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The NeuralPod AI Podcast
The NeuralPod is all about deep diving into technical machine learning topics and showing its real-world impact, bridging the gap between AI-first companies and adoption.
We chat with subject matter experts across reinforcement learning, recommender systems, deep learning and generative AI. As well as chatting with business leaders, VCs, operations and strategy experts.
Who are NeuralRec.ai? NeuralRec is a recruitment staffing agency. We build niche ML teams and represent some of the globe's best ML talent.
Artem: Some scientists afraid that AI
will replace them, but I don't think
AI will replace the all scientists.
But the scientists who use AI
replace scientists who don't use ai.
That's a common understanding
now in the industry.
Chris: Hello.
So Adam, welcome to, the Neuro Party.
It's, great to have you on today.
Artem: Chris, thank you
for having me today.
Chris: So, yeah, we was
introduced by a mutual friend
in the, biotech and AI space.
And, someone who, spoke
very highly of you.
Excited to speak to you today.
You've got a unique, blend
in the biotech space.
You've been a co-founder.
it seems like you've also done a lot
in business development to develop your
own startup and also been a researcher.
so is there.
You know, having spoke to a lot of
people, it's a really unique blend.
And now you are at neas,
leading, life sciences.
seems like you're working on some
really exciting projects there, which
we're gonna dive into today, as always.
And, I thought a great place to start.
you know, you've got a background in
mathematics and then now you're at
the intersection of genetics and ai.
Again, which seems really exciting.
But, yeah, let's start at the
beginning and what drew you into,
genetics because it's a niche field.
Artem: Yeah, so as you mentioned, I
studied mathematics and a real love
mathematics since I was very young and,
because I was doing pretty good at it.
I choose this path to move forward,
Always, when I was studying
this, I was really interested
in some real life applications.
So at the beginning, like when I
was studying in university, I was
thinking that some engineering, like
closer to mechanics or some business
applications, be irrelevant to the
mathematics backgrounds that I have.
But when I was graduating,
I started to speak with.
People who do some great
research in molecular biology.
And at that point of time,
understood that genetics is
really connected to mathematics.
It started to connect because,
like 20 years ago, it was more
about, say molecules or something
maybe more abstract things,
but with the era of.
Human genome sequenced we have the
full genomes of people, decoded.
a lot of big data.
Appeared from the genetics field
and that's why it's so interesting
to work, on it these days.
And it sounds niche, but I think
it can influence a lot of lives
and, our planet in general because
Chris: Hmm.
Artem: to all the life sciences fields.
Chris: Yeah, it's, it is interesting.
do you think that there was a defining
moment for you that made you realize
data, AI and biology would converge?
Because, up until recently it's,
it wasn't as, you know, in the
mainstream as it quite is now.
Artem: I think it was not
any particular moment.
It was a lot of moments.
when I understand that
it's growing rapidly and a,
Chris: So.
Artem: dived, deeper in it and,
still fascinated with the field.
you may know, there is a more slow
fast computers are progressing.
Yeah.
The progressing exponentially
and genetics is even faster.
It's even quicker.
the cost and the speed of genome
sequencing is, Rapidly, rapidly
improving in the last 15 years.
Chris: Yeah, so it seems like
a really exciting time in the
industry because, like you say,
It used to be super expensive.
But now, AI is democratizing a lot of
what's happening in the genetic space.
And we're gonna talk about crispr
a little later on, which is
certainly helping with that and
seems like a super exciting product.
Before we do that, let's
start at the beginning.
You know, do you wanna just give people
an overview of your career and how you've
got to, working on these projects today?
And, yeah, we'll dive
into that a little bit.
Artem: Yeah, sure.
As I mentioned, I was studying math
and my first job was, Accenture.
It's a very big IT consulting company.
But then I switched to companies
in a field of genetics,
bioinformatics, and med tech.
I co-founded several companies.
One of them was doing diagnostics
for infection diseases.
point of care device for diagnostics.
It was based on, say,
genetic technologies as well.
another company was a genetic
lab, that actually did DNA tests.
Another one was a software
service for bioinformatics, and
the last one was, companies that
provided so-called confidential
computing for biomedical research.
So confidential computing
is very interesting.
field when you can be confident.
the data that is, processed on CPU or
GPUs on any processors stored, securely.
