>> [Announcer] Live, from
New York, it's theCUBE.
Covering theCUBE New York City 2018.
Brought to you by SiliconANGLE Media,
and its ecosystem partners.
>> Hello everyone,
welcome back to CUBE NYC.
This is a CUBE special
presentation of something
that we've done now for
the past couple of years.
IBM has sponsored an influencer panel
on some of the hottest
topics in the industry,
and of course, there's no
hotter topic right now than AI.
So, we've got nine of the top influencers
in the AI space, and
we're in Hell's Kitchen,
and it's going to get
hot in here. (laughing)
And these guys, we're
going to cover the gamut.
So, first of all, folks, thanks so much
for joining us today,
really, as John said earlier,
we love the collaboration with you all,
and we'll definitely see you
on social after the fact.
I'm Dave Vellante, with my
cohost for this session,
Peter Burris, and again, thank you to IBM
for sponsoring this and organizing this.
IBM has a big event down here,
in conjunction with Strata,
called Change the Game, Winning with AI.
We run theCUBE NYC,
we've been here all week.
So, here's the format.
I'm going to kick it off, and
then we'll see where it goes.
So, I'm going to introduce
each of the panelists,
and then ask you guys
to answer a question,
I'm sorry, first, tell us a
little bit about yourself,
briefly, and then answer one
of the following questions.
Two big themes that
have come up this week.
One has been, because
this is our ninth year
covering what used to be Hadoop World,
which kind of morphed into big data.
Question is, AI, big data,
same wine, new bottle?
Or is it really substantive,
and driving business value?
So, that's one question to ponder.
The other one is, you've heard the term,
the phrase, data is the new oil.
Is data really the new oil?
Wonder what you think about that?
Okay, so, Chris Penn,
let's start with you.
Chris is cofounder of Trust Insight,
long time CUBE alum, and friend.
Thanks for coming on.
Tell us a little bit about yourself,
and then pick one of those questions.
>> Sure, we're a data
science consulting firm.
We're an IBM business partner.
When it comes to "data is the new oil,"
I love that expression because
it's completely accurate.
Crude oil is useless, you
have to extract it out
of the ground, refine it, and
then bring it to distribution.
Data is the same way,
where you have to have
developers and data
architects get the data out.
You need data scientists and
tools, like Watson Studio,
to refine it, and then you
need to put it into production,
and that's where marketing technologists,
technologists, business analytics folks,
and tools like Watson Machine Learning
help bring the data and make it useful.
>> Okay, great, thank you.
Tony Flath is a tech and media consultant,
focus on cloud and
cyber security, welcome.
>> Thank you.
 Tell us a little bit
about yourself and your thoughts
on one of those questions.
>> Sure thing, well, thanks
so much for having us
on this show, really appreciate it.
My background is in cloud, cyber security,
and certainly in emerging tech
with artificial intelligence.
Certainly touched it from
a cyber security play,
how you can use machine
learning, machine control,
for better controlling
security across the gamut.
But I'll touch on your
question about wine,
is it a new bottle, new wine?
Where does this come from,
from artificial intelligence?
And I really see it as a whole new wine
that is coming along.
When you look at emerging technology,
and you look at all the deep
learning that's happening,
it's going just beyond being able to
machine learn and know what's happening,
it's making some meaning to that data.
And things are being done with that data,
from robotics, from automation,
from all kinds of different things,
where we're at a point
in society where data,
our technology is getting beyond us.
Prior to this, it's always
been command and control.
You control data from a keyboard.
Well, this is passing us.
So, my passion and perspective on this is,
the humanization of it, of IT.
How do you ensure that people
are in that process, right?
>> Excellent, and we're going to
come back and talk about that.
>> Thanks so much.
 Carla Gentry, @DataNerd?
Great to see you live,
as opposed to just in
the ether on Twitter.
Data scientist, and owner
of Analytical Solution.
>>Welcome, your thoughts?
 Thank you for having us.
Mine is, is data the new oil?
And I'd like to rephrase that
is, data equals human lives.
So, with all the other
artificial intelligence
and everything that's going on,
and all the algorithms and
models that's being created,
we have to think about
things being biased,
being fair, and understand that
this data has impacts on people's lives.
>> Great.
>>Steve Ardire, my paisan.
 Paisan.
>> AI startup adviser, welcome,
thanks for coming to theCUBE.
>> Thanks Dave.
So, uh, my first career was geology,
and I view AI as the new
oil, but data is the new oil,
but AI is the refinery.
I've used that many times before.
In fact, really, I've moved from just AI
to augmented intelligence.
So, augmented intelligence
is really the way forward.
This was a presentation I
gave at IBM Think last spring,
has almost 100,000 impressions right now,
and the fundamental reason
why is machines can attend
to vastly more information than humans,
but you still need humans in the loop,
and we can talk about what
they're bringing in terms of
common sense reasoning, because big data
does the who, what, when,
and where, but not the why,
and why is really the Holy Grail
for causal analysis and reasoning.
>> Excellent, Bob Hayes,
Business Over Broadway,
welcome, great to see you again.
>> Thanks for having me.
So, my background is in
psychology, industrial psychology,
and I'm interested in things
like customer experience,
data science, machine learning, so forth.
And I'll answer the question
around big data versus AI.
And I think there's other
terms we could talk about,
big data, data science,
machine learning, AI.
And to me, it's kind of all the same.
It's always been about analytics,
and getting value from your
data, big, small, what have you.
And there's subtle
differences among those terms.
Machine learning is just
about making a prediction,
and knowing if things
are classified correctly.
