In this 349th episode of data driven, we are pleased
to interview Pavel Goldman Khaledin, where he's the head of artificial
intelligence and machine learning at Sumsub.
Sumsub isn't your average AI startup. They're
globally recognized for their work in k y c, AML,
and anti fraud technologies. Our guest is the
wizard behind the curtain, crafting tech to outsmart financial
fraud does and deep fake artists. Quite the
digital Sherlock Holmes, if you will. Now here are
Frank, Andy, and Pavel.
Hello, and welcome to Data Driven, the podcast where we explore the emergent
Fields of data science, artificial intelligence, and,
of course, data engineering, which is basically the underpinning of it
all. And with me on this, journey is my favorite data
engineer of them all, Andy Leonard. How's it going, Andy? Good, Frank.
How are you? I'm doing alright. We we were recording this, the day
after we did a 2 hour show,
Kinda by accident, don't I see our guest in, it look kinda had this
look of, uh-oh. No. It's not gonna turn. I can't do that today.
But we are very excited here to in spite of our issues with Microsoft
Bookings, in spite of our crazy hectic schedules, And in
spite of your allergies and, really tasty jelly jam and
and and biscuits Really sorry about that. No.
I I don't know what it is on the East Coast this week, man. It's
it's well below freezing, and I'm sneezing. Oh, that rhymed.
Allergy station should be over for me. I don't know what's going on. For real.
But our guest is actually, from Berlin,
and one of my favorite cities in the world. In fact, they were singing the
virtual green room. Had I lived in Berlin instead of Frankfurt, I probably
never would have come back to New York,
or the US, but he is
our guest today is Pavel Goldman Kaledin. Hopefully, I said that
right. He is the head of AI and ML
at Sumsub, a global know your customer anti
money laundering, anti fraud company, and,
we're we're welcome to we're happy to have him. Although, I don't think he's in
Berlin today. I think he's somewhere a bit warmer. Welcome to the show, Pavel.
Yeah. Hi, guys. Happy to be here. Good. Good. So I have
a lot of questions. You know,
first off,
I think I can kinda see the map, but What's the
connection between know your customer, KYC,
anti money laundering, and anti fraud? I think I think
I see it, but I wanna hear you you kinda walk me through it because
I haven't had enough coffee either today. So so what's the, like, what's
the common thread? Because, like, because I I've not seen those 3
kinda put together in kinda 1,
sentence, but I can kinda see why. But
I I I I can try to explain. But the thing is and we actually
this is what we focus on. So we try to secure as a company.
We try to secure the whole customer journey from
onboarding. So this is the first step of when, for instance, like, I'm in a
bank. So So I want to onboard some of my customers, and I want to
make sure that this has real persons, for instance, that are not fraudsters.
So I want to onboard them, make sure they are,
that person, they actually pretend to be. And then and
here's the thing. If I can, for instance, like, I'm a Journey
person. But a month later. There could
be some, you know, strange patterns of, you know,
financial transaction happening. So probably, there are some sort of a pattern of
money laundering. So this is where transaction monitoring comes.
So you can actually this is a person. So this is but knowing customers are
very simple. You can actually I mean, you
can So basic basic attack is to be just pretend to be,
a person. You you are not, basically. But then even if I'm
not, I'm just a real person, I can actually, yeah, come up with some sort
of, you know, few things to
do. And then where just we try to monitor it, and then from a permit,
make sure that, Okay. We can actually flag the transaction and
then make sure it's it's it's getting looped. And then, I mean, there is a
flag raised, and then, Probably, we can do
something about that. This is just, like
this. If we're talking about anti fraud, and here's the
thing. Sometimes it's very easy to see that something fish is
happening. So for instance, like, A very like, 2 years ago, it
was a very typical attack. So I tried to, you know, open a bank
account or, like, remotely, And I actually, I'll leave somewhere
else, or I don't I I use a stolen document. What
what I can do To do that, I can actually just print out the
image of a person and just try to make sure that actually the
KFC provider like us Tried to make us
believe that I'm a real person. That was a very, you know, typical attack 2
years ago. Now it's very easy to detect. Still peep some people use
it. And that's it. And that's that for us. It is very easy to do
that. But probably, I mean, this is not a real person. Some of you trying
to use the printed out images. This is
Fraud. We can actually or reject it or or ask a person. Can
you well, I mean, we need your real real pay real real image.
