Copyright of Manufacturers' Monthly is the property of Prime Creative Media and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. 62 September 25, 2017 “It’s about what you do to communicate to people why these things are important. It’s not about a tweet” Megan Murphy: Artificial intelligence. People may not know that IBM doesn’t call it AI. They call it “cognitive computing.” Tell us why that is. Ginni Rometty: I have actually had to explain this to my husband as well, because he said to me, “Ginni, of all words, why cognitive?” It was really a very thoughtful decision. The world calls it AI. There’s so much fearmongering about AI. When we started over a decade ago, the idea was to help you and I make better decisions amid cognitive overload. That’s what has always led us to cognitive. If I considered the initials AI, I would have preferred augmented intelligence. It’s the idea that each of us are going to need help on all important decisions. I’m always reminded of an interesting statistic: When you’re asked what percentage of your decisions are right, what percentage would you get? What would it be? A study said on average that a third of your decisions are really great decisions, a third are not optimal, and a third are just wrong. We’ve estimated the market is $2 billion for tools to make better deci- sions. That’s what led us all to really calling it cognitive and getting The leader of America’s oldest tech company talks to Bloomberg Businessweek Editor Megan Murphy about the promise and threat of AI (and Watson), Charlottesville, and being a role model Ginni Rometty CEO, IBM Photograph by Stephanie Sinclair through to people that, “Look, we really think this is about man and machine, not man vs. machine. This is an era—really, an era that will play out for decades in front of us.” The world discovered IBM’s Watson after the computer system beat human competitors and won $1 million on Jeopardy! It’s named after your company’s first CEO. What does Watson mean for the future of AI—and for your business? Everything you know until today is program- mable—an entire era for decades has been pro- grammable. Watson would be the beginning of a new era where you didn’t program. Machines would look at data, understand, reason over it, and they continue to learn: understand, reason and learn, not program, in my simple definition. That to us is a very big difference between what ◼ DEBRIEF 63 Bloomberg Businessweek Month 00, 2017Bloomberg Businessweek Month 00, 2017Bloomberg Businessweek Month 00, 2017 6464 you might experience in what I call consumer AI—that is, general purpose—vs. business. We set out to build an AI plat- form for business. There would be two big differences between business and consum.

1. Copyright of Manufacturers' Monthly is the property of Prime
Creative Media and its content
may not be copied or emailed to multiple sites or posted to a
listserv without the copyright
holder's express written permission. However, users may print,
download, or email articles for
individual use.
62
September 25, 2017
“It’s about what you
do to communicate
to people why
these things are
important. It’s not
about a tweet”
Megan Murphy: Artificial intelligence. People may not know
that IBM
doesn’t call it AI. They call it “cognitive computing.” Tell us
why that is.
Ginni Rometty: I have actually had to explain this to my
husband
as well, because he said to me, “Ginni, of all words, why

2. cognitive?” It
was really a very thoughtful decision. The world calls it AI.
There’s so
much fearmongering about AI. When we started over a decade
ago,
the idea was to help you and I make better decisions amid
cognitive
overload. That’s what has always led us to cognitive. If I
considered the
initials AI, I would have preferred augmented intelligence. It’s
the idea
that each of us are going to need help on all important
decisions. I’m
always reminded of an interesting statistic: When you’re asked
what
percentage of your decisions are right, what percentage would
you get?
What would it be?
A study said on average that a third of your decisions are really
great decisions, a third are not optimal, and a third are just
wrong.
We’ve estimated the market is $2 billion for tools to make
better deci-
sions. That’s what led us all to really calling it cognitive and
getting
The leader of America’s oldest tech
company talks to Bloomberg Businessweek
Editor Megan Murphy about the
promise and threat of AI (and Watson),
Charlottesville, and being a role model
Ginni
Rometty
CEO, IBM

