>> Narrator: Live from Boston, Massachusetts,
this is the Cube, covering Spark Summit East 2017,
brought to you by Databricks.
Now, here are your hosts, Dave Vellante
and George Gilbert.
>> Welcome back to Boston, everybody,
where the town is still euphoric.
Mike Gualtieri is here, he's the principal analyst
at Forrester Research, attended the parade yesterday.
How great was that, Mike? >> Yes. Yes.
It was awesome.
>> Nothing like we've ever seen before.
All right, the first question is
what was the bigger shocking surprise, upset,
greatest win, was it the Red Sox over the Yankees
or was it the Superbowl this weekend?
>> That's the question, I think it's the Superbowl.
>> Yeah, who knows, right? Who knows.
It was a lot of fun.
So how was the parade yesterday?
>> It was magnificent.
I mean, it was freezing.
No one cared.
I mean--but it was, yeah, it was great.
Great to see that team in person.
>> That's good, wish we could talk,
We can, but we'll get into it.
So, we're here at Spark Summit,
and, you know, the show's getting bigger,
you're seeing more sponsors,
still heavily a technical audience,
but what's your take these days?
We were talking off-camera about the whole big data thing.
It used to be the hottest thing in the world,
and now nobody wants to have big data in their title.
What's Forrester's take on that?
>> I mean, I think big data-- I think it's just
become mainstream, so we're just back to data.
You know, because all data is potentially big.
So, I don't think it's-- it's not the thing anymore.
I mean, what do you do with big data?
You analyze it, right?
And part of what this whole Spark Summit is about--
look at all the sessions.
Data science, machine learning, streaming analytics,
so it's all about sort of using that data now,
so big data is still important,
but the value of big data
comes from all this advanced analytics.
>> Yeah, and we talked earlier, I mean, a lot of the value
of, you know, Hadoop was cutting costs.
You know, you've mentioned commodity components
and reduction in denominator,
and breaking the need for some kind
of big storage container.
OK, so that-- we got there.
Now, shifting to new sources of value,
what are you spending your time on
these days in terms of research?
>> Artificial intelligence, machine learning,
so those are really forms of advanced analytics,
so that's been-- that's been very hot.
We did a survey last year, an AI survey,
and we asked a large group of people,
we said, oh, you know, what are you doing with AI?
58% said they're researching it.
19% said they're training a model.
Right, so that's interesting.
58% are researching it, and far fewer are actually,
you know, actually doing something with it.
Now, the reality is, if you phrase that
a little bit differently, and you said,
oh, what are you doing with machine learning?
Many more would say yes, we're doing machine learning.
So it begs the question, what do enterprises think of AI?
And what do they think it is?
So, a lot of my inquiries are spent
helping enterprises understand what AI is,
what they should focus on, and the other part of it
is what are the technologies used for AI,
and deep learning is the hottest.
>> So, you wrote a piece late last year,
what's possible today in AI.
What's possible today in AI?
>> Well, you know, before understanding was possible,
it's important to understand what's not possible, right?
And so we sort of characterize it
as there's pure AI, and there's pragmatic AI.
So it's real simple.
Pure AI is the sci-fi stuff, we've all seen it,
Ex Machina, Star Wars, whatever, right?
That's not what we're talking about.
That's not what enterprises can do today.
We're talking about pragmatic AI,
and pragmatic AI is about building predictive models.
It's about conversational APIs,
to interact in a natural way with humans,
it's about image analysis, which is something
very hot because of deep learning.
So, AI is really about the building blocks
that companies have been using,
but then using them in combination
to create even more intelligent solutions.
And they have more options on the market,
both from open source, both from cloud services that--
from Google, Microsoft, IBM, and now Amazon,
at their re-- Were you guys at their reinvent conference?
>> I wasn't, personally, but we were certainly there.
>> Yeah, they announced the Amazon AI,
which is a set of three services that developers can use
without knowing anything about AI or being a data scientist.
But, I mean, I think the way to think
about AI is that it is data science.
It requires the expertise of a data scientist to do AI.
>> Following up on that comment,
which was really interesting,
is we try and-- whereas vendors try and democratize access
to machine learning and AI, and I say that with two terms
because usually the machine learning is the stuff
that's sort of widely accessible
and AI is a little further out,
but there's a spectrum when you can just access an API,
which is like a pre-trained model--
>> Pre-trained model, yep.
