Hey there, welcome to Data Democracy, a podcast where we explore ways to make data and AI
more accessible to everyone.
We do this by interviewing experts across industries, asking them how they think about
data, what are some of the challenges they face when it comes to data, and if they had
a magic wand and time and resources were not constraints, what kind of intelligence and
models would they wish to have.
We're honored to have Karina Namanis with us today.
Karina is an entrepreneur and a thought leader in the world of AI.
She is the CEO of Ukraine and US based computer vision company and a data annotation company
label your data.
Karina and her team are pioneering innovative solutions across industries from enhancing
agricultural practices through computer vision to improving national defense capabilities
with AI-driven projects.
I've been following Karina's work in the field of AI and computer vision and I'm
both inspired and fascinated by her journey.
I'm super excited to learn more from her philosophy on leadership and her vision for
the future of AI.
Karina, welcome.
Yes, thank you very much for such an extensive introduction.
I'm super happy to be here.
Glad we finally made it.
I'm super excited to share my thoughts and always offer a great chat about things that
I'm passionate about.
That's awesome.
We're so glad that we were able to meet and chat about all things data and AI.
Let's start with our journey.
Let's start with your journey.
I'm sorry.
How did you get where you are?
Thank you for the question.
First of all, my career started almost eight years ago in a company called Support Your
App, which now is another company of our company, Label Your Data.
I was only 18 at that time.
That's an easy math.
I'm 25 right now.
I've worked with a customer support consultant at Support Your App for two years, which helped
me to learn how to get along with different people and different levels of different problems.
Then I worked as an onboarding specialist for two years.
Then for the past four years of my career, I've been building Label Your Data.
Label Your Data started off an amazing spin-off idea of R2 co-founders Daria Leschenko and
Peter Bondariewski.
To be honest, I never thought I'd end up in a place like this at this time.
My background is in journalism, and I never thought I'd be the CEO of a tech company ever.
But when I'm usually thinking about this question, I think I got to where I am right
now thanks to two main factors.
The first one is that I was lucky enough to have the right people around me.
I was lucky to have Daria Leschenko as my mentor for the past six years.
She was the one who taught me everything I know about building and managing a company.
But she was also the one who saw the potential in me in my early 20s and was always giving
me the heads up that I can do it.
The second factor I think is my life motto, as I call it.
It stands like this, don't overthink and grab the opportunities presented to you by life.
I was, as I mentioned, 21 when I was offered to join a team of four young and passionate,
amazing professionals and to start building a company.
But then, after some time, I was the only one who made it till the end.
For me, it was definitely a scary experience, full of self-doubt at times.
But nonetheless, it's been super exciting.
Right now, I strongly believe in what we do in my team, in our mission.
This is what gives me the boldness to move on.
That's amazing.
I think that's a short recap of the story.
No, that's amazing.
Yeah, it's a fascinating story and I'm excited to learn more about it.
I love what you're doing at Label Your Data with data annotation and AI.
As AI picks up steam, data annotation becomes more important and will likely be widely adopted.
Can you please tell us more about what data annotation is and what you do at Label Your
Data?
Label Your Data, we'll label your data.
It's a data annotation company and we help AI enthusiasts, evangelists, designers, data
scientists and machine learning engineers around the globe to focus on AI development
while we take care of what's often considered not a sexy part of AI, which is humans in
the loop training, but it is essential for nearly any AI model training.
We work with applications in computer vision and natural language processing.
Over the four years of our operation, we have worked with more than 100 projects around
the globe and our current team is nearly 1,000 people.
But apart from services, we also built products.
One of them, which was launched late 2023, is a destination platform where anyone from
any background of any age, well as long as you're about 18, can register on a platform
and start earning money by training AI by doing simple and interesting data annotation
tasks.
It's super automated, easy to use and I strongly believe in our social mission there too because
it provides so much needed workplaces for people who for some reason cannot work full
time or who just want to earn extra cash for themselves or their family.
And there is another product that we're working on, but it's coming soon, don't want to give
you any spoilers, it's going to be out in a few months and you'll see it.
And then if we talk about data annotation, in simple words, it's the process of using
unstructured data and transforming it into a dataset which then can be used to train
machine learning models.
So for example, the basic example that I usually give is that think of a self-driving
car, imagine you want to build another Tesla.
So you want to teach it to distinguish between crossroads and just road marks or a squirrel
passing by the car or a pedestrian.
And so in order to do that, you need a dataset, you need to show the machine examples of different
situations where a squirrel in the pedestrian can cross the road.
But to machine the computer, the images are just a bunch of colored pixels that don't
really make much sense as they do to us.
So what we do, we build managed teams to go through those images and identify the objects
on them and assign them specific labels.
So say that, hey, this object right here is a pedestrian, this object right there is a
squirrel and then by feeding your model with so many examples of these, it starts to pick
up, you know, what are the similarities.
And then in the end, it was able to identify the pedestrian from a squirrel on its own.
But I'd say that this is the easiest example.
And since, as you said, AI is a buzzword, it's all over everywhere right now, everybody
wants, everybody's chasing AI applications in their work.
We are seeing fewer and fewer of these easy cases.
But we are seeing more and more projects with niche AI requests.
I think we're entering the era of niche AI products where, for example, LLMs are adapted
to oil and gas industry, or when you use radar and AI to navigate and explore the surface
of the ocean.
So yeah, that's in simple words, what data notation is, applications, I can go on and
on about all different kind of cases we have.
But as I said, it's literally everywhere right now.
That's awesome.
Yeah, absolutely.
And then the gen generative AI, I think there's been so much talk about AI and machine learning
that, you know, it's the place to be and the time to be in the AI space.
You're at the right time in the right place.
Can you talk more about, I know you did talk about a few of your projects and examples.
What are some of the real life projects that you're working on now at Label Your Data?
Yeah, well, we work with all kinds of projects where you need to use reinforcement learning
from human feedback, both computer vision and an OP.
So not only computer vision, we work with a lot with text and audio data as well.
