Home » IBM CEO Ginni Rometty Presents at IBM Watson Group Launch Event (Full Transcript)

IBM CEO Ginni Rometty Presents at IBM Watson Group Launch Event (Full Transcript)

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IBM CEO Ginni Rometty and Watson Group SVP Michael Rhodin introduced a new business with Watson clients, partners, and executives on January 9, 2014 in New York. Here is the full transcript of the IBM event….

 

Ginni Rometty – CEO, IBM

I am so happy to see everyone here. This is a wonderful day, not only for our company but for our clients, for this industry. And what I am here to tell you about is the formation of something called the IBM Watson Group.

Now, for those of you that watch us, we don’t create new units very often. But when we do, it’s because we see something that is a major, major shift that we believe in. It happened in the 1960s. If you study IBM, we had a unit all around something then called the System/360, later known as the mainframe.

Then again in the ‘80s, it was the IBM PC. The ‘90s, we started IBM Global Services. Today is another such moment.

Today is an important moment in our company’s history, and it is also an important moment in the history of technology. And you’ll hear all about this from Mike Rhodin, our Senior Vice President of the Watson Group. He, along with clients, launch partners who are incredibly excited, will all be up here to tell you about Watson. As well, I mean this journey is only beginning; you will hear from Research on everything yet to come.

Eras of Computing

But what I want to do with you is I want to put this in context. To date, to date there have only been two prior eras of computing. The first was called tabulating. It was machines that did just what it says, they counted. This was, as you would guess, the mid 19th century, Herman Hollerith. This was punchcards, this is when IBM did things like the census, Social Security systems. It was the foundation for finance, control, inventory control.

Then, the second era, programmable era. Just what it sounds like, if, then. If, then. You had to program it, tell it what to do. And it is everything that you know to this day. We did it first with the mainframes in the ‘50s and ‘60s. Then it was PCs, tablets, smartphones, anything that’s out there today, it is programmed.

But 2011 — 2011 we introduced a new era to you. And it is a third era, it is cognitive. It is systems that learn. They are not programmed; they learn, and we debuted this on the video you saw, with the something called Watson, and it played Jeopardy.

Now, many remember it defeated the two all-time human champions. But I don’t think everyone understood what was happening behind the icon as it ran. It was a new species, if I can call it that. It is taught, it’s not programmed. By design, it learns by experience, and it learns from interaction. And by design it gets smarter over time and better judgments over time.

But I think what’s most important for right now, for all of us, why we took Watson on, it’s built for a world of big data: 2.5 billion gigabytes per day gets created, and it has — and I underscore the word — “potential” to transform industries and professions everywhere. And to be unleashed, I want you to think about this, though. To unleash all that insights of all this data and I know many of you work on this, you need this new era. In my view, Watson is just in time. A cognitive era is just in time. And this is not just data that the world thinks of as structured; the data you and I can picture in rows and columns.

But 80% of the world is unstructured, tweets, blogs, pictures. And then there’s all this other data about data. Right? So my location, about an object, about a test. And in fact, I think to understand this, what Watson does because you don’t program it, it’s thousands and thousands of algorithms that run, and they improve and they get better and then more algorithms are created.

In fact, I was talking to many of you out there, those of you on Watson now, you have to experience it to see the difference because it is not a super search engine. It can find a needle in a haystack, but it understands the haystack. It is about relations, correlations that you will never see. And this is why we called it a grand challenge when we undertook it.

And you interact with data in a new way. Natural language, and it understands the implications of your questions. And in fact, soon you’re going to hear from Guru, it will ask you clarifying questions back. So, today is about Watson to a new level.

We started with what we call in Research a grand challenge — something that we don’t think the world has yet solved or could solve. It starts as a grand challenge. We then did the work to be sure it could be commercially viable. And it has already begun transforming industries. You are going to hear from many of these partners and you will see and experience it outside. But, as well a growing ecosystem.

So, the world will experience Watson four ways that you will get a taste of today: transformational solutions; enterprise solutions; I said, a huge ecosystem; and then, something called Watson Foundations. So, let me give you just a real fast word on each.

Transformation solutions. Look, this is about transforming industries, professions, like I said. We made the decision to tackle the world’s most difficult problems first. We started with health care, we started with oncology. We have had partnerships with world-renowned experts. They are “the” best in the world. To me, the greatest testament to Watson is they have dedicated their time, their life for years here working with us; they only do that when they see a breakthrough in science, that this will change the face of health care. And I know every one of our partners on this agrees, that we will change the face of health care.

So, you will see Memorial Sloan-Kettering, the cancer center, two years, oncologists working on how to predict best treatment, evidence, confidence, behind that. Cleveland Clinic, how to use Watson to do teaching, students, to pass the U.S. medical exam. You will see Wellpoint, how is an insurer to approve, but approve based on evidence, evidence, fact-based and to have it done fast. And then, MD Anderson. Those that have ever tried bridging the gap between researchers and clinicians, and that’s what they’re working on.

Then you have the enterprise solutions. So, one was transformative; enterprise. Great projects, great problems, but I consider these more scalable, repeatable. Higher volume, quicker deployment. And we’ve already had in market something called the Watson Engagement Advisor, how to give you relevant answers to lots of questions. Today, you will meet new forms of advisors, and more ahead.

Then, the Watson ecosystem. We want, by design, partners, entrepreneurs, venture capitalists to all build their solutions around Watson. So we are announcing a Watson Developer Cloud. So, think of this, for those of you in technology, the API world ahead of you. And this is going to be APIs, content, talent, in the first clouds up, retail, travel, consumer health care.

Now, we did a really sort of quiet launch of this in November, and overnight 750-plus applicants to build businesses. And that’s with hardly telling anyone here. And then, I said Watson Foundation. What that is, a portfolio of information analytics, capabilities, because that’s what does underpin this cognitive era.

So, everyone here today, I can’t be prouder to announce this group on behalf of the IBM Company. Another billion of investment over the next several years. Over 2,000 researchers, developers, business experts. We’re going to go ahead and put another 100 million in to fund an equity fund for the ecosystem. And very symbolically, this group is going to be headquartered in New York City, in Silicon Alley, 51 Astor Place. Hundreds of people, incubator, design center, solution center, all there.

And Mike and all our partners here today are going to talk to you about what they’re doing. So, you’ll meet on stage, Dr. Craig Thompson, the CEO of Memorial Sloan-Kettering, the cancer center. Dr. Tom Graham, right in front of me, the Chief Information Officer for Cleveland Clinic. Jay Katzen, all around President, Elsevier Clinical Solutions. Kent Deverell, CEO, Fluid Retail. And Terry Jones, who is, as many of you know, the co-founder of Travelocity and Kayak.

Now, I said we don’t form a business unit very often. And when we do, it’s because we believe we can help our company, our clients and our partners establish leadership in a new era. I can’t be more excited. You can tell that. I can’t be more excited about the impact that Watson will make not only on IBM as a company, but our clients, their companies, institutions and society at large. And a moment about society at large.

You know, it’s been 18 months, two years, Memorial Sloan-Kettering, Dr. Kris has been working and training Watson with a whole staff of other physicians. Think about this: infusing Watson with the world’s best knowledge and experience. I want you to think about what that means to share that kind of expertise from this institution and many more. More physicians, more patients, than they could ever physically ever see. Now, you imagine that, that same thought for any profession.

And then, those people get the chance to dialogue with the best collaborator they could ever have, Watson. Unprecedented learning, constantly getting better, and making sense out of all of this world’s data. So, you are going to hear many examples today. They are enormously promising. They are a downpayment on Watson’s potential to transform industry, enterprises, and actually new levels of knowledge, for whether that be citizens or the masses. You just have to talk to anyone here who’s experienced it. The interest, the excitement from clients, it is unending. They view this as the very beginning of a journey.

So, when an earlier Watson, Thomas Watson, Jr., son of our founder, a half a century ago announced the System/360, I will share with you, at that time computer science was an arcane thing. It was not experienced by many people. So, now today, this is a new era. It’s an era of machine-human collaboration, and it is dawning now. You will see the Watson Cognitive Cloud Services, and you will see how they, and you look at it, you will see how it will understand me, it will engage me, it will learn and get better, help me discover. It will build trust, and it has an endless capacity for insight. This is a new era, and I can’t be prouder of the IBMers and the clients that brought us to this day today.

So, it is with my great pleasure that I introduce you to the new leader, the Senior Vice President of the Watson Group, Mike Rhodin.

Mike Rhodin – SVP, Watson Group

Good morning. Good morning. And welcome to a very deep crowd of standing people at the back. This is great. This is an exciting point. I couldn’t be prouder, I couldn’t be happier, and I couldn’t be more honored to be asked to work with 2,000 of our best and brightest colleagues on how we can take this forward. Working with our partners, great companies, great institutions, that see the same vision that we see on where we’re going to take this technology as it evolves.

The formation of a new group is a big deal. But it’s part of a journey, it all starts with the germ of an idea. Someone had the grand challenge idea of, could we really answer the world’s hardest questions? Right? IBM Research did an incredible piece of work, culminating in a pretty daring display on live television. So, you start to look at television and you start to realize if it had gone the other way, right, it might not be so much fun.

But an incredible piece of work: 27 core researchers dedicated four years built on the shoulders of decades of research and technology. What we did next, we took that team and we built a team to start looking at, how would you commercialize it? We built a team under Manoj Saxena that, as many of you have met, embodied the essence of what it means to be an innovator, to be a startup. We intentionally hid them, right? A tiny group, protected. Based them out of Austin, let them play, let them experiment, let them learn. Learn in the market, working with many of you, our clients.

Now, any startup goes through many phases, right? They had to learn how to fix bugs and make it better and improve it every day. And over the last two years, working in the market, cocreating, collaborating with clients, with partners, we believe we’ve created something that is ready to go. Ready to go mainstream, and mainstream is where we’re headed.

With the creation of the new group, we’re going to take this from those 27 researchers, to the few hundred people that have been working in startup mode for the last two to three years, and we’re going to move on to the next phase: 2,000 people. That’s a lot, right? You’re going to see today examples of technology that are going to come out. New products, new capabilities that are going to really improve what we mean by cognitive systems, what Watson really is.