>> It's developer-accessible, you don't need
to be a data scientist, and then at the other end,
you know, you need to pick your algorithms,
you need to pick your features,
you need to find the right data,
so how do you see that horizon moving over time?
>> Yeah, no, I-- So, these machine learning services,
as you say, they're pre-trained models,
totally accessible by anyone, anyone who can call an API
or a restful service can access these.
But their scope is limited, right?
So, if, for example, you take the image API,
you know, the imaging API that you can get
from Google or now Amazon, you can drop an image in there
and it will say, oh, there's a wine bottle
on a picnic table on the beach.
Right? It can identify that.
So that's pretty cool, there might be a lot
of use cases for that, but think of an enterprise use case.
No. You can't do it, and let me give you this example.
Say you're an insurance company,
and you have a picture of a steel roof that's caved in.
If you give that to one of these APIs,
it might say steel roof, it may say damage,
but what it's not going to do
is it's not going to be able to estimate the damage,
it's not going to be able to create a bill of materials
on how to repair it, because Google
hasn't trained it at that level.
OK, so, enterprises are going to have to do this themselves,
or an ISV is going to have to do it,
because think about it, you've got 10 years
worth of all these pictures taken of damage.
And with all of those pictures,
you've got tons of write-ups from an adjuster.
Whoa, if you could shove that
into a deep learning algorithm,
you could potentially have consumers take pictures,
or someone untrained, and have this thing
say here's what the estimate damage is,
this is the situation.
>> And I've read about like insurance use cases like that,
where the customer could, after they sort of
have a crack up, take pictures all around the car,
and then the insurance company could provide an estimate,
tell them where the nearest repair shops are--
>> Yeah, but right now it's like the early days
of e-commerce, where you could send an order in
and then it would fax it and they'd type it in.
So, I think, yes, insurance coverage
is taking those pictures, and the question is
can we automate it, and--
>> Well, let me actually iterate on that question,
which is so who can build a more end-to-end solution,
assuming, you know, there's a lot of heavy lifting
that's got to go on for each enterprise
trying to build a use case like that.
Is it internal development and only at big companies
that have a few of these data science gurus?
Would it be like an IBM Global Services
or an EXIN SURE, or would it be like a vertical ISV
where it's semi-custom, semi-patent?
>> I think it's both, but I also think
it's two or three people walking around this conference,
right, understanding Spark, maybe understanding
how to use TensorFlow in conjunction with Spark
that will start to come up with these ideas as well.
So I think-- I think we'll see all of those solutions.
Certainly, like IBM with their cognitive computing--
oh, and by the way, so we think
that cognitive computing equals pragmatic AI,
right, because it has similar characteristics.
So, we're already seeing the big ISVs
and the big application developers, SAP, Oracle,
creating AI-infused applications or modules,
but yeah, we're going to see small ISVs do it.
There's one in Austin, Texas, called InteractiveTel.
It's like 10 people.
What they do is they use the Google--
so they sell to large car dealerships, like Ernie Boch.
And they record every conversation,
phone conversation with customers.
They use the Google pre-trained model
to convert the speech to text, and then
they use their own machine learning
to analyze that text to find out
if there's a customer service problem
or if there's a selling opportunity,
and then they alert managers
or other people in the organization.
So, small company, very narrowly focused
on something like car buying.
>> So, I wonder if we could come back
to something you said about pragmatic AI.
We love to have someone like you on the Cube,
because we like to talk about the horses on the track.
So, if Watson is pragmatic AI, and we all--
well, I think you saw the 60 Minutes show,
I don't know, whenever it was, three or four months ago,
and IBM Watson got all the love.
They barely mentioned Amazon and Google and Facebook,
and Microsoft didn't get any mention.
So, and there seems to be sentiment
that, OK, all the real action is in Silicon Valley.
But you've got IBM doing pragmatic AI.
Do those two worlds come together in your view?
How does that whole market shake up?
>> I don't think they come together
in the way I think you're suggesting.