Or we can just tell our customers that, you have to take a look because
there was something fishy going. And then it goes and goes and goes. And the
whole customer journey, We try to make sure that the fraud is not happening. This
is basically it. So
fraud is kind of, I think, Cyber fraud or whatever the cool
kids call it, I think is has has infected
every industry. I mean, if I just I
mean, I I get 2 factor authentication logging in the
roadblocks, like, for my kids. Right. And I'm like,
they'll they'll they'll they'll get in front of their device, and they'll be like, can
you tell me what the passcode is that they texted you? Like, Sometimes
some days it's the only way I see 1 of my kids. But,
has the because I I wonder, like, has the pandemic kind of Accelerated
kind of virtual fraud, or is that just independent?
I think it I think it is. Because it, right now, it's but it's not
Related to fraud. Exactly. But the thing is is that now
people are used to actually work
remotely, Or it's so it's not that common for you
to go to bank in person. So you just call there. You just I
mean, use over the internet, basically. It's like easier
So and now you can actually, there is no way, you can actually verify
that this is the only person. Right. Yep. And this is a final thing because
for instance, in Germany, where I reside, most of the
time. There is a regulation called it's called
video ident. So for in Germany, in order For for
me, if you are going to open an account, anyway, I really have to call
a person, a live in person operator, And talk to him, and he makes
sure that or she makes sure that, a a million person. But everybody
do not like it, basically. Because, I mean, it it takes time. You have to
talk, talk to a person. I I just want to open an account. So it's
it's it's it's fast as I'm but but except Germany, all of the rest
of European Union, I think across the world as well. It's, I mean, you
just Send your image or video, some of your documents,
and then the the account is up. So it's very easy. And people get you,
getting used to it. And that's why it's easier to to to actually,
do fraud because it's, I mean, it's it's a soldier to trade off,
Make it easier, and then it's easier for fraudsters to actually do their business. So
that's that's the thing. Gotcha. Do you see,
you mentioned you see, Like, new scams, people
are running as well. And you also mentioned a lot
of what I I thought would be pretty effective ways to to
combat those scams, without really
giving anybody any ideas. Are there, like, brand new
scams that have happened maybe in in the very recent past
that, you're still working on ways to combat?
I must say that, there is there will always
be some sort of, you know, arms, right.
Competition? Yeah. So you have to say or. There will always be,
like, a new prod Of yours.
And then we have to actually deal with that. But I can tell you a
story. So for instance, like, so we asked him so not a big company. Yeah.
The technology team is not that So big, we have to move fast. But
in my team, the AI slash, ML, it's not
anti money laundering, but artificial intelligence slash machine learning. We have
a very small department aimed at creating defects.
So we do not detect defects. We have to actually learn how to create
them So you actually know how I mean, how people actually read Oh,
that makes sense. So synthetic data. Interesting. Yeah. Yeah.
And this is at and I can also tell you that I mean, and this
is for me, it was, like, so sorry if, you know, a surprise because,
Most of the like, let's talk about defects. So, yes, then what what is like
recent type of fraud? Deepest, for sure. We had a report. I
I think it, We published it 3 years 2 days ago or like
yesterday on friends. So what's actually happening right now?
