>> Anthony Weaver: This episode is sponsored by
podvantage AI.
>> Sumedha Rai: Uh, to a certain extent, it's a supervised learning
algorithm, which means that you tell the machine, you take this
type of content, you put it into this category, and it's going
to do it really fast. So those type of jobs I do feel
are going toa get replaced. But what can you do? You can
see how to upskill yourself in that,
um, domain. Can you do something,
um, similar to the domain, similar to the expertise, but
can you do it using tech? Because if you're a person
who is now using thei solution
to enable your job, you're already an expert in that
field and people need that. People need more
workers who understand AI so that they can
integrate it. That's in demand.
>> Anthony Weaver: Welcome back everybody, back to another exciting show of day.
About that Wallet podcast. I'm, um, your host, Anthony Weaver, where
we are focusing on the Saich generation,
building strong financial habits so that they can spend
money, talk about money and enjoy their money with
confidence. Today I have somebody who has
been in and out, out of the financial
realm, but also with the AI spin
and I cannot wait to get this started. So how
you doing today, Samita?
>> Sumedha Rai: Uh, I'm doing very well. I'm very happy
to be on the show and um, I'm really
looking forward to these conversations because I've enjoyed your podcast
in the past. So it's an honor to be one of the guests.
>> Anthony Weaver: O thank you so much, so much. Um, and I'm
really dying to really dive into your story
because you've been,
um, I would say internationally
exposed to so many different things in the world.
And what brings you here today
is what brings this question up is,
um, so many people are pretty much
scared of how powerful AI
is getting and what is your take
on the impacts on the human psyche?
>> Sumedha Rai: Um, so I think
with any new change that comes into
our world, there is
a lot of excitement, be a lot of
panic, see a lot of misinformation.
And I see this with AI as well.
People are really excited to try out the new tech. Every
business wants it as a, ah,
solution so that they can go out there and
tell everyone, tell their competitors or the
consumers're powering our solutions through AI
and generative AI. Um, the
other hand, there are also people, uh, really panicking
about job security and they're saying it's going to take
away all the jobs and um,
replace the human jobs that we have
with like, I don't know, some robotic jobs or A.I.
jobs. Um, I think the
first step to any New change or any
new tech in this case is
understanding. So since everybody
is talking about it, oftentimes what happens
is that we get really affected
by other people's opinions. And we see this
one article that talks about
how, you know, the next 10 years are gonna be really grim
for us, and another article that talks about how it's
gonna completely take over all the human jobs and
we start feeling the sense of panic. So at
this point what I'd like to do is I'd like to tell the
person, um, is this your
opinion, uh, which has been formed because of
some research that you have done, or is this just
something that you've been hearing a lot? And I totally understand that if
you hear something repetitively, um, it will
make an impact on you. So the first thing that you need to do
is understand what the technology is.
Understanding is the absolute key. And in
this case specifically, knowledge will empower
you because um, when you're introduced
to new topic and you don't know anything about it, you go online
and you read five articles, um, you feel really
confident and you're ready to have a conversation about it.
>> Anthony Weaver: There'five articles.
>> Sumedha Rai: Y. I mean I've seen people who go like,
yeah, I'm not too sure about it. And then like one
week later they'll come back to me and say I did my research
and uh, they want to talk about it now. So there's a sense
of confidence that you get when you understand
something. And I'm not saying that you're going to understand all the technical
aspects of it, exactly how the model's working,
but just having an understanding of what this
solution looks like. What can AI actually do,
what kind of jobs can it replace? Because at the
end of the day there are certain jobs that are taking
say like 8, 8 or 10 human hours
and they're repetitive in nature and they can be automated.
And if those jobs can be automated, um,
people and businesses will want to do
it like that. It's going to be done at a fraction of a cost with less
error because there is no human fatigue
or um, there is less
um, um, human error due to
monotony. So they will become more efficient.
But we also need to understand that there is like this whole
new uh, spectrum of jobs that is not going to get
affected just because we're not there yet.
AI is just not there yet. So um,
if you're panicking because you've been hearing
about this a, ah lot and you don't understand
it completely, go out there, read about
it and um, if you're scared
about your jobs, understand what is the nature of
your job and how is ainna affect it. So I
was talking to someone recently and um,
she mentioned that as a part of
her team, um, there was one
person who was categorizing a lot of data
into uh, 150
categories. And she used to take 10
hours, 15 hours to go through one sheet because like just
the sheer amount of data that was on the sheet needed that much amount of
time. And then she said, we started talking to this
vendor and they said they can categorize it, which makes
complete sense to me to a
certain extent. It's a supervised learning algorithm, which means that
you tell the machine, you take this type of content, you put it
into this category and it's going toa do it really
fast. So those type of jobs I do feel
arenna get replaced. But what can you do? You can
see how to upskill yourself in that,
um, domain. Can you do something,
um, similar to the domain, similar to the expertise, but can you do
it using tech? Because if you're a person who
is now using thei solution to enable
your job, you're already an expert in that field and
people need that. People need more workers
who understand AI so that they can integrate
it. That's in demand. So I
think, um, TLDR as we call
it.
>> Anthony Weaver: Yes.
>> Sumedha Rai: Um, don't be scared
immediately when you read something.
Please go and do your research. That's gonna give you a little
more confidence, that's gonna give you some more knowledge and see
if you can take some steps, um,
to go in a different direction. Or maybe
you're just gonna realize, okay, um, I'm a
nurse, this is not going toa get replaced anytime
soon. So you'renn do better.
>> Anthony Weaver: So what are your thoughts on working harder, not
smarter first and then work
smarter?
>> Sumedha Rai: Uh, okay, I think
it's a cool thing, uh, to ask. I think
even with the tech that we have, the tech
has already worked very hard. There are humans
behind it that have worked hard
to enable some humans to work smart.
So, um, personally I feel
any solution, any tech, has both
these already embedded in it, whether you see it or
not. So you can say that with the
solutions you're going to work smarter, but also making the solution.
