Introverted Forbes Under30 AI Innovator

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There are so many young people
in the world who are introvert

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who think often before they
actually take any action, before

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they actually kind of speak,
speak up in front of others.

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So there is that kind of
hesitation which might come

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across as a lack of confidence,
but actually a thinking more

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before you speak up.
That is actually a quality which

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you can leverage to do things
like.

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Welcome to Low to Pro, the
podcast transforming life's

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toughest moments into
opportunity for growth.

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I'm Annie, a Folks under 30
technology founder who's

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entrepreneurship journey brand
parallel to a mental health

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awakening.
In every episode, I sit down

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with inspiring individuals and
delve into how they managed to

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turn their personal or
professional challenges into

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opportunities for growth.
If you're facing uncertainty in

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your life, feeling down, or
simply need a kick of

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inspiration to keep moving
forward, this is your space for

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the honest and uplifting
conversations that you will want

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to hear.
Hit follow so you never miss an

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episode and let's dive in.
Today on Lotic Road, we're

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joined by someone whose story is
as humbling as it is inspiring.

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Dr. Georgette Chatterjee was
once a quiet, shy kid, often

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underestimated by classmates and
adults.

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He was even told that he lacks
what it takes to survive in the

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real world.
But through personal patience in

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a resilience and a quiet
determination, George, it proved

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that being an introvert is
actually a superpower rather

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than a weakness.
Now, George.

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It is a Forbes on the 30 honoree
in Manufacturing and industry,

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also working as a lead data
scientist at EPAM and is a

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global speaker on AI and
sustainability.

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He has worked on smart factories
across continents, earned

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international recognition for
his research and also shared

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stages with Fortune 500 leaders,
all the while staying grounded.

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Today is a conversation about
the power of patients listening

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deeply and just letting your
work speak louder than your

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voice ever could.
Georgette, I am delighted to

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have you here on Low to Grow.
Who do you think will benefit

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the most from listening to our
conversation today?

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There are so many young people
in the world who kind of are

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introvert, who kind of think
often before they actually take

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any action, before they actually
kind of speak, speak up in front

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of others.
So there is that kind of

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hesitation, which might come
across as a lack of confidence.

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But actually it's like you're
just trying to be humble.

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If you are trying to be grounded
and if you are trying to give

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others the stage before you kind
of jump in and make your views

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kind of evident, I think that's
a lot of young people these

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days.
They would understand this, that

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it really helped to understand
that being introvert thinking

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more before you actually take
any steps further, before you

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speak up, that is actually a
quality which you can leverage

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to do things like research, do
things like innovation.

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So how young professionals can
actually transition from the

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society which might treat it as
a lack of confidence to actually

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something, as you mentioned,
like a superpower which will

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actually help you transition and
grow in your career.

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So yeah.
Wonderful.

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Thank you.
I am very excited to delve deep

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into that with you.
Let's start at the beginning

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then.
So JoJo, you've spoken about

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being shy and introverted when
you were a child.

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How do you feel that those early
experiences actually shaped the

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way that you approach
communication and leadership in

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your work today?
I would say, you know, like

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during my childhood, because
that's where most of our life is

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shaped, right?
When we are kids, that's where

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the beginning of our life is
shaped.

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I was very shy, like really,
really shy, really quiet.

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I would hesitate to even ask
simple things in front of

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teachers and to speak up when I
maybe was struggling to

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understand something on stuff,
stuff like that.

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So I wouldn't really be that
kind of outgoing in terms of my

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personality.
So that was something that was

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by nature only, you know, like
it, it wasn't something that I

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had kind of learned or anything,
obviously.

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It was something that my
personal nature was always like

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that I used to speak whenever I
felt like this is like the right

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movement.
These are the right people.

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Like a comfort zone, right?
I had a particular comfort zone.

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Maybe it might be my parents,
maybe some best friends whom I

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would be most comfortable in
speaking to, but not really to

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strangers, not really to new
people, students, other

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classmates, especially the ones
who are more outgoing.

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They would mock me and they
would tell me this nature, it

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won't help you in your future
career.

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It won't help you in your life
because really, if you don't

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speak up, if you can't like put
in your views or if you don't

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ask questions, then you you will
struggle to grow in your life.

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It came across as something that
was more destructive, you know,

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like it, it would actually
hamper and it, it could lead you

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to depression, it could lead you
to anxiety and stuff like that.

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So that was kind of, you know,
like the moment that made me go

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into the low point of my life,
like during childhood when I

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realized that, OK, like, society
views introspection and

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quietness as a weakness rather
than something that is just

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coming across from our nature,
which we can't really change

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when we are kids.
Do you feel that there was a

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turning point where you actually
realize that you're naturally

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quiet?
Nature was actually an advantage

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and a strength that you have.
Yes.

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So I would say that once I moved
to university, that was the

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point when I realized that there
is a bigger picture behind this

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nature, which means that I have
got certain skills which some

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other people might not have in
terms of giving others the

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opportunity to speak up rather
than being the loudest person in

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the room.
It makes me understand things

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deeper and I can go into the
intricacies into the details

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which other people cannot.
So that was where I got really,

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really interested into research
right from my undergrad, right?

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So as soon as I moved into UNIF
in my undergrad, I kind of

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published quite a few research
papers, which is quite rare

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there for undergrad students.
And that got me interested in

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participating in conferences and
giving presentations, stuff like

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that.
And obviously that led to the

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PhD and then even more
upliftment as a researcher, as a

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scientist.
So yeah, I think that low point

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in itself, it became like the
strength when I realized that,

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OK, this is something that could
be a trait of a scientist

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growing up in their life.
That's so interesting.

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And I remember you actually told
me when you were a child.

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Other adults told your parents,
or rather they asked your

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parents, how will you be able to
survive in the real world?