And the thing is that deep fakes, They use usage of
defects for fraud. It maybe it rest
like 5 times. So like 2 years ago, like nobody actually
knew so About defects. But now it's it's very easy to craft. It's
very easy to craft. I mean, people like I mean, you are a fraudster. You
have to actually, it's very rare
prefer for you to just craft just 1 defect. It's usually something
we call the serial fraud. You create like hundreds of defects. So now it's easy,
very easy to create them. So now it's like a craft, like, hundreds
of identities. And then I tried to bypass our security checks. So that's why this
is like the recent trend. I mean, as so it's on the news,
basically. And then we have to actually try to make sure that our solution,
can detect it. And it's not sometimes, it's not that easy. Well,
it sounds like, you know, there's there's stuff that people used
years ago, and you've got that figured out. And it's probably not being used
as much, at least alone. But now you've got,
people coming up with, first, new ideas, and then second, they're
doing combinations new plus older ideas. Is that
accurate? But but, it is actually. And the thing is Okay. So,
these are also like, Okay. Just imagine. We have a very
sophisticated deep fake detector. So I I'm pretty sure that our, like,
models are more or less, good. So, like,
I mean, it's not 100% for sure. Mhmm. But what
happens next? So can I actually, I mean, combat defects, 5
years later? Maybe it's I'm so advanced. I so make like,
our customers, like, ask us about it, like, once in a
month. So what do you actually what is your plan, to talk about defects
in 2 years. Right. Because now, you know, AI is like, it's very hard problem
to solve. But here's also problem. There is a thing
called mules. Have you heard about mules or money
mules? This is, the the thing is
that you actually go, hire a person.
Usually, buy, pay some €50. And then
actually this person passes a KVST check for you.
And then Oh, wow. The person just sells sells here his or her
account to you. And then this is a real person. I mean, it's not a
defect. I found it that I could defect. Wow. It's not obvious and not defect.
Yeah. But that well, this is that looks suspicious. But
but I if I'm in a bank, I'm in a I'm a bank, for me,
it's like a real person just trying to open up in a bank account. Yeah.
And now we actually have to look around. So that's why so I
like working with Deepgrams. I mean, it's very, you know, cool technology. You have to,
like Yeah. It's technology. But Now you actually have
to look around. You have to make sure what is, I mean, the
pattern. What are the devices do you use? It's like lots
of small Features or, signals, you have to actually
combine or merge them altogether and then make a decision. Is it, like,
specia or suspicious sorta? And this is like, but this is fun. This is
like, you have to really look around, look collect lots of data, and then try
to find, you know, your way into making a decision.
Interesting. It's it's it's a fascinating the simple things are no
longer simple. Right? Just signing up for an account, You know,
it's just now it's become like this massive multinational worldwide
cyber Security kind of exercise. It's a
fascinating, Yes. For a
customer, it is it must remain easy. Yes. I don't know like
I mean, since, like even, you know, the really, really
typical KBC check is includes recording your
video. You usually have to do something like, you know, turn your head
or something. I mean, if you have this experience. People do not like it. For
them, it's like, why do you have to do this? That's it's it looks strange.
I mean, just can I just open an account? And then it's like so it's
also trade off unless you have to be simultaneously
secure and busy. And this is Yeah. Those those are
those are very much contradictory, forces. Yeah.
Well, the other thing too, like, if I'm if I'm If I'm an average
customer or paranoid me. Right? Like, if I go to a
thing and they want me to look this way, look that way, Am I training
their deep fake model of me? Do you know what I mean? Like, I mean,
I'm kinda like, you know, obviously, I've done a lot of live streams and stuff
like that, so I shudder Better to think what you know, where that could lead.
But, what are your thoughts on that? Like, I mean, are do do you have
people who are Do savvy customers
do they get a little suspicious? Like,
what are your thoughts on I'm not. I I
must said that I mean, the defects that we see, they they
can be crafted just for 1 1 image. Right. So like,
here's the problem. So so like, there are, none of that, I mean,
you can see them, but Usually, people send, you know,
low quality images. So it's even harder for us to see it. Even harder for
for human person for human to see that this is a problem.
But there is also, I think, if I find a story that I
know, that some of our models
actually detect defects better than humans. So
it's actually easier for a fraudsters to treat a leading
person than a model. This model, like, can look back from certain artifacts with
eyes or just, like, some sort of, you know, glitches.
It's easy. But for person, especially the quality of the image is It's bad.