Data science is more about
understanding why things work,
and understanding maybe
the ethics behind it,
what variables are
predicting that outcome.
But still, it's all the same thing,
it's all about using
data in a way that we can
get value from that, as
a society, in residences.
>> Excellent, thank you.
Theo Lau, founder of
Unconventional Ventures.
What's your story?
>> Yeah, so, my background is
driving technology innovation.
So, together with my partner,
what our work does is
we work with organizations
to try to help them
leverage technology to drive
systematic financial wellness.
We connect founders, startup
founders, with funders,
we help them get money in the ecosystem.
We also work with them to look at,
how do we leverage emerging technology
to do something good for the society.
So, very much on point to
what Bob was saying about.
So when I look at AI,
it is not new, right,
it's been around for quite a while.
But what's different is the amount
of technological power
that we have allow us
to do so much more than what
we were able to do before.
And so, what my mantra is,
great ideas can come from
anywhere in the society,
but it's our job to be
able to leverage technology
to shine a spotlight on
people who can use this
to do something different, to
help seniors in our country
to do better in their financial planning.
>> Okay, so, in your mind,
it's not just a same wine, new bottle,
it's more substantive than that.
>> [Theo] It's more substantive,
it's a much better bottle.
>> Karen Lopez, senior project manager
for Architect InfoAdvisors, welcome.
>> Thank you.
So, I'm DataChick on
twitter, and so that kind of
tells my focus is that I'm here,
I also call myself a data evangelist,
and that means I'm there at organizations
helping stand up for
the data, because to me,
that's the proxy for
standing up for the people,
and the places and the events
that that data describes.
That means I have a focus on security,
data privacy and protection as well.
And I'm going to kind of
combine your two questions about
whether data is the new wine bottle,
I think is the combination.
Oh, see, now I'm talking
about alcohol. (laughing)
But anyway, you know, all
analogies are imperfect,
so whether we say it's the new wine,
or, you know, same wine,
or whether it's oil,
is that the analogy's
good for both of them,
but unlike oil, the amount
of data's just growing
like crazy, and the oil,
we know at some point,
I kind of doubt that we're
going to hit peak data
where we have not enough data,
like we're going to do with oil.
But that says to me
that, how did we get here
with big data, with
machine learning and AI?
And from my point of view,
as someone who's been focused
on data for 35 years, we
have hit this perfect storm
of open source technologies,
cloud architectures
and cloud services, data innovation,
that if we didn't have those,
we wouldn't be talking
about large machine learning
and deep learning-type things.
So, because we have all
these things coming together
at the same time, we're
now at explosions of data,
which means we also have to protect them,
and protect the people
from doing harm with data,
we need to do data for good
things, and all of that.
>> Great, definite differences,
we're not running out of data,
data's like the terrible
tribbles. (laughing)
>> Yes, but it's very cuddly, data is.
>> Yeah, cuddly data.
Mark Lynd, founder of Relevant Track?
>> That's right.
 I like the name.
>>What's your story?
 Well, thank you,
and it actually plays
into what my interest is.
It's mainly around AI
in enterprise operations
and cyber security.
You know, these teams that
are in enterprise operations
both, it can be sales, marketing,
all the way through the organization,
as well as cyber security,
they're often under-sourced.
And they need, what Steve pointed out,
they need augmented intelligence,
they need to take AI,
the big data, all the
information they have,
and make use of that in a
way where they're able to,
even though they're under-sourced,
make some use and some
value for the organization,
you know, make better use
of the resources they have
to grow and support the strategic
goals of the organization.
And oftentimes, when you get to budgeting,
it doesn't really align, you know,
you're short people, you're short time,
but the data continues to
grow, as Karen pointed out.
So, when you take those
together, using AI to augment,
provided augmented intelligence,
to help them get through that data,
make real tangible decisions
based on information
versus just raw data, especially
around cyber security,
which is a big hit right now,
is really a great place to be,
and there's a lot of stuff going on,
and a lot of exciting stuff in that area.
>> Great, thank you.
Kevin L. Jackson, author
and founder of GovCloud.
>>GovCloud, that's big.
 Yeah, GovCloud Network.
Thank you very much for
having me on the show.
Up and working on cloud computing,
initially in the federal government,
with the intelligence community,
as they adopted cloud computing for a lot
of the nation's major missions.
And what has happened is
now I'm working a lot with
commercial organizations and
with the security of that data.
And I'm going to sort
of, on your questions,
piggyback on Karen.
There was a time when you
would get a couple of bottles
of wine, and they would come in,
and you would savor that wine, and sip it,
and it would take a few
days to get through it,
and you would enjoy it.
The problem now is that you
don't get a couple of bottles of
wine into your house, you get
two or three tankers of data.
So, it's not that it's a new wine,
you're just getting a lot of it.
And the infrastructures that you need,
before you could have
a couple of computers,
and a couple of people,
now you need cloud,
you need automated infrastructures,
you need huge capabilities,
and artificial intelligence
and AI, it's what we can use
as the tool on top of these
huge infrastructures to
drink that, you know.
>> Fire hose of wine.
 Fire hose of wine. (laughs)
>> Everybody's having a good time.
>> Everybody's having
a great time. (laughs)
>> Yeah, things are booming right now.
Excellent, well, thank
you all for those intros.
Peter, I want to ask you a question.
So, I heard there's some
similarities and some
definite differences with regard
to data being the new oil.
You have a perspective on this,
and I wonder if you could
inject it into the conversation.
>> Sure, so, the perspective
that we take in a lot
of conversations, a lot
of folks here in theCUBE,
what we've learned,
and I'll kind of answer
both questions a little bit.