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Do you remember as a child when
you heard that, how you actually

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felt?
Oh yeah, it was terrible.

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It felt like I couldn't do
anything literally for days that

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that thing was in my mind for
like several days.

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Like I even like struggle to eat
honestly, like if if someone

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tells you that right, like when,
especially when you are a kid,

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when you don't know how to
tackle these things.

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Any negative perception that
comes across from not just other

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children, other students, but
even teachers, right?

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I, I think the sad thing is even
teachers thought that, OK, this

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is like something that would be
negative, negative thing enjoys

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it.
But fortunately my parents were

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quite supportive.
My grandfather, who is no more,

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but he was like, he was very
much like me.

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I think like, so maybe it comes
from genetics as well.

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He was also a writer, author,
you know, like he used to edit

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books, he used to write stuff
and stuff like that.

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So I think like that quality of
being a writer, being a

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researcher and all of that, it
comes from genetics and kind of

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my fam family was quite
understanding that.

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OK, maybe Joy just grandfather
was also quite quiet and like

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him during his all his life.
They gave me the opportunity to

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not take that as a negative
thing, but rather take that as

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an opportunity to work on
myself.

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Maybe this is something that I
can work on and I can start to

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speak to strangers.
I can start to collaborate with,

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you know, other people during my
education journey and stuff like

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that.
So yeah, like the parents helped

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me a lot in recognizing this as
something that I could actually,

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you know, like, improve.
Based on your experience, then,

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Georgia, to someone who was a
quiet child, if some of our

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listeners out there have young
children and they notice that

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maybe one or two of their
children tend to be more on the

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shy side, so similar to how you
were when you were younger, how

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would you advise the parents
then to help their children to

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grow out of that shyness or to
really take ownership of that

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shyness?
I would say that definitely

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don't push, push the children to
like completely change their

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nature.
You know, that would be the

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very, very first advice for the
parents that let them be their

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authentic self.
There is nothing wrong with

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being shy.
There is nothing wrong with

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being an introvert.
So it it is actually something

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which they can leverage as their
biggest superpower, as their

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biggest quality as they continue
to grow in their life.

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What could be improved is maybe
yes, like speak at the right

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moment.
Like instead of being completely

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quiet all the time, you know
which I realized that you need

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to speak at the right time,
right?

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Otherwise will will take you for
granted.

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So speak at the right moment,
the right opportunity whenever

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you see like, OK, this is the
right point when I need to jump

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in, then jump in.
But otherwise, give others the

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space.
Also give give others the

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opportunity to speak up first
before you kind of jump in to

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that.
So I would say that let them be

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their real self, Let them be
authentic, but kind of inform

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them and help them to become
more outgoing by maybe

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encouraging them to participate
in, you know, like events,

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encouraging them to go out in
competitions, be it poetry, be

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it singing and all of that.
So hobbies, I think they help a

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lot.
I used to play guitar and all

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when I was, you know, growing
up.

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That helped me a lot to come out
in front of the public, right,

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and increase my public public
perception in terms of being

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someone who is kind of more
interactive with the public and

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the society in general.
So yeah, encourage them to do

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whatever hobbies your children
are interested in.

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Jojit, I know that you were
selected for reforms and a 30

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list in manufacturing and
industry.

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Could you share with us a bit
more about your journey there?

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What took me into Forbes under
30 is definitely my research,

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you know, so it started with my
PhD at the University of Hull,

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which was in AI for the
renewables industry with like

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wind farm operators and
organizations like the Offshore

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Renewable Energy Catapult in the
UK to develop AI algorithms that

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can perform explainable
predictive maintenance.

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So not just like highly accurate
maintenance, but also like you

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can get like transparency and
accuracy both in your decisions.

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I used variety of concepts
ranging from causal inference to

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natural language generation to
knowledge graphs to bring trust

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and confidence into the AI
models, which is something the

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industry, especially as of
today, it's very, very much

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looking forward to.
No one wants to use black box AI

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models.
They want trust and confidence

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in your decision making.
So that research got quite some

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attention.
It was published in lead leading

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journals, well sighted.
After my PhD, I continued my

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research trajectory in
explainable AI.

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I worked at Racket, which is one
of the leading consumer goods

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companies, which makes Jet all
eyes all and all those products.

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So I worked there and I actually
applied the same explainable AI

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foundations into the work in the
factories of the future of the

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future, how we can reduce waste
in the factories, how we can

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improve or optimize the shelf
life of products that they make

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in R&D and those kind of areas.
It kind of got that attention of

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Forbes and like I, I think
that's, that's where it led to,

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you know, like the manufacturing
and industry category.

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And what really helped, I think
to get that attention was my

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participation in several
conferences, writing several

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research papers in leading
journals, even organizing

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workshops and social events at
leading AI conferences like

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ICLR, new ribs and those kinds
of things.

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So that really helped to, you
know, bring together like

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industry plus academia plus
public sector, like everyone

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could see that, OK, there is
some guy who is working on AI

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that connects to sustainability,
that connects to explain

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ability.
George it for some of our

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listeners who might be just
starting to understand to learn

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about what AI is, what does
black box AI mean?

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And also, how would you explain
what explainable AI is to

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someone who is completely new to
this area?

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In very simple terms, I would
say that everyone is using like

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ChatGPT right now.
Everyone knows of it as AI, but

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behind the scenes it's using
neural networks.

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And neural networks are very
complex, like machine learning

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models, actually deep learning
models which work very much like

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how your human brain functions.
So they are based on the

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operations of the human brains.
The analogy is like that, but

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they are kind of algorithms or
models which can make highly

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accurate decisions.
But as of today, it's

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practically infeasible to
understand the workings of how

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these large models work or
operate.