Somebody worked hard to create a solution and now we're
working hard to improve the solution because you
might have seen it. Um, a lot of companies
are coming up with new solutions every day. Not all
of them are vetted. Some of them might be trained on
biased data. Some of them might have problems
related to knowledge gaps. Some of them might have been trained
till a certain date so they don't have a lot of recent
data. Some of them are not showing you the sources.
We're still putting in a lot of hard work to make sure
that um, it is a
solution that is promoting uh,
a better society, so to say. So
we're still working hard and smart, but
once we get to a point where we need to
work a little less hard and a little more
smart, that's when I would say, yeah,
concentrate towards working smarter. Don't be one of those
people who says, I'm not going to touch it. I'm very
comfortable. Because at some point uh,
you're going to be pushed in that direction, whether it's by your
company, by your peers, whether by
the sheer fact that your uh, job might
get um, obsolete. You will get.
>> Anthony Weaver: Yeah, because what I'm thinking about is when it
comes to job replacement and
promote promotability, one of the things that I
always promote or say to
myself or anybody who's looking to get into their
field for the first time is to
understand the business as a whole. Write the processes and
procedures in place and streamline it so that you
can promote or uh, replace yourself
in that sense. Uh, so say if somebody is
looking at their job and they're like, hey, this is really
monotonous. Is it a way that,
like what tool or program
should they look into learning if they're looking
to replace some of those repetitive things? So like, the company
might not be on go about this
AI thing but say if they can just write a
small Python script or
small. I mean, I know c, I'm aging myself
here, But a small JavaScript to
kind of do what they do day to day. Uh,
what are your thoughts on that?
>> Sumedha Rai: Um, I think if you're a person who can actually
take the initiative to do it, I would support it
completely. Um, um. A lot of solutions
nowadays are built on Python and there
are so many different environments that
um, will enable you to do it free of cost.
Google, um, Colab, for instance, is going to give you GPUs
for free. So you can even try out computer vision
models or you can try out language models which are very
heavy in nature and sometimes cannot be done on someone's machine.
So if you're that kind of a person, I, um, would say
the first thing that you need to do is uh,
to understand what your problem statement is.
So um, you break it down into parts
and you see which part needs to Be automated
and then you do a little more research about it. If
it's ah, a general question, um,
like I don't know, classifying it into certain categories
as I was talking about before,
the chances are the likelihood is that there is already
an open source library that exists to do that
work for you. If you're someone who already knows a little bit
of Python, that's actually a language that's used a lot
in um, AI machine learning, some flavor of
Python. It could be Piparark, which is used
for uh, making sure that you can handle a lot of
data. It could be Pytorch, which is used for
more deep learning algorithms. It could be something
else. But people have put in a lot of hard
work already to create open source libraries for you.
And if your solution, um, the problem
that you're looking to automate um, is, is something
that is, that has been done before, there is a chance that
somebody's already created a library for it. So then your job becomes
even easier. You import that library, you
use it, you test it out and you see if things are getting
better for you from then on. If this is something
uh, that you fancy, uh,
you're going to organically get pulled into it. Trust
me, if you're a person who enjoys
seeing the automation work, there is no
chance that you're not going to want to do more.
>> Anthony Weaver: Yes, I love it, uh, because you
have a talk coming up, um, in New York
City say in June, uh, talking about
the navigating the bias in AI, just
kind of promoting the ethical decisions making
in fintech. Can you just kind of give us a quick
synopsis for those people who have
no clue about the bias or
the non bias in AI?
>> Sumedha Rai: Right. Um, I think that talk was last
year, uh, but it was a very interesting talk.
I tried to capture some of the ideas. No, you're good.
I'm so glad that you actually looked it up. I'm
thrilled. Ah, there's another one coming up in April
that's about fraud detection.
But let me just talk about the bias in AI and why I think
it's a topic that we should be talking about. So
um, most of the training data, um,
as we call it, and I'm just going to explain what training data is real
quick. And AI model needs to learn
from something, um, it needs
to predict something for you and for
that it needs to understand what it's trying to
predict. So as a simple example, if you want the
AI model to understand numbers,
handwritten numbers, you need to show it a lot of Pictures
of handwritten digits and then it's going to understand
writing styles, it's going to understand a two
is with a curve, but a one is a straight line, stuff like
this. So it needs a lot of training data
to understand what it's supposed to predict.
Now a lot of the training data that we
had in the past, um, which
has already been generated was based on
decisions that could have been biased
because you know, um,
it could be because of human cognitive errors, it could be
because of, you know, the society that existed at that
time. But we're using any and all of data
that we can find right now to train models that they
understand a lot. So ah, the AI
model becomes better if it sees a lot more
examples. It's like a child
saying, if you show me an orange five times
I um, will understand that it's an orange. But if you show me an
orange a hundred times, I will definitely understand that it's
an orange. So uh, we try to give the
model as much data as possible. Unfortunately,
uh, we might be feeding it biased data. And as
part of our systems we're not always
very careful that the results
that we're getting are not
biased. Um, if we use such
models to make automated decision
making, it might give out biased results and
it might affect actual human lives.
So for me the question
of understanding the results of a model
from an ethical and um,
unbiased perspective is very important.
But a lot of companies that are integrating AI, ah,
solutions are not making it as
an important, like an absolute necessary
part of their pipelines. Right now they,
you fine tune the model or train the model, they
see how it performs on a uh, on a set of
data, testing data, um, and they
deploy it. But there needs to be this other
chunk that needs to go before the results
are deployed which somehow overlays the
demographic data on top of your results to
understand is it somehow producing biase results?
If it's producing biase results, what can we do?
We recalibrate it for a while and then refeed
it. Do we not use the solution as an automated
solution? We could have a human in the loop.
And is this something that is going to really critically affect
the lives of people, for instance in credit decisions
or in some medical decisions?