First off, on the question
of data as the new oil,
we definitely think that
data is the new asset
that business is going
to be built on, in fact,
our perspective is that
there really is a difference
between business and digital business,
and that difference is data as an asset.
And if you want to understand
data transformation,
you understand the degree
to which businesses
reinstitutionalizing work,
reorganizing its people,
reestablishing its mission around
what you can do with data as an asset.
The difference between data and oil
is that oil still follows
the economics of scarcity.
Data is one of those
things, you can copy it,
you can share it, you
can easily corrupt it,
you can mess it up, you can do all kinds
of awful things with it
if you're not careful.
And it's that core fundamental proposition
that as an asset, when we
think about cyber security,
we think, in many respects,
that is the approach
to how we can go about privatizing data
so that we can predict who's
actually going to be able
to appropriate returns on it.
So, it's a good analogy, but as you said,
it's not entirely perfect,
but it's not perfect in
a really fundamental way.
It's not following the laws of scarcity,
and that has an enormous effect.
>> In other words, I
could put oil in my car,
or I could put oil in my house,
but I can't put the same oil in both.
>> Can't put it in both places.
And now, the issue of
the wine, I think it's,
we think that it is, in
fact, it is a new wine,
and very simple abstraction,
or generalization
we come up with is the issue of agency.
That analytics has historically
not taken on agency,
it hasn't acted on behalf of the brand.
AI is going to act on behalf of the brand.
Now, you're going to need both of them,
>>you can't separate them.
 A lot of implications there
>>in terms of bias.
 Absolutely.
>> In terms of privacy.
You have a thought, here, Chris?
>> Well, the scarcity is our compute power,
and our ability for us to process it.
I mean, it's the same as
oil, there's a ton of oil
under the ground, right, we
can't get to it as efficiently,
or without severe environmental
consequences to use it.
Yeah, when you use it, it's transformed,
but our scarcity is compute power,
and our ability to use it intelligently.
>> Or even when you find it.
I have data, I can apply it
to six different applications,
I have oil, I can apply it to one,
and that's going to matter
in how we think about work.
>> But one thing I'd like to add, sort of,
you're talking about data as an asset.
The issue we're having right now is
we're trying to learn
how to manage that asset.
Artificial intelligence is a way
of managing that asset,
and that's important
if you're going to use
and leverage big data.
>> Yeah, but see, everybody's
talking about the quantity,
the quantity, it's not
always the quantity.
You know, we can have just
oodles and oodles of data,
but if it's not clean data,
if it's not alphanumeric data,
which is what's needed
for machine learning.
So, having lots of data is great,
but you have to think about
the signal versus the noise.
So, sometimes you get so much data,
you're looking at
over-fitting, sometimes you get
so much data, you're looking
at biases within the data.
So, it's not the amount of data, it's the,
now that we have all of this data,
making sure that we look at relevant data,
to make sure we look at clean data.
>> One more thought, and
we have a lot to cover,
I want to get inside your big brain.
>> I was just thinking about it from
a cyber security perspective,
one of my customers,
they were looking at the
data that just comes from
the perimeter, your firewalls,
routers, all of that,
and then not even looking internally,
just the perimeter alone,
and the amount of data
being pulled off of those.
And then trying to correlate that data
so it makes some type of business sense,
or they can determine if there's
incidents that may happen,
and take a predictive action,
or threats that might be there
because they haven't taken
a certain action prior,
it's overwhelming to them.
So, having AI now, to be
able to go through the logs
to look at, and there's so
many different types of data
that come to those logs,
but being able to pull
that information, as well
as looking at end points,
and all that, and people's
houses, which are an extension
of the network oftentimes,
it's an amazing amount of data,
and they're only looking
at a small portion today
because they know, there's
not enough resources,
there's not enough trained
people to do all that work.
So, AI is doing a wonderful
way of doing that.
And some of the tools now
are starting to mature
and be sophisticated
enough where they provide
that augmented intelligence
that Steve talked about earlier.
>> So, it's complicated.
There's infrastructure, there's security,
there's a lot of software,
there's skills, and on and on.
At IBM Think this year,
Ginni Rometty talked about,
there were a couple of themes,
one was augmented intelligence,
that was something that was clear.
She also talked a lot about privacy,
and you own your data, etc.
One of the things that struck me was
her discussion about incumbent disruptors.
So, if you look at the top five companies,
roughly, Facebook with
fake news has dropped down
a little bit, but top five companies
in terms of market cap in the US.
They're data companies, all right.
Apple just hit a trillion,
Amazon, Google, etc.
How do those incumbents close the gap?
Is that concept of incumbent disruptors
actually something that is
being put into practice?
I mean, you guys work with
a lot of practitioners.
How are they going to close
that gap with the data haves,
meaning data at their
core of their business,
versus the data have-nots,
it's not that they
don't have a lot of
data, but it's in silos,
it's hard to get to?
>> Yeah, I got one more
thing, so, you know,
these companies, and whoever's
going to be big next is,
you have a digital persona,
whether you want it or not.
So, if you live in a farm out
in the middle of Oklahoma,
you still have a digital persona,
people are collecting data on you,
they're putting profiles of you,
and the big companies know about you,
and people that first interact with you,
they're going to know that
you have this digital persona.
Personal AI, when AI from these companies
could be used simply and
easily, from a personal deal,
to fill in those gaps, and
to have a digital persona
that supports your family, your growth,
both personal and professional growth,
and those type of things,
there's a lot of applications
for AI on a personal, enterprise,
even small business, that
have not been done yet,
but the data is being collected now.
So, you talk about the
oil, the oil is being built
right now, lots, and lots, and lots of it.