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That's like the black box behind
this.

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So you you can use ChatGPT, you
can type in a prompt and you can

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get a response.
You also can get the reasoning

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in the latest models that are
out there.

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You can use O3 and stuff like
that to get the reasoning.

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But that is something that the
model doesn't really understand.

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The text that goes into the
model, the data that goes into

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the model, the model only sees
it as a bunch of numbers.

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It is just tokens for the model
and it doesn't really understand

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the things in the same sense as
a human does.

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So that's like the black box.
And in the industry, for

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example, the wind industry, like
I was talking about in my PhD,

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you would have data from wind
turbine sensors which could lead

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to decisions on faults, right?
You could use that data to build

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predictive models that can tell
you that in the next 24 hours

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the gearbox of the wind turbine
is gonna fail.

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But what actually leads to that
message, like what is making the

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model think that in next 24
hours the gearbox would fail, is

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where explainable AI comes into
play.

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That is where techniques you
could use to decipher that these

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are the most important
parameters from my wind turbine

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sensors, which actually
contribute to the fault.

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And same goes for factories.
I can predict with an AI model

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that the product that I'm making
right now, it is going to have

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more waste produced in the next
year.

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But what actually leads to that
waste?

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What is the root causes of that
waste and how I can actually go

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ahead and make changes at the
ground level in my factory so

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that I am minimizing the waste
and maximizing the profits and

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revenue for the business?
So that's where explainability

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comes in.
That's where causality comes in.

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And you actually don't just make
predictions, but you can get

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actionable insights out of the
AI in the Gen.

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AI age like it's, it's quite
important to use these models to

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make sure we use these models in
the trustworthy and responsible

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manner.
In your opinion, George, it what

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are some of the current
misconceptions or myths about AI

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that really, really annoys you?
One of the biggest

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misconceptions is that Jenny I
is all AI.

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Jenny I is such a buzzword in
which billions of dollars of

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investments are being poured in
as AVC.

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You will see so many varieties
of Jenny I products that have

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been released into the market
and which makes a lot of the

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people especially at the senior
levels, right like the VPS and

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00:16:11,480 --> 00:16:15,200
the C level leaders, they might
not know that Jenny I is not

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something that can accomplish
everything that we need to

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change in our business.
So the hype and the buzzword

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that has been created and on
Jenny I, it's like it has

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created the biggest
misconception that Gen.

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AI can solve every single
problem that we have in the

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business.
To be honest, in my experience,

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most of the problems that we
face in everyday life is

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something that we don't even
need Gen.

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00:16:39,720 --> 00:16:43,040
AI for.
How the R&D operates, how the

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supply chain operates, how the
logistics operates, how the

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finance operates, different
business functions.

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They actually have either
structured or unstructured data

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00:16:53,440 --> 00:16:56,640
which you can use to build
traditional machine learning

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00:16:56,640 --> 00:17:00,080
models or even statistical
models and apply data science on

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00:17:00,080 --> 00:17:02,920
top of it.
And then where Chennai could

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00:17:02,920 --> 00:17:06,880
help the most is when you have
to build that human interface to

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00:17:06,880 --> 00:17:10,000
any predictive model to create
like a conversational layer or

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to create like an interactive
question answering system.

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That's where it could be quite
powerful.

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00:17:15,359 --> 00:17:19,079
But if you want to do something
trustworthy and you can't like

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00:17:19,079 --> 00:17:22,839
really use an LLM to do
predictive maintenance in a

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00:17:22,839 --> 00:17:24,839
factory, which would be very,
very risky.

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00:17:24,839 --> 00:17:27,319
And you might just get more
false alarms and missed

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00:17:27,319 --> 00:17:31,040
detections than actually saving
money by using AI.

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00:17:32,320 --> 00:17:36,320
What is the area of focus for
your research or your work right

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00:17:36,320 --> 00:17:40,520
now?
I'm quite focused in using AI

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00:17:40,520 --> 00:17:43,920
for different real world use
cases in the connected lab

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00:17:43,920 --> 00:17:47,680
space, like in the labs of the
future, how we can use AI to

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00:17:47,680 --> 00:17:49,680
optimize the new product
development process.

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00:17:49,680 --> 00:17:52,800
So whenever you are making new
products, then you are trying to

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ensure that the quality of the
products is the highest, it is

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safe, it is efficacious.
You can leverage a lot of data

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that comes from your historical
laboratories, the data that

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comes from formulators and
analysts to who make all these

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00:18:07,240 --> 00:18:11,440
new products, which can be used
as a knowledge pool to really do

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00:18:11,440 --> 00:18:14,720
something impactful and it could
help you create your simulate

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00:18:14,720 --> 00:18:18,000
new scenarios, new products that
OK, I'm making a new product

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with these active ingredients.
What would be the impact if I

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change this, if I change my
container, if I change my sample

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from from a tablet to a capsule
and stuff like that.

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That is one of the areas.
Plus I am also quite heavily

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involved these days in grounding
AI models into domain specific

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00:18:35,080 --> 00:18:36,840
knowledge.
Quite important in the Gen.

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00:18:36,840 --> 00:18:40,400
AI age because we cannot really
use a foundational model like

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00:18:40,440 --> 00:18:45,720
GPT 4 to understand the context
of our own business, of our own

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00:18:45,720 --> 00:18:49,360
industrial specific use cases.
Grounding it on knowledge by

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00:18:49,360 --> 00:18:52,640
using knowledge graph.
I am quite interested these days

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00:18:52,640 --> 00:18:54,560
in creating my own knowledge
graphs.

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00:18:54,560 --> 00:18:58,400
For example, for different use
cases, I can model a wind

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00:18:58,400 --> 00:19:01,920
turbine as a knowledge graph, as
a system with different sub

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00:19:01,920 --> 00:19:05,280
components with different alarms
and different connections

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00:19:05,280 --> 00:19:08,880
between these alarms to the sub
components and things like that.