And so far, we've been delivering projects in such industries as robotics, agriculture,
manufacturing, automotive, environmental protection, advertisement, entertainment, hyper
automation, civil engineering, fintech, the least goes on and on.
But there are two I'd say most fascinating industries that I probably would want to talk
about.
Number one is universities.
We work a lot with different universities.
We have collaborations with Yale, Carnegie Mellon, Miami universities, universities from
the Middle East.
And because we get the opportunity to work with young researchers on their PhD projects
or their experiments and researches, the variety of these applications where they want to use
AI never seems to surprise me.
So we have, for example, been working on a landfill detection using satellite imagery
with one university.
Well with another one, we've been annotating the drone imagery of ships in Australia, because
apparently the student wanted to develop an autonomous drone shepherd.
So it was a pretty fun project to work with.
So I think that AI is disrupting the industries in general.
But another thing that I also wanted to mention is that so far we have a pretty extensive
experience with miltec industry, which is also booming right now.
So label you that comes originally from Ukraine.
And since the full scale invasion of Russia, which happened more than three years ago,
we have been cooperating closely with the Defense Ministry of Ukraine on multiple projects.
So from annotating drone imagery for the enemy detection to identifying the sounds of flying
missiles to better understand their trajectory and to protect civilians on time, hundreds
and thousands of people.
We got those projects in our portfolio.
So yeah, I think that covers pretty much the main kind of projects that we work with, but
they are not limited to it.
So we have been working with healthcare quite a lot on skin diseases detection with hyper
automation for fintech industries, robotics and manufacturing.
That's a huge thing right now.
And agriculture too, we see more and more of those requests and I'm super happy to see
them because I think these developments will help us to solve some of the world problems.
Absolutely.
You've got a really diverse portfolio in your company and very, very cool to hear that.
You're a thought leader in the field of AI and machine learning specifically, computer
vision and of course data annotation.
We talked about a little bit of how generative AI has impacted your field and also how you've
been helping with national defense capabilities and across different industries.
Are there any public infrastructure projects that you worked on or is how can AI and data
annotation and computer vision help with infrastructure and public services?
I know you've written and talked about it a little bit, so I just wanted to ask you
about it.
Well, speaking of the public services, I think bottom line, I'd start right away that I'm
confident that AI will transform the way public services are now governed.
It will definitely improve the efficiency.
It will definitely improve the satisfaction of the citizens.
So as I mentioned before, we have experience working with things like smart cities projects,
waste management, environmental protection, energy, public health solution and all of
them based on what I hear from our clients, see AI as a tremendous boosting tool towards
bringing solutions at a quicker speed towards the current public sector problems.
And apart from that, we also have experience working with solar panel management, urban
safety, as I mentioned before, skin disease detection and so on, so better diagnosis things.
Moving back to what you mentioned earlier from Gen AI and how it impacted our field,
I probably want to take some time to talk about that as well.
So I think one of the applications that we see in how our clients work with data is that
Gen AI helps them to create data sense they need to train their models faster and at a
higher level.
So then they can use these data sense at an extensive level to train the models, right?
But you have to be very careful there because you might end up working on a very thin ice
by having synthetic data, lots of it and fitting your model with it, you really have to oversee
the quality and the relevance of this data.
Because the annotation may be good, everything may be great, but you might end up with the
biased decisions made by your model or biased outcomes because the data wasn't representative
enough, for example.
Another application that we see is how there is a growing demand for human evaluation of
the data generated by AI.
So when there is a proper feedback needed to be given to the LLM output, for example,
or to a computer vision model, whether this was a good or bad image generated, maybe there
is something lacking or maybe a response to a specific question wasn't correct enough.
And right now we build teams for our clients to improve the efficiency of these existing
JNAI models by providing that feedback.
And as I said before, sometimes it's super niche, sometimes we should onboard the scientists
or people with some very, very specific background like in finance or in legal field because
you really want to train your model with the feedback from people who understand what this
model is going to produce in the end.
So that's a very, very important thing to be careful about when either building an in-house
data annotation team or outsourcing it somewhere is that you really want your team to understand
the end result of your model so that they can shape their answers or annotations or prompts
in the very correct way, understanding the depths of what you want to receive from the
model at the end results.
Yeah, absolutely.
And yeah, I mean, like you said, I think both quality of the data that's coming into the
model is really important.
And also, especially with the JNAI, I think there needs to be a quality check on what the
models are putting out and that there is definitely a need for quality check on the
output end as well.
In our company, yeah, I mean, I work for, I run our data science team in our company
called Legal Shield.
What we've come across is that I think JNAI in my mind is not ready yet for customer-facing
tasks in my mind because of the consistency issues and hallucination issues that we're
facing with the current models.
So what we're doing right now is utilizing JNAI as a productivity tool.
So to help, you know, we brought in GitHub Co-Pilot to help our software engineers and
data engineers get better at coding or get quicker and faster at coding.
We're also planning to bring in JNAI to help write emails and read through documents and
things like that.
So I think that's super helpful.
Yeah, that was a good point that you made on the quality of the data.
What are some of the things that you're doing to improve data quality in outputs of your
models?
Right.
Well, our main goal is to improve the output of the models of our clients by feeding it
with the right data.
It's as simple as following the standard quality assurance procedures that we've established
in the beginning of our work because to us, there are four main qualities in how we do
business and label your data.
Number one is security because oftentimes we deal with very sensitive, very personal
data of our clients, especially those coming from FinTech or healthcare industries.
And you have to be compliant with lots of different things like HIPAA, PCI, DSS, ISO
certifications, CPA compliance, 3DPR, and the list goes on.
So that's our number one value out there.
And number two is quality because having poorly annotated data regardless of how much money
you put into it won't bring you any good view on your model.
So at Label Your Data over the years, we've been implementing a couple of different strategies
and approaches.
Some of them were also things that I personally found when we just started Label Your Data
because at the very beginning of our journey, at 2020, right when the world shut down for
the current team, this is where we were starting a new company.
It worked out great.
We had our first clients in April 2020.
From that time on, we've been growing.
But yeah, at the very first, at the very start of this journey, I was the one first doing
the pilots for our project.