I think you’ll see that what you knew Watson as was merely the tip of the iceberg. The depths of our IBM researchers that have been working in parallel to the commercialization team have built a whole new wave of technology that now today is moving over to the new group. That technology is going to be rapidly commercialized and put in market and you’ll hear about some of those advances as we go through the morning.

We’re going to take some technology from our world leading software business, stuff that will help us move this along much faster and join the group. Right, so today we’ve gone from a few hundred to several hundred and over the course of the year that will continue to expand up to 2,000. Rapid growth environment.

Now, many of us have, you know, heard about Watson, we’ve read about Watson, we see articles about Watson, we see people speak about Watson. There’s YouTube on Watson, right?

So, what is Watson?

Right. If you take it at its essence, at its core, it’s a system that understand natural language. You don’t have to write programs, you don’t have to learn things like Fortran or Java. You just ask it questions. It reads. Think about it as reading, right? When it reads a lot, it adapts and it learns. It gets smarter.

When it gets smarter, you can start to ask it questions. When you ask it questions, it will generate and evaluate hypothesis, potential answers with a level of confidence. When you think about that, that’s how we work: we read, we learn. We start to answer questions. That’s how we know whether we’ve learned that or not.

Watson learns like our children do. How do you know when your children are learning a new subject? How do you know they’ve actually learned it? You ask them a question. You see whether it gets the right answer. And when it doesn’t get the right answer, you help them discover the right answer, and it learns. It gets smarter.

And the next time, it gets that right answer and it builds upon things. But it just doesn’t learn from what it knows today. You can add more data to it. It reads new books, every day. And as it reads new books, it learns. It connects the dots with what it just read with what it already knew. Sometimes the new reading contradicts what it already knew. It has to sort that out. The same way we do. Right? It has to understand new information in the context of its relevance — the connection — to the old information that it had, and how important is this new piece of information.

So Watson has come a long way. But this is really, think of this really as just an engine in a cognitive system. It’s not the end state; it’s the beginning state. So as we start to move forward, Watson is getting smarter, we’re adding new capabilities to it. It’s learning to reason, to think through things. To help people using it move along a journey to come up with the right answer, the right diagnosis. It’s using that first engine I talked about as a subroutine, as something that it calls. That it asks questions to. That is new technology from IBM Research called IBM Paths, WatsonPaths.

Watson is learning to explore, visual exploration technology, to help you wander through massive amounts of big data to find that relevance, find that hidden jewel in a haystack. It’s learning how to visualize answers, not just speak back to you. Draw pictures for you. The art of human communication is not just text. Right? It’s pictures, it’s text, it’s images. All of those things have to join in to the system we call Watson.

So, today we’re going to talk about this next wave of technologies that are adding to the cognitive system family. We’re going to go through some examples with some world-leading experts. We’re going to talk about some of the new products that we’re going to announce today.

And then, we’re going to peek off into the future as to where this may go next, because I think there needs to be more circles on that chart, and Guru is going to talk about those a little bit later. So, Ginni mentioned the new headquarters. Whenever you have a growing family, sometimes you have to get a bigger house. Right? And when you’re getting a bigger house sometimes you want to focus on location, because location matters. Right? When you walk through the areas of Silicon Valley, or Silicon Alley and you look at the real estate, you look at the buildings, the history of that area, the edge of the East Village, historic Manhattan property.

In the center of it, there’s this brand-new iconic beautiful building. On a block all its own, standing out from everything else in the area. This is our new home. This will be the headquarters for IBM Watson. It will be a place where our people, our best and brightest, work with our partners, with our clients to imagine the future, to help create the future. It’s going to have an incubator that helps businesses get started with Watson. We’re going to have some of our best and brightest designers, graphical experts, help those products become really what they should be. And it’s right in the middle of the trendiest, hippest area of town. Not exactly where you would have thought IBM would open a new headquarters. So, we’re pretty excited.

We’ll be in our environment a little bit later this year. The building is done, as you can see. That’s the live picture of it. But the inside of the building looks pretty much like this. So we have a little bit of work to do. Even though, I think this is pretty cool, right? So I don’t think we’re going to do too much work as we go forward. So, we’re putting the right people, as Ginni said, we’re investing a billion dollars in this over the next few years. We’re creating a incredible environment to put this together. And we’re going to share Watson with the world. Right? Eras are not ours alone; we just happen to have a history of shepherding them and bringing them to life for the rest of the world.

We make markets, we create entire industries, and that’s what we’re going to do with this. So, what we’ve learned as we’ve worked with Watson, as we’ve worked with many of you. As Ginni said, there’s three kind of classes of things that we see happening, and there’s some homework that you have to do to get ready. And that’s what we’re going to talk about now.

First, we truly believe based on the work we’re doing, that this is going to transform entire industries. It’s going to make people rethink how business gets done, how their organization works, how we treat patients, how we sell things to clients. It may in fact start to rehumanize the Internet. As we’ve worked on those kinds of solutions, we’ve recognized that there’s a set of repeating patterns that we’re seeing over and over again. And in our industry, when we see repeating patterns, it’s a clear indicator that you can productize something, so we’ve started to do that.

We’ve launched products for the enterprise that can deliver value faster, repeatability is important. And normally you would view transformational solutions and enterprise solutions as right in IBM’s alley. We recognize that the power of this technology is really about what it can do for everyone. And to get to everyone, we need help. We need an ecosystem, we need partners. Right? And we’re opening Watson up to the world and we’re asking for that help, because we think everybody that decides to help, that decides to join us, is going to change the world and we’re going to make it better. That’s what the ecosystem is all about.

Transformation

So let’s start talking about transformation. Now, when the Jeopardy match occurred and we were all holding our breath as it came down to Final Jeopardy, we weren’t the only ones watching. It turned out that many of our clients were watching. And when the match was over and finally aired on TV, our phones started ringing. And it wasn’t who we would have normally expected: doctors, health care was the first to call. They saw something that could be the light at the end of the tunnel. They’re faced with an enormous sea of information. Not just the medical reference material you have to learn in order to become a doctor; it’s the enormous amounts of information that are published every single day, from the researchers around the world.

And then you intersect that with everything that’s being published, by the pharmaceutical companies on new drugs and drug trials, and you start to see the magnitude of the information they’re having to keep up with. And at the same time, these doctors are having to see X number of patients per hour, eight to 10 hours a day. And they don’t have all day to read all that information and learn. They need help.

Now, the tunnel gets longer. The tunnel gets longer because this idea of DNA sequencing and genomic medicine is going to bring on a flood of information that has to be looked at in conjunction to all of that other information. And then, add in electronic medical records, add in family histories, add in the demographics, add in what’s going on in the city around you, what bugs are flowing through. That becomes a real problem. And the doctors said, you know, this could help me sort that out. And they didn’t say, install it so I can use it; they said, come with us on a journey. It will be worth it, because with this we can actually change the world.

Now, as we get ready here to kick off this next section, we have a short video to kick it off, and I’ll bring up my first guest.

[Video Presentation]

[Mark Kris: I thought Jeopardy was just a great example of what this system could do. Watson can probe every nook and cranny of their record and try to learn more about them than any one doctor can. It truly is personalizing the care of that patient in a way we were never before possible. We’re developing a set of solutions that will bring Watson’s cognitive computing capabilities to decision support around medical technology. The cognitive computing capabilities are truly unique, and unparalleled. Done correctly and applied correctly they can democratize the use of clinical evidence in a way that’s never been done before.

The Watson technology is a leapfrog in computing technology. And to me, it affords the capability to learn the art of medicine not just science of medicine.]

 

So, as we think about health care and the implications of Watson on health care, we think it’s not just helping a doctor see a patient and diagnose a patient. We think it’s far reaching. We think it’s everything from how medicine is taught, how medicine is practiced. How medicine is paid for. The end-to-end ecosystem, cognitive computing can actually start to pull together a massive sea of information to streamline that and make it work better. And that’s really what we’re trying to do, is make it all better.

So, with that I’d like to invite my first guest up. Dr. Craig Thompson, CEO of Memorial Sloan-Kettering Cancer Center.

Dr. Craig Thompson – CEO, Memorial Sloan-Kettering Cancer Center

Well, I’m incredibly pleased to be here. Mike’s just laid out the challenge for you that goes on in the health care community, and we read about it every day. Memorial Sloan-Kettering Cancer Center is incredibly pleased to have a partnership with IBM Watson’s team to develop a research or support tool, decision support tool for medical professionals as they help patients and their families deal with the most complicated and difficult challenging decision that patients face in their lifetime, and that’s a diagnosis of cancer. I want to give you just a little overview flavor.

You heard a little about it at the very highest level in the video we just saw, but how we’ve gone about it in partnership with the Watson Research team. So, as Ginni just said, this is a machine approach that provides a cognitive approach to understanding complex problems.

So, how do we go through the cognitive training? That started with a collaboration between the two research teams, two years ago, as Ginni said. We put our best physicians, of the thousands of physicians we have taking care of every year 125,000 patients with cancer, all the experience built into those individuals, and went through a training process of the cognitive abilities of Watson with the research team.

We have refined that over two years, you met the physician leader in that video, Mark Kris going forward. Out of that two-year process, we have reached a point where we have developed a partner for the health care professional in making the best and most informed decisions for patients and their families about the diagnosis of cancer.

How does Watson actually do this? In the largest overview, there are really three parts of the process. Watson has learned how to retrieve all the relevant information that’s necessary for a personal decision of that patient and their specific circumstances relative to their cancer. All the medical information that exists in the world as well as all the personal information that’s in that person’s health care record.

Second, once it’s retrieved, it can use its cognitive ability to integrate all that information. And in the way of the world, often patients worry that someone else is making the decision. Watson is a collaborative partner with the health care professionals, because from that information and that integration of cognitive abilities, it provides a prioritized list of what are the best possible choices of among the full range of choices for the best diagnosis and treatment of that patient’s illness. And it works in partnership, as a collaborator, with the health care professionals that are dealing with that patient and their family about their treatment. That’s the overview.

I’m very pleased now to introduce our physician in chief, who is in charge of all thousand of those health care professionals that have been training Watson from the medical perspective to give the specific overview – Dr. Jose Baselga.