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00:19:09,080 --> 00:19:12,840
Then I can connect my knowledge
graph to an LLM which will give

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00:19:12,840 --> 00:19:16,480
that LLM the historical context
or the knowledge that okay, this

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00:19:16,480 --> 00:19:20,440
is my domain specific knowledge
or the foundations which the LLM

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00:19:20,440 --> 00:19:23,160
should refer to whenever it is
making any decisions.

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00:19:23,160 --> 00:19:27,120
And that's where Agent TKI
again, a big buzzword comes into

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00:19:27,120 --> 00:19:29,800
play.
What I am kind of doing right

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00:19:29,800 --> 00:19:35,280
now is trying to connect it all
to additional ML plus knowledge

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00:19:35,280 --> 00:19:39,080
graphs so that we build models
that are, you know, like safe

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and like I was telling,
explainable.

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So any, any decisions I get from
an LLM, I can actually attribute

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00:19:45,160 --> 00:19:48,320
it to the responses I have in my
knowledge graph.

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So that I know that OK, there is
actual enterprise data that

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00:19:51,960 --> 00:19:55,120
tells that this is the way to
resolve the fault in a wind

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00:19:55,120 --> 00:19:59,280
turbine, or this is the way to
optimize the incorporation of

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00:19:59,280 --> 00:20:01,800
vitamin C in a new tablet that
I'm making.

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00:20:02,600 --> 00:20:06,400
I'm going to ask you to briefly
explain what some key terms are.

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00:20:06,760 --> 00:20:09,480
So could you explain to our
listeners who might not be that

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00:20:09,480 --> 00:20:12,440
familiar with AI, what
foundational models are?

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00:20:12,920 --> 00:20:17,080
Could you also explain why is
necessary to ground AI

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00:20:17,080 --> 00:20:18,800
frameworks?
And lastly, could you also

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00:20:18,880 --> 00:20:23,240
explain what AI agents are?
Foundational models are

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00:20:23,240 --> 00:20:27,240
basically your large models that
are trained with any almost

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00:20:27,240 --> 00:20:29,320
anything and everything from the
Internet, right?

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00:20:29,320 --> 00:20:33,360
So GPT that we all use.
It's a foundation model which is

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00:20:33,360 --> 00:20:36,440
built on huge amounts of data
from across the Internet, which

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00:20:36,440 --> 00:20:40,240
might even include Reddit, which
might include Wikipedia and

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00:20:40,320 --> 00:20:42,720
stuff like that.
So it has got a lot of

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00:20:42,720 --> 00:20:47,240
knowledge, but it is not
something that is specifically

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00:20:47,240 --> 00:20:50,400
going to have your competitive
advantage because it doesn't

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00:20:50,400 --> 00:20:52,680
know your data.
It doesn't know your problem

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00:20:52,680 --> 00:20:54,320
statement.
So that's like a foundational

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00:20:54,320 --> 00:20:57,080
model for you.
Why we need to ground these

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00:20:57,080 --> 00:21:00,640
models in our own data?
It's because of hallucinations.

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00:21:00,640 --> 00:21:05,240
So as as you know, ChatGPT we
all use a lot of the time, there

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00:21:05,240 --> 00:21:08,440
is a huge possibility it will
hallucinate, it will make

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00:21:08,440 --> 00:21:12,320
plausible real sounding
responses which might actually

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00:21:12,320 --> 00:21:16,120
be completely inaccurate.
And in factual it is tolerable

381
00:21:16,120 --> 00:21:20,320
when we are using it to generate
images or do some fun stuff,

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00:21:20,320 --> 00:21:23,040
experimental stuff.
But when we use it at the

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00:21:23,040 --> 00:21:26,560
corporate level, at the
enterprise level, it comes with

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00:21:26,560 --> 00:21:30,040
huge implications.
There is EUAIA and there is

385
00:21:30,080 --> 00:21:35,400
regulations that come in usage
of AI models like FDA and EMA

386
00:21:35,400 --> 00:21:38,000
and stuff like that.
There is like huge regulations

387
00:21:38,000 --> 00:21:41,000
that are coming up these days
and huge penalties for

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00:21:41,240 --> 00:21:44,240
businesses if they do not adhere
to these regulations.

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00:21:44,240 --> 00:21:46,920
So that's why it's quite
important to choose your own

390
00:21:46,920 --> 00:21:50,480
data to ground these models.
Maybe it would be a lot less

391
00:21:50,480 --> 00:21:52,600
powerful.
It cannot do everything in this

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00:21:52,600 --> 00:21:54,880
world, but it can do something
very well.

393
00:21:54,880 --> 00:21:58,280
That is where the power of
grounding your model comes into

394
00:21:58,280 --> 00:22:01,640
play.
And agentic AI basically aims to

395
00:22:01,920 --> 00:22:05,520
make sure that LLMS are
something that can work

396
00:22:05,520 --> 00:22:08,360
autonomously.
So instead of us having to go in

397
00:22:08,360 --> 00:22:11,520
and type in a question every
time and get stuff accomplished

398
00:22:11,520 --> 00:22:15,040
every time with a prompt, LLMS
can work autonomously when they

399
00:22:15,040 --> 00:22:17,680
are agentic.
So they can actually approach

400
00:22:17,680 --> 00:22:20,520
your data, they can approach
your systems, they can actually

401
00:22:20,520 --> 00:22:24,400
work 24/7.
They can keep an eye on any

402
00:22:24,400 --> 00:22:27,720
tickets that might be raised in
your company, any issues that

403
00:22:27,720 --> 00:22:29,920
are going on and stuff like
that.