So doing that annotation myself because I really wanted to understand the pain points.
Because I think that, you know, well, data annotation can be perceived in the somewhat
unmanned task, you know, something that you can get easily bored over, but then it can
also impact your productivity.
So we take care about that a lot, about how people see this job and what we can do from
our end to make it better for them and to ensure more productivity.
And then after I was doing the annotation, at some point of time, I was also the quality
assurance specialist.
So because I really wanted to make sure the data that our clients receive, it is of a good
quality because they're first clients, a reputation, and it still goes on like this, right?
So every project that we work on, it goes through the obligatory QA.
So there are two levels, the annotation level and then the quality assurance level, which
is all held on our end.
And only then the client can go on and, you know, check the data for the quality and maybe
return some things, you know, if there are some edge cases or gray areas, so to say.
So the data goes through the obligatory, another second pair of eyes from our end from the
quality assurance team, but we also offer hybrid approaches.
So sometimes if we are talking about, well, it's more applicable to NLP than computer
vision to be honest, because in NLP you receive more subjective data, right?
So the text can be perceived with certain emotions or sometimes the words out there really stand
for something different, right?
If you don't understand the context or something.
Or the reviews of the restaurants, for example, or the hotels or different places, they can
mean different things depending on how you read them.
So to ensure high quality of that kind of data and to make sure the client receives what
they want, we can sometimes implement cross-reference quality assurance method.
It's when the same piece of data is annotated simultaneously by different people, sometimes
from different cultural backgrounds, because we recently had this interesting project where
we had to listen to the audio recording of people, but the annotator didn't have to know
the language spoken, they just had to grab the emotions.
But it was super fun because people speak differently already, depending on their culture,
depending on their mood.
So not knowing the language and then detecting the emotion of the person speaking on the
other side of the phone is very, very interesting and challenging.
And then we can also implement things like embedding a golden data set into the data,
the work of the data annotator.
So it's just imagine the annotator annotates 1000 images, 10% of which have already been
annotated by us and we know they are right.
So we did that random spot check whether the annotator does a good job or not.
And then the data never knows whether it's a new piece of data or it's part of that golden
data set.
So we are also always open to some new techniques and sometimes we even use elements to do the
annotation and to compare it against what our annotators produce.
So it acts like the third or a fourth or a fifth annotator.
So yeah, we're always trying to bottom line is that there is always a quality assurance
in our end.
And then based on our experience and our use cases and as we learn the specific request
of the client, we can then offer the better way of ensuring the quality so that the client
doesn't have to go over however many thousands of images or data points they sent our way.
Yeah, that's very interesting.
Like you said, I'm glad that you have all these techniques and redundancies in place.
So your quality is maintained, especially in data annotation.
I think you'll have to think about both human errors and also machine errors, which you're
already doing.
That's super interesting.
Well, apart from that, we sometimes also offer a hybrid approach.
So it's not always right now, it's more and more not about classic data annotation.
As we think of it, right, you just get a data set you annotate it, you feed your model with
it and there you go.
But more and more, we are being onboarded by our clients to the existing models that
they have.
And so they want that humans in the loop team to always be there to evaluate the results
produced by their model, fix things here and there, or just review if it was done correctly,
for example.
So more and more, it's that hybrid model of cooperation, humans in the loop team working
together with the machine, with the model.
Yeah, it sounds like you have clients from all over the world and across different industries.
So that's definitely fascinating, exciting, and also challenging.
So that was a great example of, you know, label annotating data, annotating conversations,
emotions of the conversations without knowing the language.
That is fascinating, right?
So, yeah, it's super exciting.
You recently wrote an article in Europe's conference of AI rules.
Can you please tell us what they are and how can other countries across the world learn
from it?
Well, that's an interesting thing, right?
Well, in simple words, as I said, in simple words, as I said, this European Parliament
AI Act, the main goal of it is to prevent or manage rather than prevent the risks of violating
human rights with the existing emerging AI technologies, while at the same time trying
not to limit the development of the technology.
Because I don't think AI can be perceived as a good or bad, like in a black and white.
It's a trajectory in a black and white perspective.
I think it's both, right, depending on how you use it.
And overall, I'm happy to see the notion, because I think this is the kind of the step
that brings us closer towards preventing the many concerns people have about AI technology.
And not only concerns about, not only concerns, but actual potential threats.
So I think it's important to categorize risks and put the policies in place to prohibit
things like what the Act prohibits rights of social scoring, emotional recognition,
violating human behavior, or categorization by biometrics.
I think it's undoubtedly something we should only continue to work on.
And I'm excited to see what's next.
I think it's a very important first break.
But I also believe that it's not only about passing a law, but it's also about a strong
cooperation between tech businesses and government, because one of the risks, I think, lies in
the fact that the laws are sometimes passed by people who lack understanding of how tech
and AI actually works.
And so this is where the competence and expertise of AI leaders and businesses can really help
to bridge that gap and make sure these laws and governmental decisions are put in place
thoroughly.
Yeah, absolutely.
There's a lot of ethical considerations.
And the policies are always catching up to technology and more so with AI, I mean, with
the pace of development.
It's really hard for governments to really catch up.
So I think, yeah, definitely there needs to be a lot more collaboration with tech companies
and policymakers across the world.
And also, I think between countries, leaders of countries, between governments of different
countries as well.
Could you discuss the potential for international collaboration in AI projects?
You've got plans from all across the globe and a lot of different countries.
What are the potential for international collaborations in AI projects?
Particularly those aimed at common global challenges like climate change or anything.
Yes, of course.
Well, it's hard for me to give my general opinion on it.
I'll be talking only through my perspective as I can see it.
I think we really need success cases, which will then be implemented.
Because you can't just take something out there and, okay, let's do it.
It's important to see something that, for example, worked for one country and then scale
it up.
I can talk, for example, in Ukraine, we have this amazing initiative called DIGIA, which
is basically a digital country in Europe.
We do our European vision voting through it.
European vision voting through it.
We have our passport.
We have our language.
Recently, we were having our diplomas in it, we're paying our fines through it.