It's the applications to
use that, and turn that
into something personally,
professionally, educationally,
powerful, that's what's missing.
But it's coming.
>> Thank you, so, I'll add to that,
and in answer to your question you raised.
So, one example we always
used in banking is,
if you look at the big banks, right,
and then you look at from
a consumer perspective,
and there's a lot of talk
about Amazon being a bank.
But the thing is, Amazon
doesn't need to be a bank,
they provide banking services,
from a consumer perspective
they don't really care if you're a bank
or you're not a bank, but what's different
between Amazon and some of
the banks is that Amazon,
like you say, has a lot of data,
and they know how to make use of the data
to offer something as
relevant that consumers want.
Whereas banks, they have a lot of data,
but they're all silos, right.
So, it's not just a
matter of whether or not
you have the data, it's also,
can you actually access it
and make something
useful out of it so that
you can create something
that consumers want?
Because otherwise, you're just a pipe.
>> Totally agree, like, when you look at it
from a perspective of, there's
a lot of terms out there,
digital transformation is
thrown out so much, right,
and go to cloud, and you migrate to cloud,
and you're going to take everything over,
but really, when you look at it,
and you both touched on
it, it's the economics.
You have to look at the data
from an economics perspective,
and how do you make some
kind of way to take this data
meaningful to your customers,
that's going to work
effectively for them, that
they're going to drive?
So, when you look at the
big, big cloud providers,
I think the push in things
that's going to happen
in the next few years is
there's just going to be
a bigger migration to public cloud.
So then, between those,
they have to differentiate themselves.
Obvious is artificial intelligence,
in a way that makes it
easy to aggregate data
from across platforms, to aggregate data
from multi-cloud, effectively.
To use that data in a meaningful
way that's going to drive,
not only better decisions
for your business,
and better outcomes, but
drives our opportunities
for customers, drives opportunities
for employees and how they work.
We're at a really interesting point
in technology where we get to
tell technology what to do.
It's going beyond us, it's no longer
what we're telling it to do,
it's going to go beyond us.
So, how we effectively
manage that is going to be
where we see that data
flow, and those big five
or big four, really take
that to the next level.
>> Now, one of the things
that Ginni Rometty said was,
I forget the exact step, but it was like,
80% of the data, is not searchable.
Kind of implying that
it's sitting somewhere
behind a firewall, presumably
on somebody's premises.
So, it was kind of interesting.
You're talking about, certainly,
a lot of momentum for public
cloud, but at the same time,
a lot of data is going
to stay where it is.
>> Yeah, we're assuming
that a lot of this data
is just sitting there,
available and ready,
and we look at the desperate,
or disparate kind of
database situation, where
you have 29 databases,
and two of them have unique quantifiers
that tie together, and
the rest of them don't.
So, there's nothing that
you can do with that data.
So, artificial intelligence is just that,
it's artificial
intelligence, so, they know,
that's machine learning,
that's natural language,
that's classification, there's
a lot of different parts
of that that are moving,
but we also have to have IT,
good data infrastructure,
master data management,
compliance, there's so
many moving parts to this,
that it's not just about the data anymore.
>> I want to ask Steve to
chime in here, go ahead.
>> Yeah, so, we also have
to change the mentality
that it's not just enterprise data.
There's data on the web, the biggest thing
is Internet of Things,
the amount of sensor data
will make the current data
look like chump change.
So, data is moving faster, okay.
And this is where the
sophistication of machine
learning needs to kick in,
going from just mostly
supervised-learning today,
to unsupervised learning.
And in order to really
get into, as I said,
big data, and credible AI
does the who, what, where,
when, and how, but not the why.
And this is really the
Holy Grail to crack,
and it's actually under a new moniker,
it's called explainable
AI, because it moves
beyond just correlation
into root cause analysis.
Once we have that, then you have the means
to be able to tap into
augmented intelligence,
where humans are working
with the machines.
>> Karen, please.
 Yeah, so,
one of the things, like
what Carla was saying,
and what a lot of us had said,
I like to think of the
advent of ML technologies
and AI are going to help
me as a data architect
to love my data better, right?
So, that includes protecting it, but also,
when you say that 80% of
the data is unsearchable,
it's not just an access problem, it's that
no one knows what it was,
what the sovereignty was,
what the metadata was,
what the quality was,
or why there's huge anomalies in it.
So, my favorite story about
this is, in the 1980s,
about, I forget the exact number,
but like, 8 million children
disappeared out of the US
in April, at April 15th.
And that was when the
IRS enacted a rule that,
in order to have a dependent,
a deduction for a dependent
on your tax returns, they had to have
a valid social security number,
and people who had accidentally
miscounted their children
and over-claimed them, (laughter)
over the years them, stopped doing that.
Well, some days it does feel like you have
eight children running around. (laughter)
>> Agreed.
 When,
when that rule came about, literally,
and they're not all children,
because they're dependents,
but literally millions
of children disappeared
off the face of the earth in April,
but if you were doing
analytics, or AI and ML,
and you don't know that
this anomaly happened,
I can imagine in a hundred
years, someone is saying
some catastrophic event happened
in April, 1983. (laughter)
And what caused that, was it healthcare?
Was it a meteor?
Was it the clown attacking them?
>> That's where I was going.
 Right.
So, those are really important
things that I want to
use AI and ML to help
me, not only document
and capture that stuff, but
to provide that information
to the people, the data scientists
and the analysts that are using the data.
>> Great story, thank you.
Bob, you got a thought?
You got the mic, go, jump in here.
>> Well, yeah, I do have
a thought, actually.
