World of Watson: Day 1 General Session

World of Watson: Day 1 General Session

[ MUSIC ]>> I first learned about Watson
on television watching Jeopardy!.>> Ah, that’s the supercomputer
that beat Jeopardy!.>> It was Jeopardy! Jeopardy! did it for me.>> It was a little bit of an argument in
our house because most of us were rooting for the computer to win versus the humans.>> I thought it was some sort of IBM mascot
— the idea of a hyper intelligent robot.>> One of the challenges is, can
Watson apply to areas beyond Jeopardy!.>> Healthcare.>> Finance.>> Helping farmers.>> Education.>> Cooking.>> Toys.>> That gets done step by step through
hard work, industry by industry to solve problems that we haven’t solved before.>> Our clients are multi billion dollar
investment firms, and every day they’re looking at hundreds or possibly thousands
of opportunities to make investments on behalf of their clients. But the goal is to generalize
this technology to help people who are researching in any type of domain.>> There’s a company called Elemental
Path that has used a little green dinosaur to front end Watson and helps
kids learn and grow.>> We saw kids interacting with
the toy and just being amazed on how the toy could answer any question it had. One of the questions a kid would
ask is, where do babies come from? And the dinosaur would respond, ask your mommy.>> Watson became instrumental in actually
making it all possible without putting years of development of our own time into it.>> Watson is sort of a second
opinion on steroids. You know, it has the information
from all kinds of sources, all kinds of physicians that
have helped train it. And I think patients get a lot of comfort
knowing that the decision of their doctor is in line with the decision that
comes out of a system like this.>> When you combine the strengths of people
and machines and apply the combination to a real world problem, you can actually solve
the problem more effectively then either people or machines could solve it themselves. This is real. It’s actually making a difference today, and it can really transform the
world in such a fundamental way. [ MUSIC, APPLAUSE ] RHODIN: Hello. [ APPLAUSE ] Thank you. [ APPLAUSE ] Good afternoon to a very warm
spring day here in Brooklyn. I’d also like to extend a warm welcome, if you
will, to those of us joining from other parts of the world on Livestream today, and the
hundreds of hackers that are next door at the hack-a-thon that is completely sold out. I couldn’t be more excited to be here with you
today, because it’s been just over a year now since we started the commercialization
of the Watson project. We’ve made remarkable progress
since we started this. Together, we’ve built and brought to market
technology that’s hard at work solving some of the world’s hardest problems while we’re
reimagining the way humans work with computers, to learn, discover; and most
importantly, make better decisions. Here today among you are people, teams and
organizations that are leading that change. We’re at the start of an information revolution. What the industrial revolution did for the
scaling of automation and productivity; the computer revolution meant for the scaling
of business; and today, the fundamental promise of information technology will be able to
give us the ability to scale human knowledge. During our time together here at the World
of Watson, you’re going to hear many stories about scaling knowledge and about the incredible
things that you are accomplishing today. But to get us started on the right foot, I’m
honored to introduce Watson’s biggest supporter, IBM’s Chairman, President
and CEO, Ginni Rometty. [ MUSIC, APPLAUSE ] ROMETTY: Thank you. You see this, that as Watson’s
biggest supporter, he’s afraid if I fall he won’t get any more
money, so this is why he helped me there. So, let me welcome everyone here today. And really tell you how much I appreciate you
being here, because it wasn’t that long ago — at least doesn’t feel like it, that it wasn’t
that long ago — when we announced Watson. And today is all about the World of Watson
— a great name, the World of Watson. That time we announced, Watson was just one
system; now, it is an ecosystem, of which, boy, this room embodies that,
really being an ecosystem. And I know you’re here, just from the
conversations I already had at the front, because you share that vision with
us that I can’t say it more clearly, I really believe that what we are
doing together will change the world. It will change the world. And I also know in the 16 months since we
announced the formation of a Watson Group, I believe we have unleashed everyone
around artificial intelligence with a very broad definition of
what is artificial intelligence. But I’m going to come back to
what is so different about Watson. Now, those of you that know it, you know
it already; and in fact, we just had Watson out at Berkshire Hathaway’s annual meeting, and
someone asked me, why do you have Watson here? I said, because if you experience
it, you actually understand it and you will see how different it is. So, let me thank, I don’t know if Mike
mentioned this, there are 26 industries, 32 countries in the room, and you are
all different sorts of professions. So, let me thank you for
supporting our vision, very much. Let me thank you for rallying around Watson. But more than anything, let me
thank you for teaching Watson. I mean, Watson learns. And I’m looking at so many people,
like Dr. Kris, who has been almost for years teaching Watson now a
very difficult topic, oncology. But it is about your expertise and commitment. I have got to start there, to
thank so many in this room, on behalf of IBM, for what you’ve done. Now, as I begin my sort of
kickoff here, even though it is, I know it is tremendously
hot, by the way, in the room. I did ask them to do something. You all look like you’re
about to pass out, okay? [ LAUGHTER ] Or I am, too, I don’t know. So, let me, I’m going to start first with
just a little bit of history on what happened in the past 16 months since we launched
Watson, because it was January, 2014, I stood up on a stage, first thing
of the year, and we said we’re going to dedicate a billion dollars
to commercialize Watson. So, let me give you some
statistics for those of you that either are participating
or thinking about it. So, who is building on Watson? All right. So, far 250 ISVs — independent
software vendors — who are reselling have built
cognitive applications already. There are 5,000 companies in the pipeline for
partnership, tens of thousands of developers. And one of the things I’m most proud about, the
team has now got 100 of the best universities in the world teaching Watson and they’re
going to be graduating the next entrepreneurs that we’re all going to be up against. So, 100 universities. Now how are we building? To date, we have now made Watson, as you
know, a cloud — I’m going to come back to — available on Bluemix, our platform as a
service, 20 different services so far. But we’ve also now started to
have Watson learn new languages. And this is not about just speech recognition. So, he thinks in these languages — Spanish,
Portuguese, not so hard, but Japanese. So, those of you that know
Japanese, it is not just speaking it, it is understanding how to think in Japanese. And in fact, announced a
partnership with SoftBank. And then, what are we building? Well, most of this session is going to
be about what the ecosystem is building, but we are building some things to help. We will tackle some of the biggest
challenges, big professions like oncology, things we’ve done to help
with wealth management. But then we’re building what I call
repeatable solutions that you use or clients to build your own solution. So, there’s the Discovery
Advisor; does what it says. There is the Engagement Advisor, Decision
Advisor, Policy Advisor, the Explorer; and then most recently this
year, Watson Analytics. The idea was put Watson in the palm of any
business user to take whatever data they have and have it do its best to give
it some cognitive analysis of it. So, the who, the how, the what. And then last month, I know, I hope many
of you read about it might have been one of my proudest moments as an IBMer, we
announced something called IBM Watson Health. Now, this was building on the great work we’ve
done with some wonderful health institutions in this country between Memorial Sloan
Kettering, Cleveland Clinic, MD Anderson, Mayo Clinic, the New York Genome Center. But this goes further. We announced a whole new business that would
create a platform for health and wellness. So, without going into all the details, I
mean, the future of health and wellness, it’s going to be about information and
about connecting all different kinds. So, it’s an open cloud platform,
HIPAA compliant, the initial partners. ; many more, though, are Apple,
Medtronics, Johnson & Johnson. And then as well, we made two big acquisitions:
a company called Phytel and Explorys. This is about population health management,
it’s about evidence-based medicine; and in fact, together, they manage almost 100 million lives. So, think of all that information. And all that will be a platform
in addition to all of the work already done with
Watson around healthcare. And then, scaling. You’re going to get a chance
to hear from clients. In fact, I got the question
about, who’s scaling and how fast. So, Development Bank of Singapore, DBS, the
wealth management app, 300 professionals. Or Deakin University, 50,000 students
are now having Watson as an advisor. If you look at and go a little
further out with Watson Bumrungrad, many of you don’t know who they are. It is, Southeast Asia, is largest
private medical facilities…facilities and organizations, will be using the
Oncology Advisor for 1.1 million patients. And Singapore Tax has now rolled
out Watson as an advisor on tax for five and a half million citizens. So, vision to reality starting with
what was hundreds of users to millions. So, let me thank you for
everything you have done to help. Now, in the next, kind of rest of today and into
tomorrow, you’re going to see a lot of progress. And let me just do three things
and then hop off the stage. The first, though, I want to step back
and just recap one more time the historic and the historic era that is the context
for Watson; give you a little bit of taste of the next sets of transformation;
and, what lies ahead is my third topic. So, what do I mean by the historic context? Look, we are on a journey. In fact, somebody else as I talked to
ahead of time said, this is a journey. There is no doubt this is a journey, and why. If you think about the history of
computing, there have been two eras to date. Watson is really, someone said the mascot, the
poster child, symbolic, embodies the third era. The first era were all systems
that counted things. Just easy to say, tabulated. The second era is everything
you and I know to date, it is programmable systems: if this, do that. They just do them faster with more information. It’s your watch, it’s your phone, it’s
the fastest supercomputer — if, then. But Watson’s cognitive, and
what makes it different, as you know, is it’s a third era, cognitive. It learns. It’s natural language as well as
unstructured — images and pictures. But more than anything, what makes it different
than just artificial intelligence is its ability to augment the decisions you and I make. As I said simply, I was doing an interview
this last week, I said think of it this way. Watson deals in the gray area, the gray zone. It’s where there’s not a
perfect right and wrong answer. That’s the hardest thing we do as humans. It’s the idea of reasoning. So, what Watson is doing simply is take,
you know, forms millions of hypotheses about what the answer to something is,
tests it against everything it knows. That’s why you can see a percent
confidence, you can see evidence reasons. And then, helps you make your best decision. There’s no right and wrong; very different. And that’s why with pharma, we see