So that's a huge success case, which is now being implemented globally.
But it wasn't the thing that naturally said, okay, we need to have the digital country
in a phone that's cooperating and do it globally.
It comes from those local successes.
And this is the way it should be.
You have to test it small scale, see it works, and then implement it on a global scale.
But maybe, and not maybe, of course, adapted to specific country needs or culture needs
or geographic needs, because not always something that worked for us, for example, will work
for some other countries.
Assessing the threats, cybersecurity threats, environmental threats.
So it's a long process.
It's not an easy thing to do.
But I think learning from success cases and then implementing it maybe slowly but steadily
is a way to go for the international cooperation for the DEI development.
Yeah, that's a really good point on identifying local successes and then scaling it up across
other countries.
Because I'm originally from India and I live in the US now and hadn't been to India for
about five years.
And then when I went back last year and I was so surprised by the adoption of digital
payment systems in India.
So it's PayTM and Google Pay are the kings of the, you know, kings of the road.
So basically, there's, I pulled out my credit card and gave it to them at a salon.
Literally they had to pull out the card reader and then dust it out because nobody has used
it in months.
So everybody just pays through their, you know, mobile phones or smartphones.
So it's fascinating how I think South Asia and Africa are kind of leading in the digital
payments systems.
And you gave a great example of how Ukraine is leading the charge in terms of, you know,
it doesn't, you don't have to carry your documentation, your, you know, every, I mean, basically
it sounds like the whole anything that you need from a documentation government standpoint
can be done digitally.
That's really cool.
And what I also love is the openness of the creators of the government about it, right?
So we are ready to share it with people with those countries who want it.
What's great as well is that, you know, the government is open to, to cooperating with
the tech minds, right?
So, okay, if you think we have any vulnerabilities, okay, go ahead and tell us, you know, we'll
pay you the check for it or something like this, you know, to make sure we all work together
on improving it.
And it's important to be that open minded, open for sharing.
So, yeah.
Yep, yep, absolutely.
Yeah, that's, that's, you know, that's kind of the tech.
That's the beauty of tech, right?
It's always been open source as idealized in the tech industry.
And hopefully it will continue in the AI space as, you know, it started off with science
and then, you know, the tech industry adopted the openness and then hopefully, again, AI
and other engineering fields do that as well.
Of course.
Well, another great example I can give you will give it my, my origin is in the middle
tech industry.
So, as I mentioned before, since the full scale invasion, terrific events, terrific
invasion of Russia and Ukraine, we have been working with on different middle tech projects.
And one of them was about removing that manual analytics of drone imagery and so, you know,
switching it towards more automated things, whether it's enemy or target detection, whether
it's observation of certain points.
We have worked together on it.
And it is the technology that is now presented in NATO used for the defense purposes.
And so I think that, you know, we will, there will be a lot to share even in this year because
this is just one of the projects we've been working on.
So I know another super successful commercial project, which started off also as on a volunteering
basis here in Ukraine, where we together with another amazing Ukrainian startup and a couple
more amazing tech people from our industry have been working on building a system that
will use, they will analyze the sounds of flying missiles around the, around Ukraine.
Ukraine is huge.
So it'll detect the sound, it'll tell whether it's a wind or an animal or a helicopter
or a missile.
And these small stations located around the country will be able to track it, track it,
locally detect it and understand, okay, what this missile is heading, how we can prevent
it from hitting the potentially civilian building or something.
And it has also become a potential commercial project.
So I think it's a great technology that will help to protect, philosophically tonight to
protect civilian population against some horrific terror attacks.
Yeah, like you said previously, AI is a tool and it can be used for good and bad and you've
given great examples of how it can be life saving, right?
So it's definitely fascinating.
Although I've never really grown anything myself, coming from the US and having lived
in small towns in the US, coming from India and live, sorry, having lived in small towns
in the US, I know friends with ranches and farms who are into agriculture.
You wrote a great piece on the role of computer vision and agriculture and you've worked
on a lot of projects in that.
Can you please talk to us about those projects a little deeper and how can AI and computer
vision or data annotation help farmers in agriculture?
Yeah, of course.
I mean, it's a growing industry.
First thing first, I think it's aiming at solving a very global food production problem.
In terms of our expertise at Labelgear data, we have been working both with startups and
enterprises in this field.
They were mostly about developing a robotic solution to either detect some diseases or
automating the crop selection, right?
Where you have, I don't know, strawberries or some other plants that you are growing
to basically gather your harvest.
This is a super fun project for the editors to work on, so that they always enjoy it.
It's that kind of project where you're just doing the work and you're really enjoying
it.
But it's not limited only to the cases that I've mentioned.
So you look back to the piece that was written.
Yes, there are so many applications of computer vision in agriculture, not only robotics,
not only things like disease detection, but also things like water management, better
soil management, better prediction, right?
So I think overall, we will be seeing a huge improvement in the final metrics of the industry.
It's really disruptive.
It's amazing to see these things.
So yeah, I'm also excited to see if this prediction really come to life.
And I strongly believe that projects like AI and agriculture will help us to, might sound
sentimental, but make the world a better place.
No, absolutely.
I mean, it's one of the fundamental industries that's never gone away.
So more productive we can be, the better.
And on a smaller scale, just an off-top story, I've been hunted by this Instagram ad of an
app that helps you to detect the diseases of your plants.
And me, like, well, I've grown in a small village, so we had a lot of, you know, planting
potatoes and cucumbers and tomatoes going on.
And for my whole childhood, I said, I hate it.
I'm never going to do it because it was, you know, you have to do things as a child and
you have to help your parents.
And it's not always the best thing.
It's not always the thing you dream of doing on your summer vacation, right?
But then recently, and I've never had, as an adult, I've never had like home plants
in the apartment.
But recently, I got five plants.
I bought five plants, almost five in a row, because for some reason I just had this, you
know, excitement about having some home plants in the apartment.
And I was so scared that I'm not going to manage it, that I will fail, that they will
die.
And then, you know, I thought, well, I knew things, right?
I know how to run a company.