So it's, very important, privacy
enhancing technology for this field like
healthcare or finance or any other fields
when the data should be very secured.
and now I'm, head of life
science and healthcare at nabe.
And, it's really fascinating to be
at that point because I can work
both with AI and life sciences.
It's amazing to be here.
Chris: Yeah, the intersection
is really unique.
And, that segues nicely
onto the next question.
I think you've got through the
unique experience of being a
researcher then, commercial leader.
It's something that some researchers
might struggle with making that jump into
the commercial world because, they're
required completely different skill sets.
So, I guess it might be people out there
listening to this that, You know, we're
interested in doing the same, especially
with the way some, areas of AI are going.
What were the biggest, surprises
and lessons learned in your career?
to this point, would you say?
Artem: I think the biggest lesson
is that there is no unique lessons
or unique advice for everyone.
People are super unique.
Like we are like even genetically.
one size doesn't fit all.
So, if you look at, different leaders
and different successful people, don't
just copy them because what works for one
person, will probably not work for you.
every person is very unique, so
I think that's, One of the super
important understands that I had
through my career as well as one
of the outcomes of, the current
genetic revolution that is going on.
Chris: is there any advice you'd give to
researchers now trying to, navigate the
commercial world or commercial teams?
navigating fast, changing
environments today?
Artem: Yeah, it would be super
simple or try to use and understand
AI Some scientists afraid that AI
will replace them, but I don't think
AI will replace the all scientists.
But the scientists who use AI
replace scientists who don't use ai.
That's a common understanding
now in the industry.
So if you can use some tools, Like,
let's say some scientifical pilots,
you'll be much more effective than
other people than don't use it.
So even if your work is not exactly,
connected with computer science, if you're
not bioinformatician, but if you're a
regular biologist, let's say, you still
need to understand a little bit about the
concepts of ai, or at least to be a user
Chris: I was having this conversation
recently actually, in terms of, you
know, we all thought AI might start
replacing engineers and maybe it has
in certain areas, but companies like
Google are now realizing they can get.
More outta one engineer and they're
hiring more engineers because
they realize they can do more.
so it's, having the opposite effect
in some areas, which is great to see.
Okay.
Well, thanks to the introduction
into your career, let's jump
into the meat of the podcast.
how do you see the current
state of genetics and.
AI at the moment in terms of
what's happening at a high
level across the industry.
Artem: So my understanding is
that Justin's the beginning.
We didn't have this, let's
say genetic GPT moment.
but there are already some
interesting models exist and some
improvements, that exist in the field.
And for example, you can imagine like,
large language model LLM that is based
on some English or Chinese or whatever,
alphabet or, different words and symbols.
DNA is also like a text, this
alphabet consists of four,
simple letters, A, c, g, T.
It's a small molecules
that every D consists of.
And if you have enough of this
text, enough of these DNAs,
also train later language model.
And it's very interesting that
there's can be used not only
to understand the text better.
To also be a generative va, like GPT.
there are some models for it and I
think that's already a good start.
I think the next waves, will
be also some copilots connected
with the real physical world.
When you have the models that can help you
to make some experiments understand them.
But you can work in your
lab or like anywhere.
In the physical world
with this, in real time.
So I think it's, will be as
the, the next step, like fully
automating the laboratories, but
together with the people in it.
Chris: Nice.
And, I guess, you know, I'm not an expert
in genetics while we're speaking today,
but the main challenge for a lot of AI
companies now is the compute costs or,
access to relevant data, et cetera.
what do you see as kind of
the biggest challenges the
industry's facing right now?
Artem: Yeah, you definitely mentioned,
compute, cost, and available data.
I think for compute costs
now it's becoming cheaper.
The GPUs are becoming more effective
and there are many, like many
more providers nowadays exists.
Data, I think is more relevant problem.
is always a lack of good data
for life sciences and genomics in
particular, but other things that
I think is really important that in
the current situation, you need a lot
of talent will work on your problems
and always compete for the talent.
Like, for the best talent and give the
best, salaries, bonuses, et cetera.
it's for tech buyer companies
to compete with them.
So I think even in AI era, the
like, value of the actual talent
becoming, even, even higher.
you need the T points
actually, do great research.