It's like there is no way anybody can actually spot this is the
problem. And this is great. It it is a problem. I I I must I
must admit this is, I think, this is what we
actually have to be have to hear
about about creating deep fakes. I know that that
is a very interesting thing. So, you know, about I mean, there are lots of
things happening, around AR regulations, Especially in the
European Union. Sure. And then so we actually tried to follow and then to
make sure that everything is compliant. And actually, I wanted to say that we touched
upon k y c KYT, which is know your
transaction. There was also KYB and all your business, which is basically, you
know, how we make sure that the company you work with is is
I know fraudsters. And there is also a thing called k y
a I, know your AI. And it says about transparency.
So many people out there want to be to know actually how AI is used.
So the k l it's it's a very new trend, I think. You have never
heard about it because, I mean, it was going to be a week ago. Since
I like, I want to actually know what's happening with all of this model of
error, not just about touch prod, ground everywhere. But back
to the problem with defects. The thing is,
what to to say that,
Oh, sorry. I lost the my my train of thought. But this is the all
the time. Yeah. We I was just about to say that. But what you know,
one solution to this, I I think, Pavel, would be
if people did something, you know, like, I don't know, colored their
hair Or grew a cool beard. I'm just
throwing that out and with apologies to people listening and not
watching. No. You know? I'm just
saying. But but if you did but if you did
grow a beard, would would or or or change your hair color or
altered their face? Like, I know that, like, facial most facial recognitions
use landmarks on, like, the eye sockets. Right. The a lot harder to change I
was joking. Didn't mind. But, like, would it would it would that
I don't know. Like, does that have any impact on these kind of systems or
are they more like facial recognition systems? They are,
it's, so we operate on the if you're talking about defect detectors or
defect, models for defect detection. Yeah. There are
some, I can't say that I face recognition. The
models, they mostly focus on artifacts. So so for
instance, like, a defect of a year ago, usually,
had problems with eyes. Your eyes of a defect, they usually are
very, you know, not really human.
So it will be changed. It will be like as as as the technology,
is getting more advanced. But like a few years ago, you can actually just crop
Eyes of an image of a person, pretending to be a human person, then they'd
make sure that this is actually a defect. Also I must say that
Yeah. So a video is is is easier to detect because you can actually
so, there is a thing called, I don't like the term in blindness because
No, but nobody actually know what Linus is, but Linus is a detection.
Linus detection is detection. If this is a
leading person or not. And before, like, 5 years ago, it was
mostly a distinction between, a video of a person or
a printed out image. Now it's a detection of an image,
defect, and the linear person. And at that time,
you actually there are 2 types of fly misses. One tool that's passive,
and we actually use also sometimes our customers actually ask us for
pacifying. Let's adjust 1 image. But it's easier for
us and for everybody else to ask a person to actually do something.
And for defects, for instance, like, if I ask them to rotate, Sometimes some
artifacts can appear. Some artifact. And then you can actually see that probably. I
mean, this is not the only person. There are some sort of problems with visual
artifacts. So it is it is like this.
Also, I must say that there was also a challenge for us because there
are, certain cameras. They have some sort of a
beautifiers. So I'm pretty sure as I'm calling from my,
my computer, and then my camera actually
Advances my image. So my image is a little bit, better
than I'm in the real life. So my my skin is is is a little
bit better. So it's it is actually, Embedded into
hardware. And for us, it looks like, some sort of, you know so there is
a signal for us. It does some sort of, you know it's Oh, I see.
So It's hard. You know? And you have to make sure that make sure that,
okay, it's not defect. It's just the person using that, camera off my,
computer. It's like, you know, you have you have to be really, a
yellow error. Apple, I mean, installs
another camera, and then you have to be actually tune your models to make
sure that you actually do not penalize people from with
I think about that. Yeah. The cameras are gonna behave differently if you use different
cameras. So I'm here using my 4 k,
camera. Kind of an outdated one, but it's still it does the job. But what
if I pick up my droid Or, you know, my wife
my wife, you know, she's the the device. She's got an
iPhone. And if I'm trying to log in through her device, That would be different
images, and it may change. You know, it may tell me, nope. That's not
you. Those are gonna be different artifacts. That's fascinating. And I also
think it's funny that you have an old four k camera, which
is a pretty funny thing to say. Like For for podcasting, I won't
No. I know. I don't wanna throw back to, theme from yesterday's
2 hour show, but I'll just make this note. We we
learned that we're in the top 2 a half percent of podcasts.