404
00:22:29,920 --> 00:22:33,440
So you could have an HR agent
that looks into HR tickets.

405
00:22:33,440 --> 00:22:37,560
You can have AAR and D agent
that is going to, you know, look

406
00:22:37,560 --> 00:22:41,000
at your R&D data from the labs
and it is going to keep an eye

407
00:22:41,040 --> 00:22:44,640
or audit your R&D data to make
sure that all the quality

408
00:22:44,640 --> 00:22:47,480
control, all the quality
assurance and test like that

409
00:22:47,480 --> 00:22:50,920
they are being done properly.
That's the power of agent like

410
00:22:50,920 --> 00:22:55,160
the autonomous nature and the
ability to work without human

411
00:22:55,160 --> 00:22:57,800
intervention or with minimum
human intervention.

412
00:22:57,800 --> 00:23:01,040
It's quite important to realize
that these things, if you leave

413
00:23:01,040 --> 00:23:04,120
it without any human
intervention at all, then there

414
00:23:04,120 --> 00:23:07,480
is a huge possibility that it
will lead to very, very, very

415
00:23:07,480 --> 00:23:11,240
costly failures or scenarios for
you.

416
00:23:11,240 --> 00:23:15,840
So it's quite important to make
sure that the agents, whenever

417
00:23:15,840 --> 00:23:18,960
you are using it for something
quite critical, for example, you

418
00:23:18,960 --> 00:23:22,080
are using it to give refunds to
a customer, then it's important

419
00:23:22,080 --> 00:23:26,440
that you keep an eye and make
sure that people cannot misuse

420
00:23:26,440 --> 00:23:30,720
your AI agent and make it do
things that you do not intend to

421
00:23:30,800 --> 00:23:34,600
have it do in front of them.
OK, understood.

422
00:23:34,880 --> 00:23:37,800
In a way, it sounds like
foundational models.

423
00:23:37,800 --> 00:23:41,280
For example, if we take ChatGPT,
that's probably something that a

424
00:23:41,280 --> 00:23:42,720
lot of people are quite familiar
with.

425
00:23:43,080 --> 00:23:46,240
It's a type of conversant AI
because it's a chat box, You

426
00:23:46,560 --> 00:23:48,240
message it and it messages you
back.

427
00:23:48,240 --> 00:23:51,040
It's been trained on a
foundational model, which means

428
00:23:51,040 --> 00:23:54,080
that it's been trained on
basically most things that's

429
00:23:54,080 --> 00:23:57,360
publicly available out there.
So it can be compared to a

430
00:23:57,360 --> 00:24:01,360
worker being trained in a huge
variety of different techniques,

431
00:24:01,720 --> 00:24:04,040
but not really specializing in
anything.

432
00:24:04,240 --> 00:24:07,680
So there's worker, it can do
work, but if you ask it to

433
00:24:07,760 --> 00:24:10,720
create a very specialized
product, it will really struggle

434
00:24:10,720 --> 00:24:14,480
because maybe the skills that it
needed to have hasn't been

435
00:24:14,480 --> 00:24:17,320
focused yet.
And this is where your work such

436
00:24:17,320 --> 00:24:20,280
as what you were doing in
grounding these models come in.

437
00:24:20,280 --> 00:24:24,560
So it's really actually teaching
this AI more specialized, more

438
00:24:24,560 --> 00:24:27,920
focused, more relevant skills
for the particular job that you

439
00:24:27,920 --> 00:24:30,360
want it to do.
And then finally, with the AI

440
00:24:30,360 --> 00:24:35,240
agents is actually turning AI
models or turning AIS into

441
00:24:35,240 --> 00:24:39,600
almost like individual workers,
but know what they need to do

442
00:24:39,600 --> 00:24:41,480
and they can just do it.
And you don't really need to

443
00:24:41,480 --> 00:24:45,560
have 24/7 oversight or to have
as much hand holding as

444
00:24:45,560 --> 00:24:48,080
possible.
Would you say that's like a fair

445
00:24:48,080 --> 00:24:50,640
comparison?
Yeah, yeah, definitely.

446
00:24:50,640 --> 00:24:54,640
So it's, it's basically kind of
ensuring that you go from like

447
00:24:54,640 --> 00:24:58,600
just models that work with a
developer or a data scientist,

448
00:24:58,600 --> 00:25:02,560
kind of always keeping an eye on
those two models that would be

449
00:25:02,560 --> 00:25:04,640
more accessible or democratized,
right.

450
00:25:04,640 --> 00:25:07,800
So I think AI agents is really
helping to democratize these

451
00:25:08,200 --> 00:25:11,800
models and bring it into use by
people who are not like orders

452
00:25:11,800 --> 00:25:14,480
traditionally or not from the
technical background.

453
00:25:15,200 --> 00:25:19,520
Outside of work, what do you
think one of the most exciting

454
00:25:19,560 --> 00:25:24,200
potential applications of AI
tools into humans day-to-day

455
00:25:24,200 --> 00:25:28,280
lives could be?
AI should be reusable in

456
00:25:28,280 --> 00:25:31,880
scenarios where it couldn't
really reduce the amount of

457
00:25:32,000 --> 00:25:33,720
things that we humans have to
do.

458
00:25:33,720 --> 00:25:35,800
Not really doing everything for
us.

459
00:25:35,800 --> 00:25:39,480
Because I feel that honestly, if
AI is doing everything for us,

460
00:25:39,480 --> 00:25:43,000
from like writing an e-mail to
reading an e-mail to writing a

461
00:25:43,000 --> 00:25:46,280
presentation to even reading a
presentation, then no one will

462
00:25:46,280 --> 00:25:48,920
do anything.
That's not the future we as

463
00:25:48,920 --> 00:25:51,880
humans would want or for our
future generations.