José Baselga – Physician-in-Chief of Memorial Sloan Kettering

So, good morning everybody. And this is a standing room only, gee, you are all the way in the back so I hope you can see the slides well. I’d like to present some practical aspects on the challenges that we are facing today in taking care of patients with cancer and then it all will click together. And I hope I can demonstrate clearly that Watson will become a critical partner in the way we deliver care to thousands of patients not only at Memorial Sloan-Kettering but all around the country and hopefully around the world. This is our traditional oncology decision making process. This is what Dr. Thompson learned when he was a junior faculty; that is what I learned when I was a junior faculty; and, this is the tradition of centuries of practicing medicine.

Our process to make decisions in cancer care was based on looking at the chart of the patient, and it was a paper chart with limited information. Looking at some x-rays, very basic x-rays. You know, things got complicated and then we had CAT scans, but we had very few CAT scans, and that was it, what we had. Some laboratory data. We had a few drugs, not too many.

When I trained in breast cancer we had one hormonal therapy and three types of chemotherapy, that was it. So, our decision making process was in this way simplified. And then of course, we had books at that time. Actually, in oncology, we had “the” book that we had in our clinics and that we would consult often, but it was just a book. And the number of papers that were published every year was very minimal and there was no Internet. So, that was something that we felt comfortable in doing.

We had these data that was being analyzed in the clinic and then we offered to our patients our best decision and our best proposal for therapy. Fast forward, this is not any longer what’s happening today. The field of medicine has changed in a way that we could never have predicted. And there’s a sea of data — like Mike has mentioned to you — that we have to deal with in every single aspect of our patient care delivery.

To begin with, who is reading books any longer today? We have thousands of articles that come up every day to address every single issue of our patients. Every single need, every single question, it’s written in thousands of articles that come out. We have electronic medical records with tremendous amount of data about the patients: their prior therapy, family history, response to therapy, et cetera, allergies, interactions.

An imaging revolution: we are talking about CAT scans, we are talking about PET scans, we are talking about sonograms, we are talking about multiple imaging techniques that on their own are extremely complex to interpret and to understand and to integrate into our clinic space.

Therapies. Well, in breast cancer now, we have today 80 therapies, but we have 800 therapies that are being studied in clinical trials. Eight hundred therapies — from four to 800 in 20 years. And then of course, what was being referred to, we are just not looking at the pathology slides any longer; we are sequencing our tumors, and we are analyzing 20,000 genes and we will analyze even more complex parameters in every tumor. And our medical knowledge at Memorial Sloan-Kettering, from a few hundred physicians to a thousand physicians that we have right now. So, a tremendous amount of knowledge that is in our system.

So, this is the rise in medical publications, exponential, and this is not going to get easier. The need to develop physician-based medicine based on genomic data — every patient’s tumor is different, every tumor is different. We don’t have just two types of breast cancer; we have now at least 50 types of breast cancer, and this happens to every tumor type. And we will need to have a matching of the patient’s tumor characteristics with the specific therapies and be able to monitor this very carefully.

And just to give you an example, this is what’s happening today at Memorial Sloan-Kettering Cancer Center. We are routinely sequencing the tumors of thousands of patients by these new platforms that I will not go into in detail. But just to give you the sense of complexity, we can genotype genes in full. And this is our list. And you can read the list, and this is done on purpose. You’re not supposed to read the list, because even if you read the list, you will not understand what it means and nor will a physician understand what it means. And every one of these genes can have multiple mutations. And these mutations, among them, interact. You cannot deal with this even if you have an hour per patient in the clinic, which is not the case, by the way. So, the traditional process is not working any longer, is not working, and that is where Watson comes to play, and that’s why we are so terribly excited.

Watson becomes then our partner, it becomes our colleague, it becomes the source for integration of all the data and the source for advice. And then, one more important component to all this: the patient today plays a role, and the decision is not just in one direction. Patients are engaged in expressing their preferences, in expressing their desires, in expressing their priorities with us, and Watson takes this into account as well. So, Mark Kris has led a terrific team of our best physicians in this process in downloading thousands of clinical charts from Memorial Sloan-Kettering, downloading our guidelines.

I’ve taken guidelines from all over the world, teaching Watson to think medically with medical logic. And Watson begins to make really good decisions and choices that then they propose us. And the system is self healing and self improving on a continuous basis.

So, I’ll show you a very brief example of how this is playing out. This is the case of a patient, Ms. Yamato. This is a young patient being diagnosed with lung cancer. And we — the data is downloaded from the electronic medical record, and this is a patient with a diagnosis of lung cancer and has a number of key points by cascading the imaging. And Watson does its first round of analysis with the data that the medical record is providing. And it’s telling us, what you have today from this patient is insufficient to make the right decision. And it’s telling this on the left side.

But if you have to make a decision — because sometimes in medicine you need to make a decision with what you have — these are the options, and this is our level of confidence based on our physician’s experience that the machine has been learning. But the machine is telling us, we would…you should consider doing this molecular analysis, because this is a lung cancer in a non-smoker from Asian descent, and these have mutations that are important for the right choice of therapy. So, we do that.

We follow the advice of Watson. We don’t have to, but we do. And sure enough, Watson had a feeling about that, because we find a mutation on the EGF receptor. So, here we go. We have a mutation that could direct therapy. So, we go to our guidelines and the ones that we use across the country are from the network of cancer centers called NCCN. And in the guidelines, it’s very clear. It says if you have a tumor with a EGFR mutation, you should consider a therapy with a very specific and exciting therapy called Erlotinib, and that’s what we would do. I would do that. I would propose that.

Yet there is a paper — and this is a true story — that has been published recently, that shows that of all the mutations in EGF receptor, there is one that does not respond to this therapy. One, there are only about 10 physicians in the world, maybe less, that would know this offhand. I’m telling you, we have a thousand physicians at Memorial Sloan-Kettering and at most two or three know this. And I have been very…I’m boasting this because that means we have 30% of the world knowledge. But only two or three would have that information.

So, based on this, Watson is telling us, do not give this patient erlotinib; offer this patient conventional tumor therapy. And this is what was done. Do you think this was a difficult case? You probably think it’s a difficult case, right? This is easy. We are dealing with much more complex decisions to make every day in our clinical practice. So, this is why we are so terribly excited about this partnership and that’s why we’re devoting so much time. We believe in it. And we feel strongly that this will change the way health care is being delivered. It’s about integrating data, it’s about integrating knowledge and it’s about creating wisdom together between IBM Watson and our physicians.

Thank you very much.

Mike Rhodin – SVP, Watson Group

So, outstanding example. That is what this is all about. That’s about making the world better. Right? That’s about making all of us better. Right? Now, while we were working with Memorial Sloan-Kettering on this, John Kelly’s Research team was off working on what’s next, and we were lovingly calling it Watson 2.0 for a while. But they were working on a set of technology that starts to transform how Watson itself behaves — where Watson just becomes an element of a bigger system. We’ve been calling this WatsonPaths.

And what WatsonPaths is, is a capability that enables the system to start to reason. And with that, we started to work in partnership with Cleveland Clinic to think about, how could this type of reasoning, this type of capability, change the way medicine is taught? Could we work with students? And in turn, could the students work with Watson, and could it be a mutually beneficial arrangement? I’m really pleased to welcome Dr. Tom Graham up, the chief innovation and head of orthopedics at Cleveland Clinic.

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Thank you.

Dr. Tom Graham – Chief Innovation and Head of Orthopedics at Cleveland Clinic

Thank you, Michael. Humbling to be sharing the stage with these luminaries and on such a propitious day. Congratulations to the entire IBM team. The center of the medical universe is where the patient and doctor get together. Don’t make any mistake about that. And my previous colleagues and I took an oath, written by this fella, that said we have to bring our A game to every one of those interactions. And I’d have to say, and I’m not one to try to avoid difficulty, but I think it may have been easier then, because it was all right in front of you. All the tools you needed: your eyes, your hands and the patient were there.

Well, we know what’s happened in the last few centuries. This explosion of data, medical information, doubling every five years. What a challenge, to do that, and bring our patients the most expert inferences that we can bring to improve diagnostic and therapeutic outcomes. We have to be accurate. We not only have to have the right diagnosis, patients want to know what’s going to happen, and at all the different milestones that they’re going to face. This is a timed event. It’s like sports; there’s an immediacy to it.

And obviously today, we don’t want to just say, geez, in general this happens. This patient wants to know what their problem is, their solution is, and their journey through their medical engagement will be. And we have to make it understandable for them. We have to be able to converse to them in a way they understand, they can go home and tell their families. The genius of debuting Watson on Jeopardy just wasn’t its ability to sort through, what, 200 million pages of data in a couple seconds. It was to bring it out and introduce us all to an amazing capability.

And I think, Mike, why you got those calls from health care is because we didn’t see it as a competition. It wasn’t man versus machine. Medicine is a team sport. We said, hey, this is now somebody with whom we can think together, learn together and build a team because medicine needs to be delivered with multiple different perspectives, knowledge capabilities and different tools.

So, health care is the natural application, we believe, for Watson technology. Huge data sets. Again, not just in the library, but down in patient records. And this is how I started my career, sorting through paper records. Now, in the big data explosion in health care informatics, we have all the information parcels through which to go through. We’ve already talked about how it expands — Jose did a great job of talking about that. I’ve already told you how it’s important to have timely answers. And you know, medicine is still an art. We think algorithmically, and I think that that’s important, to have a partner who does so also.

When we started working with the Watson team, we understood, I think, what they sought. They literally wanted to know more about the art of medicine. They wanted that humanistic approach to medical decision making, the clinical perspective. We at the Cleveland Clinic needed to have a different kind of conversation with our data. It needed to be more intuitive, it needed to be a back and forth. And like I said, it needed to be quickly performed. It’s no surprise that the great advances in art, music, philosophy, science, happen in the port cities. That’s where ideas had discourse.

Innovation happens at the intersection of knowledge domains. So, if you have a partner that has these competencies to discover and evidence all the passive information you may need and can understand you, and here we sit at the Cleveland Clinic with an approach patient centric, centered around the actual patient, it’s a case-based learning model. We’re the highest acuity hospital in the world. The thickest patients are our laboratory. And we were early in the pool at the adoption of electronic medical record keeping. These are two natural partners. So, I call this the virtuous cycle.

Who is the student and who is the teacher? Well, both. And it’s constant and consistent and escalating all the time. What a great paradigm — that these two groups, professionals in medicine, can be having a conversation with somebody scanning the world’s data and an individual patient’s data to elevate quality, access and fiscal responsibility. So, we had two projects: one, the WatsonPaths as Mike has been discussing; and one, focused on electronic medical record applications.

