>> Narrator: Live from San Francisco,
it's theCUBE covering RSA Conference 2020 San Francisco,
brought to you by Silicon Angled Media.
>> Welcome back to theCUBE coverage here in San Francisco,
the Moscone Center for RSA conference 2020
for all the coverage we've been here for three days.
I'm John Furrier, host of theCUBE.
As cyber security goes to the next level,
as cloud computing goes, continues to go,
more enterprise, large scale.
AI and machine learning have become critical in
managing the data.
We got a great guest here from Intel Casimir Wierzynski,
Senior Director of the AI price with Intel.
Thanks for joining us.
>> Oh, thanks.
>> So a huge data problem when it comes down
to cyber security
and generally across the enterprise now.
It's well known, well documented
but you're here giving a talk
about machine learning privacy
because everyone wants to know who the bad guys are.
(chuckles)
Do the bad guys deserve privacy?
Okay, we'll get to that later.
But first, tell us about your talk,
you gave a talk here at RSA.
We'll get into the other stuff later.
>> I gave a talk.
So thanks for having me,
I gave a talk on a whole suite
of exciting new techniques known as
privacy preserving machine learning.
So this is a set of machine learning techniques
that help people realize the promise
of AI and machine learning.
But we know that machine learning systems rely
on underlying data to train.
So how can you also respect the privacy
and the security of the underlying data
while still being able to train and use AI systems?
>> And just to explain, where are you within
the Intel sphere
because Intel obviously serves
obviously chips and power to all the enterprises
and large scale
but are you on the software side AI group.
Explain where you are within Intel?
>> So I'm in the AI group at Intel
but I have the most fun job at Intel I think,
because I work in the CTO office of the AI group,
which means I get to think about more futuristic,
where is AI going?
What are some of the major inflection points?
One of these that we've been looking at
for the last couple years is this kind of collision course
between the need for data to train machine learning systems,
to unlock all the power of AI
but still the need to keep data private.
>> Yeah and I think that's generally consistent
with our editorial and our research,
which is the confluence
of cloud native, large scale, cloud computing, multi cloud
and AI, machine learning, all kind of coming together.
Those are multi generational technologies that are coming.
So this wave is big.
>> That's right.
And I think one thing that's kind of maybe underappreciated
about machine learning,
especially in production,
is it's almost always a multi party interaction.
So you'll have let's say,
one party that owns data
and other party may own a model.
They're running a system on somebody else's hardware.
So because of the nature of digital data,
if you want to share things
you have to worry about what other parties may be doing
with those data.
>> Cash, you bring up a great point
I want to get your reaction, thoughts on is that
it's multidisciplinary now.
>> Oh, yeah.
>> And as people aren't breaking into the field,
people are really excited about AI.
You talk to someone who's 12 years old,
they see a Tesla, they see software,
they see all these things,
they see all this cool stuff.
So machine learning which powers AI is very enticing
to anyone that's got a technical or nerdy background
and so it's attracting a lot of young people.
And so it's not just getting a computer science degree,
there's so much more to AI
because talk about why?
What someone needs to be successful
to engage in the AI wave?
You don't need to just be a coder,
you could be outside the scope
because this thing it's an integrated model or is it?
>> It's very much so,
my group at Intel is very, very heterogeneous.
So I've got kind of mathematicians
but I also have coders,
I have an attorney who's a public policy expert.
I have cryptographers,
I think there's a number of ways to get involved in meaning.
My background is actually in neuroscience.
So -- >> Makes sense.
You can stitch it all together.
>> Yeah.
>> Well, societal changes has to be
the algorithm needs training, they need to learn.
So having the most diverse input seems to me
to be a posture the industry is taking.
Is that right?
Is that the right way to think about it?
How should we be thinking about
how to make AI highly effective versus super scary?
>> Well, one of the efforts
that we're making part of my message here is that
to make these systems better,
generally more data helps, right?
If you can expand the availability of data
that's always going to help machine learning systems
and so we're trying to unlock data silos
that may exist across countries, across organizations.
So for example,
in health care, you could have multiple hospitals
that have patient data.
If somehow they could pull all their data together,
you would get much more effective models,
much better patient outcomes
but for very good privacy reasons
they're not allowed to do that.
So there's these interesting ideas
like federated learning
where you can somehow decentralize
the machine learning process,
so that you can still respect privacy
but get the statistical power.
>> Let's double down on that for a second,
because I want to explore that.
I think the most important story
that's not being talked about
because it's nuanced a little bit.
Healthcare.
You had HIPAA. >> Yeah.
>> Which was built for all the right reasons back then.
But now when you start
to get into much more of a cross pollination of data,
you need to manage the benefit
of why it existed with privacy.
So, encryption, homomorphic encryption,
for instance. >> Yeah.