3. Photograph
by Stephanie
Sinclair
through to people that, “Look, we really think this
is about man and machine, not man vs. machine.
This is an era—really, an era that will play out for
decades in front of us.”
The world discovered IBM’s Watson after the
computer system beat human competitors and
won $1 million on Jeopardy! It’s named after your
company’s first CEO. What does Watson mean for
the future of AI—and for your business?
Everything you know until today is program-
mable—an entire era for decades has been pro-
grammable. Watson would be the beginning of
a new era where you didn’t program. Machines
would look at data, understand, reason over it,
and they continue to learn: understand, reason
and learn, not program, in my simple definition.
That to us is a very big difference between what
◼ DEBRIEF
63
Bloomberg Businessweek Month 00, 2017Bloomberg
Businessweek Month 00, 2017Bloomberg Businessweek Month
00, 2017

4. 6464
you might experience in what I call consumer AI—that is,
general purpose—vs. business. We set out to build an AI plat-
form for business.
There would be two big differences between business and
consumer AI. For example, if you were on your phone and
searched for the best song in 1950, you don’t think, Well, who
voted on that? Why did they pick that song? But if you asked
for the right diagnosis of a type of cancer, you’d want to know
who trained the computer, what data and what was the evi-
dence behind it. It would be the same for business: AI would
be vertical. You would train it to know medicine. You would
train it to know underwriting of insurance. You would train it
to know financial crimes. Train it to know oncology. Train it to
know weather. And it isn’t just about billions of data points. In
the regulatory world, there aren’t billions of data points. You
need to train and interpret something with small amounts of
data. Guess what percentage of the world’s data is search-
able? What would be your guess?
Four?
The answer is 20 percent. The other 80 percent lives with all
of us who’ve established businesses—and my view is that data
has got a lot of gold in it. It leads me to the second big differ-
ence between consumer and business AI. If that’s my data, and
it’s my IT and my competitive advantage, I’m training
algorithms,
and I want to be sure those algorithms become mine. I want a
platform that’s my AI even if it operates in a cloud. Business AI
knows the domain and the profession, and it can protect your
insight. Not just your data, your insight.
Now, some of you may or may not know this, we also own
the Weather Channel. Any of you on your phones, that’s IBM

5. you’re hitting when you do your weather. Now introduce
Watson
into that. Over the weekend of Hurricane Irma—a new weather
forecast every 15 minutes recalculated across all 3 billion points
of the earth—we helped a million conversations. It was interac-
tive conversation, natural language on how to prepare for the
hurricane. We also had half a trillion interactions with Watson
to help 140 airlines reroute.
You touched on some of the criticism of Watson that’s been
out there: that it’s still too dependent on humans, it can’t learn
fast enough, and it hasn’t been transformational enough to
live up to some of the expectations both of IBM and how
it’s being marketed. How do you respond to those critics?
IBM is an $80 billion company. So when people say, “My
goodness, why hasn’t this thing grown IBM by two?” I think
that’s a very unrealistic expectation. You teach these systems.
Those of you that work with them, you and they have to learn
and teach. Watson is exactly where we thought it would be.
When we did our very first oncology teaching with Watson—
the very first was lung, breast, and colon cancer—it took the
doctors a year to train Watson.
This is really another key point about professional AI.
Doctors don’t want black-and-white answers, nor does any
profession. If you’re a professional, my guess is when you
inter-
act with AI, you don’t want it to say, “Here is an answer.” What
a doctor wants is, “OK, give me the possible answers. Tell my
why you believe it. Can I see the research, the evidence, the
‘percent confident’? What more would you like to know?” The
first cancer Watson took almost a year. We are down to less
than 30 days now. By the end of this year, Watson will have
been trained on what causes 80 percent of the world’s cancers.
And so I find that kind of criticism completely out of line.