people thinking it and using it for the discovery of drugs in months not years. If it’s insurance, how do you
rationalize a complex policy? Or, if you’re in legal, you take evidence
against a very complex legal system. So, it’s all these connections that get made. So, very different, point
one, historic era, cognitive. The second thing I just really
want as an ecosystem everyone to know is our commitment to
this being an open platform. It was a very strategic decision for us. We knew that we would do some innovation,
but 90 percent’s going to come from you and around the world — a) it’s too
powerful for us to keep to ourselves. We’ll tackle some of the biggest
issues and give you lots of foundation. But this innovation is going to be yours. And to do it, if you think about it, innovation
means…an open platform means a couple things. Open standards, so you can take it
and integrate it into other things, especially because it’s helping
somebody make a decision so you’ve got to integrate it into real world business stuff. Then second, it’s got to be a business model
that a tiny company or a big company can use. And then third, cloud-based. Now, why cloud-based? Easy. It is huge amounts of data you’re
going to have to deal with and connect, one. Second, cognitive uses a lot of processing
capability, so we’ve got to scale it, and trust me, you can’t afford to
scale it; we’ll scale it for you. And then the third thing is,
you’re going to mix and match it with things you build, all
that’s perfect for cloud. But by the way, a cloud offering of Watson’s not
enough; you’ll see announced today hybrid cloud. And why? Again, any company you go to, you
can offer them a new solution with Watson. Often they’re going to want to connect it to
either data they have or to their, you know, current claims processing process,
so you’re going to need that. So, let me kind of pause on my first point. I just wanted to reiterate, it’s an era. I believe it’s got an innovation runway that
we have not yet to even begin to go down. And then third, it will remain
an open cloud-based platform. So, the kinds of innovations that are
out there today, I always say, you know, our job is not just technology; it’s
to help people transform industries and to help them transform professions. So, let me sort of recap one more
time what we’ve done with healthcare. I’ve got to pause, because there’s nothing
that’s clearer to me than to say to you that together we will help
change the face of healthcare. I am absolutely convinced that
we will make our contribution to what is a very large problem,
that we will help change it. And when you see the progress
that’s been made with oncology, now Watson’s up to, I think,
300 medical journals. 200 textbooks, 23 million articles,
all of the clinical trials ever done. And then, taught by the finest
physicians in this world. It is going to make a difference. I mean, and maybe not in a city
like New York where you can go to the finest physicians in the world. But think of all the places
in the world you can’t. Or think about Baylor College, the work we just
did to identify what would be prospects here for targets, targets to go after,
to say, this looks like a good one that we could maybe could solve with a drug. All of life sciences does only a few a year; what we did with Watson retrospectively
was seven within a few weeks to a month. And then, you’ll also see announced here
a very large collaboration between Epic and Mayo Clinic all around electronic health
records, in fact, almost to the number of 80 to 90 million that’s going to extend this. And then, from the group out here, you’ll see
innovations all around health and wellness. So, we’re going to do some heavy lifting,
as I’ve been talking about, with oncology, because when people say, well, why
isn’t Watson going faster in oncology? I’m like, we’re only trying
to help solve cancer. This is tough stuff, people dedicate lives
and lives to, their lives, professional. But many of you have got some
great ideas around extensions here and some are in the wellness area. So, one of the partners in Welltok, the
CafeWell concierge you can see back there. Now, that takes me to, there’s an advantage
and the advantages of an ecosystem. So, please take the time. Like I said, this is about what you can do. The travel industry. You’ll hear from Terry Jones. Terry Jones was the founder of
Travelocity and as well Kayak. And now Watson, something called Wayblazer,
it’s about concierge travel services. Right? On average, you know, and I was
thinking, and this is probably true for me, too. If you ever take a vacation, I was
thinking, if I…where would I go? On average, you and I will go to 20
websites looking for where to go. All right? I mean, how many of you have ever done that? Raise your hand, it will be
good for the heat, by the way. Now move your hand like this for a
while and it will cool the place. So, everybody’s done that. You’re going to see that. Or a company called LifeLearn,
veterinary medicine. Another company in oil and gas. We have got, you know, up in that little
video you saw a company called Elemental Paths with the talking dinosaur; or,
company called Ross looking at legal. I just talked to my friends
in the front row here looking at how to deal with contaminated soil. I mean, these are millions of
ideas that are going to go ahead and have great breakthroughs around. So, what lies ahead that you can expect from us? Well, as I said, I am quite
confident that together we’re going to change the face of healthcare. But as well, as Watson gets smarter, his ability
to reason is going to exponentially increase, and there will be bigger and bigger
problems we will continue to take on. Today, we will announce as well
something called Watson Genomic Analytics. It is with 14 extraordinary cancer centers,
and it is the matching of the DNA in tumors in multiple tumors and potential
with your own DNA with the target kind of drug to help solve that. You will hear from several
doctors as part of this later. We’re also announcing today the launch
of something called Alchemy Data. Now, what is it? For those of you that want to use high volume
news data, you know, and this is an issue. You want to use it; it’s very difficult to use. If you looked at every piece of news in
the world — real news, in any format — try to put it in a format you could use
so you could make business decisions with it or you could analyze trends. Alchemy helps you do that. It’s all around how to curate that data and
then use it as part of your Watson application. So, what should you expect next from us? Well, standardizing and scaling of Watson. I know, talked to enough of you, that
we need more tooling, more self-service, all the things that will let
you go faster, number one. Second thing, Watson is learning not just new
speaking languages’ new languages of industry — so, be that metallurgy, be that law, and
there’s a whole list behind that that Watson has to learn like he learned medicine. You have to learn the language of the
industry and how to operate in it. But the third — and John Kelly’s
going to talk about this tomorrow, and I think this is another one
of the great breakthroughs — Watson is getting new senses: sight, eyes. I mean, this is not like, you know, identify a cat in a picture or
tell me how old this face is. What Watson is learning is how to
recognize anomalies, anomalies in images, be that melanoma, breast cancer,
a cardiovascular blockage. And to date, he’s already ingested
45 million images — 45 million. I mean, you’ll have to tell me
how many an average doctor sees in his life, but it’s not 45 million. So, this idea of sight is going
to take us into, and you, I hope, into a whole new realm of
what you can do for clients. So, let me just end and wrap up
my time with you to kick this off. Final thought. You know, we are all living at this intersection
of bills, technology but also of society. And I never forget that this is a
new era that we’re building together. So, I would be bold, and I
am going to boldly predict that in the future every decision mankind
makes, every decision, it’s going to be informed by a cognitive system like
Watson; and as a result, our lives in this world are
going to be better for it. And so, I can’t thank you enough or actually
encourage you enough to stay with this. These ideas are transformative. And together, this is one of those great times in one’s career you have a
chance to change the world. So, let me thank you for your dedication
to Watson, for your dedication to IBM, and wish us all a great conference. I look forward to meeting as many of you
as I can when we take a break afterwards. So, thank you, guys. [ APPLAUSE, MUSIC ] RHODIN: Thank you. Thanks, Ginni. I think really, you know, to sum up that,
it’s really this idea of living and working at the intersection of technology, business and
society, where is where we’re stuck in today. And there’s probably no greater example of that
than healthcare, as Ginni just talked about, and there’s no industry where the
potential for impact is going to be greater. Just think about it, in our lifetimes we
will generate 1,100 terabytes of data. That’s about 300 million books
just about us, each one of us. Ten percent of that’s going to come
from electronic medical records, another 20 to 30 percent is
going to come from genomic data. But the remainder is going to come
from something called exogenous data, an incredibly broad array of sources and devices
think Fitbits or iWatches, medical devices. Every day both individuals and healthcare
professionals will make decisions with only a piece of this information. But by understanding all of this
data, I think we can truly start to really understand our health
and how to make it better. Last month, we announced IBM Watson
Health as Ginni said and partnerships with Apple, Johnson & Johnson and Medtronic. It’s an initiative to bring all this clinical
research and social, health data together in a HIPAA enabled cloud so that we can mine
it for insights and build new applications. Today, we’re going to be talking about the
human impact of this work in three ways. First, a major advance in solving
the problem of matching patients to lifesaving treatments like clinical trials. Second, a brand-new development in how Watson
can analyze genomics for personalized treatment. And third, new opportunities to use
devices to personalize treatments for chronic conditions like diabetes. Joining me on stage are Carl Dvorak, Dr. Lukas
Wartman, Dr. Norman Sharpless and Annette Bruls. [ APPLAUSE ] Carl, you’re changing one of the
most difficult tasks out there, matching patients to clinical trials. Why is this so tough, and what
are we doing to work on that? DVORAK: Well, I think a couple of
reasons it’s tough is that we’re, much like computing is moving to a
new era with cognitive computing, medicine’s moving into a new
era with precision medicine. So, the number of variables that it takes
to truly understand the clinical trial and to truly map to a patient
record is growing exponentially. And the opportunities are huge if we
can pinpoint the right opportunity to the right patient and the right situation. And today we do that with that second
generation technology, second era technology. We do if,-then-else, we try to exclude,
include, exclude all the criteria to the patient and make it match. And we do that with metadata, we do that
with rule writers, but we don’t do it with cognitive computing; the
next era is cognitive computing to match those clinical trials
in a standardized way. So, what we bring to the game is health
record information at a highly detailed level, and connecting it with Watson,
we can use that technology to find the right trial for the right patient. And I think in two vectors, primarily. We’ve historically brought trials to physicians
so that they can talk to a patient about them. Today, through MyChart and the patient
engagement tools we’re bringing trial opportunities directly to the patients and
they can mention them to the clinicians and try to advance the topic of joining a