Let me just talk a little bit about those for just one second. So, on the WatsonPaths side, this case-based learning and care delivery model was a great foundation. Watson assisted us in researching the world’s data and justifying particular diagnoses and in order of probability. The data that could be accessed by our students was real time. And then, they interacted with Watson. They described what they were seeing, what they thought Watson would come back to that. It would just refine the data exchange further, and honing it in with greater confidence.

So, the concept of powering the decision support with this inferential model is right up the alley of an art like medical care delivery. And then again, to go out and find that one paper by that one group is exactly what we all need because if it’s your loved one or you in that room, that’s 100% of your experience that day. And maybe the most important thing you’ll do that day, week, maybe in your life. And so, it has to be at that focused level.

And then to be presented with, on the physician side, with likely/unlikely, possible, probable opportunities, that’s when we get to exercise all the great things that we’ve learned from our mentors. And so as you probably know, the concept of a simulation center where you can get hands on opportunity with mannequins, et cetera, has really been the explosion and a real paradigm shift in American medical education.

To practice on a mannequin, hey, heck of a lot better than practicing on your loved one, right? So, take that three-dimensional and physical concept that the simulation center has been doing, now we have a cognitive simulation center. That’s why our students are so enthusiastic and they spent the kind of time to…they put this together.

So, essentially, when you have an opportunity to not have a deterministic outcome — “this the answer” — well, that’s sometimes too unidimensional and sometimes not right. But how about a probabilistic answer? Hey, these are the things it could be.

Here’s the level of confidence. Use your brain, interact with your partner, come up with the right solutions. On the other side, when we’re talking about an individual patient and scanning electronic medical record, we know that all we have done heretofore is just archived a tremendous amount of data. We have to know how to extract from it, navigate through it and look for those gems of hidden insight that might be the answer to the problem that’s facing us.

And so, if we’re going to improve diagnostic accuracy, essentially having somebody over your shoulder — in this case it’s a dashboard, which we’re all used to looking to — is one of the greatest tools you can have. And that’s what Watson does. It organizes it, it highlights at an individual’s electronic medical record. Stuff we might have glossed over. Those pages might have been stuck together or the chart got dropped on the way. That’s the old model.

But even now when we have access to electronic medical records, it’s really hard to look through pages and pages and pages. I’d love somebody to organize that and present that to me, and that’s what Watson has done for us. I love the power of the partnership. We have to solve the biggest problems facing us in health care. We all know where health care stands on our nation’s and the world’s priority list. Taking care of the individual, large populations, managing the expanding information explosion that we’ve been discussing. And then frankly, there’s just not enough of folks with a MD after their name going forward.

And we have to all do this in a way that’s extremely fiscally responsible with your resources. So, it’s an extreme thrill to be here to talk about, I think, a paradigm shift from a computer at worst being a paperweight and at best being an archive to now being a partner. It is our partner. And we’re very proud to say that Cleveland Clinic and IBM are in this partnership for our parents, converting knowledge to the wisdom we need to improve the world’s health care.

Thank you very much for allowing me to have the stage.

Thank you, my friend.

I hope that was okay.

Ginni, I hope that was okay.

Mike Rhodin – SVP, Watson Group

So, we’ve talked a lot about health care. Health care is not the only industry that Watson can help transform. Watson is particularly good at understanding large amounts of information — information that doesn’t change instantaneously but changes regularly and starts to correlate information. Banking, wealth management, client experience is another such domain.

A few short hours ago, one of my colleagues, Bridget Van Kralingen, who runs Global Business Services in IBM, was in Singapore signing a Watson deal with the largest development bank in Asia. DBS has signed on to start to transform and reshape what they believe will be the future of banking in Asia, perhaps the world. So we’re starting to expand not just within industries but also around the world as the promise of Watson continues to spread globally.

Now, we’ve talked about transformation. As we work with clients, we’ve started to recognize that there are many things that we do in business that are somewhat repetitive, that we have large numbers of people doing, and they don’t really use our brain power in the way that we would like to use it. Right? Call centers are a great example, where people are answering questions all day long, and it’s a high turnover business, people are coming and going. Training costs are one of the key drivers of how efficient a call center operates.

So, we started to study that space, and we said, well, what if there’s a better way to engage clients than to operate a call center? What if there’s a way to actually put an advisor on the front of the company that allows an interaction and also allows a seamless escalation between interacting with information and interacting with a human in the call center, understanding what it knows but also what it doesn’t know. And changing the entire experience of interaction between clients and the organization they’re working with.

We’ve recognized this plays out in a lot of different industries: insurance, banking, anybody that runs a call center, which is just about every business that we know. So, we started to recognize repeating patterns. Repeating patterns are things we build products out of. And once you build a product out of it, you can install it faster, you can get it up and running faster, and the time to value is faster. That’s what this is all about.

We’re going to bring a series of solutions to the enterprise that we believe can deliver value in months, not years, and will continue to have the same capabilities, the same opportunities that you just heard about in transformative solutions but on a smaller scale and faster delivery. So, we call these cognitive solutions across the enterprise.

A little video to get us started, and then we’ll move along.

[Video Presentation: In 15 seconds, IBM Watson can analyze terabytes of data to help operators quickly find answers to a caller’s question. Soon, Watson will help transform customer service. Let’s build a Smarter Planet. In 2011, an IBM cognitive system won Jeopardy. Now it’s tackling bigger challenges in health care, finance and customer service. IBM Watson is ready for work. Let’s build a Smarter Planet. – Video ends]

Okay, so how do we make this work? How do we make this come to life? The first product that we came up with was this idea of helping organizations improve how they interact with their customers. Making it easier, more natural. Using natural language; not forms, not, you know, keys that you have to type in in order to use a system. You talk to it, you type in questions, it gives you answers. It helps you think through things. It has a conversation. It was mentioned earlier that these conversations can’t just be one question and one answer; they have to continue to evolve so they become dialogues. They remember context from the first question to the next question. One answer feeds into just the next question. We built a technology called Watson Engagement Advisor that we’ve been working with a couple dozen clients now around the world to start to explore how they take their data — their data — put it in Watson and allow people to ask questions so that they can deliver value faster to their clients.

To help us take a look at what Watson Engagement can do we’re going to have one of our newest partners, Scott McKinley, an EVP of Innovation at Nielsen.

Run the video, please.

[Video Presentation:

Scott McKinley – EVP, Innovation at Nielsen narrates:

Nielsen is a global market research company. In fact, we’re the largest research marketing company in the world. We’ve organized our business into what we call watch and buy. In our watch business, we measure everything that consumers watch, all the ways they spend time with media. That includes television, radio, digital, mobile and any new device that comes along. In our buy business, we ingest and measure trillions of data points from major retailers such as Wal-Mart. We look at the data and analyze it for indications of buying patterns, we look at demographics, we help the retailers understand who is buying the products and where those products are being bought. And that’s happening on a global basis. What we’re doing more and more now is bringing together what people watch with what people buy, and that allows us to comment on the impact of advertising expenditures on sales, which is, of course, what every marketer wants to know. Our clients are advertisers themselves, the agencies who service them and the media who deliver media to consumer audiences. We produce lots of data across our watch and buy functions, and that data feeds the ecosystem and allows marketers to understand what’s working and what’s not across the media landscape. Our existing tools are good, but we need to innovate to find better and better tools to do this. We need to do it faster, more efficiently.

When we look at Watson, we see this as a tremendous opportunity, in two ways. First of all, we’ve got decades of structured data, as I said before. We have syndicated data sets around television or around consumer behavior, around buying behavior at the retail level. This stuff is stable, but we need better and faster access to that. More interestingly is the unstructured side — again, these firehoses of data coming out of Twitter and Facebook and other social media and digital media channels. We don’t have tools to very quickly paw through that amount of information and uncover insights that a media planner at an agency can use to do a better job of understanding who a person is and where reach them in the media to deliver a message about a product. So, that’s where we think the opportunity is. We envision a world where a media planner aiming a product at millennials can quickly query mountains of data in a way that they can’t today and then uncover an insight about where to most efficiently reach those folks.

What do they care about? Where do they spend their time? We today don’t have tools that can do that, and we think that Watson provides that opportunity for that. We partner with IBM and Watson to uncover new ways to use this sophisticated technology to help the marketer make better decisions. We envision a world where a marketer can use natural language queries of enormous data sets — the types of which I’ve described earlier — to uncover on the spot insights about how to most efficiently and effectively reach their audiences; to help understand why audiences care about a product or a product category; and, how to better create and deliver advertising to folks as it’s more relevant and more effective. – Video concludes]

 

Scott had wanted to be here with us today but they’re having an all-day conference with the entire senior leadership of their team talking about using Watson down in Florida and about this time I’m on a video down there. So, we ended up swapping video stories across the two events to make this come together. But Watson Engagement Advisor has been about helping organizations find answers to questions. One of the other patterns we uncovered is that it’s not just about finding answers; sometimes it’s about finding the right question. It’s about moving through a sea of data and looking for the white space, something that hasn’t been explored before.

Think about pharmaceutical companies. Think about all of the chemical compounds that have ever been tried and patented. You don’t care about the ones that have been tried as much as you do the ones that have never been tried, if you’re doing research. So, it’s all about discovery, finding new things. So, today we’re announcing the Watson Discovery Advisor, which is a companion product to the Watson Engagement Advisor, our second major Watson enterprise solution.

And here to help us understand what it might do for a particular industry, I’d like to bring Jay Katzen up from Elsevier.

Jay, thank you.

Jay Katzen – ‎President, Clinical Solutions at Elsevier

Thank you. Again, my name is Jay Katzen, President of Elsevier Clinical Solutions. And what I want to talk about here a little bit is just give an introduction to Elsevier, talk about clinical solutions, talk a little bit about the health care market and some of the challenges and why we believe a partnership or collaboration with IBM can really help transform this market and solve some of the problems today.

Elsevier is part of Reed Elsevier. Reed Elsevier is a $10 billion company; Elsevier is about a $3 billion business unit. We’re a world leader in providing scientific, technical and medical information to clinicians and students across the world. We have customers in 180 countries. We serve more than 30 million scientists, students, health and information professionals worldwide. We drive innovation by delivering authoritative evidence based content with cutting edge technology, allowing our customers to find the answers more quickly.