So now I feel like I should have, I don't know, 16 k studio
and Yeah. I should have a lot of time like Joe Rogan has in a
brick wall. Exactly. Right. I don't I need something better than this
old four k camera. But
if all of a sudden You just want to open a bank account right
now. Yeah. It looks strange because, I mean, a typical person is like you
use your iPhone or you're like a regular computer. Like, with 4 k or 16
k camera, it's like very strange. It's some something, you know. It's it's a signal
for for every model and make sure that It's an outlier. Right? And
it sounds like a big this is still obviously, there's way
more complicated things than what you do, But outliers
detecting outliers is probably 1 1 big tool in your tool belt.
It is. Yeah. That's very hard if you have a Genuine person,
and you are an outlier somehow. I mean, everybody can be an
outlier in some sense. It's very hard because, yeah,
So this is hard. So, like, at some point, yeah, colored hairs
can be also an outlier. I don't No. It's just interesting. So I imagine, like,
Instagram filters and things like that probably also cause
chaos and things like that. Yeah. Of course. But, yeah, I
mean So usually use, yeah, filters,
a strong signal for us. I mean Right. And also I must I must have
this defects. So going back, thing with defects is that
it's not, like, specifically use the fraudsters. Here's the
problem. You know, there are lots of cool things for defects. You can press
advertising. Right. I don't know what what else. But, usually,
you can actually adopt a person to, like, Replaced an
actor in the movie. This is also a defect. It's a very cool defect, very
sophisticated defect, very high quality defect. Still a defect. So those
are our usage is actually for for that, I mean, not just for fraud.
And then going back to our problems, it's like, I mean, And the
even even that and even that from that, I like this example,
but, the guys from the,
I mean so we focus on financial fraud. Yeah. So it's more or less like
people trying to actually sue money on, like, take over your account, something like
that. But the thing is the defects, they are mostly created
not for that. And this is a very interesting thing, I think. They are created.
And, actually, I didn't know about that, but we actually knew that When they started
to try and to create our Deepak's. So we went, you know, to the Internet,
some strange forms to make sure what what people actually use
What they create deep eggs for. And they create
deep eggs for porn. It's like 98%, 89%
Deepex, I slide 4. And this is also a problem because in in there is
a thing called nonconsensual port. Deepex are used for that, And this
is also a problem. So it's not our business, but the thing is that the
same technologies is there. And you actually I mean, if you,
I mean, work in the area, you can actually so the same model can actually
be applied to detect, this type of defects. Right. So it's
different, but, I mean yeah. Yes. It's, That was expressed to
me maybe a year ago. It's fascinating how
quickly this space is just Evolving or
devolving, I guess, depending on your point of view. Yeah.
But, no, you're right. Like, most of it is
Those a lot of the deep fake kind of work is done
for adult content. And, you know, and it's there
the The legislation around this is gonna vary
widely from place to place. But, like, you know,
revenge porn laws don't apply. And there. I I think that was a big thing
in, and there was a controversy somewhere. I think it
was New Jersey, Where somebody had
created deep fake images of either high
school or middle school girls, which adds an extra level of
legal Concern I have a whole lots of extra
levels of concern. Let's be honest. But, like, you know and and and and
there was this, you know, the big debate. And my first reaction was, I'm
actually kinda surprised it took this long for that to happen,
which is a very cynical take, I'll admit. But I can tell I I can
tell you the reason. The thing is that Technology moves so fast. Yes. And
legislation actually is always, like
so even with with EAU, AI act,
those I mentioned defects just a little because they started working on
the regulations 2 years ago. And 2 years ago, it was not a problem.
And now it's, like, all over, you know, the Internet, and then you have to
actually tweak the, wording,
but it takes time. Well, even still, like, you know, like, there's,
a few months ago, they had these fake commercials that were created by with
combination of 11 Labs and A few other companies to name them, so I
forget. But, you know, they had a picture of Elon Musk, you
know, eating spaghetti, and it looked weird. But you can easily see,
like, You know, I was messing around with v q early versions of v
q grant d q GANs in early
2022, And that stuff looked
weird, and it it really evolved. And this morning, I saw
Pika AI, I guess, just went Yeah. Yeah. Yeah. Went to a wider beta.