So um, for certain things
this is almost an irrepraceable part of your
pipeline and we need to have more
conversations about it.
>> Anthony Weaver: Yeah, because I'm thinking about the models back
in like reading some of the books of
how credit was actually dispersed
to uh, different neighborhoods or people
of color during that timef frme
was like, they call it redlining and based
on, you know, the credit score or they have
the valability of their business
actually being successful or the banks are willing to
take that type of risk. So is this kind
of where we getting back into, hey, they don't
know enough information about us, uh,
meaning us as people of color because we
were always left, it seems like we always left behind and it was always
trying to play this catch up game. How can
we now be part
of that um, that
conversation or be part of that data set so that we
aren't left out of it?
>> Sumedha Rai: Right. I think this is a very, very important
uh, topic that you're raising right now. Very important question
to address. And you're right. There were certain
subsections of the population that were not even given the credit. So there
is no data about them or you know, the
decisions were all adverse, which means there is
negative data about them. So uh,
one of the things that we start with is to give it
more representative data, which means
if you pick out a certain subsection of the
population, you give it both positive and
negative cases and it needs to be more
balanced. So for instance, in your data set, if you
have say five different
um, geographies, you give
it enough positive and negative data in each of those
geographies, uh, four for
um, the model to understand that this should not be
the primary factor that I can make a decision
on. And this could be something to do with
age, it could be something to do with um,
um, racial bias, um,
that we've been seeing. It could be something to do with geography,
it could be something to do with um, the credit
score. So unfortunately, credit scores have
been linked with um, geographies and races
for some time. And this is the link that we're trying
to del link now. Now, assuming you still see the
results, I understand in certain cases you actually do not have
data, so you need to train it. Assuming you see some
results, once you see the results,
um, AI models are inherently predictive in nature,
which means they're giving you probab scores. Can you do
something to the probability scores to bring these results
on the same um, level so you
can recalibrate the scores to say
this negative is equal to,
to the same negative in terms of like the absolute
number. In terms of the probability number that I just gave
you, these two negatives look equal to me.
Um, and then you use this data again to
train your model so then it understands that I'm looking for
patterns right now. I'm not going to just concentrate on these,
like probability measures. Another thing that's really
important is to quantify the bias.
So, um, let's imagine
that, uh, you're training a model and
there's a person who's going toa evaluate it. The person needs to evaluate
it on a certain metric. Now what I'm
saying is that when you overlay the
demographic data on top of your results, the false
positive rate, like a person
being denied credit, for instance, but
wrongly, should be equal in both the
segments. Like if the model is denying something
incorrectly, it should deny it in both the segments equally.
And now if it's not, let's work towards getting this
number correct. So there are things that you can do and
hopefully, you know, we're going towards a society
that's more equal. So the data that's going to be generated now, now
it's going toa be more equitable. So when we use this to retrain the
models, things are going toa look better. And again, for this
thing, we can t have the solution be automated
completely. We need not just one,
but several humans in the loop. And I say several
humans because we as people are also biased. We
have human cognitive biases. So you cannot just
have, um, one person's opinion. Sometimes
for crucial things, you need to have five people's
opinions. It's like taking the average of a model.
So you get the results from the model, you run it through like five
people's decision making and then you generate
some results which are hopefully more
equitable, more accurate.
>> Anthony Weaver: Yeah, okay.
Um, because I'm just thinking of,
I'm glad that there are people like you who are actually
stepping up and saying something in this world
of tech. And I'm sure you had your
set of boundaries to kind of push through,
uh, what were the hardest challenges of actually getting
into this industry?
>> Sumedha Rai: Um, I think one of the things that
is hard for people historically is
to not come from a tech background that can sometimes be
a blocker. Um, I actually
started, um, in economics and then
I did a master's in finance and I spent some time in investment
banking and private equity. It had nothing to do with
tech. And, uh, when I entered the
field of tech, there were already people who were
working, uh, in tech for the last like five
years or something. So for them, programming was
something that was second nature. And for me it
was something new that I had to spend some time on. I taken,
I'd taken lessons in Java and C
during high school. Yeah, and
you know, like, it builds, uh, your logical
thinking, it builds Your reasoning, thinking, you know how if statements
work but then um, you're now
programming in a new language uh, which has a different syntax
and it takes some time to get used to it. Whereas the people who
have already been working in this. But then every
person can have their strengths when they're entering a
new field. So for instance for me uh, domain
knowledge was a point of strength and statistics was a point
of strength. So um, I was entering the field of
data science and AI where the decision making also
relies on uh, the statistical assumptions that we
make. Some math driven uh, things that we have to take
into account before we reach the programming stage.
So um, I found my strengths over there and then I
built up on this other side of like the tech knowledge
and infrastructure knowledge and programming knowledge.
So if you're a person who wants to
get into tech but is not exactly from a tech
background, it is not exactly a
blocker. In today's world at least I see so
many people entering it, taking some time to upsill
themselves and we have reached a point where
we're trying to democratize AI as much as possible. There are
so many open source options available.
When I started my journey, um, I was very
clear on the fact that I have to do a formal
master's program and I did it
and it was very useful for me. But then I also saw
people just going to boot camps. You
know it's harder like compress that
amount of knowledge in just like 16
weeks or something. But people were using this option
and then there is uh, like this whole segment of
people who just go online and they pick up courses
and over time they are doing very well
as well. Like they, some people choose to specialize
in a certain subsectionion of tech or
AI and they get very, very good at it. Like um,
visualizations. It's a very very
important part of conveying your point. And
I still think there is a lot of work that's needed from my
side to find the perfect graph
or the perfect um, um
PowerPoint slide. That makes it,
it so much better for a person who's from a non tech
background to understand my point and people have mastered the
skill and they are absolutely required. Like
I'd love to get some tips from
them. But uh, I think
again my point is if you're
interested in the field of AI but you don't come from a
tech background, don't let that be a blocker.
>> Anthony Weaver: Got you.