I think
Chris: Yeah, I think that's, you know,
we have a mutual friend in Alexi Ko
who, we help relocate from, Russia
to the uk, who's, described by a
lot of people are quite brilliant,
but he's, also working on, Some
areas of synthetic data for biotech.
And, a lot of companies that we,
help, I guess we moved out of a
really quiet time in biotech hiring
in the first half of the year.
But, I can tell you that Q3 has been
crazy and moving into Q4, you know, people
are getting quite astronomical pay rises
and, it is really, a war for talent and
it's certainly, areas that we address.
Cool.
Okay.
So in terms of the wider AI ecosystem,
you know, how do you see AI as
kind of helping solve some of those
challenges, specifically to genetics?
Artem: Yeah.
First of all, thank you for helping Alex
to relocate because, as a company that,
he co-founded is a very good example of
how AI and sciences can work together.
his company is doing quantum chemistry for
drug discovery and material science, and
like real fascinating science behind it.
really could say that in general, if
we're talking about AI for life science
and healthcare, I think, we can divide
like life science and healthcare
actually, because healthcare is when you
work with a patient and more regulated
and there are specific tasks for it.
medical imaging or note
taking, et cetera, and.
can mention like we're an
interesting company, like
customer of NAOs called X eight.
They're doing medical imaging based on ai.
It's real fascinating if we talk about
life sciences, life sciences is something
that provide insight and inventions that
will be later used in actual healthcare.
and medical implementations
and life sciences can be
divided into several parts.
let's say drug discovery
and drug development.
it's the main part of life sciences
and there are a lot of companies and,
let's say.
Interesting models, in
all of these fields.
But probably drug discovery
field is the most advanced now,
because it's super expensive.
It really ineffective in a way.
I can give one very famous
example of Alpha Fault.
So is a model that is actually
noble winning, like technology
that was made as far as I know,
kind of accidentally, in Google.
So, they had a hackathon in Google and
one of the persons there had an idea if
they can implement, AI for the problem of.
folding.
The thing is when you need to
understand the 3D structure of protein.
You need to spend several dozens
of thousands of dollars can take
one or two or three years to
do only once for destruction.
And if you need a lot of them and the
combinations possible, combinations
of protein structures is huge.
It's, you just, cannot go, for example.
But if you need like one, you spend a lot
of time, you spend a lot of money, and
find this 3D structure, but with alpha
fault and similar technologies, you can
get any sequence of the protein and have
with 3D structure just in a seconds.
Or like, even if.
In minutes.
It's still fascinating.
Yeah.
It's still impossible to
imagine like 10 years ago.
And that's a very good example.
maybe the most, famous example
right now, how sciences and drug
discovery in particular was approved
with artificial intelligence.
And I think if we go to genetic field
or for example, clinical trials field.
It'll also be some fascinate results
and already see some great companies.
in clinical trials there's company called
Trial Hub and they are implementing
LMS and they gather data from many,
many different sources to enable faster
and more accurate clinical trials.
It's super expensive and difficult.
Of drug development process.
Chris: And what, what for the everyday
I get that, you know, AI makes a
researcher's, job a lot easier,
which in turn makes it a lot easier
to develop a product, or a vaccine.
But are the only recent examples where,
you know, AI has been used that you can
think of to, to develop a, vaccine that
has actually, the right trials, clinical
trials to be used in the real world.
Artem: Yeah.
The problem is that even if you have.
Accelerated, drug discovery process.
You just cannot jump through
several years, like five,
10 years of clinical trials.
And because it's all happened in recent
years, there are several, drugs or
therapies that are proceeding through
the clinical trials, but it just was not
enough time for them to come to market.
But in five years or 10 years, we'll see.
plenty of them.
And I can say a more, stronger, prediction
here, that every drug that will go to the
market in some sense will be AI enabled.
Because even if it will not be fully,
produced from scratch by AI at every
stage, of the development scientists,
chemists already use AI to some extent.
any drugs that will go to
market will be AI enabled.
Chris: Interesting.
I think the real change that I've
seen over the last years is it used
to be the big players in big pharma,
like Johnson and Johnson almost had a
monopoly over what was being developed.
And now, you know, it's amazing
to see all these, small biotech.
start really developing,
some amazing things.
