>> Announcer: From the Corinium Chief
Analytics Officer Conference,
Spring, San Francisco, its theCUBE.
>> Hey, welcome back everybody.
Jeff Frick here with theCUBE.
We're at the Corinium Chief Analytics Officer
event in San Francisco, Spring, 2018.
About 100 people, predominantly practitioners,
which is a pretty unique event.
Not a lot of vendors, a couple of them around,
but really a lot of people that are out in the wild
doing this work.
We're really excited to have a return guest.
We last saw him at Spark Summit East 2017.
Can you believe I keep all these shows straight?
I do not.
Alfred Essa, he is the VP, Analytics and R&D
at McGraw-Hill Education.
Alfred, great to see you again.
>> Great being here, thank you.
>> Absolutely, so last time we were talking
it was Spark Summit, it was all about
data in motion and data on the fly,
and real-time analytics.
You talked a lot about trying to apply
these types of new-edge technologies
and cutting-edge things to actually education.
What a concept, to use artificial intelligence,
a machine learning for people learning.
Give us a quick update on that journey,
how's it been progressing?
>> Yeah, the journey progresses.
We recently have a new CEO come on board,
started two weeks ago.
Nana Banerjee, very interesting background.
PhD in mathematics and his area of expertise
is Data Analytics.
It just confirms the direction of McGraw-Hill Education
that our future is deeply embedded in data and analytics.
>> Right.
It's funny, there's a often quoted kind of fact
that if somebody came from a time machine from,
let's just pick 1849, here in San Francisco,
everything would look different
except for Market Street and the schools.
The way we get around is different.
>> Right.
>> The things we do to earn a living are different.
The way we get around is different,
but the schools are just slow to change.
Education, ironically, has been slow
to adopt new technology.
You guys are trying to really change that paradigm
and bring the best and latest in cutting edge
to help people learn better.
Why do you think it's taken education so long
and must just see nothing but opportunity
ahead for you.
>> Yeah, I think the...
It was sort of a paradox in the 70s and 80s
when it came to IT.
I think we have something similar going on.
Economists noticed that we were investing
lots and lots of money, billions of dollars,
in information technology,
but there were no productivity gains.
So this was somewhat of a paradox.
When, and why are we not seeing productivity gains
based on those investments?
It turned out that the productivity gains
did appear and trail,
and it was because just investment in technology
in itself is not sufficient.
You have to also have business process transformation.
>> Jeff Frick: Right.
>> So I think what we're seeing is,
we are at that cusp where people recognize
that technology can make a difference,
but it's not technology alone.
Faculty have to teach differently,
students have to understand what they need to do.
It's a similar business transformation in education
that I think we're starting to see now occur.
>> Yeah it's great, 'cause I think the old way
is clearly not the way for the way forward.
That's, I think, pretty clear.
Let's dig into some of these topics,
'cause you're a super smart guy.
One thing's talk about is this algorithmic transparency.
A lot of stuff in the news going on,
of course we have all the stuff with self-driving cars
where there's these black box
machine learning algorithms,
and artificial intelligence, or augmented intelligence,
bunch of stuff goes in and out pops
either a chihuahua or a blueberry muffin.
Sometimes it's hard to tell the difference.
Really, it's important to open up the black box.
To open up so you can at least explain
to some level of, what was the method
that took these inputs and derived this outpout.
People don't necessarily want to open up the black box,
so kind of what is the state that you're seeing?
>> Yeah, so I think this is an area
where not only is it necessary that we have
algorithmic transparency, but I think
those companies and organizations
that are transparent, I think that will become
a competitive advantage.
That's how we view algorithms.
Specifically, I think in the world of
machine learning and artificial intelligence,
there's skepticism, and that skepticism is justified.
What are these machines?
They're making decisions, making judgments.
Just because it's a machine, doesn't mean
it can't be biased.
We know it can be. >> Right, right.
>> I think there are techniques.
For example, in the case of machine learning,
what the machines learns,
it learns the algorithm,
and those rules are embedded in parameters.
I sort of think of it as gears in the black box,
or in the box.
>> Jeff Frick: Right.
>> What we should be able to do is allow
our customers, academic researchers, users,
to understand at whatever level
they need to understand
and want to understand >> Right.
>> What the gears do and how they work.
>> Jeff Frick: Right.
>> Fundamental, I think for us,
is we believe that the smarter our customers are
and the smarter our users are,
and one of the ways in which they can become smarter
is understanding how these algorithms work.
>> Jeff Frick: Right.
>> We think that that will allow us to gain
a greater market share.
So what we see is that our customers
are becoming smarter.
They're asking more questions
and I think this is just the beginning.
>> Jeff Frick: Right.
>> We definitely see this as an area
that we want to distinguish ourselves.
>> So how do you draw lines, right?
Because there's a lot of big science
underneath those algorithms.
To different degrees, some of it might
be relatively easy to explain as a simple formula,
other stuff maybe is going into some
crazy, statistical process that most layman,
or business, or stakeholders may or may not understand.
Is there a way you slice it?
Is there kind of wars of magnitude
in how much you expose, and the way you expose
within that box?
>> Yeah, I think there is a tension.
The tension traditionally, I think organizations
think of algorithms like they think
of everything else, as intellectual property.
We want to lock down our intellectual property,
we don't want to expose that to our competitors.
I think...
I think that's...