So giveniving your experience growing up um, and studying
in India, the UK and also the
US like how does could these diverse cultural
and educational settings like really shape your approach
to problem solving?
>> Sumedha Rai: Uh, I think collaboration is one of the
biggest strengths that I have gained uh because
I worked in teams that were
culturally different, that had a different set of
opinions and how they put it in front of you
and they were also you know
um, on different skill levels.
So um, what I saw in
one country was not necessarily how things
were uh managed in another country both in
terms of like communication, in terms of their um
approach to problems. But what really helped me
understand is that you can gain something out
of every opinion, every perspective.
Um um one of the things that we do in
statistics sometimes or you know in modeling
sometimes is pooling averages.
And um, if the model is not performing
great we get a lot of smaller
models together and then get the average out of that.
And to me uh, my cross country experience,
my cross cultural experience was somewhat of a pooling
average because I try to
listen to everyone's opinion with the
mindset that I'm going to pick out this one
thing that's going to really resonate with me or I'm going to
have this h. I didn't think of it like this
moment and then I'm going to work on it and I'm going to understand
and uh, how to go about it from a different perspective.
And I actually I do gain a lot from conversations.
It could be a person sitting in the UK and South Africa
and India or my co worker in the US
and talking about the same topic
like how do you approach fraud detection in AI
gives me so many different perspectives that my resulting model
is actually very strong.
>> Anthony Weaver: Nice. Uh because you
also mentioned like um I'm thinking about from
other things that you've talked about before but
considering your work with like natural language
processing or NLP's that you've talked
about before and understanding like the user sentiments,
um are they like the
emotional challenges that the Sadwich generation have which
is um the design of
AI doesn't offer really practical
assistance for them. So like how there
are ways that they can actually utilize it
in their day to day. So I would say they got
kids and then they got parents to deal with. Is
there any AI tools or suggestions
that you have for them?
>> Sumedha Rai: Um, I think since we're thinking of
um a person who wants to us
AI in their day to day lives I could come up with so many
suggestions because um,
we've really handed these uh, machine
learning solutions to people's uh
hands literally like fed them saying here's
a $20 subscription much like
net go interact with chat GB. But
uh, since I'm on a finance podcast,
let, um, me probably talk about some solutions
that people should absolutely start
using if they're not using it already.
Um, I think money is an important topic in
every family, for every individual. Um,
so sometimes we do not know
how to get started with money conversations.
We don't know what to do with our money. And one of the worst
mistakes that you could do with your money is to just let it
sit in a bank account without doing anything
with it. Um, so you absolutely need to start
your journey to do something with it. Um,
it could be that you want to save a lot more and then invested
later. But you're, you know, to begin with, you're not
saving at all and you're finding trouble
understanding what am I doing with my money. Start with a
budgeting app. Um, I've seen people who create
these beautiful Excel sheets. Everything is
color coded, everything is linked. But as you mentioned,
a person with two kids and a full time job
may not have the time to do it. So there are these
apps that make budgeting really easy for you.
Um, they look back at the transactions that you've
made in the last, I don't know, two years, five years.
And um, given a set of goals that you
have, the future, they're gonna start nudging you in the right
direction, saying, you know, okay, in the last
24 months it looks like, um,
you did not spend so much on travel, but suddenly I see a lot
of Ubers right, happening. Are you
just doing this because you're, you're not,
you really needed it, or is this because of something that you
could have avoided and you save instead? Now once
you've started saving, you, um,
have to start investing in something, uh, in somewhere because
your money needs to make more money,
so to say. So, um, what are you doing
in your investment journey? Um, you, are
you using, are you comfortable enough to say I already know where I
want to put my stocks? In which case maybe a robo advisor could
help. Like, you know, it a fraction of the cost and you could
just do with some extra financial advice that a
financial expert could give you. But maybe you couldn't afford the
financial expt, so you could look at a robo advisor if you don't want
to do that. There are investments that are more
passive in nature. So, um, you
could, uh, you pay a subscription cost or
you could pay um, a dollar cost for every
transaction that's made. But you start
uh, tying up your investments to an index
and you're passively investing it. And um, there are certain
AI solutions that also like, tailor to your needs. Like,
um, um, this is the ROI that
I want to achieve in the next two years. These are my long
term goals. I'm saving to buy a house. This
is how much the house can cost me. Um,
these are my risk preferences. So you give it a whole
bunch of parameters and then it starts
rebalancing your portfolio, given the market conditions,
and starts putting the money in there. So those are also good solutions
to have. Um, I've seen
people who are very good at investing. They're like,
look at the market every single day. I'm going through my own articles
and I'm doing my own research for such people.
Um, um, getting a
summary of the financial news every single day could be helpful as
well. Uh, I think the Bloomberg
terminals have it where when you open the terminal, the first thing that
you see on the screen, or like it's a part of the terminal somewhere,
is five short bullet points about how the financial
markets are looking. And for me, that's going to be really useful
information because I might say, okay, this is
something that I don't even invest in, so maybe it's not
useful. But then I might find something to do with the Magnificent
Seven and I will click into it and I'll start reading about it.
So there are solutions, uh, for a
person to look into their money
more closely. And, um, I
think people should absolutely start doing it
today. Like, start getting close to
where your money is going. How is it going? What can you
do to make it go in a better place today if you're
not already doing it?
>> Anthony Weaver: And that brings up a point of now
want to learn more about you, if you don't mind.
>> Sumedha Rai: U.
>> Anthony Weaver: Uh, it's like, did your
parents, um, bring
you into the financial realm or is this something that you just
kind of like? You know what? I want to learn more about finances because we didn't talk about
it in the house.
>> Sumedha Rai: Uh, right now I think
this is a really cool question. I would say
yes and no. Okay, so,
uh, my parents are actually
doctors, so n, um,
topics about investing were not exactly table
conversations. Uh, not,
um, we were not discussing this over dinner.