So, and that leads nicely
onto, crispr, which is, an LLM
inspired, multi-agent system.
basically democratizing
access to, gene editing.
I hope I've got that right.
do you wanna just
introduce people to crispr?
It seems like there's some really
amazing people working on that.
Artem: Yeah, CRISPR GPT is one of
the favorite AI applications for life
science and genetics in particular.
And I'm super proud and happy that
NAOs participated in that one.
this project was made in Sanford
by Professor K and some other
collaborators from different,
companies and organizations.
The thing is that there is a CRISPR
technology, and CRISPR technology
is noble winning technology that
allows very accurately and let's
say not very expensively to the
genes of people and other species.
gene editing technologists exist
for several decades already, but
they were not effective and they
were expensive, CRISPR technology is
something that scientists didn't advance
themselves, but they it in the nature.
They saw that bacterias.
Can fight with the viruses with a very
specific type of, immunity, that was
called CRISPR Cas nine or similar crispr.
when you have this, CRISPR technology
implemented to the medical field,
can very accurately change some,
letters in your DNA in your genome.
It allows you to treat different,
very dangerous, disease, like
different cancers, as well as very
difficult to treat genetic disorders.
if you implement crispr, you
can very effectively change
letter by letter or like.
Cut some pieces of genome.
But the problem is that our genome
is very difficult, it's very complex.
if you will change something,
it can be obvious for you, in
terms of getting some result.
You want to delete, some protein that you
think can be a case for specific disease.
Often it's not the best solution
because, which is very interconnected.
It's complex and you need to,
all the potential outcomes
and how it'll influence, all
your health, your whole body.
That's why it's very tough problem, and
you need to keep that in mind when you
do different crispr as well, Even if it's
understandable right now, how to make
a new crisper chemicals for specific,
gene editing, it's still bit tough.
Engineering problem, problem.
still need to be very accurate in
this chemicals that you produce.
And often when people, scientists
do them, lead to some mistakes There
is not any CRISPR system right now
that's 100% effective all of the time.
It usually, have some efficiency.
because we're speaking about gene
editing, because we're speaking about,
actually changing the genomes of people,
we should be very accurate with it.
here is where Crispr g.
Pt.
Appears what Stanford, team did is
they took a lot of information of
how scientists actually create this
crispr chemicals and they saw, like
they feed it, into their model and
make an genetic product out of it that
can help you to new crisp per systems
much more effectively and much faster.
They did several, experiments when it
was molecular biologists who didn't
have any experience particular in gene
editing, and they could make a EU crisp
system that was very effective and
targeted several genes just in a few days.
it's amazing because before, I think
it'll take at least several months too.
Person to disease.
even if molecular biologist will
understand it in a few months, he or she
will still make pretty much, mistakes.
So imagine that it's copilot tool allows
you to progress in a very specific
bioengineering thing much, much faster.
as they end it, it means.
More effective drugs and
more effective therapies
Chris: Interesting.
And, yeah, we spoke earlier about
how the biotech space has changed.
A lot of smaller startups are
developing now, and I think I've seen
recently, a publication in the agen
space developed by just one person.
which is extremely rare, and I think
that's a sign of things to come.
But, you know, I think Crispr from what
you said there has got the potential
to help a lot of smaller teams.
what do you think the main challenge
is, for researchers using CRISPR
right now in smaller teams and,
how could, this product help them?
Artem: You
Chris: I.
Artem: it can help to
democratize inventions.
Yes.
And it can help to democratize
scientific, inventions.
there is still a problem
for smaller companies.
You just cannot overcome
the need for a good lab.
So if you are like some great molecules
or some great CRISPR systems, whatever
on your computer, that's good, you
need to validate it In real life.
You still need to have some
cells, you still need to have some
chemicals, some, lead people and.
validate in real life.
And that's where bigger companies usually
have an advantage, to be honest, because
when you are operating on a scale, when
you consume a lot of chemicals, when you
have, your lab can be full optimized.
You can, you can test much
more the, like the one person.
Somewhere, I dunno, in MIT or in
Harvard, even have access to a lab.
Still big
can be more effective than you, but still
there is a very big place for creativity.