From a clinical solutions perspective, we cut across the entire health care spectrum both from an academic standpoint as well as pharma companies, retail pharmacies. But our focus is really on the provider setting: on the hospitals, the experience of physicians, nurses, pharmacists and extended care team. Our mission is to lead the way in providing health care practitioners access to the most relevant evidence based information and tools wherever and how ever they need it, to empower them to make better informed decisions, to save lives, to improve care and reduce cost.

This next point is really about, how can Watson help us, delivering information at the point of decision. How can we ensure that we take all the context about a patient into play and make sure we give the clinician the right information to make a better decision? Health care is changing. To be honest with you, health care is a mess. It’s in a state of disarray, and it’s been in a state of disarray for decades. We talk about the sea of information, information explosion.

If you look at this, 5,000 volume of medical articles published every month. I’d have to read 164 and change articles every single day to keep up. We have a ton of information, an explosion of information, and yet now we have more information, with images, with genomic databases.

Do we want more information? Absolutely. We need more information, but we need to tailor that to the specific patient, to the individual encounter, and we need tools to help us do that better. There’s incredible waste in health care today, about $700 billion of waste in the health care system. A lot of that is due to unnecessary care, lack of care coordination.

Can information help us improve the overall care? Reimbursement. The government is finally starting to get involved from a health care standpoint. They’re demanding change, regulatory changes, ensuring that hospitals have to improve outcomes. They have to reduce readmits and improve patient satisfaction. At the end of the day, it all comes down to, how do we improve the quality of care delivered, and what can we provide our clinicians to help them in those roles?

Focus on increasing quality and reducing cost. This is going from, it used to be volume-based metrics; now they’re encounter-based, it’s about the individual patient. I’m going to give you an example. There’s a 55 year old woman, had metastatic breast cancer for six years. She was battling cancer. She had a double breast reduction. She had three surgeries. A year and a half ago, she wasn’t feeling well. She went to the hospital over Thanksgiving — not the ideal time to go to the hospital over the major holidays. And she was having trouble breathing. So, they put her on some antibiotics. They didn’t look at her history, per se. They intubated her because she wasn’t breathing very well.

A few days later, they looked at her, they gave her some other antibiotics. She really wasn’t doing better. A week afterwards, a new doctor came on rounds, said, you know what? There’s a protocol out there that says after seven days of intubation we have to do a trach. They didn’t call in a specialist, and they called in a general surgeon, put the trach in. They didn’t look at her history, and they didn’t realize that actually her airway was compromised based on a couple of the surgeries. A couple days went by, she was still having trouble breathing. She went into respiratory arrest, she developed sepsis. She died 23 days after entering the hospital — not from cancer; from something that could be avoided if they had the right information, if care was coordinated the right way, if they looked at her patient history to bring all this information in. And it’s not the physicians’ fault per se. They’re challenged. We’ve heard about this. They have limited time between patients. They don’t have access to the information they need to make the right decisions.

Now, is this an anomaly in the health care system? So, if this is one-off, maybe it’s okay. Maybe this is leading into the next bullet point here. What would you guess — it used to be the sixth — what would you guess the third leading cause of death is in the United States? Preventable medical errors. There’s a study that came out back in September that said it actually used to be around 100,000; it’s about 400,000 people die every year from preventable medical errors. This has to stop. This is real. Okay. We can help prevent this stuff.

So, why are we looking at Watson? Why do we think Watson can help us? We’ve invested tens of millions of dollars in our content, in our technology, to develop comprehensive broad and deep information to provide to our clinicians to help them make better decisions. We’ve tried to integrate it at every step of the workflow, whether referential, whether you’re looking at it up on your iPad, on the Internet, whether there’s a mobile device, whether it’s integrated into the CPOE system, your electronic medical record. But it’s still not taking into account everything it can to help make better decisions.

We have trusted evidence based information. We don’t look at information from an individual hospital; guidelines, procedures that they have, genomic databases. There’s an ability for us to transform the way information is delivered and how questions are answered by working with the Watson technology: provide fast, clinically-tuned search, speed to a relevant answer.

There was an article in December about a woman that was going to have a hysterectomy. She selected a noninvasive procedure and was basically going in, small incision, doing a morcellation, which is basically breaking up her uterus. Very safe, very effective, heals pretty quickly. Unfortunately what they didn’t realize was she actually had cancer. So, by doing this, spread the cancer throughout her entire stomach. She went into stage four cancer. So the question is, using Watson — and actually there’s some examples earlier today, and there was one in Austin about this — can we look at the patient, look at the person’s history, understand what’s happening out there and prevent something like this? I don’t know if they could have prevented that case, but I guarantee you it can prevent some of these things in the future.

The combination of what we’re doing with the Watson technology can improve health care. It can transform the type of information we provide and save lives. This is why I think Watson is important, why we’re working with the IBM folks and why we think we can transform health care.

Thank you.

Mike Rhodin – SVP, Watson Group

All right. Now, Watson reads. It knows how to answer questions in natural language. My good friend Terry pointed out that it also needs to learn math, right? And we have an answer for that. Today we’re going to launch Watson Analytics.

We previewed this technology in fourth quarter under a codename of Neo as a natural language way to start to interact with data sets. Simply ask a question like you would of Watson, but instead of getting a sentence back, you might get a suggested data set or set of data sets to choose from that might be relevant to your question.

Once you select the data set, the system interprets the data, understands it, figures out what the best visualization of that data should be and creates an interactive visual graph for you to interact and play with and explore your data. In fact, it’s just another answer to a question, but it may be mathematical as opposed to in natural language — another important component of the system as we go forward. This capability is going out in beta next week, it will be live on our cloud.

Another capability that we’ve added is Watson Explore. Part of this art of discovery is exploration, being able to use tools that allow you to graphically start to explore, to navigate, to look at large sets of data, to look for that needle in a haystack so that you can find it and then maybe do something with it. It’s another key, important product in what’s becoming a system. It’s not just a question and answer thing anymore. It does questions and answers, it reasons, it explores, it visualizes, it analyzes.

We’re seeing the build-out of the platform for cognitive computing of the future. We’ve seen the tip of the iceberg. It’s getting a little bigger as it comes into focus, but there’s still a lot more behind this. Now, one of the biggest learnings of the last two years as we’ve worked with many of you and with clients around the world is that everybody wants to work with Watson but not everybody is ready for Watson.

One of the key problems is, how do you sort through all of your information, all of your data? How do you get rid of the duplicates, get rid of the conflicting information? How do you actually find it all? We built a portfolio of capabilities across our information management business that helps organizations start to sort through all the different silos of information from SharePoint sites to enterprise content management sites, to social sites, to pull together that information and to make it ready for Watson.

These are the foundational technologies that help speed up the time to value, help you get through the data, sort it out, clean it up, so that it can be ingested by Watson. So, Watson Foundations becomes an important part of how we help customers get ready and drive the road to the next platform, next era of computing.

Now, transformations, products, where you expect us, ecosystem.

Why an ecosystem? Because we can’t come up with all the ideas. In fact, when we started this idea, Manoj and Steve started working with me saying, we need an ecosystem. I said, well, what kind of ecosystem are you going to build? And we started going through it, we started thinking through it. And we said, well, what ideas are they going to come up with? And they said, we have no idea. Right? We have no idea what they’re going to come up with, because that’s why we need them.

We need entrepreneurs with bright ideas that can imagine the future that we can’t see. So, we picked a handful, a few, really close partners, to experiment with us, to play with us, help us figure out how to build a SDK, how to build the tools that are needed to build a ecosystem.

How do you build a content store? How do you get a developer cloud up and running? How do you test your application? How do you make sure this makes sense? That’s how you get started in an ecosystem. So, let’s run a little quick video and then we’ll get started.

[Video Presentation: So, if you can take the best practice of a lawyer and make it replicable through Watson, or the best practice of the best oncologists in the world and help people in hospitals around the world to diagnose patients, that’s really, I think, the true beauty of a Watson-based system. I think the biggest challenge facing retailers and brands today is the fact that consumers are now digitally connected at all times. Consumers empowered like they never have been before with information. If we are able to put this content into an ecosystem, we’ll be able to make that content that we have usable in ways that we haven’t even thought of. It’s interesting when you show the ideas and concepts and the vision to retailers, they get it.

There’s an instantaneous aha! moment when they understand that they will…they can have this great experience, they can interact with their consumers in a way that they’ve never been able to before, and that most importantly, the content that they spent a lot of time developing, the content that’s out there, can really be used in a way that it hasn’t been used before. The ecosystem itself is an environment in which more innovation will occur and will help us understand even better how we should be producing content in the future.

Very few organizations, I think, have the scale to really do something meaningful, and I think the fact that the ecosystem approach in terms of allowing companies like Fluid and enabling companies like Fluid to tap into Watson can do is a great thing. We have no doubt that it will have profound implications on how companies are organized, how customers are served, how people will work. And the possibilities are truly endless. – Video concludes]

All right. So, what is an ecosystem? When we worked on this throughout the last part of last year, we said the first thing you need to have is a developer cloud, some place that people can log on, use the technology, develop their applications and test them out.

The second thing you need is to be able to get access to content. The fuel of a cognitive system. How does that content get into the system? They have to get added together. So, the environment makes it so that you can pick the content, pick the system, put them together, start to work on your application. But it’s not simply enough to put it up on the Web and say, play with it. Making a pool of talent available to the ecosystem so that we can really accelerate how the ecosystem will build out, the speed it will build out, is another key element of our ecosystem initiative.

As Ginni said earlier, when we announced the ecosystem environment, we had 750 entrepreneurs and companies approach us very, very quickly saying, I want in, how do I start, what do I do? Then we had to start the usual questions: are you ready? Do you have your data? You know, we start to go through the process. And we’re working through that with the 750, but since we printed the charts it’s now 890. So, it is continuing to climb.

And the news of today I have a feeling we’re going to have a lot more very soon. Once you put up a number like $100 million in ecosystem investment, you get a lot of people’s attention. So, $100 million in equity investment as Ginni talked about in startups and through VCs to help drive the ecosystem around it, and 500-plus technical experts in our talent hubs to help work and incubate those new startups.