>> Data and use. >> Yes.
>> When it's being used not just in flight or being rested,
becomes now you have the three triads of data.
>> Yes. >> This is now causing
a new formula for encryption, privacy.
What are some of the state
of the art mindset thinkings around
how to make data open,
usable, but yet either secure, encrypted or protected?
>> That's right.
So it's kind of this paradox of how do I use the data
but not actually get the data?
You mentioned homomorphic encryption.
So this is one of the most kind of leading edge techniques
in this area where
somehow you're able to,
there are ways of doing math on the data
while it stays encrypted
and the answer that comes out is still encrypted
and it's only the actual owner of the data
who can reveal the answer.
So it seems like magic.
But with this capability,
you enable all kinds of new use cases
that wouldn't be possible before
where third parties can act on,
your sensitive data
without ever being exposed to it in any way.
>> So discovery and leverage of the day,
is that what you're getting at?
In terms of the benefits, the use cases,
so staying on that, the use cases of this new idea.
>> Yeah.
>> Is discovery and usage, how would that work?
>> Well, so when we talked about federated learning
and pooling across hospitals,
that's one set of techniques.
Homomorphic encryption would be, for example,
suppose that some AI system has already been trained
but I'd like to use it on sensitive data.
How do I do that in such a way that
the third party service isn't, you know?
>> John: Yeah, yeah.
>> What makes I think machine learning different
from different types of data, security problems,
is that machine learning, you have to operate on the data.
You're not just storing it,
you're not just moving it around.
So how do you?
>> And this is a key thing.
So I got to ask you the question
because one of the things
that's a real interesting trade off these days
is AI and machine learning is really
can create great benefits.
But also people just go the knee jerk reaction of,
"Oh my god, it's scary, my privacy."
You saw front line with Amazon
just facial recognition,
"Oh, my God, it's evil."
>> Casimir: Yeah.
>> So there's a lot of scared people
that might not be informed.
>> Casimir: Yeah.
>> How should companies invest in machine learning and AI,
from your opinion?
And how should they think about the next 10 year trajectory,
starting today, thinking about how to invest,
what's the right way to think about it, build a team?
>> Casimir: Yeah.
>> What's your thoughts on that?
Because this is a number one challenge right now.
>> Yeah.
Well, I think the some of this scary issues
that you mentioned,
there are legitimately scary,
they're going to have to be resolved.
Not by companies but probably
by society and then kind of our delegates,
our lawmakers, regulators.
Part of what we're trying
to do at the technical level is give society
and regulators a more flexible set of tools around
which you can slice and dice, data privacy and so on.
So that it's not just all or none.
I think that's the kind of my main goal.
As an organization,
I think, again,
this idea of having a heterogeneous set of talents,
you're going to need policy experts
and applied mathematicians
and linguists and neuroscientist,
>> So diversity is a huge opportunity.
>> Casimir: Very much so.
>> Not just diversity of people but diverse data.
>> Diverse data, diverse kind of mindsets, approaches to
problems that are hard,
but very promising if solved.
>> Okay, let's look at the other side of the spectrum,
which is what should people not do?
What's a failure formula?
One dimensional thinking,
what's an identification of something
may not go in the right way?
>> Well, you know, one distinguishing feature
of the machine learning field
and it's kind of a cultural thing
but it's given it a lot of traction
is it's fundamentally been a very open culture.
So there's a lot of
sharing of methods.
It's a very collaborative academic field.
So I think within a company you want to kind of
be part of that culture, too.
So every company is going to have its secret sauce,
it's things that it needs to keep proprietary
but it's very important for companies
to engage this broader community of researchers.
>> So you're saying
which maybe I would agree with,
but I'm just say you can agree or disagree.
To be successful, you got to be open.
>> Yes. >> So if you're data driven,
you got to be open.
>> Casimir: That's right.
>> Because more data equals better data.
>> That's right, more data,
more approaches to data, kind of more eyes on the problem.
But still, you can definitely keep your proprietary.
It kind of forces organizations to think about
what are our core strengths
that we really want to keep proprietary,
but then other things let's open up.
>> All right, so what's the coolest thing
you're working on right now?
What are some of the fun projects you guys are digging into?
And you got a great job,
sounds like you're excited about it.
I think is the most exciting thing.
I wish I could be 20 again in computer science
or whatever field
because I think AI is more
than multi-generational thing,
it's super exciting, as a technical person,
but what are you working on that you're excited about?
>> So I'm very excited about
taking some of these things like homomorphic encryption
and making them much more available to developers,
to data scientists
because it's asking too much
for a data scientist
to also be kind of a post quantum crypto expert.
So we've written
an open source package called HE transformer.
HE for homomorphic encryption,
it allows the data scientists
to kind of do their normal data science in Python
or whatever they're used to.