I was talking about, what
Karen was talking about.
I think it's really important that,
not only that we understand
AI, and machine learning,
and data science, but
that the regular folks
and companies understand
that, at the basic level.
Because those are the people
who will ask the questions,
or who know what questions
to ask of the data.
And if they don't have the
tools, and the knowledge
of how to get access to that data,
or even how to pose a
question, then that data
is going to be less valuable,
I think, to companies.
And the more that
everybody knows about data,
even people in congress.
Remember when Zuckerberg
talked about? (laughter)
>> That was scary.
 How do you make money?
It's like, we all know this.
But, we need to educate the masses on
>>just basic data analytics.
 We could have
>>an hour-long panel on that.
 Yeah, absolutely.
>> Peter, you and I were talking about,
we had a couple of questions, sort of,
how far can we take
artificial intelligence?
How far should we?
You know, so that brings in
to the conversation of ethics,
and bias, why don't you pick it up?
>> Yeah, so, one of the crucial things that
we all are implying is
that, at some point in time,
AI is going to become a
feature of the operations
of our homes, our businesses.
And as these technologies
get more powerful,
and they diffuse, and know
about how to use them,
diffuses more broadly,
and you put more options
into the hands of more people,
the question slowly starts
to turn from can we do
it, to should we do it?
And, one of the issues
that I introduce is that
I think the difference
between big data and AI,
specifically, is this notion of agency.
The AI will act on behalf of, perhaps you,
or it will act on behalf of your business.
And that conversation
is not being had, today.
It's being had in
arguments between Elon Musk
and Mark Zuckerberg, which
pretty quickly get pretty boring.
(laughing) At the end of the
day, the real question is,
should this machine, whether
in concert with others,
or not, be acting on behalf of me,
on behalf of my business, or,
and when I say on behalf of me,
I'm also talking about privacy.
Because Facebook is
acting on behalf of me,
it's not just what's going on in my home.
So, the question of, can it be done?
A lot of things can be done,
and an increasing number
of things will be able to be done.
We got to start having a conversation
>>about should it be done?
 So, humans exhibit
tribal behavior, they exhibit bias.
Their machine's going to pick
that up, go ahead, please.
>> Yeah, one thing that sort of tag onto
agency of artificial intelligence.
Every industry, every business is now
about identifying
information and data sources,
and their appropriate sinks,
and learning how to draw value
out of connecting the
sources with the sinks.
Artificial intelligence
enables you to identify
those sources and sinks,
and when it gets agency,
it will be able to make
decisions on your behalf
about what data is good, what data means,
>>and who it should be.
 What actions are good.
>> Well, what actions are good.
 And what data was used
>>to make those actions.
 Absolutely.
>> And was that the right data,
and is there bias of data?
And all the way down,
all the turtles down.
>> So, all this, the data
pedigree will be driven by
the agency of artificial intelligence,
and this is a big issue.
>> It's really fundamental to understand
and educate people on,
there are four fundamental
types of bias, so there's,
in machine learning,
there's intentional bias,
"Hey, we're going to make
"the algorithm generate a certain outcome
"regardless of what the data says."
There's the source of the data itself,
historical data that's
trained on the models
built on flawed data, the model
will behave in a flawed way.
There's target source,
which is, for example,
we know that if you pull data
from a certain social network,
that network itself has an inherent bias.
No matter how representative
you try to make the data,
it's still going to have flaws in it.
Or, if you pull healthcare
data about, for example,
African-Americans from
the US healthcare system,
because of societal biases,
that data will always be flawed.
And then there's tool
bias, there's limitations
to what the tools can do, and so we will
intentionally exclude some kinds of data,
or not use it because
we don't know how to,
our tools are not able to,
and if we don't teach people
what those biases are, they won't know
to look for them, and I know.
>> Yeah, it's like, one of
the things that we were
talking about before, I
mean, artificial intelligence
is not going to just create
itself, it's lines of code,
it's input, and it spits out output.
So, if it learns from these learning sets,
we don't want AI to
become another buzzword.
We don't want everybody to be an "AR guru"
that has no idea what AI is.
It takes months, and months, and months
for these machines to learn.
These learning sets are so very important,
because that input is how this machine,
think of it as your child,
and that's basically the way
artificial intelligence is
learning, like your child.
You're feeding it these learning sets,
and then eventually it will
make its own decisions.
So, we know from some
of us having children
that you teach them the best that you can,
but then later on, when
they're doing their own thing,
they're really, it's
like a little myna bird,
they've heard everything that you've said.
(laughing) Not only the
things that you said to them
directly, but the things
that you said indirectly.
>> Well, there are some
very good AI researchers
that might disagree with that
metaphor, exactly. (laughing)
But, having said that, what
I think is very interesting
about this conversation is
that this notion of bias,
one of the things that fascinates
me about where AI goes,
are we going to find a
situation where tribalism
more deeply infects business?
Because we know that human
beings do not seek out
the best information,
they seek out information
that reinforces their beliefs.
And that happens in business today.
My line of business versus
your line of business,
engineering versus sales,
that happens today,
but it happens at a planning level,
and when we start talking about AI,
we have to put the appropriate dampers,
understand the biases,
so that we don't end up
with deep tribalism inside of business.
Because AI could have
the deleterious effect
that it actually starts
ripping apart organizations.
>> Well, input is data,
and then the output is,
>>could be a lot of things.
 Could be a lot of things.
>> And that's where I said
data equals human lives.
So that we look at the case in New York
where the penal system was using
this artificial intelligence
to make choices on people
that were released from
prison, and they saw that
that was a miserable
failure, because that people
that release actually re-offended,
some committed murder and other things.