And, yeah, released and and and, like, I'm seeing what's created with that,
and, you know, it still looks weird, it still looks cartoonish,
but it's not The fact that we've gone that far in the span
of, you know, less than 2 years, like, I think says something, like and to
your point, legislation Usually takes years, to
make. So, like, by the time these laws are written, they may not be valid.
In the case of New Jersey, I think there's some debate over,
does what sorts of laws that applies to? Because
the the original, The faces
were mapped on to something else, but that the
something else I'm trying to keep our clean rating here. The something else were
people over 18, but the bases were mapped onto it. So there's
some debate over, do existing laws cover that?
I'm not a lawyer. Don't look at me, and I'm not. But,
it's just fascinating to your point. Like, this is moving quickly.
Yep. It's definitely complicated. So we've
reached the point in our show, Pavel, where we, like to
ask a set of questions. They're in the chat. And
I'll start out, with the, the very first question.
How did you find your way into this field? Did this field find you,
or did you find it? Yeah. I must say I have a
story to tell. I just studied yeah. Studied computer
science at, university And I actually worked as a software engineer
at Motorola. You may remember this company, with
HQ in Chicago back then, for 5 years.
And then it was, 2011, which is, like, long time
ago, the very first, massive
online courses appeared. There was a one called AI class,
and it later turned out to be a Udacity. And there
was also a m l called ML class. It's a ML class. And
this now this Coursera. It's like 10 years ago. And I was like, okay.
Cool. I enrolled and actually, I pushed because it is like it was it was
hard. It was like, you have to really, be involved. And
then I felt like, okay, this is a cool thing. This is like a next
big thing for me and, like, for everybody else. It was like
12 years ago. So I quit my job, and I actually, so
at the same time, I started to try to run a small startup with my
friend, failed miserably. But I take, took my time, studied,
for maybe half a year, and then joined a small data
startup as a data scientist. And then it just
started there. So it's I think I I find, my way into
data. But Yeah. I don't know. So You want to
I'm sorry. Go ahead. I just I just say it sounds like you were very
intentional about finding your way into it. So that's cool. Yeah.
That's cool. And I see you were You were at VK for a while too,
which I've never seen VK, but I hear it's like a like
a Russian language version of Twitter slash Facebook. It used
to be. Yes. Yeah. Yeah. I don't I yeah. Obviously, now things are different, but
yeah. Yeah. Yeah. Yeah. I worked there for 5 years, a long time ago. Oh,
interesting. And, you know, if you're talking about the data, I mean,
the, where it's like the the place where you can
actually play with data. You can actually cool do many cool things.
Oh, yeah. Nice. Nice. And he's being modest. According to LinkedIn, he was director
of AI research, so he's super smart.
But, what's your favorite part of
your current job? Oh, I can't say it
could create some defects, but, it's not
it. I think
no. I mean, I would say that what I like is, they,
the the Samsung, Samsung is is now it's it's a product or any company. So
have our own own products, whether, like, a technology company, yet we have our
own product. And having that,
actually, our own product, Actually helps us, you know, I know what our
customer wants. Wonderful. I know the
data. So it's like, you know, I mean, you have to actually so you have
to look around. Okay. There is a problem with defects. I have to,
like, make sure that I mean, I had, I actually have to understand this. This
is a problem. And for many of our customers, I mean, I
don't I would not like to say that we have to educate them or actually
make make sure that they understand this is a problem with defects. And now we
have when they understand, we can actually help them with their their,
safety and security. One thing that this is, like, a little bit, I
mean, Clumsy answer, but I'm sorry if you know.
Yeah. Being closer to the product is is is is fun.
Oh, sorry. Cool. So we have 3 complete
sentence. And the first one is when I'm not
working, I enjoy blank.
Okay. Okay. Let me think for a while. There are many things I can
say. No. I can say no. This is I think of this as I can,
I can share? No. I I I I run or I can see job.
