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>> Narrator: Live from Fisherman's Wharf
in San Francisco, it's theCUBE.
Covering IBM Chief Data Officer Strategy Summit Spring 2017.
Brought to you by IBM.
>> Hey, welcome back everybody.
Jeff Frick here at theCUBE.
It is lunchtime at the IBM CDO Summit.
Packed house, you can see them
back there getting their nutrition.
But we're going to give you some mental nutrition.
We're excited to be joined by
a repeat performance of Cortnie Abercrombie.
Coming on back with Vijay Vijayasankar.
He's the GM Cognitive, IOT, and Analytics for IBM, welcome.
>> Thanks for having me.
>> So first off, did you eat before you came on?
>> I did thank you. >> I want to make sure
you don't pass out or anything.
(group laughing)
Cortnie and I both managed to grab a quick bite.
>> Excellent.
So let's jump into it.
Cognitive, lot of buzz, IoT, lot of buzz.
How do they fit?
Where do they mesh?
Why is it, why are they so important to one another?
>> Excellent question.
>> IoT has been around for a long time
even though we never called it IoT.
My favorite example is smart meters
that utility companies use.
So these things have been here for more than a decade.
And if you think about IoT,
there are two aspects to it.
There's the instrumentation by putting
the sensors in and getting the data.
And the insides aspect where there's
making sense of what the sensor is trying to tell us.
Combining these two, is where the value is for the client.
Just by putting outwardly sensors,
it doesn't make much sense.
So, look at the world around us now, right?
The traditional utility, I will stick
with the utilities to complete the story.
Utilities all get dissected from both sides.
On one hand you have your electric vehicles
plugging into the grid to draw power.
On the other hand, you have supply
coming from solar roofs and so on.
So optimizing this is where
the cognitive and analytics kicks in.
So that's the beauty of this world.
All these things come together,
that convergence is where the big value is.
>> Right because the third element that you didn't have
in your original one was what's going on,
what should we do, and then actually doing something.
>> Vijay: Exactly.
>> You got to have the action to pull it all together.
>> Yes, and learning as we go.
The one thing that is available today
with cognitive systems that we did not have
in the past was this ability to learn as you go.
So you don't need human intervention
to keep changing the optimization algorithms.
These things can learn by itself
and improve over time which is huge.
>> But do you still need a person to help
kind of figure out what you're optimizing for?
That's where, can you have a pure, machine-driven algorithm
without knowing exactly what are you optimizing for?
>> We are no where close to that today.
Generally, where the system is super smart
by itself is a far away concept.
But there are lots of aspects of specific AI
optimizing a given process that can still
go into this unsupervised learning aspects.
But it needs boundaries.
The system can get smart within boundaries,
the system cannot just replace human thought.
Just augmenting our intelligence.
>> Jeff: Cortnie, you're shaking you head over there.
>> I'm completely in agreement.
We are no where near, and my husband's actually looking
forward to the robotic apocalypse by the way, so.
(group laughing)
>> He must be an Arnold Schwarzenegger fan.
>> He's the opposite of me.
I love people, he's like looking forward to that.
He's like, the less people, the better.
>> Jeff: He must have his Zoomba, or whatever those
little vacuum cleaner things are called.
>> Yeah, no. (group laughing)
>> Peter: Tell him it's the fewer the people, the better.
>> The fewer the people the better for him.
He's a finance guy, he'd rather just
sit with the money all day.
What does that say about me?
Anyway, (laughing) no, less with the gross.
Yeah no, I think we're never going to really get to that point.
Because we always as people always have to be
training these systems to think like us.
So we're never going to have systems that are just
autonomically out there without having an intervention
here and there to learn the next steps.
That's just how it works.
>> I always thought the autonomous vehicle,
just example, cause it's just so clean.
You know, if somebody jumps in front of the car,
does the car hit the person, or run into the ditch?
>> Where today a person can't make that judgment very fast.
They're just going to react.
But in computer time, that's like forever.
So you can actually make rules.
And then people go bananas, well what if it's
a grandma on one side and kids on the other?