So, I mean, it's,
it's more than what anybody really thinks.
It's not just, oh, well,
we'll just train the machines,
and a couple of weeks later they're good,
we never have to touch them again.
These things have to be
continuously tweaked.
So, just because you built an algorithm
or a model doesn't mean you're done.
You got to go back later,
and continue to tweak these models.
>> Mark, you got the mic.
 Yeah, no, I think one thing
we've talked a lot about
the data that's collected,
but what about the data
that's not collected?
Incomplete profiles, incomplete datasets,
that's a form of bias, and
sometimes that's the worst.
Because they'll fill that in, right,
and then you can get some
bias, but there's also
a real issue for that
around cyber security.
Logs are not always complete,
things are not always done,
and when things are doing
that, people make assumptions
based on what they've collected,
not what they didn't collect.
So, when they're looking at this,
and they're using the AI on it,
that's only on the data collected,
not on that that wasn't collected.
So, if something is
down for a little while,
and no data's collected off
that, the assumption is,
well, it was down, or it
was impacted, or there was
a breach, or whatever,
it could be any of those.
So, you got to, there's
still this human need,
there's still the need for
humans to look at the data
and realize that there
is the bias in there,
there is, we're just looking
at what data was collected,
and you're going to have to make
your own thoughts around that,
and assumptions on how
to actually use that data
before you go make those decisions
that can impact lots of
people, at a human level,
enterprise's profitability,
things like that.
And too often, people think of AI,
when it comes out of
there, that's the word.
Well, it's not the word.
>> Can I ask a question about this?
>> Please.
 Does that mean
that we shouldn't act?
>> It does not.
 Okay.
>> So, where's the fine line?
>> Yeah, I think.
 Going back to this notion
of can we do it, or should we do it?
>>Should we act?
 Yeah, I think
you should do it, but you
should use it for what it is.
It's augmenting, it's
helping you, assisting you
to make a valued or good decision.
And hopefully it's a better decision
than you would've made without it.
>> I think it's great, I think
also, your answer's right too,
that you have to iterate
faster, and faster, and faster,
and discover sources of
information, or sources of data
that you're not currently using, and,
that's why this thing starts
getting really important.
>> I think you touch on a
really good point about,
should you or shouldn't you?
You look at Google, and
you look at the data
that they've been using,
and some of that out there,
from a digital twin perspective,
is not being approved,
or not authorized, and even
once they've made changes,
it's still floating around out there.
Where do you know where it is?
So, there's this dilemma of,
how do you have a digital
twin that you want to have,
and is going to work for you,
and is going to do things
for you to make your life easier,
to do these things,
mundane tasks, whatever?
But how do you also control it
to do things you don't want it to do?
>> Ad-based business models are
inherently evil. (laughing)
>> Well, there's incentives
to appropriate our data,
and so, are things like blockchain
potentially going to give
users the ability to control their data?
>>We'll see.
 No, I,
I'm sorry, but that's actually
a really important point.
The idea of consensus algorithms,
whether it's blockchain or
not, blockchain includes games,
and something along those lines,
whether it's Byzantine fault tolerance,
or whether it's Paxos,
consensus-based algorithms
are going to be really, really important.
Parts of this conversation,
because the data's going to be
more distributed, and you're going to have
more elements participating in it.
And so, something that allows,
especially in the
machine-to-machine world,
which is a lot of what we're
talking about right here,
you may not have blockchain,
because there's no need for
a sense of incentive,
which is what blockchain
>>can help provide.
 And there's no middleman.
>> And, well, all right,
but there's really,
the thing that makes
blockchain so powerful is
it liberates new classes of applications.
But for a lot of the stuff
that we're talking about,
you can use a very powerful
consensus algorithm
without having a game side,
and do some really
amazing things at scale.
>> So, looking at blockchain,
that's a great thing to bring up, right.
I think what's inherently
wrong with the way
we do things today, and
the whole overall design
of technology, whether it
be on-prem, or off-prem,
is both the lock and key
is behind the same wall.
Whether that wall is in a
cloud, or behind a firewall.
So, really, when there is an audit,
or when there is a forensics,
it always comes down to a
sysadmin, or something else,
and the system administrator
will have the finger pointed
at them, because it all resides,
you can edit it, you can augment it,
or you can do things with it
that you can't really determine.
Now, take, as an example, blockchain,
where you've got really
the source of truth.
Now you can take and have
the lock in one place,
and the key in another place.
So that's certainly
going to be interesting
to see how that unfolds.
>> So, one of the things, it's good that,
we've hit a lot of
buzzwords, right now, right?
(laughing) AI, and ML, block.
>> Bingo.
 We got the blockchain bingo,
yeah, yeah.
So, one of the things
is, you also brought up,
I mean, ethics and everything,
and one of the things
that I've noticed over the
last year or so is that,
as I attend briefings or demos,
everyone is now claiming
that their product
is AI or ML-enabled,
or blockchain-enabled.
And when you try to get
answers to the questions,
what you really find
out is that some things
are being pushed as, because
they have if-then statements
somewhere in their code, and therefore
that's artificial intelligence
or machine learning.
>> [Peter] At least it's
not "go-to." (laughing)
>> Yeah, you're that
experienced as well. (laughing)
So, I mean, this is part
of the thing you try to do
as a practitioner, as an
analyst, as an influencer,
is trying to, you know,
the hype of it all.
And recently, I attended
one where they said
they use blockchain, and
I couldn't figure it out,
and it turns out they use
GUIDs to identify things,
and that's not blockchain,
it's an identifier. (laughing)
So, one of the ethics
things that I think we,
as an enterprise community,
have to deal with,
is the over-promising of AI, and ML,
and deep learning, and recognition.