We do need to have intellectual property,
however, I think many organizations
get locked into a mental model,
which I don't think is just the right one.
I think we can, and we want our customers
to understand how our algorithm works.
We also collaborate quite a bit
with academic researchers.
We want validation from the academic research community
that yeah, the stuff that you're building
is in fact based on learning science.
That it has warrant.
That when you make claims that it works,
yes, we can validate that.
Now, where I think...
Based on the research that we do,
things that we publish,
our collaboration with researchers,
we are exposing and letting the world know
how we do things.
At the same time,
it's very, very difficult to build an engineer,
an architect, scalable solutions that implement
those algorithms for millions of users.
That's not trivial. >> Right, right, right.
>> Even if we give away quite a bit of our secret sauce,
it's not easy to implement that.
>> Jeff Frick: Right.
>> At the same time, I believe and we believe,
that it's good to be chased by our competition.
We're just going to go faster.
Being more open also creates excitement
and an ecosystem around our products and solutions,
and it just makes us go faster.
>> Right, which gives to another transition point,
which would you talk about kind of the old
mental model of closed IP systems,
and we're seeing that just get crushed
with open source.
Not only open source movements
around specific applications, and like,
we saw you at Spark Summit,
which is an open source project.
Even within what you would think for sure
has got to be core IP, like Facebook
opening up their hardware spec
for their data centers, again.
I think what's interesting,
'cause you said the mental model.
I love that because the ethos of open source,
by rule, is that all the smartest people
are not inside your four walls.
>> Exactly.
>> There's more of them outside the four walls
regardless of how big your four walls are,
so it's more of a significant mental shift
to embrace, adopt, and engage that community
from a much bigger accumulative brain power
than trying to just trying to hire the smartest,
and keep it all inside.
How is that impacting your world,
how's that impacting education,
how can you bring that power to bear
within your products?
>> Yeah, I think...
You were in effect quoting, I think it was Bill Joy
saying, one of the founders of Sun Microsystems,
they're always, you have smart people
in your organization, there are always more
smarter people outside your organization, right?
How can we entice, lure, and collaborate
with the best and the brightest?
One of the ways we're doing that is
around analytics, and data, and learning science.
We've put together a advisory board
of learning science researchers.
These are the best and brightest
learning science researcher,
data scientists, learning scientists,
they're on our advisory board
and they help and set, give us guidance
on our research portfolio.
That research portfolio is,
it's not blue sky research,
we're on Google and Facebook,
but it's very much applied research.
We try to take the no-knowns in learning science
and we go through a very quick iterative,
innovative pipeline where we do research,
move a subset of those to product validation,
and then another subset of that to product development.
This is under the guidance, and advice,
and collaboration with the academic research community.
>> Right, right.
You guys are at an interesting spot,
because people learn one way,
and you've mentioned a couple times this interview,
using good learning science is the way that people learn.
Machines learn a completely different way
because of the way they're built
and what they do well, and what they don't do so well.
Again, I joked before about the chihuahua
and the blueberry muffin,
which is still one of my favorite pictures,
if you haven't seen it, go find it on the internet.
You'll laugh and smile I promise.
You guys are really trying to bring together
the latter to really help the former.
Where do those things intersect,
where do they clash,
how do you meld those two methodologies together?
>> Yeah, it's a very interesting question.
I think where they do overlap quite a bit is...
in many ways machines learn the way we learn.
What do I mean by that?
Machine learning and deep learning,
the way machines learn is...
By making errors.
There's something, a technical concept
in machine learning called a loss function,
or a cost function.
It's basically the difference between
your predicted output and ground truth,
and then there's some sort of optimizer
that says "Okay, you didn't quite get it right.
"Try again."
Make this adjustment. >> Get a little closer.
>> That's how machines learn, they're making
lots and lots of errors, and there's something
behind the scenes called the optimizer,
which is giving the machine feedback.
That's how humans learn.
It's by making errors and getting
lots and lots of feedback.
That's one of the things that's been absent
in traditional schooling.
You have a lecture mode, and then a test.
>> Jeff Frick: Right.
>> So what we're trying to do is incorporate
what's called formative assessment,
this is just feedback.
Make errors, practice.
You're not going to learn something,
especially something that's complicated,
the first time.
You need to practice, practice, practice.
Need lots and lots of feedback.
That's very much how we learn
and how machines learn.
Now, the differences are,
technologically and state of knowledge,
machines can now do many things really well
but there's still some things
and many things, that humans are really good at.
What we're trying to do is not have machines
replace humans, but have augmented intelligence.
Unify things that machines can do really well,
bring that to bear in the case of learning,
also insights that we provide.
Instructors, advisors.
I think this is the great promise now
of combining the best of machine intelligence
and human intelligence.
>> Right, which is great.
We had Gary Kasparov on
and it comes up time and time again.
The machine is not better than a person,
but a machine and a person together
are better than a person or a machine
to really add that context.
>> Yeah, and that dynamics of,
how do you set up the context
so that both are working in tandem in the combination.
>> Right, right.
Alright Alfred, I think we'll leave it there
'cause I think there's not a better lesson
that we could extract from our time together.
I thank you for taking a few minutes
out of your day, and great to catch up again.
>> Thank you very much.
>> Alright, he's Alfred, I'm Jeff.
You're watching theCUBE from the
Corinium Chief Analytics Officer event
in downtown San Francisco.
Thanks for watching.
(energetic music)