Mhmm. Oh, cool. Cool. I run-in the the ring marathon.
This is my Nice. There are Major Martins, like, 5,
6 Martins across the world. So that's New York, Paris,
London, Tokyo, Berlin, and,
London. Nice. Like, 6 so that Very So Berlin was my 1st major
marathon. So I ran it, this this September, and it was great. No. That's
awesome. That's awesome.
When you said Berlin, the first thing that popped in my mind was, Berliner
Kendall wrote, which is like this local kinda drink.
Yeah. Yeah. Yeah. Yeah. I know. That's like Yeah.
Yeah. But I prefer there is a it's a vehicle. It's
like a craft. Oh, yeah. From Berlin.
Right. But I talking about Berlin, so I run. It was
super fun, but, on my finishing picture, so
it's my me, Ryan. So close to Bernsberg. It's a
very central grid. Mhmm. And there is also a guy in the
bottle question. And and I
wasn't it was not slow. I wasn't slow. Yeah. There was a guy in a
huge ball, like, I still running, like, finishing with me. Like, so it was, Oh,
that's funny. That's fun. It's that's fun. That's funny. Very cool.
Next, complete the sentence. I think the coolest thing in
technology today is
blank. Oh, it's it's it's hard to say. Let me I'll just
think for a while. But, I mean,
I think that so my my area
seems like I expert a personally specified natural
language processing. So I know about language models. And,
actually, we had papers on language models, like, before they they
were super big. So, like, on tuning language models. Yes. I
found it really, really exciting that it in a
year, it went from, you know, research
Prototypes to, like, everyday product. This is Yeah. This was
a compelling. So, like, my parents used Chargebee PCs. Like, I mean, this
is like this is like a mobile phone. This is I mean, this is what,
like, some sort of a milestone, last year.
I think this is this is it. And he is that the actual unit
for main things. You can build products on on language models. And
this is also like. It's wild, isn't it? Like, you know,
and and it's captured everybody's imagination in in good and bad ways.
But, like, my father-in-law, you know, So he used to
say Frank works with computers. Now he says Frank works in AI.
Okay. You know? That's good.
But I also like we used to say machine learning. So now you have to
say AI. That's right. That's right. You have to say that data mining
core something. So it's like, you know That's right. It definitely would.
I wonder what it'll be next year. Who knows? Gen AI probably.
Probably. So our next one, complete this Regulate, I think. Oh, that's
right. Regulation. That's right. Regular. Our our last completes the
sentence is I look forward to the day when I can use technology
to blank. Uh-huh. I
can't it's hard to answer because, I mean, like, I
can't say it would be cool If I can, you know,
develop drugs. And then there are very cool startups for drug design
with AI. Yet, I mean, Just imagine we have
a a a cure for cancer, but Right. We have so
many diseases to care to cure. So let's say, I think I
hope Once we fix anything, then there is gonna
be a next, you know, next milestone for us to look forward. So I'm sorry
if, you know, there's never I hope there will be
no such date, I can say. Right. Right. That's
a good one. I'm pretty sure you will agree with me. Like Yeah.
Especially work with the technology. I mean So true. For sure.
The next question, share something different about yourself, but remember, It's a family
oriented well, not family oriented, but we like we we like it so that
you can list it with your kids in the in the car. Right? Like, That's
kind of a Yeah. Yeah. Yeah. And, yeah, and I live in Berlin across, very
close. There's a very, how to say, kinky club, which is Berlin.
Was that the the the tier garden? It's it's
it's a it's a it is family friendly. It's it's like the most family
friendly place in in in Berlin. You got some. Yeah. No. It's it's
called KitKat. Yes. What I can say.
I have, purple hair. Since last month.
I don't know. So I can say that I speak
a few languages, all of that. But, no, I'm I'm
joking. So I speak Japanese. I don't I don't Japanese, for a
long time. So I I can speak Japanese. I speak
English, obviously, Russian. My parents are from
Russia. And I also speak German. So I actually Studied
German for 2 years. So I actually studied right now. So I had, like, my
German classes 3 or 4 times per week, which is
let me just go. Sorry. So I hope in a year, I will be able
to do a podcast in German as well. Oh, Wendeschon. That is
not
Yeah. Yeah. And we just lost, like, We we just
looked at our analytics, and, like, most of our listeners are from English language countries.