And one of the very good options is
for someone to make a biotech setup.
validate it to several degree
and then sell it to bigger
biotech onto pharma, onto pharma.
pharmaceutical companies, they're very
actively looking for these new ideas
and they're very happy to acquire some
groups or some molecules, or some like
technologies at the very early stage.
So I think like if you are doing some
early stage biotech, of the easiest
and the more effective ways for you
to succeed is to sell your molecule or
your genetic technique to some bigger
company, like pretty early stage.
Because it, like when you proceed with
clinical trials, if you go to phase two
or phase three, just need huge resources.
To proceed with it.
And it's almost impossible for
any small biotech to, raise
such amount of money do that.
So even in ai, that we live now, it's
still the easiest way to proceed.
Chris: Hmm.
Artem: experiment, find some
interesting product, let's say.
Sell it to bigger pharma
and then do a new one.
Chris: Certainly, it seems like the
game has changed and it's all about
acquisition these days with startups.
I think, you know, the wider
machine learning and AI communities,
it's all about agents this year.
And, multi-agent architecture and
reinforcement learning now starting
to power a lot of products, along
with traditional, with LLMs as well.
you know, let's talk about
the architecture in CRISPR
and how it's powering current
systems that you are seeing.
Artem: Yeah.
So for example, if we're talking
about crisp GPT, it's actual, system.
Yes.
So it has, it's not only one LLM model.
It's actually an agent that can
help you to do your experiments.
And if you don't mind, maybe I
can tell that, we have a webinar
with Professor Kunk how to build,
agentic AI models for sciences.
And on that webinar.
It'll be, like after our recording
with you, but depending on, when
people will listen to the podcast, they
probably will see it online already.
we'll do some demos there, and I think
Professor Khan could be the, like
a good person to actually show, how
it works in real life to different,
applications of Gentech systems,
crisp, GPT, and others as well.
Chris: Yeah, I think, you know, depending
on when this goes out, they can watch
it live or they're recorded, I guess.
So, moving on to the future, you
know, what do you think CRISPR means
for the future of gene editing?
Artem: it's also a bit
controversial, I think.
Chris: I.
Artem: not everybody knows that
we already have CRISPR gene edited
people that live on the planet.
at least two people
are like two babies, in
Chris: really?
Artem: Yeah.
it was an experiment, not an experiment,
let's say it was, I think that one of the
Chinese scientists did a few years ago.
He.
Actual gene edited, a few
embryos and these embryos
became, real babies, two girls.
what he did, he had like very good
idea that there is a very specific gene
important for HIV to, infect the person.
if you have this.
with mutation, then HIV just cannot.
In fact, you and you will not
have aids like any person like
with this mutation, will not have.
It's even if connected with, HIV and
this mutation exists in the world, around
1% of the population, mostly Europeans
have this mutation, with CRISPR you
can make this mutation artificially.
You can cut the 32 letters of one
specific gene, let's say throw
away, and then reconnect this gene.
And you have the person that is
immune to HIV aids and the scientists
implemented it to two embryos to girls.
were born this way.
The immune to HIV hopefully.
But, this experiment was a scandal
Chris: I can imagine.
Artem: In academic field.
And he wouldn't went to prison for that.
Chris: Yeah, it crosses so many different
lines in terms o obviously you've got
the, the ethics within genetics, which
I imagine most people are dear to, but
then, you know, crosses, political and
religious lines and, everybody's got
different opinions and ne never more so
in the, the current state of the world.
So, yeah, let's speak more about this.
what's the current state and.
Thought process in, the genetics community
about doing this kind of experiment.
Artem: I think right now
consensus is a following.
If you some very.
severe disease and it cannot
be treated as a way, then you
can use gene editing for that.
But if you want to enhance the person,
and there is some other way to do it,
then the consensus is let's not do it.
But I think it'll change.
As well as we will progress,
one will have much more gene
therapies for very severe diseases.
go one step further and further because
I think like having an immunity
very dangerous infection disease.
It's not actually treatment,
it's a very good feature that
some of the people want to have.
It's like a vaccine, like
genetic vaccine, let's say now.
People are very cautious about it.
But I think like in probably 10
years that something that maybe will
be done like, here and there in the
world depending on the regulation,
depending on like, religion, ethics,
whatever, and different societies.
But I think that something that will,
be, especially if we will understand
the potential consequences of different.