I know how to manage people.
But I was still so, you know, petrified about managing my five small plants in my kitchen.
So yeah, well, things like even having those small apps helping you to take, to give a
better care to your plants, I think it's also a good thing.
Yeah, absolutely.
I mean, I've never been a plant person, but I'm slowly trying to be now.
I think this plant is kind of going, I need that AI.
Oh my God, I saw there was a background and I was just about to make a joke about it in
the beginning saying you didn't plant anything, but here you go.
I thought that was a background.
Oh, thank you.
That's great.
I'll take that as a compliment.
But I do need that app you're talking about to see if this plant's going bad.
But yeah, it is definitely fascinating.
You're taking good care of it.
So.
Oh, thank you.
Yeah, absolutely.
And also, I think as you were talking about applications of AI in agriculture, I think
since the world is rapidly evolving in adoption of AI across industries, I think we'll see
a lot of cross-pollination and innovations in other industries helping something else.
Say for example, if we get really better, even better than right now at predicting weather,
maybe that'll help the agricultural industry, the rains and predicting exact quantities and
timelines of the rains and everything.
So I think we will see a lot of cross-pollination of innovations and applications of AI from
other industries of doubt in across different ones.
Well, I heard this fun story recently.
Our marketing lead shared it with me.
Well, not quite sure it's true, but apparently in Japan, I think it was Japan, they were
developing an AI computer vision model.
They've been doing it for five or so years to be able to detect between 200 different
bakeries, bakery products, because it's a lot of manual work and they wanted to automate
it, but it was so difficult to do because these 200 different types of bakery products,
they almost looked alike.
And then they finally developed it.
And then there was this guy who came in and said, hey, you know what, actually on a larger
scale, these bakery products, they look similar to some cancer cells or something like this.
So they have used this model to, and obviously they have developed it over time, but they've
used it as a foundation to develop the model to detect some cells, you know, to improve
in the healthcare industry.
So that's another example.
Well, not sure.
I'm pretty sure it's a real one, but it sounds like a made-up story, but it proves the point,
right?
Yeah, no, absolutely.
I wouldn't be surprised if that's true, right?
There's a lot of things that we don't think about as humans, and as AI gets better and
you know, it's the knowledge base of the world more so than the internet.
I think it'll be able to make those connections that we've never made before, I think.
So I'm super excited about, you know, what AI brings us in that space.
Yep, for sure.
Yeah, you've led your company and leading your company through enormous crises in a pandemic
and war.
What can you teach us about leadership and leading a team in crisis?
What can we learn from your experience?
I love talking about this because I'm just sharing my experience.
I don't think, I don't think you can pass the leadership course or management course,
and then all of a sudden become a great leader, right?
It's all about practice.
I had my mistakes and had my good things that I just learned from and learned from my lessons,
but I love this.
I recently listened to, I think it was Simon Altman's podcast with Lex Friedman, and he
was saying about the Hactic Weekend that happened last November, and he was talking about Mira
who took over, you know, temporarily the open AI.
And he said this thing that I think that just got, you know, deeply in my mind that we usually,
we usually evaluate the great leaders by how they act in crises or in stressful situations
or just with those one-time moments.
But being a good leader, I think, is, as Sam said, and I also stand by that, is in those
day-to-day things, right, in how you show up to the meetings and how you make your decisions
and how you solve challenges on a day-to-day basis.
And it's what about, it is about what you do on a regular day, regular morning, regular
afternoon or evening.
So I think genuinely what helped me a lot is that I strongly believe in what we do.
I strongly believe in our social mission.
I strongly believe in our mission to table your data, which is go building AI-driven
economy.
And this is the thing that I always tell my people, my executive teams and the leaders
of the teams about we hold strategic sessions two times a year and communication sessions
with the whole team, including tag team, marketing, HR, service delivery, business development.
And what we always look back to, we start every report with our mission.
So everybody told you, reminded where we are going.
And from that mission, we start to build our tactical steps.
And we always communicate to every single person.
We spend a lot of time brainstorming it.
And we always communicate how every single team member can help the company achieve its
goals by doing their day-to-day things, whether you're a content writer, whether you're a
software developer, whether you're an SDR or anything.
So it's all about, I think, for me, it has always been about vision, keeping in mind
regain and focus.
My day-to-day routine, by the way, is always about talking to people, whether it's potential
clients or our existing clients or team members or partners.
I do talking a lot, but then I learned to dedicate one day a week to strategic tasks,
leaving everything else behind.
Because I think it was easy to do so when you were relatively small, but as we scale
up, you get a lot of operational stuff on your shoulders.
And there are lots of things you get to deal with, including finances or interviewing
new team members or maybe just spending lots of time fighting a battle, the Google spreadsheet,
trying to get that formula right.
But it is important to stop there and to just rise above and gain that, well, you can call
it a helicopter vision or a 360 vision.
And why am I here, what value can I bring as the CEO to the company, regain in focus,
looking back to the very beginning and understanding that you're here to drive strategy.
And my main goal is to make sure that our strategic plans are being executed correctly
throughout a certain period of time.
So definitely vision, definitely about being transparent.
I think there's another key thing.
And there is a crisis, you say there is a crisis, right?
But I'm here, we're all in this together, I'm here to give you all the resources, all
the means to make sure you stay on board because I know I value you and I want you to be, you
know, to stay in the team and we're going to go through it, every crisis ends.
So you know, it's those who survived the crisis will make it after that.
So yeah, I think those key things to sum up vision, regain in focus on strategic things
and being transparent with your team and with yourself too, is what I learned from these
four years of building label your data.
That's awesome.
I mean, a couple of things really stood up to me, like you talked about everyday leadership,
right?
And it's not just in the, you know, points of crisis, but in how you operate every day
is really important as a leader.
That's super important.
And also you talked about the importance of repetition and consistency, you know, of clarifying
your messages as a leader, you know, in terms of what your vision is and what your, you
know, projects are, you know, being transparent with your team, especially on the repetition
and consistency.