I could tell you so much about, uh, Tylenol
and Advilce and the thoughts
that make up these medicines. Um,
but, uh, we were not exactly
discussing where to put the money or what to do with the
next paycheck. But I do remember that
my dad was very particular about doing his own
taxes with his accountant So I remember he used to
maintain this, uh, book which had all his
transactions written down, so that when he's
giving this information to the accountant,
he's not a person who doesn't know
what, where his money is going and how it's being treated or, like, how
his taxes are getting filed. He knows exactly what
he's giving to the accountant. Um, he knows his
transactions. There are times when he would just like, call his accountant and say, oh,
I see this over here, but I think I did this. Can you go to, like,
page 14 and check this out? I have it in my note.
And, um, that image stuck with
me. And, um, I didn't
start investing right away. Truth be told, I
entered the investing game a little later. But
now that I'm already in it, I do understand
that if I'm managing my own money,
I feel a lot more comfortable with it. Even if I'm
asking someone else to do it with me. I would feel
a lot more confident if I also had knowledge about
it. And, um, this
whole, um, uh, making sure that
you know exactly what you spending habits
is, is the key to it.
Um, my parents used to discuss these things with
each other. So, like, sometimes I would hear a
conversation. So I know the conversations were happening
in the house, but they were not exactly happening with us. So
maybe this is what I would advise people,
that there are certain conversations that you can also
start having with your kids. Um,
what does it mean to save? Um, what
is actually the value of money? There's a
really cool, uh, thing that I remember, uh,
when I was talking to my nephew this one time, I asked him what did he
want for his birthday? And he
said, you could either get
me a fidget spinner or you
could get me an iPhone.
And I remember thinking, oh, my
God. I don't think you, that he has any idea of what,
like, the cost of these two things are.
>> Anthony Weaver: Right?
>> Sumedha Rai: And this is just a funny example to say that,
um, start teaching your kids
the value of money. Like, what can work
for them? What is
$10? What can it buy? What is,
you know, a thousand dollars? What's a good
income to live off of, say, in New York City?
And I'm not saying you need to have these conversations when the kids 3
years old, but
when they're in their teens or when they're in high
school. Um, these are conversations that should
be almost a part of our, um,
daily talk.
Uh, another thing that I would strongly recommend everyone
to do is get close to your taxes.
Um, in the last five years, I've just started doing
taxes myself. It just makes
me, uh, it forces me to look into
what I did with my money retrospectively. Helps me
plan a little bit. Uh, which is not to say that
a lot of people have much more complex taxes.
They have business entities that they need to take care
of. But you could hand it to an
accountant, but also exactly know how the tax
was filed. U. You are going to learn so much
more when you do your own taxes. And you might even get
ideas about, like, what to do better next
year.
>> Anthony Weaver: So what tools are you using to file your taxes if you don't m mind, like, caus
I was thinking, like, the big box ones, and I have
a tax account that comes on, and she was like, well, you can
go with them, but if you really want the money,
you got to go to somebody now.
>> Sumedha Rai: That's true. I'm not an expert on, like, tax
structures and the best way to save money, so I use very simple
tools. Right now, I'm. I so like, pick up
TurboTax or Sprint Tax, but then it asks me
so many questions to fill out
a return that I feel like when it's
asking me so many questions, I have to go back and look
at the answers to those questions. And that informs me u.
Uh, a lot more than. Than just
like, giving everything, giving my W2 to the
accountant and, um, and just asking the
person to file the tax or e file it. Um, my
point is, like, going to an accountant and getting
the right help and getting the right ways to, uh, save money
or filing the right returns and getting the right
refunds, um, it's a very good thing
to do. But also, do not make this a
thing where you're completely uninformed. Because I've seen people just
say, I'm not really sure my accountant does
it. I'm not a big fan of
that answer. Um, honestly.
>> Anthony Weaver: Well, it shows up in your work ethic.
And actually not. It goes back to the data set that you
were providing, your AI model. It's like,
is it doing what you wanted to do? And if it's
not, you know why? Because you
understand what the data set that you've given it, in a way, it
should operate, and it's not. So it actually shows up in
everything that you do, which I have picked up on just in
our short conversation. Um, I'm sure you're like,
you know, why is my food
tasting this way? Do I need to add an extra
spice?
>> Sumedha Rai: Oh, my God, I'm such a big fan of research. Like, if
I don't Understand it. I sometimes go crazy and I've been told
this in the past that we don't exactly
need 100% accuracy.
20% will do it. And sometimes I
deliver. But I see myself going back and saying, okay, yeah,
but what happened to the 10%?
>> Anthony Weaver: Yes, yes.
>> Sumedha Rai: Um.
>> Anthony Weaver: Cause it gets you to think about like these models that go
out, um, far as like, oh yeah, you know, 29%
of the people are doing this. So I'm like,
well, what are the 80 mean or
the 71% of the people doing that? The
29% are doing?
>> Sumedha Rai: Like, uh, you know, this is actually a recurring theme
in uh, AI as well. The,
the choice between explainable versus
explainable AI versus black box models.
And um, there are certain
businesses that actually choose explainable
AI over black box models, even though black box
models are going to give them better metrics just because the
explainability part is so critical to the
business. Like, um, if, if
a doctor was using an AI model and really wanted
to understand the result, um, he or she needs to
go back and see exactly how the result was generated.
Cann I just go like, I don't know what happened.
>> Anthony Weaver: This I just handed to the AI team.
>> Sumedha Rai: Yeah. And um, believe you me,
people are actually doing that in the development world where
they say, they wash their hands off and say, I'm not
really sure how this result, um, uh,
you know, was generated because I fed this to
the model and this is what it gave me. Now
for certain things it might be okay. Like if I'm
generating a summary of something and I get high
level bullet points, I'm probably okay with a black box
model as long as the result actually looks okay for
something like, uh, you know, mark this as spam or
not. I'm not too worried about, um, just marking
something else's spam and saying the next time, just make sure this is
also included. And I'm not really fussy about why did you
mark something as spam or not spam? I mean maybe
not, but for certain things, like as
I was mentioning, credit decisioning, if a person
is denied credit, um, I need to know
exactly what happened, um,
at what part in the model pipeline was
this decision made to go from,
yes, this is what I looked at and like this is
how I came to the decision. I need to know these things.