Like gene therapies and
yeah, I can help you.
if we'll have a broader picture of how
the genes are interconnected, people
will be step by step more open to it.
Chris: You know, if someone felt CRISPR
would be good for their team, are you
happy for them to reach out To you?
Artem: Yeah, like if anyone will
see any potential of, reaching
me regarding like any things that
Nebi is doing or our partners is
doing, feel free to connect me.
I will help as much as I can.
Chris: Absolutely.
I know, like I said at the start of
the podcast, I know we've got a couple
of friends in the, in the same space,
and I know, they spoke highly of you
and the, the help you've provided.
So, yeah, I think, would recommend you
as well from, from what I've heard.
cool.
Okay.
Moving on to just a bit of fun
in terms of future predictions
I ask similar questions to people
who come on, is he a book, a film
or paper that's influenced how
you think about AI or science?
in recent years.
Artem: I think's a classical example for
anyone who want to understand, the future
of biotech and how it may look like.
The classical example is Gatica.
It's pretty old film already.
Chris: Okay.
Artem: watched that one.
Chris: It's No actually.
Artem: Yeah.
It's about the future when people
can invest their money into.
Creating better children.
And if you don't have enough
money, then you just make a
simple, regular, natural kit.
But if you have more money,
okay, you choose your embryos,
you choose the best one.
You make a gene editing here and
there, and society became preposition.
Prepositioned even much more.
Than in current situation, and
it's really interesting how.
It can work because for example,
many people in Silicon Valley and
other places around the world,
they invest money into having,
better, genetically better children.
Already without gene editing, you
can choose the best embryos with the
best, so-called polygenic risk score.
So.
If you have a naturally conceived baby,
then you just have, random result of how
your genes can, interact with each other.
But if you have an like few
embryos, then you can see the
particular combination of the genes.
And if you have, tens or
20 of them, and if you.
Sequence the genome of this
particular embr, very early stage.
when it's a few cells, then
you can put all these genomes
the models that will try to
how, smart, resistant to some
disease, tall, et cetera, will
be your baby in the future.
It's some prediction, it's some
estimation, but like it's super
popular these days, right?
Like at least two companies that, are
popular for that particular type of thing.
The future that Gaca has predicted.
Chris: Yeah.
Artem: think we're very
close to that, to be honest.
And I dunno how we'll as a
society, will work on that.
Chris: Yeah.
I love speaking about things like
this and I always wonder, are
we following the frameworks that
some of these films are providing?
Or, did someone predict
the future really well?
Because the IT section and crossover
seems to be pretty close at times.
So, okay.
And, if you could see, one AI
powered revolution in your time.
what would it be, what kind
of breakthrough do you think
could have the biggest impact?
And it can be genetics related, by the
way, 'cause that's, I would say very
much at the frontier at the moment.
Artem: What I would love to
see from AI Revolution is,
that, Tropic is talking about.
He's biophysicist by background
and in his essays, he also
mentioned how AI can accelerate.
science and healthcare, and
I really like the vision.
even if it's not absolutely
correct or accurate.
still, love the trend.
He mentions and predicts that in the
next five to 10 years with ai, will
have an ability to prevent and treat
most of the diseases, we can double with
the lifespan on the world, the world.
So.
If Air Revolution can help us to live
longer and more healthy life, let's
say, we can live for 150, years without
severe disease and be in good form.
Yeah, I think that's something
that I would love to see it's more
happy years with like your friends,
with your family, et cetera.
Contributing like very small
amount as far as I can.
Chris: Yeah, I don't think anybody
would disagree with you on that as well.
I think who wouldn't want to live
longer and better for those years?
So it's one of the reasons I covered
the longevity space, I think.
Yeah, so in its infancy and so
something I'm super passionate
about supporting as well.
and in terms of genetics and AI and the
intersection, obviously CRISPR's doing
some amazing things, but are there any
other areas that excite you in the space?
Our companies outside of, philanthropic.
Artem: Yeah, so, like the fields
that I really like and like.
Background in is, generalization
studies and polygenic risk scores.
so it's some, it's when you look at the
whole genome and trying to understand
how it influenced particular trait, and
it's really important to see right now,
not only on the level of DNA, but other
molecules, like RNA, proteins, et cetera.