I did speak to Jim James, a PR guru on this podcast a couple of weeks ago, and he said
the same thing that the most important thing for a leader is from a PR standpoint is repeating
and staying consistent with the message.
So I think you're doing the right things already.
Yeah, absolutely.
And then communicating them, right, you may think, I mean, you may stay focused on it,
you know, on your own, but it's also important to communicate it to the team.
And oftentimes we have some starting conversations with my team and they, they, they, they, I
think it's important to cultivate the safe space for the team, right?
When they can openly share ideas as well as concerns they have.
And then when you understand that you need to maybe sometimes take time to just listen
and understand, okay, I understand your concern.
I understand your doubt.
Well, maybe even share your personal story that you wouldn't have shared otherwise.
Is it any other, you know, any other in any other situation, but maybe, you know, just
being transparent and also telling people sometimes about your weaknesses and how you
acted in those moments of self-doubt.
It also brings a lot of trust towards you as a leader and as a person as well.
Those are very intimate moments, especially if we're talking about building the right
executive team, core team, people who then go and communicate the message to their team
members.
It's in those sometimes intimate moments, not about, you know, I'm the manager here,
I'm the leader here, I'm the CEO.
You do everything I tell you to do.
It's about sharing that experience which is sometimes painful, sometimes exciting, right?
And sharing the emotions.
Because then I think in the end we all work, it's all about people, right?
I do business development, I do sales, sometimes they buy your data.
And I know it's all about people, it's all about genuine interest in people rather than
make closing a successful deal or going to a conference, right?
And trying to get as many leaders as possible.
No, it's always about talking to people and showing your genuine appreciation, showing
your genuine interest in who they are and learning how they got there.
So I think that's what is value in communication.
Yeah, no, absolutely.
And as in my leadership journey, I've learned that I don't have all the answers at all
times, right?
So just being transparent and also sharing your own journey and self-doubts, like you
said, it makes you a more relatable leader and not somebody who just reads a book and
just talks just about that.
It's about personal journeys, it's about we're all people, we're all, you know.
Yeah, sure, I think being humble is important.
So for us, well, just to give you another example from a recent experience, I'm not a
marketing person, I don't have a lot of marketing experience.
But when we were onboarding a new marketing lead, I knew I have to make sure he does a
good job, right?
But then how can I do that if I don't know marketing?
And that in the end, it all came down to, for example, him showing me a report, which
I, for example, there were lots of numbers, lots of concepts, right?
I might not know.
But I'm showing my willingness to learn and to explain this to me because I really want
to know what this means.
I really want to be on the same page with you.
Well, someone can say it, my doctor is okay, you don't know things, right?
Why are you in here?
Why are you my manager?
But no, I see it, it does a completely opposite thing.
I'm genuinely interested in what you do.
I'm genuinely interested in how you want to bring me that result.
And in your experience, so please share it with me.
At any other convenient time, I'll be sharing my experience with you, maybe because maybe
there are some things that you are not, you are lacking expertise in where I can fill
in and vice versa.
And my team knows that they are always learning something, they're always passing some course
courses, watching things, and we're always learning from each other.
They come and say, I passed this course, I heard this and that.
Here is what we can implement in the company and say, okay, sure, looks like a good idea.
And then when I hear something or I pass some course or do some education, I go and I talk
to them about it and this is how we grow together.
And this creates, as I said, that safe idea lab rather than eco chamber when you can exchange
ideas and open for criticism, open to discussion, open to the out of the box ideas are not afraid
to be humble and to share what you really think.
Because in that, you see how you contribute to our common goal or common success.
Yeah, absolutely.
I mean, one of my philosophies is don't be an asshole, be a jerk, maybe.
And so, yeah, I mean, there's a lot of things you can learn from anybody and everybody, right?
So one of the things I keep a journal off or keep notes off is what did I like about
people I talked to in my in the podcast or at my work or any place we meet.
So how can I emulate some things I like from each person I meet?
You know, that that has helped me be more positive and trust people more and also be
a better leader on the whole.
But I think it all ends up to that one motto of yours.
Don't be a jerk.
Exactly.
Yeah, humility is super important, right?
So as we see more and more that being a problematic leader brings a lot of eyeballs, but I think
in my own little world, I think I'm championing a humble leader and importance of being a
humble leader.
Yeah.
You wrote an article asking if we should be afraid of AI and a lot of people are like
we talked about.
And for some good reasons, right?
There are the genuine concerns that you mentioned.
Can you please give us your take on it for our audience?
Well, that's a never ending argument, isn't it?
There is no doubt regarding how powerful AI techniques can be and impact in both good
and bad ways the humanity.
But my personal philosophical take on it is simple.
I think that AI cannot possess bigger threats for humanity than humans themselves.
That we are managing, though not always successfully, to prevent our worst fears from happening.
It somehow, the world somehow ends up being, again, somewhat balanced, right?
One party develops AI, another one regulates it.
So I think that one, we have to admit we can stop it.
I think the case with the letter to pause the development for six months really, you
know, shown that no one can stop it from happening.
It's already there.
But let's do it in a smart and sustainable way.
That's it.
Yeah, no, absolutely.
There's no, you made a good point of, you know, the world, humanity finds a way to make
things okay.
We saw this with the nuclear warfare and the ADs.
I mean, we've heard about it.
You and I weren't there.
But so I think I am positive as well that we will be okay at the end.
We will see some bad actors using AI or any kind of technology in bad faith.
But I think overall, over-arching Lee will be okay.
I think overall, it challenges us tremendously, right?
It challenges our ability to have power.
It changes in some ways the human nature.
It challenges how we think about ourselves.
It challenges how we think about other people, right?
Because one thing I think about is, okay, what if it ends up in the hands of bad people,
right?
It challenges lots of foundational things about human nature, about the current state
of things, about our future.
And I think that because we are, I don't know, we are going with such a huge speed at it
that our inability to predict things is what scares us the most, right?
So we would, even with the regulation of AI, we oftentimes hear these thoughts, okay, we
are, we are nearly late to implement the regulation because it's all over, you know, it's full
speed development.
So I think it's that inability to predict things at this current pace is what is scary.