Now, the three partners that we worked with initially, Welltok, MD Buyline, Fluid Retail, they all came up with ideas that I wouldn’t have thought of, which is exactly why you do an ecosystem. Welltok in the area of consumer health care, a personal health care concierge. MD Buyline starting to use Watson to help make procurement decisions better in the B2B health care environment; and, Fluid Retail putting the expert personal shopper on the back end of e-commerce.

Let’s hear from Kent, who was in the video a minute ago, to talk a little bit about what they’re going to do at Fluid.

Kent Deverell – CEO, Fluid Retail

Saving lives, improving health care is a tough act to follow. But I’ll talk a little bit about how we’re leveraging Watson to really transform the digital retail experience.

Quick background on Fluid, who we are, what we do as a company. We live at the heart of digital shopping. We’re all about creating great digital shopping experiences. We do that by fusing technology, strategy, design to turn shoppers into buyers, to try and get people to that moment of conversion, that aha! moment when people say, yes, I want to buy that product, have a great experience, buy it, and they get it.

We’re fortunate to work with some great brands, leading national brands like Levi’s, The North Face, ULTA. They’re great innovative partners. Everyone is very excited about what we’re doing here. I’m going to share some examples, show you how we’ve developed some initial prototypes where we think it’s really going to be game changing.

First let’s step back and talk a little bit about what’s going on in digital retail today. We all know e-commerce is massive, right? 80% of consumers research and buy products online today, pretty much everybody. Digital influences more than 60% of all retail transactions, and that’s really important, because it’s not just about buying online; it’s about the influence of digital, it’s pervasive. 60% of 4.8 trillion transactions are influenced by the digital experience, and of course, it’s growing at a phenomenal rate.

But today, the digital retail experience is really driven by three key things: price, convenience and selection. Quick show of hands: how many people out here are Amazon Prime members? Wow, more than half. So, it’s probably 75% of you are Amazon Prime members that currently have over 20 million people paying them for convenience, paying an annual fee for the factor of convenience. So, that’s amazing when you think about it, right? It’s really, it’s just…it’s just changing, it’s really, what do I want, getting convenience out of this? Is there more to it? What is more to it? How do I get better satisfaction out of this process? There’s problems.

Convenience is great, but at the end of the day, most sites have a 2% average conversion rate and a 60% to 70% purchase abandonment rate. So, why is that? We think it’s because consumers want real advice, but it’s not available online. Can you find great prices? Sure. Is there great selection? Sure. Can I ship it to house tomorrow? Yes. But am I really confident in what I’m buying? We know that 60% of consumers abandon their purchase practice because they can’t get information.

We know that 80% of consumers want advice, want help during the shopping process. And we know that fully two-thirds of consumers start their digital shopping journey with a specific product in line, which means they’re not using it as a discovery tool, it’s not an advice tool yet. So, we look at that and we know technology can really help change this equation. And there’s been an explosion, certainly. Big data, social media, personalization, content proliferation. All those things are having a big impact. But sort of just incrementally nothing is really a game changer yet. They’re all moving the needle just a small amount.

We think that making sense of it for consumers is the next big thing in digital retail: making it a more personal, relevant, intuitive experience; moving from keyword-based queries to conversations; making it visual; and again, providing real advice. So, when we think about the dynamic and we think about this thing about what is a great retail experience, I think we’ve all had the experience of going into a store and working with that great sales associate, that great salesperson that really helps you find the product versus the e-commerce experience. The great sales associate, they’re personal, they’re proactive. They’re conversational. e-commerce, it’s very impersonal and user driven.

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The great sales associate asks intelligent questions and clarifies dynamically; e-commerce, very structured, linear, form and keyword driven. The great sales associate, educates broadly and effectively; e-commerce, there’s a ton of information out there but consumers have to dig for it. The great sales associate creates intuitive and relevant upsells; e-commerce, very data-driven.

That’s a good thing, it’s important to leverage the data, but it’s just one tiny element. You’re not really understanding the person when the e-commerce upsells. Great sales associate interprets and recommends in context; e-commerce, very limited recommendations usually limited only to the site content. And finally and most importantly, the great sales associate makes you feel good about your purchase.

When you walk out of a store you’ve had a great sales experience, you feel good about what you buy. You feel confident. You feel confident you got the right product. e-commerce, you’re on your own. You click buy, you put in your credit card information and process transaction. Is that really the right product? Is that really what I wanted? There’s no real way to know for sure. So, we see a world where you get the best of both worlds.

We think the opportunity to deliver a natural conversation, understand and learn about a consumer’s specific needs, provide access to product information in context of the shopping experience. Quickly assimilate multiple content sources, go as deep as you need to, and then quickly resurface; and ultimately, provide rich and relevant recommendations.

So, our goal — we call it the Expert Personal Shopper, teaming up with the Watson team – the vision is great; how do we bring that sales associate experience on online? How do we make a more human experience in the shopping process? The cognitive cloud can bring this to life. We’re able to leverage technology through the Watson ecosystem that will come to life would never be able to create on our own, incredibly powerful and being able to put that to work for consumers in a meaningful way during the shopping experience we think is incredibly powerful.

We call it Fluid XPS — Expert Personal Shopper. It’s the ultimate shopper GPS. I’ll show you quick example.

Before I get into the example, though, I’ll talk a little bit about sort of my aha! moment when we started working with the Watson team. And it was the beginning of the summer, so just six months ago. We’ve made incredible progress in six months. But I was shopping, it was the beginning of the backpacking season, summer camping season, shopping for a new sleeping bag for my son, my 10 year old son. He had outgrown his other sleeping bag, I had to get him something. I was willing to spend some money to get something that was a good product, was going to last, was going to be appropriate for all the various uses where we need it. Went online, probably went to five, six different sites. Filled out the forms. Put in some data, how much we wanted to pay, how much I want it to weigh. Where were we going, what was the temperatures. Got back, I don’t know, 15, 20 different suitable products. Started digging through the reviews.

Tried to figure out, okay, well, which one is the right one? What am I going to buy? Three hours later, got in my car, drove to REI. Walked in the store, talked to a sales rep, told him what I needed. Five minutes later, I left with a product that we were extremely happy with, great experience. Right? And that is the problem we’re trying to solve. We want to make that happen. We want to be able to bring that to life online.

So, we went to one of our customers, The North Face, and we said, we’ve got this great opportunity, this Watson technology. It’s really cool. We think it would really help you with some of your purchases. We really think that there’s an opportunity to help change how people want to shop for gear online. So what I’m going to show you are just some examples of what we created with The North Face.

There’s a full demo in the back, I encourage you all to go take a look. It’s very exciting. We call it the Compass Gear Guide. Our goal here is really, again, to create that expert personal shopping experience. How does somebody shop for a product when they go into a store? Not when I go and type in whatever keywords I’m trying to guess to game the system on the search box and filling out all the forms to get information back.

It starts with a question: how can I help you? Very simple, straightforward, single screen. Not a lot of clutter. And I ask it a question, just like I’d ask my friends or the sales rep. I’m gearing up for a 14-day backpacking trip, what equipment do I need? Watson comes back, takes that information, says, okay, 14-day trip, backpacking, what’s the kind of equipment we need? Parsing through all the information and says, here’s a list of gear suited for a 14-day backpack trip. What would you like to start with? I can then ask it a question. Say, well, I want to look more about technical packs for my trip. Please tell me about it. And so on and so forth.

You see it becomes more of a conversation, it’s much more natural, it’s engaging, and I can quickly go deep into products and I can quickly surface back up. And it’s providing me relevant, contextual information to what I’m doing. So, we think that that kind of shopping experience is really changing the way people are going to shop.

And ultimately, it’s about two things: it’s, one, it’s natural, responsive and it’s personal. Meets intelligent, intuitive and limitless. And we think that that is going to be, you know, three years from now, we hope you’ll be shopping this way.

Thank you.

Mike Rhodin – SVP, Watson Group

Okay. So, three entrepreneurs, three ideas, three examples, up and running, starting down the productization curve and getting them out into the market. Exciting stuff.

So, what other things can get done in an ecosystem? What other ideas do people have? We’ve invited Terry Jones, co-founder of Travelocity and Kayak, to talk about, what if we could actually change the way we travel? You personally invented many of the e-commerce travel sites. What could we do differently? Terry?

Terry Jones – Co-founder of Travelocity and Kayak

Thanks, Mike. So, I’m here to talk about travel, specifically leisure travel. What is leisure travel, anyway? Well, I like this definition: travel is the sherbet between courses of reality. Right? It’s fun. It’s impactful.

But travel planning, well, maybe not so much. Right? I mean, we moved from 19th century travel planning where you went to see the agent to 20th century travel planning where we do it all ourselves, and that was great. I mean, I had a lot to do with that. And it’s been very impactful.

But 20th century travel planning isn’t always easy. In fact, the average consumer visits over 20 sites when they’re trying to plan a leisure trip. And that’s just not the right answer; it’s kind of like the sleeping bag. Right? Now, I’ve seen a lot of change in travel. I started my career as a travel agent in Chicago 40 years ago, actually, as a receptionist. And believe it or not, the very first reservations I made were extremely high tech. I sent telegrams. That’s how old I am.

So, technology’s moved a bit since then. I mean, we’ve gone, first we went to connecting computers, and that’s what I did at American Airlines with Sabre when we automated all those travel agents with reservation systems and ticketing systems. And that was a great advancement, but we still had to call or visit to get that information that we wanted. Then information found its freedom. It escaped. And we were able to do it ourselves as travel went online. And that was powerful for a lot of reasons; one is that prices became transparent.

When Orbitz put up this price grid, all of you said, I’m never going to pay a thousand dollars for a leisure ticket again. You probably haven’t in the U.S. So, a very powerful change. And we moved on with kayak to connecting pages so that you could search in one place and then go buy direct, and that’s meta search. And again, a pretty big change in travel. We moved to connecting people with mobile.

At Kayak alone we have over 30 million people using our mobile app. So, travel…travel’s come a pretty long way. We have all kinds of different bidding auction mobile portals, all kinds of different sites. And you may not be aware that travel is the biggest part of e-commerce. The blue graph is e-commerce; this is travel outside of e-commerce. Travel is larger than the next four categories of e-commerce combined.

Why is travel so big? Well, I think there are a number of reasons. One is, prices change quickly, seats fill quickly, so, we need this immediacy. And plus, on the Web, of course, video can promote the emotion to sell travel-related products. So, I think that’s why it’s grown to be the biggest part of e-commerce, but something’s missing.