It's called omics.
with ai.
We can build the better, like we, I
mean like society or like scientists and
different companies will definitely have
the option to build the better polygenic
scores to make a better, ation studies.
And I think it's really important
because, it'll understand this nuances
and we'll have the bigger picture.
Based on that, we'll go closer
to personalized medicine.
To really personalized
approach to prevent disease.
To treat disease because right now,
even in like the richest countries and
successful societies, often have our.
Some basic rules, how to treat some
disease or how to make checkup, et cetera.
And it does not include, it often
does not include the information about
your genome, about, how your biology.
If we can do that and offer
personalized, medical services,
it'll be an amazing outcome.
Chris: Super.
And I think that segues nicely onto
the next part, and you've kind of
sent me, answered this question,
but, you know, I'm a huge sci-fi
fan and it's one of the reason I
love the biotech and genetics space.
'cause it, it does kind
of intertwine at times.
So what's the, what, you know, one
sci-fi s breakthrough that you think
might happen in our, our lifetime?
Artem: That's a good question.
I think if we're talking about
something that will happen in the
field of ai, I think it'll be somewhere
around cloning gene editing 3D
printing or producing some organs.
let's say in some distant
future you can clone yourself.
You can make yourself better
based on gene editing, and you can
easily replace your organs when
they are, not effective anymore.
Chris: Yeah.
Artem: you don't like your liver anymore?
Okay.
Chris: Yeah.
Artem: I mean, it's sounds like sci-fi,
Chris: yeah,
Artem: we're moving in that direction,
Chris: yeah.
I know, we had Alex STR on previously
and he was talking about Organ
Sachs and his real company's
developing that technology right now.
And I guess the million dollar
question item, if you could, would
you, would you, clone yourself?
Artem: That's an interesting question.
There is so much ethical, concerns here.
I mean.
Me particularly, I, I would
be curious to do that.
Chris: Yeah.
Artem: sounds like
something very interesting.
Yes.
Chris: Yes, I know, I know.
the company, having next on the
podcast, they're bio, startup on the
West Coast and they're developing,
digital clones of people where,
they're much more personalized.
There's so many ethical challenges
with that as well, I guess
with deep fakes in the world.
But would you have a very accurate digital
representation of yourself if you could?
Out of interest?
Artem: I think yes,
Chris: Yeah.
Okay.
Artem: we we're talking about movies.
Chris: Yeah.
Artem: my first thought about Black
Mirror and the easy answer is yes, I
would love to have a digital copy of me.
but, yeah, in, in like on a big
scale, on terms of like society,
we need to, to think of it
Chris: Yeah.
Artem: right answer is yeah, which like.
a good thing to do to make, digital
copies, et cetera, but as usually it
should be somehow regulated and we
have need to have some rules that.
Chris: I think as a solo founder, I'll go
on record and say I would love one because
it would, help me out tremendously.
So, Thank you so much for coming on
today, art and sharing your knowledge
and being so open I've really enjoyed it.
I think this has been one of
my favorite podcasts I've done,
so thank you so much for that.
in closing, before we wrap up, is
there anything you'd like to say?
Artem: I would love to thank you,
Chris, for inviting me to this podcast.
And yeah, I would like to thank
you for attention to the field of
longevity and life sciences and to
everyone who watched the podcast.
I think it's, Great that you watch it, of
how AI can help, all of us to live longer.
It's really fascinating field.
It will change a lot
and stay tuned for that.
Chris: Yeah.
Nice.
Thank you so much.
I'm really, glad I can
support you with this.
And, to wrap up, I'll put some links
to get in touch with you If there's
any small teams or researchers or
startups are interested in how,
CRISPR or NIUs can support them.
I think you're doing some
really great things there in, in
supporting the, the small community.
I.
Include links to the, the webinar as
well where if it goes out before, wake
and watch or the recording after, I'll
include some links to the team as well.
I think when you're choosing, there's so
many products out there at the moment when
you're choosing the right products for
you, knowing that there's a good team,
Behind the product.
it is really important.
So I'll include some links to the
teams, and I think that NIUs are
doing your discovery wards and you
style a program for next year as well.
So, I'll include links to that for
anyone who needs support as well.
Thank you so much.