>> Announcer: Live from New York City,
it's The Cube, covering
IBM data science for all.
Brought to you by IBM.
>> Welcome back to data science for all.
This is IBM's event here on
the west side of Manhattan,
here on The Cube.
We're live, we'll be here all day,
along with Dave Vallente, I'm John Walls
Poor Dave had to put up
with all that howling music
at this hotel last night,
kept him up 'til, all hours.
>> Lots of fun here in the city.
>> Yeah, yeah.
>> All the crazies out last night.
>> Yeah, but the headphones,
they worked for ya.
Glad to hear that.
>> People are already
dressed for Halloween,
you know what I mean?
>> John: Yes.
>> In New York, you know what I mean?
>> John: All year.
>> All the time.
>> John: All year.
>> 365.
>> Yeah. We have with us now
the head of data science,
and the VP at Galvanize, Nir Kaldero,
and Nir, good to see you, sir.
Thanks for being with us.
We appreciate the time.
>> Well of course, my pleasure.
>> Tell us about Galvanize.
I know you're heavily
involved in education
in terms of the tech community,
but you've got corporate clients,
you've got academic clients.
You cover the waterfront,
and I know data science is your baby.
>> Nir: Right.
>> But tell us a little
bit about Galvanize
and your mission there.
>> Sure, so Galvanize is
the learning community for technology.
We provide the training in data science,
data engineering, and also
modern software engineering.
We recently built a very large,
fast growing enterprise
corporate training department,
where we basically help
companies become digital,
become nimble, and also very data driven,
so they can actually go through
this digital transformation,
and survive in this fourth
industrial revolution.
We do it across all
layers of the business,
from the executives, to managers,
to data scientists, and data analysts,
and kind of transform and upscale
all current skills to be
modern, to be digital,
so companies can actually go
through this transformation.
>> Hit on one of those items
you talked about, data driven.
>> Nir: Right.
>> It seems like a no-brainer, right?
That the more information you give me,
the more analysis I can apply to it,
the more I can put it
in my business practice,
the more money I make,
the more my customers are happy.
It's a lay up, right?
>> Nir: It is.
>> What is a data driven
organization, then?
Do you have to convince people
that this is where they need to be today?
>> Sometimes I need to convince them,
but (laughs) anyway,
so let's back up a little bit.
We are in the midst of the
fourth industrial revolution,
and in order to survive in
this fourth industrial revolution,
companies need to become
nimble, as I said,
become agile, but most
importantly become data driven,
so the organization can
actually best respond
to all the predictions that are coming
from this very sophisticated
machine intelligence models.
If the organization immediately
can best respond to all of that,
companies will be able to
enhance the user experience,
get insight about their customers,
enhance performances, and et cetera,
and we know that the
winners in this revolution,
in this era, will be companies
who are very digital,
that master the skills
of becoming a data driven organization,
and you know, we can talk
more about the transformation,
and what it consisted of.
Do you want me to?
>> John: Sure.
>> Can I just ask you a question?
This fourth wave, this is what,
the cognitive machine wave?
Or how would you describe it?
>> Some people call it
artificial intelligence.
I think artificial
intelligence is like big data,
kind of like a buzz word.
I think more appropriately,
we should call it
machine intelligence
industrial revolution.
>> Okay. I've got a lot of questions,
but carry on.
>> So hitting on that,
so you see that as being a major era.
>> Nir: It's a game changer.
>> If you will, not just a chapter,
but a major game changer.
>> Nir: Yup.
>> Why so?
>> So, okay, I'll jump in again.
Machines have always replaced man, people.
>> John: The automation, right.
>> Nir: To some extent.
>> But certain machines have
replaced certain human tasks,
let's say that.
>> Nir: Correct.
>> But for the first time
in history, this fourth era,
machine's are replacing
humans with cognitive tasks,
and that scares a lot of people,
because you look at the United States,
the median income of the U.S.
worker has dropped since 1999,
from $55,000 to $52,000,
and a lot of people believe it's sort of
the hollowing out of that
factor that we just mentioned.
Education many believe is the answer.
You know, Galvanize is an organization
that plays a critical role
in helping deal with that problem,
does it not?
>> So, as Mark Zuckerberg says,
there is a lot of hate
love relationship with A.I.
People love it on one side,
because they're excited
about all the opportunities
that can come from this utilization
of machine intelligence,
but many people actually
are afraid from it.
