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