So I think we just lost them. Maybe we
can attract new listeners. Oh, I like it. I like the way you think. We
wanna we wanna get to the top 2.4% now.
Our new goal. So,
Audible is a sponsor of the show, and I'm not sure if
Audible is big in Europe. I think it is because I've seen a lot of
German language audiobooks. It is a no. Okay.
So do you do you listen to audiobooks? And if so, you have a good
recommendation. Otherwise, we'll take a recommendation on the regular good Fashion
paper dead tree book. No. I have a couple. I think I can
give you a couple of examples. This is like,
I like this was the most, you know so so I'm so my
background is from many, places,
since Israel, Russia, and Germany in some extent. So
I would recommend, there is a very Good book. It is
in my opinion, this is very known, but not many people know about it for
some reason. It's called the good soldier's make. Okay.
Like it said, didn't Not heard of. About the, sort of
third world war by Oh, interesting. But this is
it's very good. Like, you can actually learn a lot about
Czech Republic, Germany, Austria in the beginning of
the, Last century. Oh, interesting.
Especially now, it's the very thing. It's called in the park. This is a very
good thing too. And it's very funny. It's like one of the funniest, books
ever written. And also the the second one, I have 2.
This called Arc of Triumph, by remark. Okay.
This is also about the pre war Europe, pre second World War
Europe, like, Southeast, years of the
last century. And this is also very, like, you know, you
really you really feel like what what was the I mean, living in
Germany and, France, during that time, it's very, very interesting.
So one of my favorites. So I can definitely recommend both of these
videos. Very cool. So audible detecting I'm sorry. I'm
detecting a history theme. Yes. Yeah.
Yeah. Yeah? Cool. There's a really good book. Since you live in Berlin,
you might like it. It's called Faust's Metropolis, and
it's about the history of Berlin from, like, you know, Almost
stone age time till Okay. Cool. You know, the 20
you know, early 21st century is kind of like And the basic
gist is, like, you know, a lot has happened in Berlin. Good.
Sure. Yeah. We all know the bad. Right? But, like, some good things have
happened, kinda everything in between. It's kind of it's an interesting look at, like, the
history of the city and how it apparently was built on a swamp or something
like that. Like Yeah. It's just, it's it's
interesting. And Audible is a sponsor of
Data Driven. If you go to the data driven book .com,
I think even the data driven book .com might work. Uh-huh. That was
a pronunciation joke. You'll get a free, on 1 free
audiobook on us, and And we'll get a kickback if you sign up for a
subscription. And finally, where can
folks find out about you, more about you, and what you're up to at Sumsub
And, some of the other things you you're up to.
What's up? My my connection was, Oh, where can folks find out
more about you and what you're up to? Oh, yes. It's,
yes. It's, it's a company. It's called Samsung. So Samsung dot com.
Also, like, what we have is, today is
with anti fraud. And you have to I mean, It's not about
all the product. It's actually about making people helping people
learn about, security. So how they can actually navigate the Internet or,
like, their life More safely. So we have a portal called
some suburb where we actually post a lot
of stuff on Making your Internet life,
can I say like this, safer? So, actually, I I advise you
to take a look, and then probably you'll find something interesting there.
We definitely will. And, any parting thoughts before
we end the show? Any final thoughts? I just
want to say, yeah, Just I was very happy to, to be here
and hope, it was Cool. Interesting. This is a great show. It's always good to
it's always good to kinda understand The the the intersection
of of AI data and security because some people still see
those as separate things. But I think as time goes on,
we're gonna I'm gonna we're gonna wonder how we ever saw it as separate
things. There are so many things to talk about that. Yeah. Yeah. Yeah.
Yeah. Well, awesome. Any parting thoughts, Andy?
No. Just a great show. Pavel, thank you for, for joining us.
It was our honor. Yes. Likewise. And we'll let
Bailey finish the show. That was some show.
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