I read a survey a few weeks ago
that says that 36% of
the population thinks
that A.I. will destroy humanity,
and will conquer the world.
That's a fact that's what people think.
If I think it's going to happen?
I don't think so.
I highly believe that education is one
of the pillars that can address
this fear for machine intelligence,
and you spoke a lot about jobs
I talk about it forever,
but just my belief is that machines
can actually replace some of
our responsibilities, right?
Not necessarily take and
replace the entire job.
Let's talk about lawyers, right?
Lawyers currently spend between 40%
to 60% of the time writing contracts,
or looking at previous cases.
The machine can write a
contract in two minutes,
or look up millions of data points
of previous cases in zero time.
Why a lawyer today needs to spend 40%
to 60% of the time on that?
>> Billable hours, that's why.
>> It is, so I don't think the machine
will replace the job of the lawyer.
I think in the future,
the machine replaces some
of the responsibilities,
like auditing, or writing contracts,
or looking at previous cases.
>> Menial labor, if you will.
>> Yes, but you know,
for example, the machine
is not that great right now
with negotiations skills.
So maybe in the future,
the job of the lawyer will
be mostly around negotiation skills,
rather than writing contracts, et cetera,
but yeah, you're absolutely right.
There is a big fear in
the market right now among executives,
among people in the public.
I think we should educate people about
what is the true implications
of machine intelligence
in this fourth industrial
revolution and era,
and education is definitely one of those.
>> Well, one of my favorite stories,
when people bring up this topic,
is when Gary Kasparov lost
to the IBM super computer,
Blue Jean, or whatever it's called.
>> Nir: Yup.
>> Instead of giving up,
what he said is he started a competition,
where he proved that humans and machines
could beat the IBM super computer.
So to this day has a competition
where the best chess player in the world
is a combination between
humans and machines,
and so it's that creativity.
>> Nir: Imagination.
>> Imagination, right,
combinatorial effects of
different technologies
that education, hopefully,
can help keep those either way.
>> Look, I'm a big fan of neuroscience.
I wish I did my PhD in neuroscience,
but we are very, very far away
from understanding how our brain works.
Now to try to imitate the brain
when we don't know how the brain works?
We are very far away from being
in a place where a machine
can actually replicate,
and really best respond like a human.
We don't know how our brain works yet.
So we need to do a lot of research on that
before we actually really
write a very strong,
powerful machine intelligence model
that can actually replace
us as humans, and outbid us.
We can speak about Jeopardy,
and what's on, and we
can speak about AlphaGo,
it's a Google company that
kind of outperformed the world champion.
These are very specific tasks, right?
Again, like the lawyer,
the machines can write
beautiful contracts with NLP,
machines can look at millions
and trillions of data
and figure out what's the
conclusion there, right?
Or summarize text very fast,
but not necessarily
good in negotiation yet.
>> So when you think
about a digital business,
to us a digital business is a business
that uses data to differentiate,
and serve customers,
and maintain customers.
So when you talk about data driven,
it strikes me that when everybody's
saying digital business,
digital transformation, it's
about a data transformation,
how well they utilize data,
and if you look at the bell
curve of organizations,
most are not.
Everybody wants to be data driven,
many say they are data driven.
>> Right.
>> Dave: Would you agree most are not?
>> I will agree that most companies say
that they are data driven,
but actually they're not.
I work with a lot of Fortune
500 companies on a daily basis.
I meet their executives
and functional leaders,
and actually see their data,
and business problems that they have.
Most of them do tend to say
that they are data driven,
but truly just ask them if they put data
and decisions in the same place,
every time they have to make a decision,
they don't do it.
It's a habit that they don't yet have.
Companies need to start investing
in building what we say
healthy data culture
in order to enable and become data driven.
Part of it is democratization
of data, right?
Currently what I see if
lots of organizations
actually open the data
just for the analyst,
or the marketers, people
who kind of make decisions,
that need to make decisions with data,
but not throughout the
entire organization.
I know I always say that everyone
in the organization makes
decisions on a daily basis,
from the barista, to the CEO, right?
And the entirety of
becoming data driven is
that data can actually help
us make better decisions
on a daily basis,
so how about democratizing
the data to everyone?
So everyone, from the barista,
to the CEO, can actually
make better decisions
on a daily basis,
and companies don't excel yet in doing it.
Not every company is as digital as Amazon.
Amazon, I think,
is actually one of the most
digital companies in the world,
if you look at the digital index.