clinical trial for an appropriate purpose. RHODIN: All right. Dr. Wartman, you have a pretty remarkable
and inspiring personal connection to the work you’re doing
with Watson and genomics. Can you share the story of your
journey both as a leukemia doctor and researcher and also as a patient. WARTMAN: I’d be happy to. So, I’ve had a longstanding battle with
leukemia that now started over a decade ago. I relapsed for the second time
just over three years ago. I was out of treatment options, and so I was not
in the gray zone, but I was in the black zone. And lucky for me I was at a great
institution, Washington University in St. Louis, where they were able to do comprehensive
sequencing of my leukemia genome and we found a therapeutic target that really
had an incredible effect and put me back into remission very promptly and I
remain in remission three years later. Without that technology I wouldn’t
be here sitting here today. No one doubts that. At that time, my story seemed like
it was just going to be limited to very specialized treatment centers,
very few, very…genomic sequencing centers that offered this type of sequencing and
not only sequencing but data analysis and that wouldn’t be widely
applicable for the population at large. And so, when I first went public with my
story a few years ago, I had received e-mails and I still do receive e-mails
from patients all over the world on almost a daily basis asking
how they can get the same type of sequencing-based treatment that I got. And oftentimes, my answer has been incredibly
disappointing, and it’s that we’re working on it, we’re trying to get there. And I think that Watson offers really a step
forward in actually making that a reality and making this amazing potential available to so many more cancer patients
who need it very desperately. So, that’s why I stand behind it, is because
I truly believe that there’s a great need and this is a great, great opportunity to
kind of seize that potential and really kind of transform the way we treat cancer patients. RHODIN: In many ways, we can start to democratize the best practices not
just the best medical centers but spread that best practice on a global
scale using technology. That’s a great way forward. Dr. Sharpless, what’s your vision for how
doctors and researchers will use this technology in their practice, and what are you observing
so far and where do you think it’s going? SHARPLESS: Yes, so my interest in this as
a cancer physician started when I took care of a young lady who…we had lung
cancer, an unusual presentation, and we gave her what we thought was the
right therapy and it didn’t work very well. And in the process of taking care of her
we found out she had a rare genetic event that had we known we could have done
a much better job of caring for her. And she ended up dying. And at that time as a leader of
the Cancer Center at the University of North Carolina I decided we’re going to
not have that happen, if possible, again, and I wanted to start sequencing
patients in a real wholesale way. So, we began our sequencing
program in 2011 called UNCseq. And to date, we’ve done about 1,700 patients. And I think we started it thinking that it was
going to be easy, and we learned very quickly that it’s quite difficult,
that patient information, genomic information about
patients is very complex. A lot of genes, a lot of combinations
of genes, a lot of rare mutations in the tumor genetic information. And therefore, we realized that really
the ability to make decisions based on this genetic information is going to require
help — and algorithmic like Watson type help — to understand what mutations are really
important in a patient’s cancer and are likely to lead them to respond to a
certain agent; and then also, how to match those mutations to patient care. And so, we’ve really started to try to do that,
to try and use a Watson-based approach to figure out what mutations are important
and what chemotherapy then to give the patient in that setting. So, it’s a really exciting time, you know,
because this is a very hard decision. We don’t do well with most patients
with cancer, with advanced cancer. We have too many disappointing outcomes. And so, the ability to bring a
powerful new technology is very exciting to the oncology community. RHODIN: Annette, where do
you see the opportunities to create more personalized
diabetes treatment for patients; and, how do you think that’s going
to change their lives? BRULS: You know, diabetes is really
a complex and growing epidemic. If you look at the numbers, over 380
million patients worldwide are diagnosed with diabetes, and that number is rising fast. If you think about the complications
related to a poorly managed diabetes — cardiac complications, renal
failure, blindness, amputations — these are severe for people with diabetes. And finally, if you think about the cost. In the U.S. alone, more than $270
billion are spent on diabetes every year. So, huge opportunity, huge
problem we need to tackle. If you think about the management of diabetes,
it’s really changing for every person. It’s about getting that glucose value
under control, and that is being done by making the right decision at every
moment of the day, 24/7, all the time. It’s the right lifestyle changes,
medication, insulin intake. That has to be done by the person with
diabetes nearly alone because they have access to healthcare professionals, maybe 10
minutes, 15 minutes every three months; in the meantime, decisions are theirs. And so, also on the healthcare professional
side during the famous 10 to 15 minutes, the healthcare professional needs to
digest multiple data sources from devices, from the patient journal, needs to make
decisions looking at treatment algorithms, multiple combinations of
medications are possible. How do you make the right decision for
a given patient in a given situation? So, at Medtronic, our mission is to transform
diabetes care and we are a global leader in medical devices for diabetes
treatment such as insulin pumps, continuous glucose monitoring systems. And we have the possibility to get real-time
access to continuous glucose monitoring and to a wealth of data that will allow
us to understand the patient’s status. So, combining this data, this device
data, with the power of the Watson cloud and Watson health computing systems will really
allow us to provide decision support tools and services for both people with
diabetes and healthcare professionals. So, it’s an exciting world ahead of us. RHODIN: So, I think as
Ginni said a few minutes ago, we’re at the beginning of a new era. So, I want to do just kind of
a quick lightning round here. We’ll do a quick answer and we’ll go down,
we’ll get everybody to answer the question. But let’s talk about, let’s look
two, three, four years in the future. Time’s moving very quickly right now,
technology is moving very quickly. What is your hope for technology? What kinds of things do you think we’ll have
during that three- to five-year timeframe and the promise that these types of
technologies are going to bring to market? Annette, we’ll start with you. BRULS: Yes, I think it’s
really, also as Ginni said, it’s really providing the right decision
support for the people with diabetes, healthcare professionals at the time
that the decision needs to be made. Not three months later but at that time
when impact can really be obtained. And so, I think in two to five years
from now, through our collaboration, we will transform the face of diabetes care. This chronic disease management
will look different. We will have personalized dynamic care
plans that we will develop together. We will have better automated
algorithms that take away some of the burden of the disease management. And I believe that together we can even develop
integrated care services that are targeted to both the high risk population. And all this together at the end of
the day will create greater freedom and better health for people with diabetes. DVORAK: The bedrock of progress in medicine
is success in a clinical trial; you know, a randomized comparison of
one care versus another care. I think it’s very likely within the next two
to three years we will see a clinical trial in cancer of drug picking using a human or a
computer-aided human, a Watson aided human. Can the interpretation of the
data and the choice of therapy for the patient be improved using Watson. I think that’s a relatively straightforward
trial to do, and I think it’s likely to happen and I think likely the computer will win. Or, the human helped by computer will win. The reason I say that is
because it’s very hard to do. The data are complicated and
complex and we need help. WARTMAN: Yes, I’d like to mirror
the same thing, but I think what’s so exciting is the potential to
move beyond N equal one ase reports, individual patients benefitting and really
trying to affect us more on a global scale. RHODIN: Carl, you and I were talking
about some of the ideas back stage. Why don’t you sahre those with us as well? DVORAK: We were talking about hooking
Watson up to the air conditioning system. RHODIN: Yes, that was what
we were talking about. DVORAK: The other one, sorry. Yes, two years is an interesting timeframe. I think we’ll see the fundamental connections
made so the electronic health records and patient portals can get advice from
a information from Watson pretty quickly. I think the three- to five-year
horizon much has much more intrigue. I think we’ll se Watson digest the rest of
the world’s medical information, what we know. I think we’ll see Watson begin to digest
the patient stories of hundreds and hundreds of millions of people across the globe
and learn from what’s actually happened to people through the human condition. And I think we’ll be able to harness
that information at the point of care so that when…well, hopefully even beyond
the point of care so we won’t even need to have the point of care as much
any more, but as patients interact with their health and wellness data… The ability to get advice, to get medically
appropriate advice and targeted advice in an era where we’ve got full consumer
monitoring, home diagnostics, and interact with the healthcare system in a
much more efficinet way, because we’ve only got so many resources we want to spend on
healthcare, we want to use those as frugal as we can for the most important
situations and have patients be as healthy and well as well as they can be. RHODIN: Thank you. I’d like to thank the panel. Can you join me? [ APPLAUSE, MUSIC ] Healthcare was really the first industry we
turned to when we began to commercialize Watson, but we’ve made significant progress since then. But that’s just the tip of the iceberg. We’ve also moved into many other
industries, so let’s take a look. [ MUSIC ] RHODIN: So our next two
segments are going to… [ APPLAUSE ] Our next two segments are going
to focus on clients and partners who are driving transformation within
industries like financial services and retail. To talk about a very progressive approach that
the Development Bank of Singapore is taking around wealth management, I’d
like to invite Olivier Crespin and Bridget Van Kralingen to the stage. [ MUSIC, APPLAUSE ] VAN KRALINGEN: Okay, so we’re going to talk
about banking, which I know you’re all thinking, okay, this will put me to sleep
now after that exciting session. But banking is in fact in a
very exciting transformation. Like many industries that
you are in the audience, it’s caught between the pincer movement
of shareholders, profits, need for returns and at the same time commoditizing products,
disrupting industries as well as the pincer of an increasingly high expectations
from every single consumer. And this combination of the pincer of
disruption, of the [bubble] and the expectations of consumer give some incredibly
difficult challenges to navigate. So, as an example, there will be about be
about a billion people doing mobile banking and already today we have 31 billion of