I think what Google, Microsoft, Facebook,
what they're doing is they're churning out
fundamental technology, like one of the most popular
deep learning frameworks, TensorFlow,
is a Google thing that they open sourced.
And as I pointed out, those image APIs,
that Amazon has, that's not going to work for insurance,
that's not going to work for radiology.
So, I don't think they're in--
>> George Gilbert: Facebook's going to apply it differently--
>> Yeah, I think what they're trying to do
is they're trying to apply it to the millions of consumers
that use their platforms, and then I think they
throw off some of the technology
for the rest of the world to use, fundamentally.
>> And then the rest of the world has to apply those.
>> Yeah, but I don't think they're in the business
of building insurance solutions
or building logistical solutions.
>> Right. >> But you said something
that was really, really potentially intriguing,
which was you could take the horizontal Google
speech to text API, and then--
>> Mike Gualtieri: And recombine it.
>> --put your own model on top of that.
And that's, techies call that like ensemble modeling,
but essentially you're taking,
almost like an OS level service,
and you're putting in a more vertical application
on top of it, to relate it to our old ways
of looking at software, and that's interesting.
>> Yeah, because what we're talking about right now,
but this conversation is now about applications.
Right, we're talking about applications,
which need lots of different services recombined,
whereas mostly the data science conversation
has been narrowly about building
one customer lifetime value model or one churn model.
Now the conversation, when we talk about AI,
is becoming about combining many different services
and many different models.
>> Dave Vellante: And the platform
for building applications is really--
>> Yeah, yeah.
>> And that platform, the richest platform,
or the platform that is, that is most attractive
has the most building blocks to work with,
or the broadest ones?
>> The best ones, I would say, right now.
The reason why I say it that way
is because this technology is still moving very rapidly.
So for an image analysis,
deep learning, very good for image, nothing's better
than deep learning for image analysis.
But if you're doing business process models
or like churn models, well,
deep learning hasn't played out there yet.
So, right now I think there's some fragmentation.
There's so much innovation.
Ultimately it may come together.
What we're seeing is, many of these companies
are saying, OK, look, we're going to bring in the open source.
It's pretty difficult to create a deep learning library.
And so, you know, a lot of the vendors
in the machine learning space,
instead of creating their own,
they're just bringing in MXNet or TensorFlow.
>> I might be thinking of something from a different angle,
which is not what underlying implementation they're using,
whether it's deep learning or whether it's just
random forest, or whatever the terminology is,
you know, the traditional statistical stuff.
The idea, though, is you want a platform--
like way, way back, Windows, with the Win32 API
had essentially more widgets for helping you build
graphical applications than any other platform
>> Mike Gualtieri: Yeah, I see where you're going.
>> And I guess I'm thinking it doesn't matter
what the underlying implementation is,
but how many widgets can you string together?
>> I'm totally with you there, yeah.
And so I think what you're saying
is look, a platform that has the most capabilities,
but abstracts, the implementations,
and can, you know, can be somewhat pluggable--
right, good, to keep up with the innovation, yeah.
And there's a lot of new companies out there, too,
that are tackling this.
One of them's called Bonsai AI, you know, small startup,
they're trying to abstract deep learning,
because deep learning right now,
like TensorFlow and MXNet, that's a little bit
of a challenge to learn, so they're abstracting it.
But so are a lot of the-- so is SAS, IBM, et cetera.
>> So, Mike, we're out of time,
but I want to talk about your talk tomorrow.
So, AI meets Spark, give us a little preview.
>> AI meets Spark. Basically, the prerequisite
to AI is a very sophisticated and fast data pipeline,
because just because we're talking about AI
doesn't mean we don't need data to build these models.
So, I think Spark gives you the best of both worlds, right?
It's designed for these sort of complex data pipelines
that you need to prep data, but now,
with MLlib for more traditional machine learning,
and now with their announcement of TensorFrames,
which is going to be an interface for TensorFlow,
now you've got deep learning, too.
And you've got it in a cluster architecture,
so it can scale. So, pretty cool.
>> All right, Mike, thanks very much for coming on the Cube.
You know, way to go Pats, awesome.
Really a pleasure having you back.
>> Thanks.
>> All right, keep right there, buddy.
We'll be back with our next guest
right after this short break.
This is the Cube.
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