But then they kind of flick a switch
and suddenly their model is able to run on encrypted data.
>> Can you just take a minute to explain
why homomorphic encryption trend
right now is really important?
Give it a peek into the why,
because this is something that is now
becoming much more real.
>> Casimir: Yeah.
>> The data in use kind of philosophy.
Why now?
Why is it so important right now?
>> Well, I think
because of cloud and the power of cloud
and the fact that data are collected in one place
and possibly processed in another place.
You're going to have to,
your data are moving around and they're being operated on.
So if you can know that
as long as my data moving around
and people are operating on it
but it's staying encrypted the whole time
not just in transit,
that gives a much higher level of comfort around.
>> And the applications are going to probably be on boarded.
You'd almost imagine new applications will emerge from this.
>> New applications. >> Discovery, cataloging,
API integration points.
You could almost imagine the trust will go up.
>> And you can also kind of end up
with these different business models
where you have entities that compete in some spheres
but they may decide to collaborate in other ways.
So for example,
banks could compete on lending
and so on their normal activities
but in terms of fraud detection,
they may decide,
"Hey, maybe we can make some alliance
"where we cross check with each other as models
"on certain transactions."
But I'm not actually giving you any transaction data,
so that's maybe okay.
So that's a very powerful concept.
>> It's really interesting.
I think the compute power has allowed the overhead seems
to be much more robust
because people are working on this for
in the 80s and 90s, I remember.
>> Casimir: Yes.
>> But it was just so expensive overhead was.
>> That's right.
You bring up a great point here.
So and this is one of the areas
where Intel is really pushing, my team is pushing,
these techniques have been around for 20 years.
Initially, they were maybe like 10 million times slower
than real time.
So people thought,
"Okay, this is interesting mathematically
"but not practical."
There have been massive improvements
just in the last two years
where now things are running
100 times slower than kind of unencrypted math.
But still that that means that something
that took
would take 50 milliseconds now takes five seconds.
That's still not an unreasonable amount of time.
>> All right Cash, you're my new friend now
my best friend on AI.
And I got a business to run.
And I'm going to ask you,
what should I do?
I really want to leverage machine learning
and AI in my business.
>> Okay. >> I'm investing in more tech,
I got a cloud, I'm building my own software,
how should I be investing?
How do I build out a great machine learning AI team
and then ultimately, capabilities?
How should I do that?
>> Okay, well I would start with a team
that has a kind of a research mindset,
not because you want them to come in
and like write research papers
but the path from research into production is
so incredibly short in AI.
You have things that are papers one year
and they're going into production at Google search
within a year.
So you kind of need that research mindset.
I think another thing is that you want to.
You're going to require a very close collaboration
between this data science team
and your CIO and kind of systems and
a lot of the challenges around AI
are not just coming up with the model
but how do you actually scale it up and
go to production with it.
>> And interesting about the research,
I totally agree with you,
I think you almost call that product management
kind of new fangled product management
because if it's applied research,
you kind of have your eye on a market generally.
But you're not making hardcore product decisions,
you're researching and you're writing it,
you got to do the homework.
Dream it before you can build it.
>> Well, I'm just saying that the field is moving so fast
that you're going to need on your team,
people who can kind of consume the latest papers.
>> John: Oh, you saying consume the research as well?
>> Yeah. >> Not just writing.
>> If they can consume it that's great, too.
I think this is this kind of open culture where,
people consume, they find some improvement,
they can then publish it at the next year's conference.
It's just been
this incredibly healthy ecosystem.
>> And software acceleration is a big part of with cloud.
Awesome. >> Yeah.
>> Well, I really appreciate your insight.
This is great topic, I could go for an hour.
One of my favorite things.
I love the homomorphic encryption,
I think that's going to be a game changer.
I think we're going to start
to see some interesting discoveries there.
Give a quick plug for Intel.
What are you working on now?
What are you looking to do?
What's your plans?
You guys hiring, doing more research?
What's going on?
>> Well, so we think that
this intersection of privacy and AI is kind of
at the core of Intel's data centric mission.
So we're trying to figure out
whatever it takes to enable the community
whether it's
optimized software libraries,
it could be custom silicon,
it could be even services where we really want to listen
to customers figure out what they need.
>> It's so funny Moore's law is always going to be around
the next wave is going to have more compute,
it's never going away.
It's more storage, more data.
>> Casimir: Yeah.
>> It's just gets better and better.
>> Casimir: Yeah.
>> Thanks for coming on Casimir,
I appreciate it. >> My pleasure,
thanks for having me.
>> We have Intel Inside theCUBE,
breaking down the future of AI.
Really exciting stuff on the technology front,
security day, that's all going to happen at large scale.
Of course, it's theCUBE bringing you all the data
here at RSA.
I'm john Furrier, thanks for watching.
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