wealth being run by so-called robo advisors, digital advisors, who are trying to disrupt
the role of normal bankers and wealth managers. So, when you compare that
to many of your industries, bankers are facing similar challenges — a world
where they don’t just have to respond in seconds but they have to proactively delight. They have to give the response in the context of
their individual customer, and they have to do that at a price point and they have
to differentiate from everybody else. And by the way, they have to do that very often
in augmentation or in combination with a human and a machine in order to create
a differentiated experience. So, navigating this world of transformation
for banks is a very interesting and challenging time, and DBS and Olivier
Crespin have been actually leading the way. So, Olivier’s job is he leads digital banking
for all of DBS, which he’ll tell you about. But one thing before I hand over. DBS has actually just won the
award for the best bank in Asia, and it’s the first Asian-based
bank to ever win that award. So, they really are pathfinders. And working with us, with
Watson over the last year, what Olivier has been doing is
really striving to find a way where Watson’s augmentation can completely
differentiate the banking experience that any consumer has with his
or her relationship manager. So, Olivier, just to start us off, tell us a
little bit about Development Bank of Singapore, because not everybody in the audience
will know about it and its transformation and it sets the context for why you
chose to work with us in Watson. CRESPIN: All right. So, DBS means Development Bank of Singapore,
is a bank based in Asia with headquarters in Singapore covering 17 markets mostly
across Asia with main focus on China, India, Singapore, Hong Kong, Taiwan and Indonesia. It is a truly universal regional bank;
that means that we have retail banking, corporate bank, investment bank,
brokerage and wealth management divisions. It’s a profit of around 3.2
billion net profit per year. It’s the biggest bank in Southeast Asia. We are getting quite good recognition because
in 2014 we have been the best bank in Asia from global finance; and over the last six
years, we’re also most secure bank in Asia. And we have initiated a transformation,
a digital transformation that I’m leading right now on behalf of DBS. We started three years ago, and that’s why
we have been looking at Watson as being part of the solution of what we
are trying to achieve. VAN KRALINGEN: So, what inspired
you to start working with Watson? CRESPIN: I think what’s happening in
Asia, the market is growing very fast. So, it’s really an issue of
scalability, how we can address, how can we take care the best
way possible of our clients. So, scalability and be able to provide
best client experience possible. What we have is that if you take
the middle class in Asia, in 2010, you have 500 million people; by 2020, we’ll have 1.7 billion people
– -so it’s growing three times. And a lot of these clients
are in the reach of DBS because we are also the biggest
bank in Southeast Asia right now. So, that’s one phenomena: how we can cope
with so many clients without having the need to add too many relationship managers. Also what’s happening in Asia is…and also part
of the world, obviously, the world is changing and the world is changing very fast. And we see some emergence of new
technology, the emergence of new competitors. And as a result of that,
emergence of new needs for clients. Client expectation is getting higher and
higher, and we need to cope with that. So, for the job to be done that we really tried
to address by using Watson the first time, saving time and gain productivity. You know when you have the relationship manager
who has to deal with 5,000 pages of documents on a quarterly basis that includes
chief investment officer recommendation, analyst report from our broker side,
[vicars], product recommendation. Both on the client and the relationship manager
point of view it’s very difficult to do, to read all this information and understand
it, apply it to the specific client. The second aspect is really
people want to be recognized. Clients want to be recognized. They want to be engaged on a one-on-one basis. And you know, there is not one
single portfolio that is the same; every client has different kinds of needs
and we need to be able to address that. The third aspect is the client are
expecting us to make them successful. How do we make them successful? It’s obviously by bringing to
them the expertise the bank has. So, last aspect but not least,
people want a peace of mind. They want trusted advice,
they want transparent advice, and they want to see the
evidence behind the advice. So, if you take these four boxes — saving time,
be engaged on one-on-one, get expert advice; and then, have peace of mind — we
can say Watson, check, check, check, check all these boxes and
can allow us to do that. VAN KRALINGEN: So, that’s a very
compelling potential transformation story, and we’ll go back to a minute to how the
relationship managers actually responded, because when you talk about being able to
be very rigorous and logical and fact based and why you might make a recommendation as a
relationship manager, that’s somewhat different. So, we’ll talk about how they responded
and received Watson in a minute. But if we start back to the
beginning then, you had this vision. We were together a year ago
with Piyush, your CEO, talking about this great vision for it, right? And we’ve come through a journey
of learning to implement it. So, what have you learned in the implementation? I know we’ve done…you’ve learned a lot and
I’ve got a lot of insight for the audience on things like how collaboration between you
and IBMers worked, how we’ve trained Watson, what you’ve learned about
the training of Watson. So, what would you tell us all about
the implementation, how you did it? And just to make a point, as Ginni
said, DBS have done this in scale. I mean, 300 relationship managers in the world of wealth management is a scale,
definitely a scale rollout. CRESPIN: Yes. So, it started…the journey is a journey
starting, in fact, the day we signed, you remember, was the ninth of January,
2014, the same day Ginni mentioned. So, we have a really strong
commitment here from that day. VAN KRALINGEN: The stakes were high. CRESPIN: It starts really with a shared
vision I think frmo DBS and IBM to go on that. I’ve been working with a team of IBM for three,
four months to come up with a business case that is kind of achievable, that
can make [it] happen and be able to celebrate the success before
moving to the next phase. So, that was really important, sahring this
visino of being able to give best advice to people and provide the evidence. And then, we needed to bring at every level
of IBM senior management and DBS the same and working level subject matter expert,
we needed to bring the best people. And one of the key important points was in DBS we asked our senior relationship
manager volunteer to work on Watson full time for a year, and that has been a very big
difference, because when you build it, build it from a user point of view human
centered design and you can ensure you are going to get something that is really
going to be really relevant. So, to explain in a few words and to [santitize]
the project, is relationship manager have a lot of information on the unstructured format. We saw…we started with
all the information we know, that [INAUDIBLE] information that DBS issue. So, expert of DBS, the product people, chief
investment officer and his office as well as the analysts from DBS are issuing
a lot of product recommendations. And, by the way, is open architecture
is not only the DBS product. At the same time, we have
a lot of structured data which are also information
we have about clients. And so, that includes a set of location. This includes his risk appetite
in investment objective profile, its preferences and past trade behaviors. So, as part of this project we have
been loading all this information, one year of transaction history as
well, to be able to identify a pattern. And then we have these two
sides, this unstructured data and the structured data are combined to
come up and offer clients a recommendation for five products:, equity, fixed
income, fund, insurance and currency. And for thousands of fixed income I think 3,000
funds, thousand of equities, Watson every day is for each specific client checking what
is a product that can be most suitable. And he rates it, it gives evidence and it
give it how likely it is to be a good match. So, the way it looks like is when your RM, he
will see all every morning for all his clients, he will see the product recommended
and he will see if it matches the investment
objectives of the client. If it matches asset allocation, if it
matches risk profile of the client. But also, it will make sure the outlook of DBS
on the currency on a country, on an industry, on the asset class, is positive
or neutral and he assesses that and shows it to the relationship manager. So, the relationship managers are now
able to interact and engage the client at the very high level of detail. And they really look smart, in fact. VAN KRALINGEN: Right. So, the question then, I mean,
I’m thinking if I’m a client, I would love to have a relationship
manager who would do htat. So, how have the relationship managers
received and responded to the rollout? Do they love it as much as the
client might love it, the end point? CRESPIN: They like it. We in fact, we started, we went for pilot from
20 of December with 22 relationship managers, making sure we calibrated before releasing
it early March to 300 relationship managers. And because it has been built
with the relationship manager and the client in mind, it’s very [entitive]. You have all the information you need. They use it, and they can even ask questions. So, they save a lot of time. And again, it makes the knowledge
about the product much bigger. So, to reach that, we have to
train Watson, is very important. And we needed to train it and explain, when
we say “outlook on Asia” what does that mean. So, we have to teach Watson, that
means you need to look at [draws], inflation, employment and all that. Plus all the financial tests as well. And to make sure we have the good usage from the relationship manager we are
using the capabilities of Watson to learn. So, for every recommendation,
even at the granular level, the relationship manager can
put a like, dislike or comment, and this is analyzed later on to be updated. Also very important is for any
recommendation made, like for example, we are positive on currency, the
relationship manager can click and see the underlying document. So, it’s not a black box;
it’s very, very transparent. We show the evidence and we are able to engage
RM and [INAUDIBLE] a good level of interaction. VAN KRALINGEN: So, that outcome I
know took a year of very hard work. When you think about your lessons learned
and difficulties you had to overcome, anything you’d share with the audience here? CRESPIN: Okay, so I think very
important is, start, obviously, with the vision of what you want to achieve. Be [INAUDIBLE], spend time to
analyze what you want to do. And you can do, Bridget,
don’t try to do everything in one shot; and in fact, it’s a journey. We have achieved the first
step, but we have already…now that we know it works, we
have a lot of more ideas. And we are talking already with IBM;
it’s a bit early to talk about it. So, that’s important. Bring the business into the development;
and if possible, bring the client as well by doing prototyping, agile
methodology, like work on hack-a-thon, we have been doing some hack-a-thon as well. And that’s really important. And stick with what you want to achieve and make
sure you have a full alignment on beginning. In fact, before we signed, it’s quite…we have
a few days workshop with IBM people in Singapore to really build on it and make