And what’s missing is expert advice. You can’t get expert travel advice on the Web. You can get reviews, but you can’t get advice. So, here’s a secret, never revealed before. The guy who started the online travel revolution, me, I use travel agents.

Now, I didn’t use them to get here; I used Kayak. Okay? I don’t need that for a trip to New York. But I want an experience when I go on vacation, and using an expert allowed me to cook with a one-star Michelin chef in France, allowed me to stay in a boma in Botswana, allowed me to hire a guy who could read hieroglyphics to my son when we went down the Nile in Egypt, allowed me to make noodles in Beijing with my daughter. So, expert advice is necessary.

Today, travel advice comes from books, comes from newspapers, comes from TripAdvisor, comes from Facebook. Sometimes it comes from data culled from past trips; not very often. And of course, it comes from friends, and from conversations, and from agents.

But in online, everything has to fit in a box. You know, you have to know where you’re going, you have to know the dates, you have to know the time. So, what do we do to change? Why can’t discovering my perfect trip be as easy as a conversation? Well, maybe it can be. A simple entry to answer a difficult question. I think this is one thing you need to walk away from this Watson meeting today is to understand we’re asking deceptively simple questions.

But actually, we’re making…sorry, we’re making very simple entries and asking really hard questions. It looks easy but it’s not. So, could you ask this question today of many travel sites? I’d like to go to a four-star beach resort in January with my wife and two kids, needs to have a great spa, kids activities and good restaurants? You could ask it, but you wouldn’t get an answer, would you?

So we had a Watson demo — I don’t think we have it here today — that said I want to go on an adventurous but nice vacation with my husband and children. And the answer, with cognitive computing, actually can be done. We did that, Watson analyzed 64 million reviews, 15 million blogs, 7,000 guides. It understood adventurous vacation, husband, children, so on. And it put it all together and it said, you should go to Bali, with a very high level of confidence that Bali is the place for you, 97% level of confidence. But maybe that isn’t right.

And because it’s about a conversation, how about the ability to go back and say, well, yeah, but we love the beach but we want some time on dry land. So, then Watson came back and said, well, you really want to go to Punta Cana. That’s something you can’t ask any travel site today. If we do that, we can move travel outside of the box. And frankly, if people are always telling you to think outside of the box, maybe something’s wrong with the box.

And there is something wrong with the box. We have to move travel outside the box. Now, are there other things we could do with travel? Sure. What about off-schedule operations? How many people here were disrupted last week trying to move in and out of New York? And what were you doing? You’re on the phone. Your flight has been canceled, press two. I didn’t understand your response. We can book Dallas tomorrow. What were you saying? Agent, agent, agent, agent. Right?

Because these machines can understand what you’re saying; they just don’t know what to do. So, what if you could say, I need to be in Fargo tomorrow at eight a.m., and what if the system understood have to, Fargo, tomorrow, and eight a.m.? A revolutionary change in customer service if we could do that. That’s something Watson can do.

How about an easier way to use those miles? This is the Jon Iwata program. He asked me for this. How about the ability to simply say, I want to use my miles to go first class with my wife to a romantic resort in January. Can’t do that today. So, I think if we take cognitive computing and run it against these huge data problems that we have in travel, we will pick the lock and turn data into advice and make travel advice as easy as the kind of conversations we all have today. And maybe, just maybe, we can revolutionize travel once again.

Thank you.

Mike Rhodin – SVP, Watson Group

I think that’s a great lead-in to our next speaker. We’re going to think about what Terry just said, which is, imagine the future. It’s limitless. The ideas here are limitless. The idea of being able to have an idea, a cloud, some content, pull it all together, get some investment, get some expert help to get an application up and running.

Let’s talk a little bit about what the venture community looks like today, how they think about what we’re doing with Watson. Jean Sullivan. General Partner at StarVest Partners here in New York. Come join me on stage and we’ll have a conversation.

Hi, Jean. First, why don’t you introduce yourself for everybody?

Jean Sullivan – General Partner at StarVest Partners

Hello, everyone. I am Jean Sullivan, a co-founder and general partner with StarVest Partners. We are a venture capital firm here in New York City. We’ve been in business as a firm with $400 million under management, and solely invest in B to B enterprise software. And so we all have a long experience in that. Even when people gave it up, we said we’re staying with it all the way, and as you can see it’s back.

Mike Rhodin – SVP, Watson Group

Thank you. So, as you look at what we’ve been talking about today with an ecosystem around Watson and all the investment that’s going in from IBM and actually in the world around big data and analytics, what do you see in the venture community right now about ideas, and how can this idea of cognitive computing really start to revolutionize your business as you think about where to put your money?

Jean Sullivan – General Partner at StarVest Partners

Well, Mike, did you know that many sectors are in slumberland. And guess what? Watson can wake them up. That’s what we believe, and it is so exciting. Let me give you three quick examples. One is the whole human capital management world. They’ve been sleeping, steeped in paperwork and data. It’s really been quite confusing. But they have broken out just recently, and Watson can just take them further. Huge amounts of data.

And you know, I believe that Watson could also solve one of our biggest problems — joblessness — because so many people have no idea of the array of opportunities that are out there, and think of companies that want the best people with the best skills. Talk about sifting through large amounts of data. That’s the perfect role for Watson. I love that use case, don’t you?

Mike Rhodin – SVP, Watson Group

It’s a great use case.

Jean Sullivan – General Partner at StarVest Partners

A second would be, certainly…certainly you have heard today about some of the innovations in retail, retail infrastructure, as you just heard. And we really believe in that as well as incredible innovations in health care. We think that’s a great area.

Thirdly, huge amount of actionable trends could be discovered from sifting through data in fin tech. Certainly, what about insurance? And you have many great insurance customers. Imagine their problems being solved by being able to normalize what’s going on with over 50…with 51 state regulators and the issues there of just normalizing opportunity and regulation there. There’s three quick examples.

Mike Rhodin – SVP, Watson Group

So, one of the things that you have a passion for is this idea of incubators, right? And how incubators can play a role in the VC community. We talked this morning about the new home for Watson in the middle of Silicon Alley where a lot of the startups in the area reside. How important is it for us to have an incubator as part of that?

Jean Sullivan – General Partner at StarVest Partners

Well, welcome to putting that in New York City, because we are proud that we’ve spent, we’ve been able to invest multiple billions just since 2008 right here in New York and making this one of the great tech centers. And we believe that IBM can continue to prosper these tech centers. But we as a firm did participate in a B to B incubator sponsored by the City of New York just these past six months.

We saw companies enter as projects and leave the program as high-growth businesses. That’s what incubators can do. We are strong believers in them. And that’s the kind of job creation that incubators and accelerators and certainly this Watson incubator can create. This is very exciting. We are so proud you picked New York for this. This is great.

Mike Rhodin – SVP, Watson Group

That’s great. In fact, we just actually bought a company a couple months ago out of one of those incubators with Xtify. It’s an interesting experience, because usually when we do acquisitions in this, we usually buy companies that are a little bit later stage that actually have their own office by that point. And the Xtify team was in shared office space in that kind of environment and now they’re actually going to come over and join us in Astor Place as well.

Jean Sullivan – General Partner at StarVest Partners

Well, all this is great, because that’s certainly what a venture capital investor wants. It’s a win for the company, it’s a win for IBM, and certainly a win for the investors. This is what promotes innovation and jobs for the U.S., and it is very exciting. But I’ve got one more idea related to New York. You know, thanks to our wonderful just outgoing Mayor Bloomberg, he created the Cornell Technion Center, and I would love to challenge IBM to not only continue the work — which I know you’re already doing with Watson and universities — but why not have an East Coast/West Coast bake off and get that Cornell Center really cooking? I’d love to see that one.

Mike Rhodin – SVP, Watson Group

Well, we may take you up on that. That might be a lot of fun. I’m a big fan of fun. As you can tell with the Jeopardy match, we do like to compete. So, how important is it to the VC community when a company like IBM not only talks about building an ecosystem put tools together but puts its money where its mouth is and frees up capital to invest?

Jean Sullivan – General Partner at StarVest Partners

Hey, this is critical. And you know, some of the issues we didn’t talk about. If incredible scientists and innovators can feed a million people a day in India, why can’t we go further and fix world hunger? I don’t think that’s so far out of reach for Watson to work on. Certainly joblessness, I believe in that to the max. And you heard today about potential proactive cures around cancer. I mean, that’s what it’s all about. I see great, great, profound changes that Watson can make.

Mike Rhodin – SVP, Watson Group

In the investment community that you’re participating in, how are you seeing the influence of these kind of converging trends between social, mobile, big data and cloud? And how does that kind of relate to what cognitive is going to be all about.

Jean Sullivan – General Partner at StarVest Partners

Certainly solving local problems with partnerships. I know that’s what IBM’s all about, really innovating around partnerships. In many cities, as I said a few seconds ago, creating global centers of technology, that has a lot to do with creating, again, jobs and wealth around the world. I think that’s exciting. And because IBM’s so intent on making the world a Smarter Planet, what about innovations around energy? So, there’s so many different areas that we can fix.

I heard words today like profound, innovation, these kinds of things are critical for success. And wow, how impressive that IBM’s right there putting intellectual capital and capital to work. This is very exciting.

Mike Rhodin – SVP, Watson Group

All right, Jean. Thank you very much for joining us today.

Jean Sullivan – General Partner at StarVest Partners

Thank you so much.

Mike Rhodin – SVP, Watson Group

So, this morning we’ve talked about transforming industries, we’ve talked about products, we’ve talked about ecosystems. We’ve talked about imagining the future with new ideas that ecosystem partners could start to build.

But what happens next? What could Watson become next? As we think about it, we start to see the circles populating around Watson. Watson needs to learn to do more than read. It needs to see. What do I mean by see? Images, video. The analytics around those will feed Watson. They’ll help us understand how to deal with contextual information in a different way.

We need to hear. And like most things, we’ll also need to learn to listen. We also need to find a way to help the world experience cognitive technology in a new way. So, I’d like to take a quick look at a video from IBM Research and then invite Guru up to talk about this.