It's okay, right?
It's the time it should be.
You don't know what's out there that makes you feel scared about it.
But yeah, I also tend to be optimistic.
I think it's also a lot to do with education, but with educating people on what it really
is.
As I said, with the governmental policies, right, it's sometimes people should then know
the capabilities.
They don't know what AI in essence is and they perceive it as some, I don't know, either
a magical pill, which is also a bad thing, right?
Some people just want to use AI to solve every single problem out there, which is not true.
It's not right, at least at this point of time.
But then the other camp sees it as some terrific, you know, weapon that's going to kill us all
and going to rebel against humanity and kill everyone.
Without educating people so that they can realistically assess risks like in the act
that was released.
It's not about some transformers, you know, robots coming and killing everyone.
It's about things like manipulating.
And so you have to be cautious because even without AI, a lot of people are manipulated
with the media, with social media and stuff.
So yeah, I think that's my take on it, optimistic, but definitely there is a lot of things we
should do about it, to regulate it, to do it in a smart, to do it in a sustainable way.
And be cautious that it will definitely change the way things are, not only, you know, in
the reality, but philosophically to who we are and what are our capabilities.
So yeah.
Yeah, it's basically enhancing our intelligence.
We've augmented our ability to walk and run with automobiles.
We've augmented our ability to, you know, say, shout or communicate with digital technology,
microphones and speakers and anything like that.
And AI is more of an augmentation of your intelligence, right?
No, of course, it's artificial intelligence.
But yeah, I mean, definitely no one knows where we're going to go from here, learn to
your point, regulation and keeping ourselves in check is super important.
But again, maybe 10 years from now, we'll just use AI to create tech talks and random
emojis, who knows.
No, I'm just kidding.
I think we're already doing it.
We are.
I mean, 10 years from now, that's all we'll be doing.
We're creating tech talks.
But yeah, it's there.
There's a lot of ways this will go and a lot of industries are being disrupted, right?
We talked about the role of regulators and the role of tech leaders to be involved.
We also talked about ethical considerations while building AI and the data sets that go
into AI.
There is a lot of talk about tech leaders and engineers having way too much power because
AI is taken over.
How can, what are some of your tips to engineers or building these models and AI technology
to keep their own biases in check and bring in different voices or not make sure that
it's equitable to everybody?
Well, I think you got it there in your question.
But I don't think I'm in a position to give some sort of advice.
That's just my perspective.
Again, I'm trying to be humble.
I don't know everything.
But I think on a basic level, AI leaders should be a visioner and look above the short-term
revenue goals or self-centric goals.
But focus really on understanding a mission and vision of the company or not only the
company but the product or technology or a solution you're building on a global level.
And it is a good thing, I think, to look back to on a day-to-day basis.
And as you said it there, I think you got it completely right.
It's developing that habit of self-regulation.
Not waiting for the government to pass the law and prohibit what you're doing or someone
to sign the letter to pause AI development.
But it's important to challenge yourself on what risks and opportunities my creation is
going to produce.
It's about self-check, self-assessment, self-regulation, the motives.
But also trying to look above that, what would another person or the different motive be
able to do with what I'm creating.
I think that's the basic thing that I would think of if I were to design a unique AI
patent, AI solution, for example.
Yeah, that's a really important point that you made that it's important for anybody
creating a technology to have a foresight of how else can this technology be used.
Because it's baffling that a lot of times scientists and engineers are so focused on
that their technology is answered to a specific set of problems.
We don't think about how else, if this gets in the hands of a bad actor, how can this
be used?
So I think adding those guardrails or thinking about it at least will be a really good practice
for engineers.
For sure.
Sweet.
What excites you about current advancements in AI?
I'm sure there's a lot of things that you're chatting to people about and what are you
excited about?
Gosh, well, lots of things, as you said.
How can it not excite someone in one domain or another?
Oftentimes, putting the work aside, the things that we are seeing on a day-to-day basis at
label your data, these tremendously interesting AI applications and things that people are
doing around the globe, I think in the end I'm getting more and more excited about the
day-to-day life.
That's how it challenges the current state of things and the human nature.
So just yesterday I had a chat with this woman.
I know she was talking about her 10-year-old son who is created an Instagram page for their
dog and this 10-year-old boy uses chatGPT to write the text to the post, to compose
the content plan for the next couple of months and then to ask, and then he asks, chatGPT
what kind of pictures I should take of my dog to make it an interesting looking profile.
And I was listening to it.
Well, I know that it sounds kind of common right now, but I could do, I probably wouldn't
be able to do as good, and she showed me that Instagram page and I thought, okay, I'm
not going to be able to do that as good as he does it.
So that one thing that I was just a little bit shocked to hear about, realize, right,
because I hear these stories, lots of things, but when you realize it, you know, it's different.
Another thing I think is that what excites me is how not specific developments in AI
or, you know, like models that are deployed or presented to the world, but I'm always
thinking about how this can challenge the way things are right now.
So for example, challenge of workplaces, but not only like the number of workplaces, right,
but how we will be changed, how our agility will be changed.
We hear it a lot from Yvonne Noaharari, right, that people will have to stay flexible and
agile at ages of 40, 50, 60 to learn new skills, because again, we're moving at such a high
speed that no one really knows what you're going to need in terms of the skills in the
future.
And then sometimes I'm even thinking about my future kids, right, I don't have kids right
now, but let's imagine four years ago when I was thinking about it, I thought, okay,
maybe my kids would go and study mathematics and, you know, like computer science, so that,
you know, my imaginary is done, so that by, you know, because I know that it's kind of
a thing right now, so he will be successful in his life with basic thoughts, right.
Of course, there is a lot of other things involved, but still basic things.
I'm thinking about what kind of skills were needed at that time.
But right now, like four years later, I'm pretty convinced it's going to be about creative
skills rather than some hard skills, design thinking, creative problem solving.
And now I'm not sure, you know, what in the next four years time spent, I think would
be a necessary skill.
So it's that challenge to really make us more agile as people, as humans.
It's something that I think, you know, challenge of philosophical questions, the basic question
gets me all excited about what I have about AI in general.