It's not, I don't really
consider it visual recognition
services if they just look for red pixels.
I mean, that's not quite the same thing.
Yet, this is also making
things much harder
for your average CIO, or
worse, CFO, to understand
whether they're getting any
value from these technologies.
>> Old bottle.
 Old bottle, right.
>> And I wonder if the data companies,
like that you talked
about, or the top five,
I'm more concerned about their nearly,
or actual $1 trillion
valuations having an impact
on their ability of other
companies to disrupt or enter
into the field more so than
their data technologies.
Again, we're coming to
another perfect storm
of the companies that
have data as their asset,
even though it's still not on
their financial statements,
which is another indicator
whether it's really an asset,
is that, do we need to
think about the terms of AI,
about whose hands it's in, and who's,
like, once one large
trillion-dollar company decides that
you are not a profitable
company, how many other companies
are going to buy that data and
make that decision about you?
>> Well, and for the first
time in business history,
I think, this is true, we're seeing,
because of digital, because it's data,
you're seeing tech companies
traverse industries,
get into, whether it's content, or music,
or publishing, or groceries, and
that's powerful, and that's awful scary.
>> If you're a manger, one
of the things your ownership
is asking you to do is to
reduce asset specificities,
so that their capital could be applied
to more productive uses.
Data reduces asset specificities.
It brings into question
the whole notion of vertical industry.
You're absolutely right.
But you know, one quick
question I got for you,
playing off of this
is, again, it goes back
to this notion of can we
do it, and should we do it?
I find it interesting, if
you look at those top five,
all data companies, but all of them
are very different business models,
or they can classify the two
different business models.
Apple is transactional,
Microsoft is transactional,
Google is ad-based, Facebook is ad-based,
before the fake news stuff.
Amazon's kind of playing it both sides.
>> Yeah, they're kind of all
on a collision course though,
>>aren't they?
 But, well,
that's what's going to be interesting.
I think, at some point in time,
the "can we do it, should
we do it" question is,
brands are going to be
identified by whether or not
they have gone through that
process of thinking about,
should we do it, and say no.
Apple is clearly, for example,
incorporating that into their brand.
>> Well, Silicon Valley, broadly defined,
if I include Seattle, and
maybe Armlock, not so much IBM.
But they've got a dual disruption agenda,
they've always disrupted horizontal tech.
Now they're disrupting
vertical industries.
>> I was actually just going
to pick up on what she was
talking about, we were
talking about buzzword, right.
So, one we haven't heard yet is voice.
Voice is another big buzzword right now,
when you couple that with IoT and AI,
here you go, bingo, do I
got three points? (laughing)
Voice recognition, voice technology,
so all of the smart speakers,
if you think about that in the world,
there are 7,000 languages being spoken,
but yet if you look at Google
Home, you look at Siri,
you look at any of the
devices, I would challenge you,
it would have a lot of problem
understanding my accent,
and even when my British
accent creeps out,
or it would have trouble
understanding seniors,
because the way they talk,
it's very different than a typical
25-year-old person living
in Silicon Valley, right.
So, how do we solve that,
especially going forward?
We're seeing voice
technology is going to be
so more prominent in our homes,
we're going to have it in the cars,
we have it in the kitchen,
it does everything,
it listens to everything
that we are talking about,
not talking about, and records it.
And to your point, is it going
to start making decisions
on our behalf, but then my question is,
how much does it actually understand us?
>> So, I just want one short story.
Siri can't translate a word that I ask it
to translate into French,
because my phone's set
to Canadian English, and
that's not supported.
So I live in a bilingual
French English country,
and it can't translate.
>> But what this is really bringing up
is if you look at society, and culture,
what's legal, what's ethical,
changes across the years.
What was right 200 years
ago is not right now,
and what was right 50
years ago is not right now.
>> It changes across countries.
 It changes across countries,
it changes across regions.
So, what does this mean
when our AI has agency?
How do we make ethical
AI if we don't even know
how to manage the change of what's right
and what's wrong in human society?
>> One of the most
important questions we have
>>to worry about, right?
 Absolutely.
>> But it also says one more
thing, just before we go on.
It also says that the issue
of economies of scale,
>>in the cloud.
 Yes.
>> Are going to be strongly
impacted, not just by how big
you can build your data
centers, but some of those
regulatory issues that are
going to influence strongly
what constitutes good
experience, good law,
good acting on my behalf, agency.
>> And one thing that's underappreciated
in the marketplace right now is the impact
of data sovereignty, if
you get back to data,
countries are now
recognizing the importance
of managing that data,
and they're implementing
data sovereignty rules.
Everyone talks about
California issuing a new law
that's aligned with GDPR,
and you know what that meant.
There are 30 other states
in the United States alone
that are modifying their
laws to address this issue.
>> Steve.
 So, um,
so, we got a number of years,
no matter what Ray Kurzweil says,
until we get to artificial
general intelligence.
>> The singularity's
not so near? (laughing)
>> You know that he's changed the date
>>over the last 10 years.
 I did know it.
>> Quite a bit.
And I don't even prognosticate
where it's going to be.
But really, where we're at right now,
I keep coming back to, is that's
why augmented intelligence
is really going to be the new rage,
humans working with machines.
One of the hot topics,
and the reason I chose
to speak about it is,
is the future of work.
I don't care if you're a millennial,
mid-career, or a baby
boomer, people are paranoid.
As machines get smarter, if
your job is routine cognitive,
yes, you have a higher
propensity to be automated.