sure we can deliver something. So, that’s for me, that’s the key. VAN KRALINGEN: Olivier, thank you. Olivier came all way from Singapore,
and I’m sure you’ll agree with me, he’s got a role of taking, you know, this very
high-level technology and making it practical and real for the world around us. So, thank you for that inspiration and thanks
for being our great partner here today. CRESPIN: And Ginni asked me earlier,
is DBS happy, yes DBS is really happy. And second question, as a client of DBS, I
was the first, I wanted to be the first one to do a trade and I can tell you I’m very happy. VAN KRALINGEN: He told me that story. He is. [ APPLAUSE, MUSIC ] GOLD: So, absolutely amazing what we’ve
seen so far and the work that Watson’s doing in healthcare and in financial services. But I want to switch gears a little
bit right now and talk to you about what’s happening among our partners, among
the ecosystem that Ginni referred to earlier. It’s hard to believe it was a little
over a year ago that we opened up the technology to third-party development. We opened up perhaps what was one of IBM’s greatest innovations
in its 100-plus year history. And the partners didn’t disappoint us. They brought their creativity. They brought their innovation, they
brought their passion across media, mining, manufacturing, entertainment,
healthcare, financial services, travel. Many, many industries looking
to change or redefine processes, looking to help transform entire industries. And they have a breadth of understanding
and knowledge and domain expertise that is unparalleled and they’re
moving Watson faster into the market. In 16 months, we’ve grown 80-fold
with our partnership community. We’ve grown 25-fold in terms of
the services that we now offer. And our partners are pushing us
harder to go faster, to do more. And I couldn’t be more thrilled today than
to literally provide an opportunity for you to experience three of the
partners that we’re working with. But before I get there, if I
can I’d like to share a story. It was October when we were moving
into Watson’s new headquarters in New York City, downtown, at 51 Astor Place. And I realized that we were truly
opening a new chapter in the history of Watson and the work that we would do. And I said, you know, now is a great time to reflect upon my own chapter
and where I want to go. And I said, you know, it’s
coming into the fall season. I’ve got a couple of months here, but I’d love
to get back into biking, get back into shape. And I was spirited to go do that, but I
realized I had one little problem: no bicycle. My bike had long been gone, probably sold at a
garage sale, hadn’t thought about it in years. And so, I did what I suspect
most of us would do. I went to the Web, and I opened
up my browser and I typed bicycle. And it was very efficient and it was very
fast, and it gave me 270 million responses. Now, I don’t know about you, but I
neither have the time nor the patience to go through multiple pages. And so, I looked at the first page, and
unfortunately, it was bike insurance, bike helmets, bike accessories, bike clubs, bike
paths — everything but what I was looking for. And I quickly got distracted, and lo and behold
I stand before you today without a bicycle. But that’s okay. If it had been a cognitive experience,
had I taken a cognitive powered journey, I really believe it would have
had a very different outcome. I would have interacted in a
very intuitive natural way. I would have said hey I’m looking to get back
into biking, what bicycle is good for me? Because I’m really not interested
in what’s good for my son or my daughter, my wife, my parents, right? What bike would be appropriate? And Watson not only is able to
bring back a set of responses, but with confidence it brings back
perhaps what is the best fit for me. In this particular case, it
brought back a Felt Air 5. Now, lo and behold, I was a little dismayed —
what happened to the days of Schwinn and Huffy? I guess I’m a little dated
in my bicycle etiquette. To understand what just happened behind
the scenes to power that first step in an exploratory experience, it’s my pleasure to welcome Brian O’Keefe, CEO
of SellPoints to the stage. [ MUSIC, APPLAUSE ] O’KEEFE: All right, So why are we here? We’re here to get Stephen his bike. Right? I think actually as an illustration of
why SellPoints is partnering with IBM we’re here to help understand how Watson is
optimizing the shopper journey. As Stephen said, when he
started his shopping journey, he was just like 69 percent of all shoppers. The first place they go is a search engine. And what did he do? He’d like to put in that natural
search of, I want to get back into shape, what’s the right bike for me. And he’s completely overwhelmed, as he said. He gets offers for personal trainers
to help him get back into shape. Maybe insuring his bike so he
doesn’t get it lost or broken. Maybe to accessorize his bike, get a helmet
and shoes — he doesn’t have those, either. Take a biking trip to Arizona. After five or six clicks,
though, Stephen gave up, but many shoppers then remember,
oh, yes, I want to buy a bike. Maybe I should go to an e-commerce site. So, that’s the next step,
going to an e-commerce site. This is the actual search results
from a multi billion dollar outdoor and recreational e-commerce site. I want to get back into shape. I want to get back into biking. What’s the right bike for me? Zero results. The word “bike” and “biking” are in there twice. I’d encourage you to go to a really, really
big retailer that’s up in the northwest part of the country put the same search in. You’ll actually get two Samsung
smartphone cases as the answer. So, hopefully I’ve illustrated to you that e-commerce site search and
product discovery is broken. But it’s nearly a $300 billion
market, it just shouldn’t be. To understand why it is,
though, let me show you the way that e-commerce sites are built,
the way they’re architected. They’re actually built just
like a store, physical store. They have departments, they have rows
and aisles and they have shelves. And for Stephen to find the bike he wants,
he has to go to the biking category, and then select the road category, and then a
traditional road bike, not a triathalon bike. And then and only then will he be presented
with the relevant assortment of products — in this case, bikes — that he wants to buy. And he reads the descriptions, reads
those highly valuable five-star reviews from other consumers that own the bike. That’s all well and good,
if he gets to that stage. We know we lost Stephen on
the search engine, right? After 15 years at SellPoints of serving the
Internet Retailer 500, we enjoy the position of actually being the number three provider of
rich media content to the Internet Retailer 500. We understand this concept called
the “half life of the shopper.” And what that means, with each
click that the shopper has to go through on their shopper journey
you lose 40 percent of them. And generally, that’s kind of
okay, because if you get them to the end there’s a 300 percent more likely
probability that they’re going to buy. But the problem is all that friction in the
middle, and that’s where Watson comes in. Where Watson comes in is, it
takes all this out of the middle, all that structure out of the middle. And we take the marketing text on the
right and the associated product reviews that are also written in the same natural
language of other consumers just like Stephen such that he could simply enter
the query, hey, I want to get back into shape, what’s the right bike for me? And in one click, he has that assortment. That’s really profound when you think about
the three big things that Watson fixes then. In summary, what it fixes for e-commerce
site search is this 40 percent problem. This 40 percent drop-off goes away. There’s no half life; there’s one click. The second step, then, is 300 percent increase
in shoppers’ likelihood to buy, because — the final piece — 60 times more Stephens
see their relevant assortment of bikes. That’s really fundamentally a game changer
for us and why we’ve partnered with Watson for fixing e-commerce site search. Thank you very much. [ MUSIC, APPLAUSE ] GOLD: Truly amazing, clearly,
what’s starting to happen online and redefining our own experience on the Web. But if you’re like me, have you
to have that physical experience. I’m not going to buy a bike
without the opportunity to go and touch and feel and test ride. And so, I got in the car and I
drove over to the local bike shop. Now, if you’re like me, when you go into
a retail store, you kind of quickly head to the area of interest, avoiding
all clerical people in the way. And as I walked through the bike shop
I noticed over in the corner a kiosk. And so I approached that kiosk and I entered
I logged in through my Twitter handle. I could have logged in through
Facebook or Twitter. And unbeknownst to me, what was happening now, it was going to start to
personalize the experience. So, I began to interact with the kiosk. It understood how I liked to reference
information and interact with the data. And I further explored my interest in bicycles. And I was able to interrogate the system,
look at user reviews, get an understanding of the features and functions, you know, some
of the benefits of that particular AR 5 bicycle. And to understand really how that all
takes place, I’d now like to welcome to the stage Michael Garel,
who’s the CEO of EyeQ. [ MUSIC, APPLAUSE ] GAREL: Good afternoon. The original ecommerce sites were built to
originally replicate the in-store experience. Today we’re witnessing a retail revolution
where the physical store now needs to replicate the online experience. Personalization online has shown
to increase sales over 20 percent, yet personalization inside the
physical store is virtually nonexistent. With over 70 percent of shoppers’ decisions
being made inside the physical store, brands need a way to stand out from the noise
and deliver an optimal shopping experience. Brands that failed to stand out
from the noise lose sales, loyalty; and ultimately, that coveted shelf position. So, brands are always looking
for ways to win at the shelf, and EyeQ brings personalization
techniques that currenty exist only online into the physical store where 92
percent of a brand’s revenue is earned. So, the EyeQ solution uses multiple tools
to engage shoppers in store with targeted and personalized content
at their point of solution. These tools include facial intelligence — we’re
able to determine things such as shoppers’ age, gender, emotional response and attentiveness. Or mobile location analytics,
we’re able to determine things like shopper loyalty and
in-store browsing history. Interactive touch screens that are installed at
the shelf at that person’s point of decision, delivering this one-on-one
marketing to shoppers. And now, cognitive computing with the
help of Watson’s Personality Insights API, we’re able to deliver an even
better experience by taking into account a shopper’s personality traits
and incorporating that into our algorithm — keeping shopping fun and
that physical store relevant. So, in addition to providing shoppers
with a really stellar in-store experience, we’re also providing leading
brands and retailers with insights they’ve never had available
to them before, enabling them to continue to refine their messaging until those 70 percent
of brand decisions always fall in their favor. So, now let’s look a little bit behind
the scenes at how EyeQ is using Watson. So, when a shopper enters a store
that has EyeQ displays enabled, they’re asked to enter their Twitter handle
so we can choose the best product for them, kind of gamefying the experience a little bit. So, now the EyeQ system pulls their
latest Twitter feed and sends a JSON file to the Watson Personality Insights API. The Watson API then returns a separate JSON file that includes personality
traits that are weighted. And we take those weights, along with
age, gender loyalty, into our algorithm and we deliver a really personalized
digital experience for that shopper. So, now let’s see EyeQ in action. Our first customer is a 35