Which do you go?
Or what if it's a criminal that just robbed a bank?
Do you take him out on purpose?
>> Trade off. >> So, you get into a lot of,
interesting parameters that have nothing to do
necessarily with the mechanics of making that decision.
>> And this changes the fundamentals
of computing big time too, right?
Because a car cannot wait to ping the Cloud
to find out, you know, should I break,
or should I just run over this person in front of me.
So it needs to make that determination right away.
And hopefully the right decision which is to break.
But on the other hand,
all the cars that have this algorithm,
together have collective learning,
which needs some kind of Cloud computing.
So this whole idea of Edge computing will come
and replace a lot of what exists today.
So see this disruption even behind the scenes
on how we architect these systems, it's a fascinating time.
>> And then how much of the compute,
the store is at the Edge?
How much of the computed to store in the Cloud
and then depending on the decision, how do you say it,
can you do it locally or do you have to
send it upstream or break it in pieces.
>> I mean if you look at a car of the future,
forget car of the future, car of the present like Tesla,
that has more compute power than a small data center,
at multiple CPU's, lots of RAM, a lot of hard disk.
It's a little Cloud that runs on wheels.
>> Well it's a little data center that runs on wheels.
But, let me ask you a question.
And here's the question,
we talk about systems that learn, cognitive systems
that are constantly learning, and we're training them.
How do we ensure that Watson, for example
is constantly operating in the interest
of the customer, and not the interest of IBM?
Now there's a reason I'm asking this question,
because at some point in time, I can perceive
some other company offering up a similar set of services.
I can see those services competing for attention.
As we move forward with increasingly complex decisions,
with increasingly complex sources of information,
what does that say about how these systems
are going to interact with each other?
>> He always with the loaded questions today.
(group laughing)
>> It's an excellent question, it's something
that I worry about all the time as well.
>> Something we worry about with our clients too.
>> So, couple of approaches by which this will exist.
And to begin with, while we have the big lead
in cognitive computing now, there is no hesitation
on my part to admit that the ecosystem around us
is also fast developing and there will be hefty competition
going forward, which is a good thing.
'Cause if you look at how this world
is developing, it is developing as API.
APIs will fight on their own merits.
So it's a very pluggable architecture.
If my API is not very good,
then it will get replaced by somebody else's API.
So that's one aspect.
The second aspect is,
there is a difference between the provider
and the client in terms of who owns the data.
We strongly believe from IBM that client owns the data.
So we will not go in and do anything crazy with it.
We won't even touch it.
So we will provide a framework and a cartridge
that is very industry specific.
Like for example, if Watson has to act
as a call center agent for a Telco, we will provide
a set of instructions that are applicable to Telco.
But, all the learning that Watson does
is on top of that clients data.
We are not going to take it from one Telco
and put it in another Telco.
That will stay very local to that Telco.
And hopefully that is the way
the rest of the industry develops too.
That they don't take information
from one and provide to another.
Even on an anonymous basis, it's a really bad idea
to take a clients data and then feed it elsewhere.
It has all kinds of ethical and moral consequences,
even if it's legal. >> Absolutely.
>> And we would encourage clients to take a look
at some of the others out there and make sure
that that's the arrangement that they have.
>> Absolutely, what a great job for an analyst firm, right?
But I want to build upon this point,
because I heard something very interesting in the keynote,
the CDO of IBM, in the keynote this morning.
>> He used a term that I've thought about,
but never heard before, trust as a service.
Are you guys familiar with his use of that term?
>> Vijay: Yep.
>> Okay, what does trust as a service mean,
and how does it play out so that as a consumer of IMB
cognitive services, I have a measurable difference in how
I trust IBM's cognitive services versus somebody else?
>> Some would call that Blockchain.
In fact Blockchain has often been called trust as a service.
>> Okay, and Blockchain is probably the most physical form
of it that we can find at the moment, right?
At the (mumbles) where it's open to everybody but then
no one brand section can be tabbed by somebody else.