464
00:25:51,880 --> 00:25:55,920
But what we really want is
definitely cases where AI is

465
00:25:56,040 --> 00:26:00,480
really, really great at to help
help people who might be having

466
00:26:00,480 --> 00:26:04,320
diseases and performing
diagnosis and supporting

467
00:26:04,320 --> 00:26:06,960
doctors, the medical specialist
and reaching out quick

468
00:26:06,960 --> 00:26:10,080
decisions, quicker turn around
times in terms of you know, like

469
00:26:10,120 --> 00:26:13,640
giving the patients their future
prognosis and diagnosis and

470
00:26:13,640 --> 00:26:17,560
stuff like that.
It is really adept at drilling

471
00:26:17,560 --> 00:26:20,240
down into the data.
There is so much data around the

472
00:26:20,240 --> 00:26:23,440
world which we have from our
daily life as well, which could

473
00:26:23,440 --> 00:26:25,960
be used to create so many
different useful things.

474
00:26:26,440 --> 00:26:29,720
Especially from the multi model
modality bit like it can help

475
00:26:29,720 --> 00:26:33,000
them to understand things that
are going on around the world.

476
00:26:33,040 --> 00:26:36,280
If some person is blind, then
they can actually use the AI to

477
00:26:36,360 --> 00:26:39,240
interpret what is happening in
their surroundings and it would

478
00:26:39,240 --> 00:26:41,480
be a completely game changing
thing.

479
00:26:41,480 --> 00:26:45,400
It would be a new life for them.
That is where AI could be very

480
00:26:45,400 --> 00:26:48,280
helpful.
Things like Excel and you're

481
00:26:48,320 --> 00:26:52,720
building a spreadsheet and stuff
like that and you kind of have

482
00:26:52,720 --> 00:26:56,800
to do lots of calculations and
create lots of formulas and

483
00:26:56,800 --> 00:26:59,440
things like that in which AI
could really help you.

484
00:26:59,520 --> 00:27:03,880
It could assist you to create
this formulas in a shorter time.

485
00:27:03,920 --> 00:27:07,280
It could help you to actually
focus on the analytics rather

486
00:27:07,280 --> 00:27:09,440
than the developments.
It would help you through

487
00:27:09,440 --> 00:27:12,200
decipher the data.
You can perform deep research on

488
00:27:12,200 --> 00:27:16,080
the data set and use it to
extract meaningful insights for

489
00:27:16,080 --> 00:27:18,840
your company.
How do you think that someone

490
00:27:18,840 --> 00:27:23,760
who is socially anxious might be
able to use AI tools to help

491
00:27:23,760 --> 00:27:25,800
them better go about their daily
life?

492
00:27:26,520 --> 00:27:29,880
A great example would be things
like the advanced voice mode

493
00:27:29,880 --> 00:27:31,760
that you have got in ChatGPT
these days.

494
00:27:32,600 --> 00:27:35,680
You can literally ask it, right?
To speak to me like a teacher,

495
00:27:35,720 --> 00:27:39,320
speak to me like a boss in my
company, speak to me like

496
00:27:39,640 --> 00:27:42,560
stranger and stuff like that.
So you can actually use it to

497
00:27:42,560 --> 00:27:47,720
simulate a person, a human who
you have never met before, for

498
00:27:47,720 --> 00:27:51,560
an introvert, for a shy person,
that is perfect for knowing

499
00:27:51,560 --> 00:27:54,960
beforehand what might it be like
to interact with such a person?

500
00:27:54,960 --> 00:27:57,400
What might be it like to
interact with the CEO of a

501
00:27:57,400 --> 00:27:59,120
company?
What might it be like to

502
00:27:59,120 --> 00:28:01,920
interact with a potential
investor and stuff like that.

503
00:28:01,920 --> 00:28:04,760
So it's really be useful,
especially I think for young

504
00:28:04,760 --> 00:28:07,920
professionals, for young people
who haven't seen life in and

505
00:28:07,920 --> 00:28:10,120
out.
We, we are very much at the

506
00:28:10,120 --> 00:28:12,440
beginning of our career.
We are very much at the

507
00:28:12,440 --> 00:28:14,760
beginning of our journey.
We haven't seen like the

508
00:28:14,760 --> 00:28:17,920
struggles that come later on in
life and that we learn from

509
00:28:17,920 --> 00:28:20,960
experiences.
So I think with the AI, we can

510
00:28:20,960 --> 00:28:24,480
actually understand that.
OK, like how is it really like

511
00:28:24,480 --> 00:28:30,440
to be in the shoes of ACEO?
How is it really life to like be

512
00:28:30,440 --> 00:28:34,480
in the shoes of maybe someone
who is kind of, you know, like

513
00:28:34,480 --> 00:28:37,520
older and you have like children
and how do you interact with

514
00:28:37,520 --> 00:28:38,800
your children and stuff like
that?

515
00:28:38,800 --> 00:28:42,160
So how how do I experience that
without even having reached that

516
00:28:42,160 --> 00:28:44,880
time or that age?
So I think that is where, yeah,

517
00:28:44,880 --> 00:28:48,040
I could really help the
introvert people to become like

518
00:28:48,040 --> 00:28:51,400
more extrovert.
And from my experience, as you

519
00:28:51,400 --> 00:28:55,600
start to speak to people, right,
you kind of start to become more

520
00:28:55,600 --> 00:28:57,400
outgoing.
There is no way it cannot

521
00:28:57,400 --> 00:29:00,160
happen.
Speak every day to the advanced

522
00:29:00,160 --> 00:29:02,400
voice mode, just for 15 minutes
every day.

