Rudi Falat: Hello everyone and welcome to this event out co hosted by emeritus and voice of fintech podcast we're going to talk about Ai in fintech we're going to have.
Rudi Falat: A how from nvidia, who is the global head of developer relations for consumer fintech and video so i'm looking forward to an exciting session, but first we would like to hear also a little bit about emeritus right.
Marie Mize (she/her) | Moderator: I think you ready, yes, absolutely we have a lot in store here as we make our way through this next hour together a lot to cover as we learn more.
Marie Mize (she/her) | Moderator: About exciting an explainable artificial intelligence for modern fintech and we are joined by two industry leaders here.
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Marie Mize (she/her) | Moderator: To rudy and his co host here today to learn more about this topic in modern fintech and from there we'll take all of your questions so you'll notice there are.
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Marie Mize (she/her) | Moderator: So, as we dive into today's session we want to introduce you to emeritus.
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Marie Mize (she/her) | Moderator: To today's live learning event, I see a few folks saying hello in the chat Benjamin here from Johannesburg we have pete here from the UK.
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Marie Mize (she/her) | Moderator: And so, with that let's turn the spotlight over now to our keynote subject matter experts here to tell us more about exciting and explainable artificial intelligence for modern fintech.
Marie Mize (she/her) | Moderator: you've met rudy for a lot here a moment ago, he introduced today's session you'll see he's the founder and host of voice of fintech podcast.
Marie Mize (she/her) | Moderator: he's a digital transformation, innovation and startup enthusiast a startup mentor advisor business angel executive education coach founder and host of the voice of fintech podcast I won't read all of the details of.
Marie Mize (she/her) | Moderator: rudy's bio here, but certainly we are joined by a world's expert in these topics and to take us through today's session he's joined by a hall at.
Marie Mize (she/her) | Moderator: Who is the global developer relations lead at consumer fintech and he leads global developer relations for consumer fintech companies.
Marie Mize (she/her) | Moderator: and his focus is on driving Ai adoption within the fintech industry and he helps companies to accelerate their machine learning models at scale, so he also leads fin tech oriented engagement.
Marie Mize (she/her) | Moderator: with partners cloud service providers influencers venture capitalists and startups in the ecosystem so i'll let him tell you a little bit more about his background, as we turn over the spotlight here to our two keynote presenters.
Marie Mize (she/her) | Moderator: very pleased to have you both with us here today.
Marie Mize (she/her) | Moderator: Just a reminder to stay tuned here towards the end after today's event we're going to be talking more about additional educational opportunities here at a narrative so please do stay tuned for more.
Marie Mize (she/her) | Moderator: One quick announcement, here we are recording today's session everything you see here is being recorded live.
Marie Mize (she/her) | Moderator: And we're planning to follow up within 24 to 48 hours to provide you, each with copies of not only the recording but all of the slides that we cover here as well.
Marie Mize (she/her) | Moderator: As the rest assured you'll be able to go back through and take a more detailed look at the material that we're covering here today, so with that I warmly invite into the spotlight our hosts of today's event, thank you again for being here every year.
Rudi Falat: Well, thank you so much Murray, for such a kind introduction and very professionally done, I think.
Rudi Falat: You raised the level so, but how.
Rudi Falat: You know, we need to stay there at the very least right, so thank you so much, once again, and thanks for emeritus for co hosting it with voice of fintech so.
Rudi Falat: You know, when I first heard about nvidia I obviously recalled gpus and gaming and things like this and then, when I started to be more and more interested in fintech i've seen in video everywhere right and a lot of people were talking about a.
Rudi Falat: Shooting this and, by the way, sometimes maybe a little bit too much, and this is why we have a ball here because we'd like to unpack some of the Ai.
Rudi Falat: Inflation perhaps some of the untapped opportunities in fintech world, why is in the media invested in this space.
Rudi Falat: what's going on there, so first and foremost let's start from the beginning and maybe, let us know how did you get to what you do today because there may be quite a few listeners who would like to do the same as you so how what's the way to get there.
Pahal Patangia: Absolutely thanks for having me rudy and then but esteem.
Pahal Patangia: it's been a great journey so far I started my career with psycho.
Pahal Patangia: Where I began in our data science consulting capacity to advise banks and financial institutions to mitigate risk So this was a mix of technical and business consulting where I.
Pahal Patangia: Help them on use cases like retail credit risk default prediction fraud detection etc and built a good bit of domain knowledge, as well as basic machine learning skills.
Pahal Patangia: But I had this quest in me to go deep dive further into the world of Ai and what's hot and happening.
Pahal Patangia: into this into this area and that's what got me to pursue a master's degree at the University of Minnesota after taking a break and.
Pahal Patangia: As I graduated from that program with the equipped with the tools and technologies which are using.
Pahal Patangia: Current a landscape, I went on to work with a fintech company briefly in minneapolis, and all this while and media came up to the door.
Pahal Patangia: Within the opportunity, where it was an ensemble of my business is my industry knowledge meant domain knowledge, as well as my as skills and that's what got us gets us talking, you know today.
Rudi Falat: brilliant, so you know your title is global head of developer relations for consumer fintech at nvidia So what does that mean in plain English and what's your role or what's your team's mandate.
Pahal Patangia: yeah absolutely it's quite a nice roles, so let me break down into very simple words and what essentially do is I had.
