in 1964.

we're not trying to just pokeat these individual problems; we're trying to build an entireplatform of capability for all industries with this cognitive computing capability. so, to do that, we took that large machinethat was watson that won that jeopardy! match which was about half as big as this stage, and it was so heavy it probablywould have fallen through the stage, consumed85,000 watts of power. we took the watson capability out ofthat machine, brought it to our cloud, decomposed what was at the time one system-- a question and answering system --

that had basically five technologiesunderstand it. it had other bits and pieces,but these were the big ones. we took that capability, brought itto our cloud so that it could scale. and then we proceeded to offer thatas a service but not just that. build out a suite of services on thewatson cloud that are composable assets. so, in a sense, you as a developercan go in and pick and choose and construct a mini watson fora solution for your problem. and this has been incredibly successful. we have held on one extreme we've heldhackathons where...i still say kids,

but you know, young people in 12 hours arecomposing meaningful solutions with those assets on the watson cloud in a day or two. very, very powerful sort of time to market. now, this was a fundamental decision thatwe made in ibm right after that jeopardy! match. we could have startedjust selling watson boxes and we could have sold a lot of watson boxes. but we decided that, no, we weregoing to make this cloud based and as a service composable for all industries. and that has been what has guided usover the past few years as we build this.

and as you can see, whatwe have a couple of dozen or so of these services availabletoday, this will grow. this has become the platform for our ecosystem. we have hundreds of partners nowbuilding with this capability. dozens of universities engaged in this,hundreds of universities engaged in educating and how to in a sense programor assemble with this language and to develop the underlyingskills to use this. and as you can see, our plan is to developnot just dozens but hundreds of these services on the watson cloud as fast as we can.

we have a pipeline of these servicesand i think you're going to find that this is a very rich environment. so, when you step back, then you sayokay, we're building this platform. we're building all these capabilities. what is the essence of what we're trying to do? what is the essence of thiscognitive capability? first is learning at scale -- learning at scalein data, learning at scale in those solutions. it's about reasoning or developing insights from the data whether it'snatural language or images.

it's reasoning over thatwith purpose, with purpose. with a goal. whether it's a radiologistor a financial advisor. someone that is reasoning over data to getan insight with a purpose to take an action. and then finally, to interact withhumans because as i said, in the end, it's this magic of man andmachine that i think is going to produce really leapfrogcapabilities going forward. so, what gets me excited and has kept me helpingto drive this project forward is to really start to think about how we can rethinkwhat's possible with this technology.

it's no longer about just automating orprogramming these systems for erp or back office or mobile phones and connections. the possibilities with this are immense. medical imaging. we in ibm are absolutely convinced that withwatson capability, with image analytics, with machine learning over these images,we can change the course of healthcare. vast majority, on the order of two-thirds ofhealth information is contained in images, x-rays, mris, cat scans, et cetera. we know that the diagnosis associated with theseimages by humans is not what it needs to be.

think about a radiologist who sits in a roomand looks at thousands of these images a day. obviously fatigue sets in and otherhuman, natural human issues set in. so, we are in the process of buying a companymerge healthcare, 30 billion medical images. we are going to train watsonon those medical images. and not only are we going to do the analyticson the image, we're going to bring the learnings from the electronic medical records becausewatson can read the medical records... bring that information together, bring insightsfrom previous patient and previous outcomes all in one place with a physician to makea decision on treatment in minutes. very, very powerful capability.

i think this is going to changethe course of healthcare. it's going to change outcomes and it's goingto take a lot of waste out of the system. seismology, as i mentionedearlier, we're working with a number of the world's largest oil and gas companies. they are building cognitiveenvironments for decision making. decision making around wheredo we drill the next well? do i bid on this piece of land to tryto get the oil reserves underneath? what's going to happen tothose reserves over time? these are very complex decisionsthat need to be made,

in many cases, in very short periods of time. i think that we're going to help transform whathas also become a very intensive data industry. education is another one thati'm particularly excited about. in a sense, there's a direct analogybetween education and healthcare. today in healthcare, i'm oversimplifying, but we are still diagnosing and treating to the average, by and large. you're still going to getprescription or treatment that is to the doc's best understandingwhat people sort of like you on average he or she has seen in the past.

education is very much the same. we put our children in the class and by andlarge, the teachers teach to the average and they try to adjust a little bitfor children on the two extremes. think about having a watson engage in theeducation system with the individual student where watson can observe the learningpatterns and decide, you know what? this child is not learning properlyor this child is having no problem with these concepts cannot learn thisconcept, and intervening at that moment. think about a pre-k'er. we know that between the ages oftwo and three, the number of words

that child learns is a directcorrelation to their ultimate potential. think about watson intervening and doubling or tripling the vocabularyof a two- or three-year old. an accelerating learning. huge opportunity to change the educationsystem and the outcomes that we see. think about genomics. another area of healthcare. genomic data will probablyeventually swamp image data. everyone involved in genomics will tellyou more data than i know what to do with.

i not only cannot deal with the hundreds ofmutations, i cannot deal with the thousands of pathways that may be causingthat tumor to manifest itself. think about using a watson to do...tounderstand what do those mutations mean and what is the right drug orcocktail to use for that cancer. working with a number of leading edge genomicinstitutions around the united states and canada to explore what watson can do in this area. frankly, it's the only way we'regoing to deal with genomic data. so, let me ends then by reflecting on somewords that i found from thomas watson, jr. as you all probably know,thomas watson was our founder.

tom watson, jr., ran the company during the timeof the 360 and a lot of our explosive growth. and i thought that this quote was quiteinteresting, because he describes systems that are not going to, quote-unquote, robman of his initiative but are going to start to take away some of the...what he refers to asmenial tasks, mental menial tasks and free us up for creativity and other things. and in a sense, that's whathappened during the programmable era. we took what we wanted the machine to do. we put it into memory. we made the machine to do what we wanted andwe went off and did more creative processes.

i don't know what mr. watson would thinkabout what we could do with watson itself. but think about the fact that it'sno longer about displacing work. machines displace manual labor. programmable systems displaced themenial, quote-unquote, mental processes. we're now talking about manand machine tackling problems that were inconceivable just a few yearsago, whether it's education, healthcare, energy sources, on and on and on. some of the world's biggest problems i believeare going to be solved by these technologies. so, again, i'll sort of end where i started.

i urge you throughout the course of theday, enjoy, participate in the discussion around the technology, around what'sgoing on in artificial intelligence. but don't fall into the trap of, we're tryingto reproduce a human brain and don't fall into just the deep technology trap. think about the applications of this technology. whether you're going to create a business,whether you want to apply this into your field or your discipline, there is not an industry ordiscipline that won't be completely transformed by this technology over the next decade.