year old female named Sara. So, after Sara enters her Twitter handle, the
system uses her personality traits as determined by Watson’s PI along with her age, gender and
loyalty to completely choose a bike for her. So, everything from the bike that’s selected
for her, which happens to be womens specific, to the video that’s shown above kind of
demonstrating her competitive nature, to the kinds of information she sees. She sees ratings and reviews,
full features and specifications because that’s what Watson tells
us her personality requires. So, now let’s look at Stephen. Stephen, you’re looking for
a bike to get back in shape. So, let’s see what EyeQ determines for you. We enter Stephen’s Twitter
handle, and as you can see, Watson and EyeQ have completely
changed Stephen’s digital experience. We see you have high levels of
creativity, shown by the video up top. Pretty good trait for a CMO, right? We show you a summary of information, because that’s what Watson is telling
us your personality would like to see. And you are clearly a high spender,
so we show you a high-end bicycle. So, at EyeQ, with the help of IBM Watson,
we bring the best of the digital world into the physical store enabling brands to fundamentally transform
their in store experience. Thank you. [ APPLAUSE, MUSIC ] GOLD: So, I’m starting to feel pretty
good about my decision to acquire a bicycle. I’ve done the online, a little bit of research. Had a good experience, came and shopped, interacted with some amazing technology
based on the Watson capabilities. But there’s one more thing, and that is
I’m not quite ready to pull the trigger and spend $2,600, which I’m told, by the way,
is not a lot of money anymore for a bicycle. And so I have a couple of questions for
the staff as it relates to, you know, which accessories, a seat, the pedals I need. A number of other factors, like
what is your return policy? I the bike ultimately isn’t
what I want, can I exchange it? Can you recommend a bike club to get started? So, I usher on over one of the associates
that happens to be carrying a tablet with them that is powered by a product called SellSmart. So, I become very interactive with that sales
associat and I start peppering the individual with a number of questions, and they
quickly are interacting with the iPad. And coming back and saying, well, you
know, we have a 90-day return policy but a one-year satisfaction guarantee that if for any reason this isn’t the
bicycle you want you can exchange it. I’m thinking, well, I’m feeling
pretty good about this. We talked a little bit about the
accessories, we talked a little bit about getting started, exercise routines. And I make my commitment and I buy my bicycle. That’s a far different experience, right,
than what you can possibly imagine. Now, to understand what just happened with the
interaction with me and the sales associate, I’m pleased to represent from
RedAnt the CTO Dan Hartveld. [ MUSIC, APPLAUSE ] HARTVELD: Thanks, Steve. Good afternoon, everyone. So, RedAnt specialize in connecting and enhancing the customer
experience through smarter technology. We’ve got experience working with big
retailers, delivering technology not only to the end consumer but also to
sales assistants in the store. We’ve been conducting a lot of research
over the past few years, both of customers and with retailers, and we
found the key thing is that customers are having a better
experience online now than they are in store. They get better access to information more
tailored to their needs, and that’s coupled with the opposite effect in store where nearly
two-thirds of sales assistants are having less than two hours training before they’re put
in front of sales…in front of customers, and 43 percent of customers…43 percent of
sales assistants have actually admitted to lying to customers every week due
to lack of product knowledge, so just imagine what they’re
saying to your customers. And customers are understanding
that there’s a gap here, too. A recent IBM poll has said
that trusting sales associate to provide sales knowledge ranked lower
than any other source of information. And how many of you guys have
walked in the store and felt like you knew more than the sales assistants? So, you’re smart enough to know that technology
can help you, so why can’t it help them? Gartner found that nearly two-thirds
of luxury customers are more likely to approach a digitally enabled
sales assistant than they are if there’s just a sales assistant walking around
looking like they don’t know what they’re doing. So, customers have realized
that there’s a missing piece, and that missing piece is
empowered, knowledgeable employees. So, how do we give sales associates
access to that information? How do we give them access to the
right information at the right time? So, that’s why we’ve created SellSmart. SellSmart is a voice activated sales
trainer that uses the power of Watson to bring big data to the shop floor. It helps sales staff learn more
about the products that they sell. It helps build winning conversations with
customers; and, it helps build conversations that are most likely to lead
to satisfaction in sales. And we then crowdsource that information
and learning over time to train Watson, giving Watson more and more
questions and more and more learning and exponentially increasing its power
as it’s rolled out across your estate. So, I’d like to show you how SalesSmart
works with a bit of an example. So, we talked about Steve
and his research online. He’s gone out and he’s researched
a lot of different websites. Watson’s potentially helped him
find some bikes he’s interested in. But now he’s looking for a better
idea to find what he’s looking for, but he’s still got questions and he
wants to see the products for real. He wants to talk about it with a real
person who understands his needs. So, he’s come into the store
to talk to a real person. This salesperson they approach is quite
new; actually is a seasonal worker and doesn’t really know a lot about
bikes, they’re really just helping out with stock check but
he’s approached them anyway. So, Steve asks, you know, I’ve
taken a look at a specific range of bikes online and they seem good. But I want a bike that I can take off road. Well, let’s find out how we can do that. The sals assistant can actually
ask Watson that question, and Watson understands the natural
language and returns relevant answers. Well, with SellSmart on top of this, the
sales assistant not only gets the answers but they get detailed product information
and potential purchase options. They also get follow-up questions that other
customers have asked around this topic. So, this sales assistant can help anticipate
relevant needs specific to Stephen. After the conversation, sales assistants
can feedback on the customer satisfaction and the sales success of the product. This then helps train Watson further,
improving answers over time and giving answers that are most likely to satisfy
customers and lead to sales. This then helps all sales assistants
across your entire real estate. It creates basically the best sales assistant
you can imagine, and gives that…gives all of your sales assistants access to
that so that every staff can benefit from every conversation you’re
having with each customer. So, SellSmart not only helps the sales process,
but it can also be used to train new employees on anything from sales warranties,
to parts replacement, to gift card policies, to
stock checking procedures. With all of that in mind, we believe that Watson and SellSmart were really
helping making retail human again. We’re giving sales staff knowledge not just
to answer questions but to build relationships with their customers and make
each customer feel like a VIP. So, that’s why we think with
RedAnt, SellSmart and powered by Watson we’re really helping
bring the future of retail. Thanks. [ MUSIC, APPLAUSE ] GOLD: So, let’s think about what just
happened in this scenario, this vignette. We navigated a wealth of
information, culled it down to a number of options that were contextual in nature. I wanted a bike, it gave me bicycles. It created for me a highly
personalized experience to know me, to understand me to understand what I’m trying
to accomplish with this particular purchase. And to help me fill indicate all the way
to end, the transaction that, candidly, I and in this case, this bike store had in mind. It was a win-win scenario. We got to an end state where everybody
felt confident in the transaction and the activity that just took place. We truly just witnessed the beginning of a
disruptive cycle in retail, redefining the way in which we as individuals will interact
and operate online and on premise. That’s pretty exciting, and it’s
great to share the story with you. But I think to really bring this point home
and to amplify it, all of us have been out and probably acquired a bicycle
but certainly have acquired cars. It’s one of those industries
that’s the bellwether, and staid, and been around for decades,
but it’s reinventing itself. It’s redefining the way that you and
I will acquire cars in the future. And one organization that is truly leading
the charge is FordDirect, and with us today, the Vice President of Innovation, Marc Fecker. Marc. [ MUSIC, APPLAUSE ] FECKER: ood afternoon. And first, some good news: tomorrow, the high temperature is 20
degrees cooler, so hang in there. I know what you’re all thinking. Who’s FordDirect? For those of you who haven’t heard
of FordDirect, we’re a joint venture between Ford Motor Company and
our U.S. Ford and Lincoln dealers. We’re based in Deerborn, and our focus
is driving sales and service revenue through our dealerships through
enabling digital services. My role at FordDirect is
that I run an innovation lab, and we’re tasked with revolutionizing
the automotive retail experience for our Ford and Lincoln customers. Working with great customers like EyeQ and IBM
and leveraging the most advanced technologies in the world like Watson, we intend
on creating a car shopping experience that is second to none. At FordDirect, we see the car buying experience
evolving significantly over the next few years. As systems become more connected
and more intelligent, we see a future where cars sell themselves. We like to think of that as the
personalized personified car. We also see a world where the transaction
is completely paperless and it can be done through whatever channel that the
customer preferences on their terms. But one of the challenges facing
customers today is trying to address that perception of the pushy salesman. We all know it’s what I’m talking about. So, customers struggle enough today
trying to find the right vehicle that will meet their needs and doing this
while fending off the unwanted help can result in a less than than enjoyable experience. In our lab, we have created an
experience that customers self select into and get recommended a vehicle and
then ultimately initiate a test drive that leverages both the EyeQ
technologies and Watson. You then will get matched with a salesperson
in a way that’s more meaningful and enjoyable, and I’ll tell you a little bit about that. So, today, heading into a dealership
can feel quite overwhelming. Trying to sift through all the
vehicles and find the perfect fit that meets your needs is challenging. And trying to avoid the sharks at the
same time; that’s a whole nother story. What if there was a way to get the
information you needed on your terms? In our experience, customers can
opt in to get a recommendation on a vehicle based on their personality. The content they see is tailored to the
individual who is standing in front of it. And then, when they find the
vehicle that they’re interested in, customers can choose to take
it for a test drive. And when they do, they’re going to be matched with a salesperson based on
their personality profiles. So, we like to think of this as the experience at the point of sale. Now, we don’t expect many, you know, love