6. I remember when my mother got cancer. My first reaction
was, How do I know that this is the treatment? How do I know
that this is the best thing? A 70-year-old truck driver in Florida
going to get a new job had a recurrence of cancer. He was
absolutely devastated. This is what I saw Watson do for him.
The doctor showed him, “There are people like you with this
cancer. Here are the kinds of treatments they get.” The differ-
ence in mindset is night and day. And then, on my trip to India,
I
met a woman whose doctor had never seen her kind of cancer.
Without Watson, he would never have had the idea of what the
treatments are.
Memorial Sloan Kettering [Cancer Center] was one of the
first that taught Watson. It’s the gold standard, and it illustrates
beautifully one of the principles of AI in the future. You must
know who taught it and what data is in it—and you must be
trans-
parent about it because that matters in these decisions. That
gives you a long, long, long answer, but this is why I’m so
positive
this world will have more really tough problems solved with AI.
And the dystopic view of AI?
When I went to Davos in January, we published something
called Transparency and Trust in the Cognitive Era. It’s our
responsibility if we build this stuff to guide it safely into the
world. First, be clear on the purpose, work with man. We aren’t
out here to destroy man. The second is to be transparent about
who trained the computers, who are the experts, where did
the data come from. And when consumers are using AI, you
inform them that they are and inform the company as well
that owns the intellectual property. And the third thing is to
be committed to skill.
Do you feel we’re going to get to a point where AI will dis-

7. place more jobs than it creates and we’re not doing enough
to push forward with the jobs of the future?
I do believe that when it comes to complete job replacement,
it will be a very small percentage. When it comes to changing
a job and what you do, it will be 100 percent. “Whoa, differ-
ent skills. Everybody is going to have to have a different skill
because it’s going to be a threat in all our jobs.” Let me just
park that thought. I want to come back to something I think
that’s far more important and is related. The issue of skills is
front and center in this country and many countries in the world
right now without AI. We already have a world that’s bifurcat-
ing between haves and have-nots, and a lot of that is based on
education and skills. This country has 5 million to 6 million
jobs
open. That’s about skill. This is not being caused by AI. We’ve
got to revamp education for this era of man and machine. And
that means you cannot insist that every person needs to be a
university or a Ph.D. graduate to be productive in society. You
cannot. It’s not true by the way. We’ve proven that.
You started a six-year high school program. This is a program
where they take people through four years of high school,
two years of a college equivalent, and then hopefully give
them preference in getting into the workforce, again to work
with IBM.
In the U.S., in 2015, half of our young people didn’t have an
associate’s degree or a college degree. That’s the problem
today: the number of people that need to be retrained. I’m far
more optimistic that public-private partnerships can solve
this dilemma. There will be a hundred pathways to technol-
ogy becoming viral, driven by governors and states. I always
◼ DEBRIEF Bloomberg Businessweek September 25, 2017

8. 6565
remember when President Obama came to the first one, he
goes, “Where are all the computers?” We’re like, “That’s not
what
we teach these kids.” We’re teaching them a skill about math
and problem-solving that’s going to transcend any technology
they deal with. The first part is a very simple formula: a
curricu-
lum of math, science. The second, give the kids a mentor and
then you give them a chance at a job. We will be up to 50,000
kids, and 300 other companies have volunteered. I have a whole
bunch of these kids over in Silicon Alley where we have our
Watson headquarters.
You were a part of President Trump’s decisions advisory
council which disbanded in the wake of Charlottesville. You
said in a letter to IBM employees that it was no longer fit for
the purpose for which it was created. What did you mean
by that?
It’s about policies, not politics. I’m passionate about skills
and education, about being competitive in trade for a digital era,
and of course, diversity and inclusion. We’re blessed to be able
to have an influence, and it’s our job to do that. So this strat-
egy and policy forum is what I asked to be a part of. It wasn’t a
council. It was asked to give input, and I felt we’ve made a
very,
very positive impact on this issue about education and things
that can be done. I expect the administration to continue to do
more things aligned with that. We had some very good input
on many other issues.
That is what the purpose was. If people began to believe
that by becoming and being in any of these vehicles it meant
you condone Charlottesville, no, we did not. There was nothing