So, this really shifts a number of things.
A, you have to be a lifelong learner,
you've got to learn new skillsets.
And the dynamics are changing fast.
Now, this is also a great equalizer
for emerging startups, and even in SMBs.
As the AI improves, they
can become more nimble.
So back to your point regarding
colossal trillion dollar,
wait a second, there's going
to be quite a sea change
going on right now, and
regarding demographics,
in 2020, millennials
take over as the majority
of the workforce, by 2025 it's 75%.
>> Great news. (laughing)
 As a baby boomer,
I try my damnedest to stay relevant.
>> Yeah, surround yourself with millennials
is the takeaway there.
>> Or retire. (laughs)
 Not yet.
>> One thing I think, this
goes back to what Karen
was saying, if you want a
basic standard to put around
the stuff, look at the
old ISO 38500 framework.
Business strategy, technology strategy.
You have risk, compliance,
change management, operations,
and most importantly, the
balance sheet in the financials.
AI and what Tony was saying,
digital transformation,
if it's of meaning, it
belongs on a balance sheet,
and should factor into how
you value your company.
All the cyber security,
and all of the compliance,
and all of the regulation, is all stuff,
this framework exists, so look it up,
and every time you start some kind of new
machine learning project,
or data sense project,
say, have we checked the box
on each of these standards
that's within this machine?
And if you haven't, maybe slow
down and do your homework.
>> To see a day when data
is going to be valued
>>on the balance sheet.
 It is.
>> It's already valued
as part of the current,
>>but it's good will.
 Certainly market value,
>>as we were just talking about.
 Well, we're talking about
all of the companies that
have opted in, right.
There's tens of thousands
of small businesses
just in this region
alone that are opt-out.
They're small family businesses,
or businesses that really
aren't even technology-aware.
But data's being collected
about them, it's being on Yelp,
they're being rated,
they're being reviewed,
the success to their business
is out of their hands.
And I think what's really
going to be interesting is,
you look at the big data, you look at AI,
you look at things like
that, blockchain may even be
a potential for some of
that, because of mutability,
but it's when all of those businesses,
when the technology becomes a cost,
it's cost-prohibitive
now, for a lot of them,
or they just don't want to do
it, and they're proudly opt-out.
In fact, we talked about
that last night at dinner.
But when they opt-in, the
company that can do that,
and can reach out to them in a way
that is economically feasible,
and bring them back in,
where they control their
data, where they control
their information, and
they do it in such a way
where it helps them build their business,
and it may be a generational
business that's been passed on.
Those kind of things are
going to make a big impact,
not only on the cloud,
but the data being stored
in the cloud, the AI, the applications
that you talked about
earlier, we talked about that.
And that's where this bias,
and some of these other things
are going to have a tremendous impact
if they're not dealt with
now, at least ethically.
>> Well, I feel like we just
got started, we're out of time.
Time for a couple more
comments, and then officially
>>we have to wrap up.
 Yeah, I had one thing
to say, I mean, really,
Henry Ford, and the creation
of the automobile, back
in the early 1900s,
changed everything,
because now we're no longer
stuck in the country, we can
get away from our parents,
we can date without grandma and grandpa
setting on the porch with us. (laughing)
We can take long trips,
so now we're looked at,
we've sprawled out, we're
not all living in the country
anymore, and it changed America.
So, AI has that same
capabilities, it will automate
mundane routine tasks that
nobody wanted to do anyway.
So, a lot of that will change things,
but it's not going to be
any different than the way
things changed in the early 1900s.
>> It's like you were saying,
constant reinvention.
>> I think that's a great point,
let me make one observation on that.
Every period of significant
industrial change
was preceded by the formation,
a period of formation of
new assets that nobody
knew what to do with.
Whether it was, what do we do, you know,
industrial manufacturing,
it was row houses
with long shafts tied to an engine
that was coal-fired, and
drove a bunch of looms.
Same thing, railroads, large
factories for Henry Ford,
before he figured out how
to do an information-based
notion of mass production.
This is the period of asset formation
for the next generation
of social structures.
>> Those ship-makers are going
to be all over these cars,
I mean, you're going to have
augmented reality right there,
>>on your windshield.
 Karen, bring it home.
Give us the drop-the-mic
moment. (laughing)
>> No pressure.
 Your AV guys
are not happy with that.
So, I think the, it all comes down to,
it's a people problem, a
challenge, let's say that.
The whole AI ML thing, people,
it's a legal compliance thing.
Enterprises are going to
struggle with trying to meet
five billion different
types of compliance rules
around data and its
uses, about enforcement,
because ROI is going to
make risk of incarceration
as well as return on investment,
and we'll have to manage both of those.
I think businesses are
struggling with a lot
of this complexity, and you just opened
a whole bunch of questions that
we didn't really have solid,
"Oh, you can fix it by doing this."
So, it's important that
we think of this new world
of data focus, data-driven,
everything like that,
is that the entire IT
and business community
needs to realize that focusing on data
means we have to change how we do things
and how we think about
it, but we also have
some of the same old challenges there.
>> Well, I have a feeling we're going to be
talking about this for quite some time.
What a great way to wrap up
CUBE NYC here, our third day
of activities down here at
37 Pillars, or Mercantile 37.
Thank you all so much
for joining us today.
>> Thank you.
 Really, wonderful insights,
really appreciate it,
now, all this content
is going to be available on theCUBE.net.
We are exposing our video cloud,
and our video search
engine, so you'll be able
to search our entire corpus of data.
I can't wait to start searching
and clipping up this session.
Again, thank you so much,
and thank you for watching.
We'll see you next time.