interests to be born or romances out of this, but we do hope that it will result in a better
customer experience; and ultimately, more sales. I like to think of this as true
personalization and in real time, moving away from the traditional messaging based
on historical transaction data and demographics and moving towards creating
experiences that are improved by considering an individual’s personality
and how they’re feeling at the moment. But creating an experience like this comes
with its own set of business challenges. If it proves out, we’re going to have to
look at the way that we run our businesses and how we hire and train our employees. We’re going to have to look at
how we compensate salespeople. It all is going to have to be changed. But we all must embrace change if we’re
going to be successful moving forward. While we are still in the experimental
phase, we are very excited by the potential that this brings to improve
our customers’ experience and improve the way that we do business. We are looking to test this lives in
dealerships with customers later this year. Now, Watson presents opportunities that just a
few years ago we never even thought would have been possible in our time time. But today they are. Thank you. [ APPLAUSE ] GOLD: Thanks, Marc. Amazing, what’s starting to happen. Hopefully you’re getting a sense today for the
groundswell of activity of how organizations, clients, partners are starting to put
together the cognitive capabilities to redefine how we live and how we work. But it’s not just transforming industries;
it’s also about us as individuals, us as professionals in redefining the way we
will perform our very tasks, how we can enhance, scale and accelerate human expertise. We have a great panel coming up for you right
now, led by Jon Iwata, our Senior Vice President of Marketing and Communications, to
talk about individual transformation. And joining him on stage
will be Chef James Briscione, Dr. Mark Kris of Memorial Sloan-Kettering
and Beverly Oliver from Deakin University. Jon and panel. [ MUSIC ] IWATA: Well, good afternoon. I want to start with an insight that
I gained yesterday from Tata-san of our great Watson partner, SoftBank. We were talking about Watson as a brand and
that Watson has become something that is known by millions and millions of people
and what they think of Watson. He posed an interesting question back to me. He said, is Watson an icon
of innovative technology; or, is Watson an icon of innovative business? I’ve been thinking about that for
the past day, and let me ask you. How many of you think that Watson is a
symbol or icon of innovative technology? How many of you think of Watson
as an icon of innovative business? How many of you think it’s both? I’m with you. And this panel is a discussion about what
happens at the intersection of the next era of computing and the next era of
every profession and industry. We have a wonderful panel of
colleagues who represent leadership in a variety of industries and professions. And we’re going to have a discussion about
Watson at work in the real world in education and healthcare and in the culinary arts. Beverly, let me start with you and
Deakin University in Australia. What is Watson doing there? OLIVER: Well, as of since March this
year, every student who comes to Deakin, 50,000 students, spread all around the country,
20 percent of whom never come to campus. They come to our campus in the cloud. Every student and every staff member can
ask Watson for advice and information on how to orient themselves to study, how to
find help, how to get their student card, all the details that students sometimes
fret about when they start their studies. So, we think this helps. It’s 24/7, 365. So it’s, students have information at
their fingertips to help them get started. IWATA: Great. We’ll talk more about the
reaction to Watson at Deakin. Dr. Kris, your relationship with Watson
goes back to, well, it’s like Alex Trebek and Dr. Kris at Memorial Sloan-Kettering. In a nutshell, what has Watson
been doing with you and at MSK? KRIS: So, I’ve had the
extraordinary opportunity to work with IBM Watson for the last three years. I’m a medical oncologist, I take
care of people with lung cancer. And what patients ask me is, doctor,
make the best decision for me. And as I said, my institution and IBM came
together to develop this technology to do that. And what I’ve been able to see over
the last three years is that it can go into a patient’s information and
all the nooks and crannies of it. It can go over a huge amount
of medical information. It can look for the newest information. It can have the wisdom of what I think
are the smartest doctors teaching it, and put it in my hand; and more importantly,
I can put it in my patient’s hand. So, the two of us together can sit
down, go over the various decisions that are possible and together
make the best one. So, it’s given me a unique opportunity
to give patients exactly what they want; and I as a doctor, what I’d like to give them. IWATA: Thank you. Chef, for many people their first
experience with Watson is Chef Watson. It was at the Berkshire Hathaway annual
shareholder, meeting tens of thousands of people, and they were tasting, literally
testing and having an experience with Watson. Can you share with us what
Watson’s been doing with you at the Institute for Culinary Education? BRISCIONE: Yes. You know, we’ve been working with Watson for about three years now at
ICE here in New York City. And you know, one of those words we’ve started
to hear a lot already today is “innovation.” And I think as a chef, it’s one of the things
that we all strive for, is to be innovative. And I think, you know, in the world that
we live in, I suspect hundreds of pictures of food have probably been
looked at on a smartphone since we just started this panel already today,
and Instagram and Twitter and it’s everywhere. And that ability to give someone something
new, to put a plate of food together that they’ve not seen before, is
truly, it’s the goal of every chef. And in the ways that we’re working with
Watson now, that’s exactly what we’re doing. Every dish we put together is a combination
of ingredients that no one has seen before. IWATA: Can you say a little bit more about
that, because some people think initially that Chef Watson searches for recipes. And it may even occur to some
people to ask the question, why would a chef even need a
cognitive system like Watson? BRISCIONE: You know, it’s often one of the
first reactions we get, and that’s usually when people don’t quite understand
what it is that we’re doing. And to be honest, when I first sat
down with a team of engineers from IBM and they told me what they had planned, I
kind of had the same reactino, saying, well, this is probably not going to work but let’s
give it a shot anyway and see what happens. And you know, within a day or
two we were in the kitchens at ICE cooking ingredients
suggested by a computer. And we were trying to make a breakfast pastry,
and we had things like coconut milk and saffron and black pepper and cocoa
going into a yeast risen pastry. And then things that I would
never imagine putting…combining and putting together in a single dish. And you know, that’s the idea of Watson. I think as a chef, using it
as a tool to fuel creativity. You know, normally that process of creation,
of developing a dish, means hours sitting down in a notebook and writing a list of
ingredients and trying to trace and draw lines and sort of connect all of these dots to to
come up with the dish that you want to create. And I’m pulling every cookbook off the
shelf and seeing what every chef that I know and respect has ever used to
create…to pair with asparagus. Okay, this chef, he likes
[sherbo] with asparagus. He did crawfish with asparagus. He does eggs with asparagus. And you’re trying to compile all this data
yourself, and it’s hours and hours of work, and so much time and effort goes into that. And then it’s like, well, well crap, that’s
got to be ready for the menu next week. Now we’ve just got to throw those ingredients
on the plate and get it done and move on. You know, when we’re working with
Watson, we get ingredients that have been so thoughtfully paired together with so
much different criteria, chemical compounds that these ingredients contain and
where the ingredients come fron, and the goal of the recipe —
what we’re trying to create — is all considered to give us a list
of ingredients so that I can sit down spend my time thinking,
well, Watson says mushrooms. But what is that, is that a mushroom puree? Is that powdered mushroom? Is that a new form that we
can put a mushroom into? And it just drives the creativity so
that I can think about all of the new and exciting ways I can utilize these
ingredients that frees up that mental power and that mental space to
focus on that innovation. IWATA: Thank you. Beverly, tell us about, you’ve got
tens of thousands of students as well as over 3,000 faculty interacting
with Watson right now. What has been their reaction? OLIVER: It’s been very positive. One of the strategies that we chose
was to actually employ the students, particularly the new undergraduates or
the more experienced undergraduates, to be our project team. And so, we’ve sold it to the students
partly to manage their expectations that they’re helping to train Watson. So, they’re really enjoying this experience of
being a fellow learner with Watson, if you like. And I think they’re very proud
and they see it as very clever. But we have a very, shall
we say, demanding audience, particularly among our new undergraduates,
because they have very high expectations. But I think bringing them on the journey
with us has been a really good strategy. IWATA: Dr. Kris, we were
talking about earlier an aha! moment you had, not initially, not
with Jeopardy!, but a little bit later when you saw the power of
Watson at work in your field. Do you want to describe that aha! moment? KRIS: Well, to many of the people here,
you’ve probably seen me show that Watson demo from the investors meeting many years
ago, and it just that — a demo. And it had that beautiful Watson
thing spinning around in there that people really seemed to love. But the first time I actually saw it
analyze a patient’s data and give a list of treatment choices — and frankly,
do it in the same time it took in that demo — I mean, that was truly amazing. I think the other thing is the way
that the patients have embraced it. I mean, patients really like this
idea because they feel it’s a way of getting all the information
about them to their doctor. It’s a way of matching all their information to the information that’s
available in the medical literature. And it puts it in their doctors’ hands. It helps them make a doctor’s decision. When you kind of see them light up and please,
use this for me, I think that was a moment that really changed things for me, as well
as that little Watson thing turning around. IWATA: We’re rather found of that
little Watson thing turning around there. KRIS: People love it. [ LAUGHTER ] IWATA: A final question
and a very important one. So, we’ve gained a little insight into how
Watson is transforming Deakin, MSK and ICE, but you represent, you’re
leaders in entire fields. And if you were to think about the intersection
of Watson cognitive systems and culinary arts, oncology, healthcare and education,
where do we see the future? How is this going to change the field? We’ll start with James and
we’ll end with Beverly. BRISCIONE: Well, you know, I think
for us, this Watson at ICE, you know, beyond using it to create really exciting
dishes, a lot of our colleagues in the field and other chefs are kind of really
interested in what we’re doing. So, I think there’s exciting opportunities
for us to share this with other professionals and going to spread the knowledge. But you know, also, we’re able to
use it as a teaching tool at ICE. As you’re learning the world of food — which our students are doing when they’re in
the classrooms and in the kitchens with us — trying to explain, well, why do
