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>> From Yorktown Heights, New York
It's theCUBE! Covering IBM Cloud Innovation Day.
Brought to you by IBM.
>> Hi, it's Wikibon's Peter Burris again.
We're broadcasting on The Cube from IBM Innovation Day
at the Thomas J Watson Research Laboratory
in Yorktown Heights, New York.
Have a number of great conversations,
and we got a great one right now.
Rob Thomas, who's the General Manager of IBM Analytics,
welcome back to theCUBE.
>> Thanks Peter, great to see you.
Thanks for coming out here to the woods.
>> Oh, well it's not that bad.
I actually live not to far from here.
Interesting Rob, I was driving up the Taconic Parkway
and I realized I hadn't been on it in 40 years, so.
>> Is that right? (laugh)
>> Very exciting. So Rob let's talk IBM analytics
and some of the changes that are taking place.
Specifically, how are customers thinking about
achieving their AI outcomes.
What's that ladder look like?
>> Yeah. We call it the AI ladder.
Which is basically all the steps that a client has
to take to get to get to an AI future,
is the best way I would describe it.
From how you collect data, to how you organize your data.
How you analyze your data, start to put
machine learning into motion.
How you infuse your data, meaning you can take any
insights, infuse it into other applications.
Those are the basic building blocks of this laddered AI.
81 percent of clients that start to do something with AI,
they realize their first issue is a data issue.
They can't find the data, they don't have the data.
The AI ladder's about taking care of the data problem
so you can focus on where the value is, the AI pieces.
>> So, AI is a pretty broad, hairy topic today.
What are customers learning about AI?
What kind of experience are they gaining?
How is it sharpening their thoughts and their pencils,
as they think about what kind of
outcomes they want to achieve?
>> You know, its... For some reason, it's a bit
of a mystical topic, but to me AI is actually quite simple.
I'd like to say AI is not magic.
Some people think it's a magical black box.
You just, you know, put a few inputs in,
you sit around and magic happens.
It's not that, it's real work, it's real computer science.
It's about how do I put, you know, how do I build models?
Put models into production?
Most models, when they go into production,
are not that good, so how do I continually train
and retrain those models?
Then the AI aspect is about how do I bring
human features to that?
How do I integrate that with natural language,
or with speech recognition, or with image recognition.
So, when you get under the covers,
it's actually not that mystical.
It's about basic building blocks that help you
start to achieve business outcomes.
>> It's got to be very practical,
otherwise the business has a hard time
ultimately adopting it, but you mentioned
a number of different... I especially like the
'add the human features' to it of the natural language.
It also suggests that the skill set of AI
starts to evolve as companies mature up this ladder.
How is that starting to change?
>> That's still one of the biggest gaps, I would say.
Skill sets around the modern languages of data science
that lead to AI: Python, AR, Scala, as an example of a few.
That's still a bit of a gap.
Our focus has been how do we make
tools that anybody can use.
So if you've grown up doing SPSS or SaaS,
something like that, how do you adopt those skills
for the open world of data science?
That can make a big difference.
On the human features point, we've actually built
applications to try to make that piece easy.
Great example is with Royal Bank of Scotland
where we've created a solution called Watson Assistant
which is basically how do we arm their call center
representatives to be much more intelligent and
engaging with clients, predicting what clients may do.
Those types of applications package up the human
features and the components I talked about,
makes it really easy to get AI into production.
>> Now many years ago, the genius Turing, noted the
notion of the Turing machine where you couldn't
tell the difference between the human and a machine
from an engagement standpoint.
We're actually starting to see that happen
in some important ways. You mentioned the call center.
>> Yep.
>> How are technologies and agency coming together?
By that I mean, the rate at which businesses are
actually applying AI to act as an agent
for them in front of customers?
>> I think it's slow. What I encourage clients to do is,
you have to do a massive number of experiments.
So don't talk to me about the one or two
AI projects you're doing, I'm thinking like hundreds.
I was with a bank last week in Japan,
and they're comment was in the last year
they've done a hundred different AI projects.
These are not one year long projects
with hundreds of people.
It's like, let's do a bunch of small experiments.
You have to be comfortable that probably half of your
experiments are going to fail, that's okay.
The goal is how do you increase your win rate.
Do you learn from the ones that work,
and from the ones that don't work,
so that you can apply those.
This is all, to me at this stage, is about experimentation.
Any enterprise right now, has to be thinking in terms
of hundreds of experiments, not one, not two
or 'Hey, should we do that project?'
Think in terms of hundreds of experiments.
You're going to learn a lot when you do that.
>> But as you said earlier, AI is not magic and it's grounded
in something, and it's increasingly obvious that
it's grounded in analytics.
So what is the relationship between AI analytics,
and what types of analytics are capable of creating
value independent of AI?
>> So if you think about how I kind of decomposed AI,
talked about human features, I talked about, it kind of
starts with a model, you train the model.
The model is only as good as the data that you feed it.
So, that assumes that one, that your data's not locked
into a bunch of different silos.
It assumes that your data is actually governed.
You have a data catalog or that type of capability.
If you have those basics in place, once you have a single
instantiation of your data, it becomes very easy
to train models, and you can find
that the more that you feed it,
the better the model's going to get, the better your
business outcomes are going to get.
That's our whole strategy around IBM Cloud Private for Data.
Basically, one environment, a console for all your data,
build a model here, train it in all your data,
no matter where it is, it's pretty powerful.
>> Let me pick up on that where it is,
'cause it's becoming increasingly obvious,
at least to us and our clients,
that the world is not going to move all the data
over to a central location.
The data is going to be increasingly distributed closer
to the sources, closer to where the action is.
How does AI and that notion of increasing distributed data
going to work together for clients.
>> So we've just released what's called
IBM Data Virtualization this month,
and it is a leapfrog in terms
of data virtualization technology.
So the idea is leave your data where ever it is,
it could be in a data center, it could
be on a different data center, it could
be on an automobile if you're an automobile manufacturer.
We can federate data from anywhere,
take advantage of processing power on the edge.
So we're breaking down that problem.
Which is, the initial analytics problem
was before I do this I've got
to bring all my data to one place.
It's not a good use of money.
It's a lot of time and it's a lot of money.
So we're saying leave your data where it is,
we will virtualize your data from wherever it may be.
>> That's really cool.
What was it called again?
>> IBM Data Virtualization and it's part
of IBM Cloud Private for Data.
It's a feature in that.
>> Excellent, so one last question Rob.
February's coming up, IBM Think San Francisco
thirty plus thousand people,
what kind of conversations do you anticipate having
with you customers, your partners, as they try
to learn, experiment, take away actions
that they can take to achieve their outcomes?
>> I want to have this AI experimentation discussion.
I will be encouraging every client,
let's talk about hundreds of experiments not 5.
Let's talk about what we can get started on now.
Technology's incredibly cheap to get started
and do something, and it's all about rate
and pace, and trying a bunch of things.
That's what I'm going to be encouraging.
The clients that you're going to see on stage there
are the ones that have adopted this mentality in
the last year and they've got some great successes to show.
>> Rob Thomas, general manager IBM Analytics,
thanks again for being on theCUBE.
>> Thanks Peter.
>> Once again this is Peter Buriss of Wikibon,
from IBM Innovation Day, Thomas J Watson Research Center.
We'll be back in a moment.
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