Not everyone is Google or Facebook.
Most companies want to be there,
most companies understand that they will
not be able to survive in this era
if they will not become data driven,
so it's a big problem.
We try at Galvanize to
address this problem
from executive type of education,
where we actually meet with
the C-level executives in companies,
and actually guide them through
how to write their data strategy,
how to think about
prioritizing data investment,
to actual implementation of that,
and so far we are highly successful.
We were able to make a big transformation
in very large, important organizations.
So I'm actually very proud of it.
>> How long are these eras?
Is it a century, or more?
>> This fourth industrial?
>> Yeah.
>> Well it's hard to predict that,
and I'm not a machine, or what's on it.
(laughs)
>> But certainly more than
50 years, would you say?
Or maybe not, I don't know.
>> I actually don't think so.
I think it's going to be fast,
and we're going to move to
the next one pretty soon
that will be even more,
with more intelligence, with more data.
>> So the reason I ask,
is there was an article I saw and linked,
and I haven't had time to read it,
but it talked about the Four Horsemen,
Amazon, Google, Facebook, and Apple,
and it said they will all be
out of business in 50 years.
Now, I don't know,
I think Apple probably has 50 years
of cash flow in the bank,
but then they said, the one,
the author said, if I had to
predict one that would survive,
it would be Amazon, to your point,
because they are so data driven.
The premise, again I didn't
read the whole thing,
was that some new data driven,
digital upstart will disrupt them.
>> Yeah, and you know,
companies like Amazon, and Alibaba lately,
that try kind of like in a competition
with Amazon about who is
becoming more data driven,
utilizing more machine intelligence,
are the ones that invested in
these capabilities many, many years ago.
It's no that they started
investing in it last year,
or five years ago.
We speak about 15 and 20 years ago.
So companies who were really a pioneer,
and invested very early on,
will predict actually to
survive in the future,
and you know, very much align.
>> Yeah, I'm going to touch on something.
It might be a bridge
too far, I don't know,
but you talk about, Dave brought it up,
about replacing human capital, right?
Because of artificial intelligence.
>> Nir: Yup.
>> Is there a reluctance, perhaps,
on behalf of executives to embrace that,
because they are concerned
about their own price?
>> Nir: You should be in the room with me.
(laughing)
>> You provide data, but you also provide
that capability to analyze,
and make the best informed decision,
and therefore, eliminate the human element
of a C-suite executive
that maybe they're not
as necessary today, or tomorrow,
as they were two years ago.
>> So it is absolutely true,
and there is a lot of fear in the room,
especially when I show them robots,
they freak out typically,
(John and Dave laugh)
but the fact is well known.
Leaders who will not embrace these skills,
and understanding, and
will help the organization
to become agile, nimble,
and data driven, will not survive.
They will be replaced.
So on the one hand,
they're afraid from it.
On the other side,
they see that if they will
not actually do something,
and take an action today,
they might be replaced in the future.
>> Where should organizations start?
Hey, I want to be data driven.
Where do I start?
>> That's a good question.
So data science, machine learning,
is a top down initiative.
It requires a lot of funding.
It requires a change
in culture and habits.
So it has to start from the top.
The journey has to start from executive,
from educating and executive
about what is data science,
what is machine learning,
how to prioritize
investments in this field,
how to build data driven culture, right?
When we spoke about data driven,
we mainly speaks about
the culture aspect here,
not specifically about
the technical side of it.
So it has to come from the top,
leaders have to incorporate
it in the organization,
the have to give authority
and power for people,
they have to put the funding at first,
and then, this is how it's beautiful,
that you actually see it
trickles down to the organization
when they have a very powerful CEO
that makes a decision,
and moves the organization
quickly to become data driven,
make executives look at data
every time they make a decision,
get them into the habit.
When people look up to executives,
they try to do the same,
and if my boss is an example for me,
someone who is looking at data
every time he is making a decision,
ask the right questions,
know how to prioritize,
set the right goals for me,
this helps me, and helps the
organization better perform.
>> Follow the leader, right?
>> Yup.
>> Follow the leader.
>> Yup, follow the leader.
>> Thanks for being with us.
>> Nir: Of course, it's my pleasure.
>> Pinned this interesting love hate thing
that we have going on.
>> We should address that.
>> Right, right.
That's the next segment, how about that?
>> Nir Kaldero from Galvanize joining us
here live on The Cube.
Back with more from
New York in just a bit.