523
00:29:02,400 --> 00:29:06,160
And I think literally in one
month you will be much more

524
00:29:06,240 --> 00:29:07,840
extrovert and much more
outgoing.

525
00:29:09,320 --> 00:29:12,560
That's a very creative way to
use the existing tools.

526
00:29:12,840 --> 00:29:15,360
And as you were speaking, I was
actually thinking, yes, that is

527
00:29:15,360 --> 00:29:18,200
so right.
It's a way for us, I guess, to

528
00:29:18,200 --> 00:29:23,720
also be able to more deeply
emphasize with people whose life

529
00:29:23,720 --> 00:29:26,520
experiences might be very
different to our own.

530
00:29:26,880 --> 00:29:30,440
And I think that's definitely a
great way to garner more

531
00:29:30,440 --> 00:29:34,000
understanding and more patience,
especially when we have to, you

532
00:29:34,000 --> 00:29:37,400
know, for example, work in a
very multicultural group or

533
00:29:37,400 --> 00:29:40,760
perhaps work with a group of
people who are just naturally,

534
00:29:40,760 --> 00:29:43,520
by default and by their own
experience, very, very different

535
00:29:43,520 --> 00:29:46,240
from us.
What would he say to young

536
00:29:46,240 --> 00:29:50,440
people who might feel like they
don't fit the mold, whatever

537
00:29:50,440 --> 00:29:55,240
that may be?
Believe in yourself, trust your,

538
00:29:56,120 --> 00:29:59,720
trust your skills, trust your
ability to change the world.

539
00:29:59,720 --> 00:30:03,520
Like literally every one person,
every single person in this

540
00:30:03,600 --> 00:30:06,600
planet, they have some unique
skills that we are born with

541
00:30:06,600 --> 00:30:09,600
which you can use to change the
world.

542
00:30:09,600 --> 00:30:12,440
Like every single thing that you
do in your everyday life that

543
00:30:12,440 --> 00:30:16,560
can actually help you to create
an impact that goes bigger that

544
00:30:16,680 --> 00:30:20,040
that can actually speak, speak
more than words, you know, like

545
00:30:20,040 --> 00:30:23,520
actions speak louder than words.
So you can actually write your

546
00:30:23,520 --> 00:30:26,440
own story every single day.
So that is what I would

547
00:30:26,440 --> 00:30:29,880
encourage young people that you
might be introvert, you might

548
00:30:29,880 --> 00:30:33,760
not fit the expectations of the
society or every single person

549
00:30:33,760 --> 00:30:36,680
in the society.
It doesn't really mean that is a

550
00:30:36,680 --> 00:30:39,400
weakness in you.
It is something that could be

551
00:30:39,440 --> 00:30:42,160
the positive quality, a positive
trait in you.

552
00:30:42,360 --> 00:30:45,400
You just have to leverage that.
Whatever is your weakness,

553
00:30:45,400 --> 00:30:49,400
identify how you can turn that
into your biggest superpower.

554
00:30:49,400 --> 00:30:54,400
Fittest introversion maybe
generally, maybe you are a good

555
00:30:54,400 --> 00:30:56,560
writer.
Maybe you you might be a good,

556
00:30:56,880 --> 00:30:59,160
good thinker.
So how you can become a

557
00:30:59,160 --> 00:31:01,760
scientist, how you can
potentially win the Nobel Prize,

558
00:31:01,760 --> 00:31:03,080
right?
Like have big dreams.

559
00:31:03,080 --> 00:31:06,960
I think like I never I never
imagined like even for one day

560
00:31:06,960 --> 00:31:10,360
in my life that I will be in
Forbes under 30, You know, so it

561
00:31:10,360 --> 00:31:14,920
was like have big, big dreams
and kind of never give up in

562
00:31:14,920 --> 00:31:17,440
your life.
That's the biggest encouragement

563
00:31:17,440 --> 00:31:20,680
to young people.
Who knows what you might become

564
00:31:20,680 --> 00:31:23,200
one day, right?
So just just keep come on going

565
00:31:23,200 --> 00:31:25,560
and let your work speak for
itself.

566
00:31:25,560 --> 00:31:28,200
And it will reach the right
people at the right time.

567
00:31:28,200 --> 00:31:32,800
And then that's where you can
speak up and you can share your

568
00:31:32,800 --> 00:31:35,600
views with the right people.
And you might be speaking to

569
00:31:36,160 --> 00:31:39,480
some of the biggest people,
biggest leaders in the planet,

570
00:31:40,040 --> 00:31:43,760
the best leaders in the world.
They are the ones who give space

571
00:31:43,760 --> 00:31:47,080
to their subordinates, right?
So they let their subordinates

572
00:31:47,080 --> 00:31:50,400
or their colleagues speak first.
And they are very humble.

573
00:31:50,400 --> 00:31:53,600
They are very grounded.
I think this is like a very,

574
00:31:53,720 --> 00:31:57,640
very much a unique trait which
could be turning you into a

575
00:31:57,640 --> 00:32:02,640
leader as you grow in your life.
For yourself, what kind of

576
00:32:02,640 --> 00:32:05,560
legacy do you want to be able to
leave behind?

577
00:32:06,800 --> 00:32:10,600
What I want to demonstrate to
young people is that you can

578
00:32:10,600 --> 00:32:14,920
actually shine in the big world
that we have around us by

579
00:32:14,920 --> 00:32:19,480
actually staying as you are.
What I try to do is kind of jump

580
00:32:19,480 --> 00:32:23,040
in at the right moment where I
feel it will help the company

581
00:32:23,040 --> 00:32:25,440
the most.
It will help maybe the society

582
00:32:25,440 --> 00:32:28,720
the most for other people to see
that.