So yeah, explainability is very important to
me.
>> Anthony Weaver: That is awesome and I appreciate that
you taking the level of detail to go
through, look at all the fun stuff.
That is not really many air quotes here with the fun
stuff.
Uh, because I'm thinking about like now
as we get older, we move into like the third segment of the show, which
is the features. Um, and
I'm thinking about like when your parents are getting
older, are you
planning to have them live with you doing,
um, what they call it, the assisted
living. Have you thought about or had that
conversation with your parents yet?
>> Sumedha Rai: Um, I think yes. Um, we've
almost started talking about it, I guess,
but it's really a decision that's
very, very personal to them. So
I mean, I can have the conversations with them,
um, but really, at the end of
the day, it's exactly what they want that's gonna
happen. I know
and I know this decision is a very important one. And for
different people based on different situations, it' the
result might look very different. But at least in my
case, um, it's really up to my
parents. Um, they uh,
were good with their finances, I
think, and that'that's great. So
they are able to make that choice
independently. And, um, I will
probably have very little say in this.
>> Anthony Weaver: Okay. I actually think what it be
possible that people can actually just put in their parent
information into an AI model and be like,
hey, what is the best solution in this
situation? Because we can't figure it out. Just dump it
all in and just say pick one.
>> Sumedha Rai: Uh, wow, uh, wow. Uh,
very, very sensitive question.
Uh, to answer that question, I think
yes, there could be a solution,
but also coming back to the same
conversation and um, first of all,
you understand why that prediction was made and
secondly, I mean, make sure that someone has a
choice to say yes or no.
And I'm sure like people are gonn to do that,
but were something like
this to get created, um, the
end result should not be. I think the
a said so, so this is what
we'renna do. Uh,
it should, um, also be like,
oh, uh, this looks like a cool result. Maybe we
can talk about it, I guess.
>> Anthony Weaver: Yeah, I think that would be cool. Um, at least
to get the conversation starteduse a non bias.
Yeah, not saye person, but a non biased
entity, uh, is providing a solution
based on the data that is s provided. I think that'be pretty
cool.
>> Sumedha Rai: Yeah. Again, like, was it really non biased though?
So understand what the
data was trained on. You need to go
to the very start of the solution and say,
are you sure it's not biased? And yeah, that's when you
rely on the non biased result.
>> Anthony Weaver: So to say, uh, yes, yes, yes, full
circle. I love it that you.
So, um, right now do you want to focus on
You. So what areas um,
are you focusing on improving in your
life or even your career?
>> Sumedha Rai: Right. I think having the right conversations
for me is very important to me at this point of time
in my life. Um, I feel like
I get 24 hours in a day
and um, a lot of that is spent
on working um, I don't know,
like preparing for a speech at a conference
or, or writing an article. I love doing these things.
I inna put more information
out there. Good, um, information out there
if I can. Um, and so there
isn't a lot of time that's honestly
spent talking with people, uh,
and having a uh, very long conversation.
So when I do get the chance, like if I go at
a conference or if I am m talking with a
friend, I have started making this
conscious effort to also bring up topics that
are important for me to grow as a person.
Um, and I really encourage everyone to do it. I
mean um, that is not to say that you should not have
conversations about uh, things that are not related
to this list of important topics. It's um, I
can totally understand gets very stressful. Sometimes you just need to
vend. Sometimes you need to have conversations about,
I don't know, um, shoes
and uh, um. Those are also
important conversations for your brain. But also start making
a conscious effort to start having think the right
conversations now. Um, the word
right I wantn stress on it because the word
right might um, be different for
different people based on where they are in their lives.
But if you have a list of these topics that you think are
important, um, start talking to people
about it. If they're in the industry or if they're in that
domain, you're going to get a lot of good perspectives
and it's going to help you build on top of it. If you're
a person who likes to research about topics, just
getting those conversations started, it is going to be very good for you.
So for instance, when I go for a conference,
um, and if I'm speaking at a conference,
um, I'm very alert right before the speaking topic.
I'm very very alert. So I
would actually uh, try to get a slot
that is later in the day which
forces me to listen to all the conversations that are
happening beforehand. And I would consciously make
a decision to just go very early, listen to everything
and really absorb. And as I mentioned before,
there are times and I when I tell myself
h. I did not think of it from this perspective. I
already had an opinion. But this is also an extra
opinion that could really, you know,
um, um. I could have a different take
on, on things if I looked at it from this point of
view. And those things are important for me.
So at this, at this time in my
career, I'm looking to have
good, useful, uh,
conversations with a, uh, lot of
people in my, um, in my,
um, industry.
In industry, it could be in my life, it could be a part of
my friend circle. Um, I think when I
hesitated with the word like industry,
it's because sometimes the conversations are
just about AI and how awesome it is.
And then I met someone recently
in journalism and she said,
you know, I absolutely do not understand it at
all. And my simple ask, um,
for her was, okay, like, we don't have time
right now, but let's set up some time, have a cup of coffee and just
discuss what you don't understand about AI and I don't
understand about journalism. And then let's inform each
other about how it could be maybe a collaborative, collaborative
effort. Uh, uh, some time back, I was speaking
to a person who works in economics, and
he said, oh my God, there are so many conversations around AI.
Sometimes you don't need it. That is true
causality. I need causal inference. I need
to understand what thing caused the other
thing. I don't need predictions for my
problem. And I was like, yeah, that's, that's great.