[Video Presentation: The problems that we’re facing now are too complicated for a single human to figure out all by themselves, and so, the entity that’s going to solve the problem is going to be a combination of humans and machines working together to make a kind of integrated intelligence. One way of framing the core research question is, how can people and computers be connected so that collectively they act more intelligently than any person, group or computer has ever done before? At the highest level, you could imagine a truly intelligent computer that would understand situations, would be able to figure out explanations of events and be more inventive with respect to scenarios, overcome the limits of the imagination and creativity. So, there is really no limit to what computers ultimately could do.

We have big problems to deal with as a country, cost overruns, ramping inefficiency. And now we have tools that can help us deal with that, where I think 10 years ago we didn’t; we had relatively primitive tools. So, I’m excited for the next decade. I think that we’ll be much better doctors, we’ll hopefully get much better health care as patients, and some of these tools will help us get there. I think when people imagine machines and people working together, sometimes it’s a little frightening idea of having a computer help you think. But I imagine it’s being kind of like a violin, that if you look at a violinist and violin together, it’s really the violin that’s making the noise. But the violin and the violinist are able to do something much more than either of them could do separately. I think the future is going to be humans and machines working together like that, like the violinist and the violin. – video ends]

A nice little thought there at the end of how these systems are going to continue to evolve to become collaborators, as we talked about earlier. So, as we think about the future, IBM Research will continue its groundbreaking work in the area of cognitive computing. We’ll continue to get the next wave of things ready for my teams. And Guru Banavar is going to come help us understand what the other side of the looking glass looks like. Thank you. Guru?

Guru Banavar – Global CTO, IBM

So, as a computer science researcher, a career computer science researcher, this is actually an inspiring day for me, because many of the things we’ve been working on for decades is now mainstream, as you say it, Mike. So, I’d like to give you a sense of all of those, actually step back a little bit and talk about what’s been going on in this field for quite some time and what is likely to happen.

Now, we’re talking about decades scales here. So, Watson winning Jeopardy may have seemed like a sort of, you know, it came out of nowhere, but really there’s so much of foundational science underneath it, computer science underneath it. There’s a lot of work that’s gone on in academia, but I’m proud to say that IBM Research has actually invested for over a decade — actually, I would say multiple decades — on the foundations of cognitive computing. That’s how Watson beating Jeopardy actually happened.

And if you look at all the fundamentals here, like machine learning and question answering, knowledge representation, which is at the foundation of cognitive computing, even experiential and interaction modalities and all of those things, those happen through the great work of a whole community of researchers.

And I’m happy to tell you that I have some incredibly bright and accomplished researchers who work with me to make that happen. And that’s the team that created the Watson system that beat Jeopardy. I’m also thrilled to say that we are going to be not only accelerating all of these innovations that go into the new unit that’s announced here today, but we are going to be expanding, and we’re going to be focusing our investment. Almost a third of IBM Research is going to be focused on cognitive computing. And we’re going to be delivering, we’re going to be generating the next generation of all of the foundations and applications and all of the technologies that will keep this going in this very, very wonderful and expansive set of applications that we’ve heard about today from the previous speakers.

So, I want to give you a few examples of the kinds of things we’re doing. And in order to appreciate the examples, I think it would be good to, again, just step back and think about how humans do cognition. When humans do cognition, you first have to sense the environment around you, understand what’s going on. You have to get very good at recognizing patterns of what’s going on around you. And you have to then be able to reason about things that you have seen patterns for, and then you have to be able to go into the sort of the art of it, which is the creative exploration and discovery of it.

Now, I’m going to give you examples of all of those four things that I just mentioned. There’s a number of other things that are foundational to cognitive computing and as we’ve explored in neuroscience and other areas, but I’m going to use those four fundamental faculties to tell you what we’re doing in IBM Research.

So, first, the ability to give Watson the power to see is that of learning from a very large collection of images and multimedia information — videos, audio, animations, if you will — all kinds of information that is not textual. And we’re not just talking about understanding the metadata that’s associated with these images, it’s understanding the content. It’s not only understanding it’s learning from the content over time so when you look at an application like looking for anomalies in an x-ray or in an MRI or any of the other image and video kinds of data sets that we’ve heard about, it really takes a huge amount of expertise on the part of humans to be able to do that. And the accuracy can be greatly improved when you adopt a tool that has learned the anomalies over time through a large number of data sets and through training from human experts.

When you adopt those as assistants, you can improve accuracy, improve productivity, and you can in fact get into much more real-time analysis and diagnosis of many kinds of conditions that we cannot do today. That’s going to be the power of “see.”

Next, if you look at how we perceive patterns — again, going back to neuroscience — there are these fundamental blocks of structures in our neocortex which actually hardwire things that we see as patterns around us and other kinds of knowledge that we’ve gained over time.

Now, when you get to the scale of data and when you get to the amount of these patterns that we need to learn and we can learn going forward, our traditional computing architectures do not work anymore. We call those traditional computing architectures Von Neumann machines, these have been around for half a century now, and we’ve all sort of grown up on those architectures.

But we believe and we are proving that in order to get to this new world of huge data and cognitive capabilities, you need a new architecture; we call those architectures neurosynaptic systems. And this is a fundamental rethinking of how computing happens. It actually mimics what happens in the brain through neurons and synapses, and these patterns that I’m talking about are actually the fundamental way in which you specify what these systems do: they learn, they interact, but it all happens because they understand how to work with patterns. And that’s this ability. This is actually a very long-term project that we are doing, and it’s a breakthrough in computer science. And this, we believe will be, will also become mainstream in the future, and we look forward to solving many more applications as we go forward.

You’ve heard a little bit about the power to reason. But I would like to maybe dig a little bit under the covers here and tell you about, when Watson answers a question, it tells you the answer. It tells you what the answer is. But the question of why that is the correct answer is fundamental to many professions. Like the medical profession, a practicing physician needs to understand the logic, the reasons why anyone, whether it’s a human or machine, is giving a particular answer. And they need to analyze it. They need to make a judgment about whether that sequence of logical steps and the evidence underlying that sequence of logical steps are appropriate for the particular situation, because there’s a huge amount of judgment involved here.

So, when you look at the technologies like Watson Paths that was discussed earlier, it gives you the ability to formulate hypotheses or maybe even specify arguments that says, well, what if I did this; would that be supported by any existing literature? Or, what if I wanted to…what if the patient does not want to do something? Or if the patient condition requires that we explore something brand new that we did not know about. What would be the potential options, and what would be the consequence of each one of those options? Those are the reasoning methods that are actually pushing the boundary in not only in our lab but in academia and in the broader scientific community, because those are very, very difficult questions and it really requires deep science to think about.

And when you look at the extreme or the ultimate way of doing this reasoning, it becomes interactive reasoning, because you are having a discussion, even a brainstorming session with somebody, and you’re saying, what if I did this? Would that work? And somebody says, oh, you know, these are the reasons why it might not work. These are the reasons it might work. And you pick up on one of those reasons, and then you start digging deeper and so forth and it becomes an interactive dialogue between a human and a cognitive system that can assist the person to really do what they want to do.

And we’re building systems like that in the lab today. You know, we will have other use cases where we can demonstrate this capability. And I want to get now to the creative portion of what cognitive systems can do. Right? This is about…it’s not about knowing precisely what the question is; it’s about exploring about what the possibilities are, what are the adjacent ideas, what are things that I haven’t even thought about.

What can I discover from things that I know may be plausible but I’m not sure about what the consequences are and what the evidence is. And in the case of examples I use here are those of, let’s say, chemistry, biology, materials science.

You may end up needing a combination, a cocktail of chemicals for a particular medical condition. And we may not be able to imagine what those are without the help of a system that can pull various different pieces of information across the entire space of information and put it all together and suggest something new that we never considered. Same thing with metals.

For specific applications, you may need a combination of elements from the Periodic Table that you may not have considered before, and those kinds of alloys or materials can be discovered through this exploratory brainstorming technique that cognitive computing can enable even human experts to be able to do. That actually brings me to the idea of innovation. Right? Creativity, innovation, we’ve mentioned many, many times that we are going to be opening up this whole platform and the APIs for the broader community to engage and build new cognitive applications.

We call them COGs, by the way, this is just a new terminology that we’re beginning to use. But in order to be innovative, we absolutely need those innovators, those humans, who will be working with these cognitive systems to build all sorts of applications. You know, we of course proved the technology out with our clients. That’s our step one.

We’ve been doing that, we will continue to absolutely do that in partnership with our clients. We’d also would like to encourage the broader community of partners, of academics, to work with us. And especially encourage the millennia generation who have so many new great ideas, different ways of doing things, new kinds of behaviors, all the different kinds of ideas that have sprung up in the last few years based upon the new generation of everything that can come in and imagine brand-new ways of interacting with cognitive systems and making their own lives better, their lifestyle. You know, wellness is a great example of this. So, we can think about how all of those kinds of applications can be built quickly, iteratively and at Internet scale.

I look forward to collaborating with all of you to do that.

Thank you very much.

Mike Rhodin – SVP, Watson Group

Some great ideas there. This idea of Watson Digital Life is something that we think is really cool. It’s not just enough to open up to an ecosystem of people that are going to build applications; we want to let some of our best and brightest researchers put ideas out into the wild, if you will, to let people log on to play with them try things. One of the examples that are out in the back is our Chef Watson that can generate recipes for you based upon your dietary restrictions, novel and unique recipes. And I will have to admit, I’ve eaten many different recipes from Watson. They’re all edible. Some of them are very good. But we even fed them to our investors one day. So, it was a safety test. But it worked out pretty well.

So, our Chef Watson will be one of our premium capabilities in Watson Digital Life as we make that available later this year. So, it’s going to be a place for the general public to log on, to play, to experience and understand the possibilities of cognitive computing as we go forward. Many new research activities going on.

Guru Banavar talked to very brief piece of what we’re doing here. A third of IBM Research; IBM Research is 3,000-person organization, dedicated to the next generation, Watson 3.0 and beyond. A great set of capabilities that complements what we’re talking about here, complements the investments we’re making. So, today we’ve heard a lot. It’s been a long morning. I appreciate everybody’s patience as we’ve gone through this.

I think everybody understands the level of excitement we have for what this next generation of computing is going to bring. I think you’ve seen it from all of our speakers as they’ve talked about what they see the future to behold. And we’re really, really excited to work with all of you on how we make the world a better place.

Thank you.

 

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