those two ingredients go together? And we can actually use Watson to lift
the veil on that and say, well, you know, it goes back to that they’re both
native ingredients of Indonesia. Or, they share these chemical
compounds in common, which makes those flavors
harmonious and work together. And while you would never think
of putting those two together, here’s kind of this hidden connection
that without that deep insight to the science no one would ever even know. We found some really exciting
combinations in that same way ourselves. IWATA: Thank you. Dr. Kris. KRIS: I like that term Ginni had before
about letting you do the heavy lifting. I mean, to me, I see it as being a way to
aggregate all the important information, to prioritize the information, to make sure
it’s up to date and just put it in front of me and I can share it with the patient. And then I can be the doctor. I don’t have to do this. And I have this and it gives such comfort to
the patient that I have this backing me up. And I think that is going to be the best thing
about it when it’s in the room with the patient and helping the patient and the
doctor make the best decisions. IWATA: Thank you. OLIVER: Well, we’re very happy
with what we have at the moment where our students have correct
and consistent advice. But we really believe this has barely started. So, today we’ve heard Watson helping
to solve problems in for patients, for example, and financial advice. We have great challenges in higher
education: cost access, learning outcomes. Imagine if Watson ingested all the
assessment tasks we’ve ever set and then ingested all the papers we’ve
ever marked and could be a trusted advisor, when we go to grade students and make
judgments about whether they’re ready for professional life — because that’s what
we do — I think that’s going to be amazing, and I’m really looking forward to that. IWATA: As well we all are. Well, please join me in thanking our
leaders here, Beverly, Dr. Kris and James. Thank you. [ APPLAUSE, MUSIC ] RHODIN: Feels like the sun’s setting right
on us in here as it comes through the window. So, we’re almost done. We’re in the home stretch right now. So, today’s conversations and stories
are really I think a proof point of what our overall mission with
Watson is, which is to scale knowledge and expand the possibilities of what
we as humans can accomplish with that. Our next guest leads an organization
that shares that same mission and vision. Please welcome curator of TED, Chris Anderson. [ MUSIC, APPLAUSE ] ANDERSON: Hello. I like this venue. I might have to steal it one day. I’d like to talk with you about the moment
nine years ago when we stumbled on something that has changed forever the way we do business. I’m going to call it, for want of a
better term, the generosity strategy. Nine years ago, 2006, TED was an
annual conference in California. That’s what we were, 800 people came,
or a thousand people once a year. We got inspired about talks and
ideas, but nothing else happened. And so, we tried to get these
talks on to television. No one was interested. And around about that time bandwidth
on the Web, the cost was plummeting. So, we tried this experience of putting
six talks up on the Internet for free. We didn’t think they would work online. Who would watch public health
lectures on the Internet when there were all those
great kitten videos to watch. To our amazement, these talks went viral and people told us they were
completely moved by them. And so, we thought, well, what next? Because the logical thing to do was to open
up completely and put all of our best stuff up on the Web for free if we
were serious about sharing ideas. The only thing is, that seemed like the
dumbest business move you could make. Our whole operation was based on people spending
thousands of dollars to come to this conference. Why would you give away your crown jewels? But here’s the thing. In the connected era, as actually
many TED speakers have told us, the rules about what you hang on to and
what you give away have changed forever. Several reasons for this. One is that when you give away a digital
asset that matters, it explodes online. It can fly across the world at the speed of
light withni a shockingly short period of time. Hundreds, thousands, millions of
people can have experienced it. That means that you can build
a reputation faster than you ever could before,
and actually for nothing. You just give it away and it happens. And then in the transparent era which we’re
in, reputation matters more than ever. People really care about that stuff. So, anyway, we plucked up the
courage and did it and changed TED from a conference to ideas [worth] spreading. And to our amazement, the demand for
our conferences actually rocketed. It went up. And so, ever since then that’s been our
strategy and it’s amazing what happens when you give people stuff and
let them do things for you. So, for example, by giving away our
talks, thousands of people volunteered to translate our content now
into more than 100 languages. And we eventually took the risk of giving
away our actual brand in the form of TEDx. We added a little X to say that
the X stood for self-organized. And again, to our amazement, thousands of
people offered to sweat blood and tears, months of their time, big financial
risk, to put on their own TED-like event in their own community so
that now there are about eight of those held every day somewhere in the world. So, this generosity thing
definitely has something to it. And this partnership that we’re about to
hear about is also just based on generosity. We did a little about it
of generosity, not much. We gave IBM all our videos and all the metadata
that goes with it and all the transcripts. And then they, they did something
really incredibly generous. They promised us the full
intellectual resources of one of the smartest people who actually works there. I haven’t met him yet, I think I’m
about to, some guy called Watson. Anyway, ii this is the moment to…Mike, I think
yr going to tell us about this partnership. Thank you for your generosity on this. RHODIN: Thanks, Chris. So… [ APPLAUSE ] We’re all huge fans of TED. The talks really expand our imagination
and challenge us to think differently. In fact, we’ve had members talking
at TEDx and many of the conferences. I think you’ve even done TED at IBM with us. But these talks, they help us kind
of reshape our perspectives on some of humanity’s most important questions. To even try and wrap your head around some
of the volume of insights that originate and are delivered by some of the world’s
brightest minds is kind of overwhelming. Thousands of these talks have been
delivered, hundreds of topics. But that got us thinking — if we had your
content, what if there was a way for us to quickly and intuitively discover
the insights and perspectives that a user might be looking
for about that particular topic? What if you could collaboratively
explore these with the assistance of that very smart employee called Watson. So, let’s take a look. I’d like to introduce our project
lead extraordinare, Kai Young. Welcome Kai. [ APPLAUSE ] So, Watson has now listened to almost
2,000 TED talks and organized them. Each of these icons is a talk,
clustered into similar concepts. Behind the scenes Watson has extracted
the essence and meaning of the talk, identifying ideas and concepts just like a human
would: understanding what speakers are talking about morality or consciousness
or the process of learning, things that keyword searches
would never pick up on. So, let’s now try asking Watson a question,
a complex one and see what it comes up with. Chris, when we first got together a few weeks
ago when we first looked at the prototype of the system, you had a question,
do you remember what it was? ANDERSON: I think it was,
were there any talks that built on the relationship between money and happiness. RHODIN: Yes, tThat’s right. So, why don’t we, Kai, why
don’t we look at that. So here, Watson is pulling out the actual
answers from within the videos based on the expertise of the TED speakers, differing on their different
perspectives on that question.>> Remind you of a consumer lifestyle
where you work hard to get money. You spend that money on consumer goods,
which you hope you’ll enjoy using. But then the money is gone, you have
to work hard to get more, spend more and to maintain the same level of happiness. It’s kind of an Hedonic treadmill,
you never get off.>> We see it in religions and self help
books that money can’t buy happiness. And I want to suggest today that
in fact that’s wrong and that… [ LAUGHTER ] I’m at a business school, so that’s what we do. So, that’s wrong. And in fact if you think that, you’re
actually just not spending it right, so that instead of spending it the way you…>> I think we’re starting to sort of question
and disrupt and interrogate what money means, what our relationship with it is. What defines money? Then the ultimate kind of extension of
that, is there a reason for the government to be in charge of money anymore? RHODIN: So, what you can see here is that
we’re looking for the meaning of the talks, layers of meaning beneath the spoken word, and
this is something that’s truly cognitive, right? Now the possibilities of this
we think are pretty exciting. Imagine the millions of hours of material in
fields like law or education that could benefit from this kind of collaborative
exploration application. Are you ready to give it a spin? Chris and I are happy to share that
TED Connect, powered by IBM Watson, is live today today exclusively for those of
you here along with TED’s 25,000 power users. You can sign up right in the World of Watson
app that you have from the conference here. And then soon, we’re going to be
opening up to millions across the world. Thank you for your partnership, Chris. ANDERSON: Thank you, Mike. Thank you very much. RHODIN: And thank you, Kai. [ APPLAUSE, MUSIC ] Now, what you just saw there, if you go behind
the scenes, we’re looking at something that went from an imagine-if conversation to a
very cool app in less than eight weeks. Taking TED’s amazing content, our ad tech
team plugged into the Watson APIs and some of the new Alchemy APIs that we just added
to the suite, designed the experience with IBM’s award-winning IBM
Interactive Design agency. That’s a pretty cool thing to do
in a very short period of time. Now, tomorrow, you’ll have a chance to build
your own app using the hands-on Watson lab which is going to be located in that
sauna up there on the second floor. But let’s take a quick look at how to do that. [ MUSIC ] So, I’d like to start off by really
thanking for your patience in the heat that we’ve gone through this afternoon. I think as one of the speakers said, the temperatures for tomorrow are
supposed to be a little bit lower. We didn’t expect 85 degrees in
May, it was a little unusual. We clearly didn’t ask Watson about the weather. But we have a full agenda
starting tomorrow morning. First I want to give you a few
reminders about transportation tonight. The ferries depart here tonight every 15 to 20
minutes starting at seven p.m. Tomorrow morning, the general session starts at nine. I won’t give away anything, but we will reveal
the winners of the hack-a-thon at that time. Ferries will be running in the morning from 7:30
to 9:30 a.m. But right now, we’ve got cocktail and dinners waiting for you behind the curtain. In fact, you may have already heard that Chef
Watson has launched a bestelling cookbook. Let’s see what the chef has
cooked up for us tonight. Thank you. [ APPLAUSE, MUSIC ]


  1. The music between speakers is HORRIBLE! Should have used Watson to figure that out before releasing this video.

  2. Interesting applications but in terms of processing these are just machine learning techniques which can be implemented with open source tools. I'm curious about their natural language processing advancements.

Leave a Reply

Your email address will not be published. Required fields are marked *