583
00:32:28,720 --> 00:32:32,240
OK, like coming from a shy
child, you can actually reach

584
00:32:32,240 --> 00:32:35,240
this position in life.
You can actually be like Forbes

585
00:32:35,240 --> 00:32:37,040
under 30.
That's just the beginning.

586
00:32:37,040 --> 00:32:39,280
So there, there's so much more
that could be done.

587
00:32:39,280 --> 00:32:42,320
If I can become ACEO or
something someday of a big

588
00:32:42,320 --> 00:32:45,800
company, like I think that would
be something I would love to

589
00:32:46,040 --> 00:32:48,480
accomplish.
But yeah, let's see where life

590
00:32:48,480 --> 00:32:50,440
takes us, right?
Wonderful.

591
00:32:50,440 --> 00:32:53,680
Well, I will have to re
interview you in five years time

592
00:32:54,280 --> 00:32:56,520
and see just how much further
you have gone.

593
00:32:56,920 --> 00:32:59,120
Thank you so much for sharing
your thoughts George.

594
00:32:59,120 --> 00:33:03,480
Let me finish by asking you our
podcast staple, which is what is

595
00:33:03,480 --> 00:33:06,800
one thing that you think will
allow more people to have better

596
00:33:06,800 --> 00:33:10,000
mental health?
Because we talked a lot about AI

597
00:33:10,000 --> 00:33:13,880
today, AI, I feel it could be
useful.

598
00:33:13,880 --> 00:33:16,800
It could be harmful as well when
it comes to mental health.

599
00:33:16,800 --> 00:33:21,520
So if you are just completely
using AI for every single thing

600
00:33:21,520 --> 00:33:24,400
in your life and you are losing
human interaction, that's not

601
00:33:24,400 --> 00:33:25,960
good.
It could lead you to depression.

602
00:33:25,960 --> 00:33:29,960
It could lead you to, you know,
like just be doing everything

603
00:33:29,960 --> 00:33:32,800
with AI.
But where AI could be helpful is

604
00:33:34,080 --> 00:33:36,000
it could help with your mental
health as well.

605
00:33:36,000 --> 00:33:39,880
So for example, imagine a lot of
people, especially the older

606
00:33:39,880 --> 00:33:43,440
people, they might leave alone
in today's life, there is not

607
00:33:43,440 --> 00:33:45,720
much social interaction between
everyone.

608
00:33:45,720 --> 00:33:49,320
Like everyone is busy in their
everyday life and they don't get

609
00:33:49,800 --> 00:33:52,120
get to spend that much time with
their friends and family.

610
00:33:52,120 --> 00:33:54,000
So that's where AI could be very
helpful.

611
00:33:54,000 --> 00:33:57,000
You have the companion at the
fly of a button.

612
00:33:57,000 --> 00:34:00,600
You just click on that button
and then you have an AI that can

613
00:34:00,600 --> 00:34:05,160
speak to you and it can just be
your best friend.

614
00:34:05,160 --> 00:34:09,159
It can be your philosopher.
It can help you to act like your

615
00:34:09,159 --> 00:34:12,320
grandchildren, for example, and
it can give you that happiness

616
00:34:12,320 --> 00:34:15,480
which you might not be
experiencing right now because

617
00:34:15,480 --> 00:34:18,360
you are away from your friend,
friends and family.

618
00:34:19,040 --> 00:34:23,239
In a nutshell, use AI in the
ways which gives you happiness

619
00:34:23,239 --> 00:34:26,560
rather than in the ways which
makes life monotonous for you.

620
00:34:26,960 --> 00:34:31,760
Stay confident, stay grounded
and humble and keep on working

621
00:34:31,760 --> 00:34:34,520
on life like keep keep on doing
things that you'll have to do.

622
00:34:34,520 --> 00:34:37,159
If you like, like to write
papers, write that.

623
00:34:37,159 --> 00:34:38,880
If you like to write books to do
that.

624
00:34:38,880 --> 00:34:43,159
If you are good in sports, then
do so, but do something that can

625
00:34:43,159 --> 00:34:45,880
kind of will give you a
competitive advantage and that

626
00:34:45,880 --> 00:34:49,679
will set you apart in society
and be 50 years from now, people

627
00:34:49,679 --> 00:34:51,719
will remember your name, you
know, like that.

628
00:34:51,719 --> 00:34:55,120
OK, This person was the
contributor for so and so thing

629
00:34:55,120 --> 00:34:58,760
in the society and he made this
change in the society which

630
00:34:59,080 --> 00:35:02,200
which the world would always
remember and your name would be

631
00:35:02,200 --> 00:35:06,560
carved in history.
What a wonderful way to end our

632
00:35:06,560 --> 00:35:10,480
conversation, George It thank
you so much for taking the time

633
00:35:10,480 --> 00:35:14,640
to share your personal journey,
the comments that you received

634
00:35:14,640 --> 00:35:18,280
as a child, which actually
inspired you to keep working and

635
00:35:18,280 --> 00:35:21,480
stay true to who you are.
And so thank you for educating

636
00:35:21,480 --> 00:35:24,200
us about some of the key terms
within AI.

637
00:35:24,480 --> 00:35:27,200
It was a pleasure to have you on
the podcast.

638
00:35:27,880 --> 00:35:29,880
Thanks a lot.
Yeah, nice speaking to you as

639
00:35:29,880 --> 00:35:30,440
well.
Cheers.

640
00:35:30,480 --> 00:35:33,840
Thank you.
That's a wrap for today's

641
00:35:33,840 --> 00:35:35,920
episode of the Low to Grow
podcast.

642
00:35:36,520 --> 00:35:40,280
If it resonated with you, leave
a review and hit follow to help

643
00:35:40,280 --> 00:35:42,800
more people to find important
conversations.

644
00:35:43,400 --> 00:35:45,400
Keep growing and see you next
time.