I'm putting aside all of the amazing things that are currently happening.
So things like Sora, Jennie and artists and, you know, implementation of AI in industries
previously where we couldn't think, like just recently I had a call with Oil and Gas Company,
they are implementing AI, they're implementing to predict it or to analyze the data that
they have to create business intelligence tools.
So data that they have been gathering for the past 10 or 20 years.
This is the kind of the industry I thought it's just, you know, a couple of years ahead
there, no, it's right there.
As you mentioned it early, disrupts industries, and as I said, it disrupts human nature.
So you know, it gives me chills, but also super excited to see this exciting time to
live in.
So exciting that sometimes you think I'd love to live in some boring times because no, it's
too much exciting when the boring time is coming because I want to just, you know, chill
down a bit.
I know.
I always talk about having a week where nothing happens.
Maybe, you know, I need a boring week, but no, never happens.
But I mean, you made some great points there.
Maybe with a will to do something or, you know, that wants to create something can do
it, can do that wants to complete a project, can do it like you gave a great example of
a 10 year old, you know, using generative AI to do a lot of things.
And we also have, I was talking to Robert Brill, a media guru on the podcast a few weeks
ago as well.
He talked about that he's excited about seeing a single person company that's evaluated at
a billion dollar.
Yeah, that's not true.
Because it's democratized and the productivity gain is so much that there will be a tipping
point where there will be, you know, a solo entrepreneur will create a billion dollar
business.
And what's more exciting, it can be anyone, right?
Exactly.
Because you don't have to, you know, be born in a particular country or go to a particular
university or being surrounded by a particular set of people.
Of course, everything is important.
All of this stuff is important.
But yeah, as I said, democratization of these things and it could be anyone, which means
that it exposes us to such a huge talent pool out there, which we previously hadn't seen
into so many other perspectives to the world problems, challenges and other situations.
Yeah, I think as I think about it, like, like, like you said, hard skills, like coding, I
mean, I can talk to chat GPT or these gen AI tools and create code.
But as I've been thinking about this, one of the things that became just more important
in the world is your English speaking or writing skills, right?
Because a lot of these AI tools are being developed in English, more so in English than
in other languages, right?
So because I've come from India where there's a ton of different languages.
So I'm acutely aware of importance of language and importance of English as a global language.
And I think we're in the middle of the time in history where English just became a little
more important than it already was.
True, but don't you think it in a way became less important with the development of the
technologies that we have, right?
Do you sometimes you don't necessarily have to know?
Well, I think for the display right now, maybe not so much, right?
But it's going to a way where, I don't know, you just, my mom moved to the US almost a
year ago with zero knowledge of English and how to speak English.
She lived there successfully for a year and a half.
She goes to a bank, she puts up her phone, translate her and she solves her things perfectly.
So it's just a matter that you have to pull it up and to say something and then to wait.
But I know there are so many technologies that kind of bridge that gap already.
So yeah, well, that's an interesting topic to discuss as well.
Yeah, that's a good point that you made.
So it's definitely going to be more accessible to people who doesn't know English as an end
user, but as a developer of technology and data, maybe English is more, I need to think
about it a little more, but yeah, that's a really well made point.
Cool.
And this is one of our key staple questions on this podcast.
If you had a magic wand and time and resources were not constrained, what is the AI model
and data models that you would build and have access to?
Oh gosh, when I was thinking about that question, I spent a decent amount of time thinking about
it.
Spoiler alert, haven't come up with some fascinating answer or something unique, but I thought
about it in two different perspectives.
One, self-centric, I think that I'd be really excited about.
I'm not a scientist myself.
I'm not a physicist or an astrophysicist myself, but I think I'd love to do something
related to prediction or exploring the outer world out there because I think as much as
AI challenges us today, that will challenge us too.
We have been challenged by what we've seen in terms of how small we are, how really we
are there because of a coincidence of lots of different things and how we continue to
live on because for some reason things are the way they are.
So I think the development in going above what we currently see and know is exciting.
And I oftentimes like to think, I'm not a conspiracy theorist myself, but I often like
to think that people know something that is so grand that it might be just a little bit
too much to think about as a single person not knowing certain concepts.
So given that I have no constraints and resources and money, that would be something I'd explore.
But other than that, I mean, I think I cannot pick one problem I'd solve.
There are so many problems out there in the world that need immediate solutions, including
wars, right, that's something that is my pain point right now, including environmental
changes, famine and all these things.
So it wouldn't definitely be one thing, but making the world a better place in any way
I can would be definitely something I'd strive for, given that I have access to all the amazing
technology and all the money around the globe.
Well, that's some sentimental answer, but that's one thing I really like to the core
of it.
I think that's what I'd do.
No, absolutely.
And you're already doing a lot of it, right?
So you are running a company and making the world a better place.
It's really fascinating.
I mean, I do share your sentiment on space, Neil deGrasse Tyson, the director of Hayden
Planetarium in New York, he always talks about how if you look at things from a cosmic perspective,
problems of our world become so tiny or you have a different perspective, we don't need
to be fighting.
We don't need to be just because the colors of our skin are different.
We're still the same species.
We're still humans.
You know, everything about we have more things in common than not.
If an alien looks at both of us, maybe you're almost the same to me.
So I think the differences and the wars and everything kind of comes puny.
So I think developing the space consciousness and how AI can help us get there is super
fascinating to watch.
I agree.
Awesome.
No, thank you so much, Karina.
It's been a pleasure.
I appreciate your time and I learned a lot.
Thank you very much as well.
The pleasure is all mine.
As I said, I was super excited about the chat.
I'm super excited right now because I got a chance to talk about things that I'm passionate
about.
And I think it ended in a very good note.
In the end, we're all humans.
There are things that matter, right?
We have to remain humans.
We have to remain humble.
We have to remain kind to each other and together make the world a better place.
So that's a good ending, I think, of the conversation.
Absolutely.
Hopefully AI technologies can help us with that.
Absolutely.
Let's end on a positive note.
Thank you so much.
Thank you too.