9. to condone about Charlottesville. But we would continue to
engage, because it’s incumbent upon us. It transcends any
kind of electoral cycle everywhere in the world. I have 380,000
employees. So it helps to always explain why we believe these
things. We’re the only tech company that makes no political
contributions, no PACs. Never have, never will. We’re the only
one that can say that.
You come from a background that’s a bit different from most
people’s. Your father left home when you were young, leaving
your mother. You talked about food stamps, entitlement pro-
grams, getting back on your feet, and those lessons that your
mother taught you. When you look at the country and some
of the anger and, frankly, that a section of the people feel
for this establishment, are we headed in the right direction?
For this country itself, I would never count America out, never.
And I think you need to look no further than the weekend of
Hurricane Irma. When things don’t go right, people help each
other. I didn’t hear a single person say, “Well, what did my
◼ DEBRIEF Bloomberg Businessweek September 25, 2017
government do this weekend for me?” IBM pledged $4 million,
and we’re not even counting all the volunteer work that we’re
doing. Everyone looked out for each other. We had people
buying boats going in and helping people. It’s a country that
when there’s a problem, people look to each other before they
look somewhere else. This is the culture of America.
But you have to pay attention that people have to believe they
have a better future. That’s what people ground themselves in.
We pledge to hire 25,000 people over the next four years. It’s
important to me to go to the middle of America where compa-
nies are not necessarily always putting high-tech jobs. We will
do it. Like others, I’m very bullish on this country.
One of the things IBM has recently been engaged on which

10. people may not realize is the transgender bathroom bill. How
are those decisions made about what issues to engage on?
Our history of diversity goes back to 1943, when IBM had
its first woman vice president, so I’ve been surrounded by a
culture of diversity and inclusion my whole professional life
there. This is a matter of where what you need to be a thriving
business, to be competitive, intersects with your values. You
can’t speak out on everything. By the way, I don’t think speak-
ing is the most important part—it’s doing.
But we spoke out. Why? We had large parts of our compa-
ny’s LGBT population, which we’ve embraced very, very
strongly,
afraid about North Carolina and Texas. In Texas we actually
did 150 meetings with the state House of Representatives. It’s
about what you do to communicate to people why these things
are important. It’s not about a tweet. It’s about getting in there,
rolling your sleeves up, communicating why it’s an issue,
under-
taking grass-roots efforts. That’s what we’ve done on the select
issues that we think really do drive home what our values are.
We can’t have a workforce afraid of coming to work.
You’ve talked about your journey as a female leader and
about being a role model. There are a lot of women who sym-
pathize with not wanting to be known always as a female
CEO. How has that become more important to you during
your career?
Early in my career, I would have always said, “Please, don’t
ever reference me being a woman.” This is not about being
a woman. I’m on my own merits here for many, many years.
Then at some point, I realized wait a second, people do need
role models, and whether I like that or not, you do have to take
that onboard. I watched my mom. Yes, she struggled, and I’m
a proponent for programs in the world that are a safety net for

11. people. When we had no money and she had to go on food
stamps, I had also watched the pain in her face. She could not
wait to get off of those. She went back to school to get her
degree, get a job so that we would be OK. The world would
not define her as a woman whose husband left her, as unsuc-
cessful, never educated. She wasn’t going to let the world do
that. What she taught us transcended what a woman leader,
as well as just a leader, is. Don’t ever let someone else define
who you are. Only you define who you are.
We have come full circle, me and IBM. I say to people, “Look,
we’re the only 106-year-old tech out there.” So this isn’t one
generation, two generations, three generations, it’s four or five.
And we’re the team reinventing it for another generation. The
part that’s never changed about IBM is to innovate technol-
ogy and apply it to business and society. That’s our core, even
when those technologies change. <BW>
On being a role model:
“Don’t ever let anyone
define who you are. Only
you define who you are”
Copyright of Bloomberg Businessweek is the property of
Bloomberg, L.P. and its content
may not be copied or emailed to multiple sites or posted to a
listserv without the copyright
holder's express written permission. However, users may print,
download, or email articles for
individual use.