I understand. I'm working in AI, but
not every cause has to be
championed by AI. There are certain things that might
not require it. So I'm really trying to have
these conversations with so many different people,
whether it's at a conference, whether it's at
a meetup, whether it's with people in my
own company. But I'm consciously making
an effort to carve out some time to have
these important conversations. And another thing
that I'm trying to do is to put my
efforts in the right direction. Um, that's
because in the industry that I'm working
in, there is so much noise. Like
the word AI is an umbrella term for
so many different things about it.
Yeah, I really need to do my research to
understand in the next three years, what am I going to
concentrate on? Am I going to start
learning a new language? Am I going to start
learning a new concept? Am I going to start looking at it
from a project point of view, a research point of view,
a business point of view, what am I going to do in
the next three years? And I'm trying to chart a course for
myself because this,
um, tech market is changing overnight
sometimes. So I need to make sure that I'm, you
know, I'm up to date with the topics.
I'm in a way ahead of the curve a little bit.
So I'm putting in a lot of time and effort to research
my path moving forward.
Um, conversations, uh, are a part of.
>> Anthony Weaver: It, um, because you got me thinking
about conversations just in general. Um,
and these are the things that I talk about, um,
with other podcasters. It's like the future of
podcasting. Um, what tools are people
using, how they streamlining their services
and so forth. Like, we can talk hours. Like, I could
talk your head off about this stuff. Um, and I'm sure you
can talk my head off about AI and like, how you guys are
looking into pretty much the same topics but
just at a different take of, of like, how are you streamlining your
process? How are you streamlining your code? How are you looking
in power consumption, uh, with these models? Because you
hear about, I think it says it's almost like an ocean's worth of
cooling that you need just to run some
programs.
>> Sumedha Rai: Energy corprint is very high right now. Yeah,
yeah.
>> Anthony Weaver: Um, and so it's just like how we
all have the same general topics.
It is just how do we approach it and
the mindset about how of it all, like,
then again goes back to do you even really need it?
Because sometimes the simplest solution, like how I think it was what
NASA when they went up in space and it was like, we gota find a pen
that can do anti gravity.
>> Sumedha Rai: My God, use a pen'a.
>> Anthony Weaver: Pist.
>> Sumedha Rai: Right, yeah, I've
heard about that. Uh, yeah, there were issues
with that as well. The lead can break and
stuff. But I understand, like, coming back and
saying simple can help, doesn't need to be
overco complicated. Don't overkill.
>> Anthony Weaver: Yeah, I think that might be the simplest,
uh, way to sum up this episode.
>> Sumedha Rai: Yeah, of course, yeah.
Um, I say this to, uh, startups
and businesses looking to integrate AI. Think about three
things for sure. Need time and
money. Do you need it? Do you
have the time to, um, create
it? Do you have the money to afford it? And I think the
need part is very important. Like, answer that question
first. Can it be done in a simpler way? Uh,
do you need to throw this into a chat bot or
a generative AI solution and pay for each of
those API calls? Or can you do it
in a simpler way? Yes.
>> Anthony Weaver: Love it.
U. Um, before we get to the final four, is, ah, there anything that you want
to leave the audience with before we dive into the final
four?
>> Sumedha Rai: I think this became like a recurring theme in this
podcast for me, but my mantra is research
it and prepare for it. So
specifically for this
technology that has, uh, so much going
on for it. There's so much talk about
this technology. Research the
technology, research how to use it, research how
to, um, how other people are using it, research how it's being
used in your industry, and then sort of like start
preparing for what comes next. Um, if you feel
that you need to upckill yourself, start taking those steps
right now.
>> Anthony Weaver: Perfect. All right, you ready for the final four?
>> Sumedha Rai: Yes, of course. Looking forward to it.
>> Anthony Weaver: Awesome.
Number one, what does wealth mean to
you?
>> Sumedha Rai: Um, uh, a, ah,
really, really cool question. And
I actually thought about it and I think,
um, I would say that
wealth for me is having the right
skills and the competence to
be able to generate the tradable currency
in the future if I need to. And
I say this because your skill
set is very important. Something could happen
tomorrow. You might not have your job, you might have
to use up your savings. Um, a lot of
things could happen to this, this fiat money
that you keep in your bank accounts or to this commodity
money or to like, uh, a land you on or
something. But do
you actually have the skills to generate more
tradable currency that we need, um, to
survive, to like, buy goods and
services for us? Do you have the skills? Do you have the competence
to do it? Do you feel confident, couldn't enough to do it? And if
you feel like, okay, I could
lose my job and be unemployed for the next six months,
but I'm pretty sure with my current skill set, I would be
able to get, um, on the right track to start
generating this wealth again. Um,
your skills, in my opinion, are then your real
wealth. And um, the
second more philosophical take on it is
that the definition of enough can be
very different for different people.
So do you feel
like you have enough and does that give
you a sense of contentment?
So that sense of contentment for me
is tied to the idea of wealth. A
person with two kids might have a different
sense of what wealth means than
a person who is maybe, you know,
um, does not have kids or has like dependent
parents or is working in a different industry
that, that you know, does not generate enough
numbers to get to a certain, um, salary
bracket. But at the same time, given
your current situations, given your current
geography, given how you've been
working, do you feel content with where
you are? Do you feel content with the number,
um, that you have in your bank account?
If yes, yes, you do have wealth,
and then you can plan for how to sustain this
sense of contentment. But if you feel
like I m. Don't think this is enough,
then ask yourself, what is your definition
of enough? And work towards it.
>> Anthony Weaver: Right. This is a sad
question. I'm just thinking of, like,
when you say enough, because I know you like to,
you know, like gu. Um, um, to do your research.
When is enough research for you to say, yes,
I'm moving forward and I believe this to be the
case.
>> Sumedha Rai: Know at some point I just get tired of the screen.
Uh, no, let me, uh. Yeah, that's
a good question. That's a good question. Uh,
I once told someone, you did not go to the seventh
page of Google Page.
I think enough for me is to start
feeling confident that I can,
um, start the Endeav.
So if I'm creating a
model for fraud detection and I know
nothing about it, enough for me
is to start looking into what space
I'm working on. What do the features mean to me?