But if we extend that concept philosophically,
it also includes a lot of the concept about identity.
Identity.
I as a user today don't have an easy way
to identify myself across systems.
Like, if I'm behind the firewall I have one identity,
if I am outside the firewall I have another identity.
But, if you look at the world tomorrow
where I have to deal with a zillion APIs,
this concept of a consistent identity
needs to pass through all of them.
It's a very complicated a difficult concept to implement.
So that trust as a service, essentially, the light blocking
that needs to be an identity service that follows me around
that is not restrictive to an IBM system,
or a Nautical system or something.
>> But at the end of the day, Blockchain's a mechanism.
>> Yes.
>> Trust in the service sounds like a--
>> It's a transparency is what it is,
the more transparency, the more trust.
>> It's a way of doing business.
>> Yes. >> Sure.
>> So is IBM going to be a leader
in defining what that means?
>> Well look, in all cases, IBM has,
we have always strove, what's the right word?
Striven, strove, whatever it.
>> Strove. >> Strove (laughing)?
>> I'll take that anyway. >> Strove, thank you.
To be a leader in how we approach everything ethically.
I mean, this is truly in our blood,
I mean, we are here for our clients.
And we aren't trying to just get them to give us
all of their data and then go off and use it anywhere.
You have to pay attention sometimes, that what you're
paying for is exactly what you're getting,
because people will try to do those things,
and you just need to have a partner that you trust in this.
And, I know it's self-serving to say, but we think about
data ethics, we think about these things when we talk
to our clients, and that's one of the things that we try
to bring to the table is that moral, ethical, should you.
Just because you can, and we have, just so you know
walked away from deals that were very lucrative before,
because we didn't feel it was the right thing to do.
And we will always, I mean, I know it sounds self-serving,
I don't know how to, you won't know until you
deal with us, but pay attention, buyer beware.
>> You're just Cortnie from IBM, we know what side you're on.
(group laughing) It's not a mystery.
>> Believe me, if I'm associated with it, it's yeah.
>> But you know, it's a great point, because the other
kind of ethical thing that comes up a lot with data,
is do you have the ethical conversation before you
collect that data, and how you're going to be using it.
>> Exactly. >> But that's just today.
You don't necessarily know what's going to,
what and how that might be used tomorrow.
>> Well, in other countries.
>> That's what gets really tricky.
>> Future-proofing is a very interesting concept.
For example, vast majority of our
analytics conversation today is around
structure and security, those kinds of terms.
But, where is the vast majority of data sitting today?
It is in video and sound files, which okay.
>> Cortnie: That's even more scary.
>> It is significantly scary because the technology
to get insights out of this is still developing.
So all these things like cluster and identity and security
and so on, and quantum computing for that matter.
All these things need to think about the future.
But some arbitrary form of data can come hit you
and all these principles of ethics
and legality and all should apply.
It's a very non-trivial challenge.
>> But I do see that some countries are starting
to develop their own protections like the
General Data Protection Regulation is going
to be a huge driver of forced ethics.
>> And some countries are not.
I mean, it's just like,
cognitive is just like anything else.
When the car was developed, I'm sure people said,
hey everybody's going to go out killing people
with their cars now, you know?
But it's the same thing, you can use it as a mode
of transportation, or you can do something evil with it.
It really is going to be governed by the societal norms
that you live in, as to how much you're going to get away with.
And transparency is our friend, so the more transparent
we can be, things like Blockchain, other enablers
like that that allow you to see what's going on,
and have multiple copies, the better.
>> All right, well Cortnie, Vijay, great topics.
And that's why gatherings like this are so important
to be with your peer group, you know,
to talk about these much deeper issues
that are really kind of tangental to technology
but really to the bigger picture.
So, keep getting out on the fringe
to help us figure this stuff out.
>> I appreciate it, thanks for having us.
>> Thanks. >> Pleasure.
All right, I'm Jeff Frick with Peter Burris.
We're at the Fisherman's Wharf in San Francisco
at the IBM Chief Data Officer Strategy Summit 2017.
Thanks for watching.
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