What exactly are the rules that govern
the model that I'm looking at? And then at a
point where I feel, okay, let me just start coding this
out, I feel like at that point the research is.
And then I obviously, like, there are certain
questions that come up, um, when I'm
coding, then I come back to it again.
But the first part of research
is always longer than the successive
parts after that. It's like a marginal addition on top of your
knowledge. So, yeah, I think
typically enough for me means I'm ready
to get started.
>> Anthony Weaver: Okay.
>> Sumedha Rai: After that, it's going to be, um, an incremental value
to every data point that you give me.
>> Anthony Weaver: I like that. All right, number
two, what was your worst money
mistake?
>> Sumedha Rai: Uh, I think I said before I let my money sit in the
bank account, a, uh, savings bank account, generating
no, uh, interest for some time. And
I, I could have started sooner,
and I did start, but if there
is anyone out there who's still not doing it, get started
today. You need to make your money work for you
as well.
>> Anthony Weaver: Love it.
Number three, is there a book that inspire
your journey or change your perspective?
>> Sumedha Rai: My. Ah, God, this is a very tricky one, honestly.
Uh, there are so many different books that give
you so many different takes on
things. So, um,
sometimes it really depends upon what I
was looking for at that point in my life.
Um, and the book might change,
but then there's this book, um,
called the Alchemist. A lot of people have read it, of
course, and I think the reason why I'm
mentioning it is that it brings out a very, um,
very famous Kind of, um,
way to approach things in life. Start
learning from the journey. Like, start learning from
your experiences. Of course, like, having an end goal is very
important, but while you're chasing that end
goal, uh, don't forget that
every small thing, every small thing that goes
wrong is actually like, not really going wrong. You're
learning something from, um, it. Because
depending upon our goals, it could take
days, months, or years to get there.
And if we don't start learning from
our experiences, it's gonna feel like it's a
very long journey to get where we're trying to get.
But at the same time, if we have this mindset
saying, let me learn from every small
thing. Journal it if you want to, because when you look back at
it, it'll feel like a positive reinforcement and it's going
to make you, um, work in a more positive
way towards your new goal. But
start learning from every little thing. Um,
and I think that book really
talked about it in a very nice
philosophical way. So I actually ended up reading it
twice at different times in my life.
Um, another thing that I'm reading right now is Atomic Habits.
>> Anthony Weaver: Love that book.
>> Sumedha Rai: Yes, it's a great book. Um, we
are all, um, a slave to our habits. Sometimes we want
to break the old ones, the bad ones, and
we want to start with the new ones. And again, it has,
it has a very similar theme saying, you know, learn from the little
ones as well. But, um, if you guys haven't
checked out that book, you, you should think about reading it.
>> Anthony Weaver: Definitely. U, uh, yeah,
I'll leave that one. We'll talk offline.
>> Sumedha Rai: Yeah.
>> Anthony Weaver: U, uh, number four, what is
your favorite dish to make?
>> Sumedha Rai: Wow. Uh, I like
making a vegetarian lasagna. So,
um, I'm vegetarian, I love Italian
food. And, um, I
started experimenting with it, I think two or three years
ago. I burnt a lot of them over B.
But I've come to a point where I know how to
layer them properly and, uh,
to use different kinds of cheese. I know which one
works and I can customize it based
on dietary requirements at this point. And
honestly, this is sometimes my catchphrase
for someone who I'm inviting over, um,
as a guest. I'd be like, yeah, let's play some board
game. And you know what? I do a mean lasagna.
And, and just leave it at
that.
>> Anthony Weaver: Check them. Are you using, um, are
using like tofu? Like, what are you using as your, your meat
based, quote unquote meat bas. Uh, because I heard some people
use like lentils, um, or like some Type of
toefu base.
>> Sumedha Rai: I like to mix vegetables, uh, and
create like a medley. So I chop them up real
small. And so there's squash,
zucchini, um, um, spinach, carrot,
carro, um, mushrooms. And I like
to chop them up real, real nice. So then
it almost feels like, um, it's. It's
one base, but it's actually a medley of these bases. And
it. The flavor is really nice in your mouth.
>> Anthony Weaver: Yes. Oh, man, this is. I need to see
a picture. I. To follow your social. To see.
>> Sumedha Rai: Yeah, I'SEND one to you or, or
the next time that I'm inviting you to a party again,
I'm going to use this. I'm gonna say, hey, uh, let's play some board
games. Let's talk finance. But also, I make a mean
lasagna.
>> Anthony Weaver: Okay. I'll be sure to be hungry.
>> Sumedha Rai: All.
>> Anthony Weaver: Ah, right.
The very last question of the show, which is where could people
find out more about you?
>> Sumedha Rai: Um, I am on, um,
LinkedIn. I have a personal
website. Um, the website has a link
to shoot me a personal message as well, if you want
to. I love having conversations about
AI Whether you're a person working in it, it.
Whether you're a person looking to find out more about it,
just shoot me a message now. We'll make sure that I get back to
you. I want to enable as many conversations as
possible. Sometimes one on one is not always possible, so
I do a group conversation. Um,
but yeah, I'll make sure I get back to you.
Um, so hit me up, please.
>> Anthony Weaver: Awesome. Well, thank you so much,
Samitha. I greatly appreciate all of the
information that you provided to us. Um, especially about the
AI stuff. Like, I'm thinking I'm doing AI, but
then people like, well, as long, large, uh, language
models, and I'm like, okay, well, whatever is AI in
industry. So, uh,
you know, I greatly appreciate everything you
provided us. And one of the things, everybody, if you're listening, if
you made it to the end, I just want to let you know that you have what it
takes to go to that next level. You know, you listen
to Samita'story um, you saw
where her parents came from. You saw that she took a whole different
path than what they did. You have the ability to
change your life, your trajectory. All you have to do is just put in that
effort and take time out of your day to invest in
yourself. I wish you all the best. We out.
Peace.