Pahal Patangia: A data scientist machine learning engineers C suite executives at fintech companies to help them adopt Ai and enable them with tools and technologies which get them accelerated from a machine learning Ai perspective.
Pahal Patangia: Now this role involves me working on two facets, I would like to call the first part, being the one to one part where I work with key strategic accounts which are part of any media strategy and.
Pahal Patangia: Go to market plan and intervals and honestly discovering use cases that these accounts understanding their pain points and.
Pahal Patangia: making them aware of what they could do using and video students technologies and frameworks and and run their machine learning models their Ai models more efficiently, and you know, in an accelerated manner, so this involves working with them closely at a hands on level.
Pahal Patangia: In pilot projects conducting PCs and making them realize the value potential which nvidia brings on the floor with its accelerated competent platform.
Pahal Patangia: The other part of the role is more of a one to many approach which I take to evangelize meds position in the industry and establish ourselves as a leader in the accelerated computing space.
Pahal Patangia: This involves me working with different influencers speaking on podcasts like this writing blogs having partnerships with cloud service providers, like aws tcp azure etc, so that we can enable and we can position and spread nvidia's.
Pahal Patangia: Word into the industry and make developers data scientists machine learning engineers and C suite executives aware of what nvidia is doing to help them.
Pahal Patangia: To help them accelerate machine learning models.
Pahal Patangia: have been important part of my role is also working with startups with the meds inception program which is like an accelerator program for startups of kind of housing more than 9000 startups and.
Pahal Patangia: What is essentially doing it as its opening doors for startups who are in seed stage of the seed stage of CDs a stage, even in later stages to enable them with the with the resources from a compute standpoint, provide the right network partnerships or.
Pahal Patangia: Also, or also get them on to cloud credits, as they progress into their journey, so this is how I I am evangelizing and bds full stack computing platform to the Community in both of one to one and a one to many fashion.
Rudi Falat: All right, that sounds like a lot of work to do, but you know days long right behind so.
Rudi Falat: Look, you think that let's maybe let's level set a little bit because Ai has become quite crucial to development of many, many fintech startups around the world.
Rudi Falat: But let's start from the beginning, why is nvidia.
Rudi Falat: Interested or invested in this space right and it has to do with your portfolio the origins, when we come from the hardware, the gpus and now, maybe also the software solutions that maybe some people don't know about right so.
Rudi Falat: Of course we had lots of Ai summers mo he also had Ai winters, especially not from the late 80s and you can.
Rudi Falat: Read different books and listen to different audio books, perhaps as i'm doing right now, where.
Rudi Falat: In one of those books, they say, look it's because the first generation of Ai people wanted to really mimic how the humans think.
Rudi Falat: And the performance just wasn't there, so the funding dried up now, we are in a different position where we.
Rudi Falat: You know, we are no longer purists, but we are pragmatists that's one way to look at it, but the other way, is that in last couple of days or last.
Rudi Falat: couple of years we we developed or recreated 99% of the data that is out there, and at the same time, the computer power has increased so much right so into that backdrop, how does nvidia fit in there and and their efforts around fintech.
Pahal Patangia: Absolutely let's start with financial services and banking, so we have been in the space for more than 13 years now sure our key product offering which.
Pahal Patangia: The gpus graphic processing units had their first use cases designed around gaming and rendering graphics and accelerating simulations where it became a landmark was in 2008 when JP Morgan used it for simulations at at their place for.
Pahal Patangia: Conducting Monte Carlo simulations for portfolio optimization and options pricing and if are taking the journey to 2012 when some deep learning researchers found out the value of using gpus for.
Pahal Patangia: For training deep learning models which earlier was very cumbersome and time taking now what this led to is a revolution.
Pahal Patangia: which was continued by the advent of frameworks like by dodge and tensorflow in 2015 the kind of democratised the development of applications within fintech and financial services.
Pahal Patangia: And made Ai accessible to all four easily developed for to easily develop, and this is where we saw our customers like pay Pal beginning to use.
Pahal Patangia: beginning to use deep learning models for fraud detection etc and let's cut to today where we are today, we are taking a full stack approach in.
Pahal Patangia: Taking our product to customers, so what full stack meanness means so it's not just the hardware it's not just the software.
Pahal Patangia: it's not just the networking units it's the entire stack it's it's from hardware to application so at the bottom layer, which is our key offering the gpus now these.
Pahal Patangia: are served by the software gets so and media has software kits the cuda X API stack, which is responsible for regular machine learning processes like.
Pahal Patangia: etfs which is data preparation well model building model, training and influencing at scale, so the stack is working in tandem with the acceleration hardware, to bring in value in terms of speed and performance to the.
Pahal Patangia: To the machine learning pipelines, and on top of this, a business needs to develop applications these these libraries don't.
Pahal Patangia: don't offer any value if they are just being used in our in in our training environment so.
Pahal Patangia: that's where our application stack comes in, we have identified business use cases for different fintech customers say we have our SDK for.
Pahal Patangia: Recommendation systems for Personalizing and personalization on offers and next best action to take, and we have our merlin SDK for that.
Pahal Patangia: For a conversational Ai and virtual assistant applications, we have SDK like Riva for that So you see from hardware.
Pahal Patangia: To the application level it's a full stack which is coming across and what these frameworks and applications of punish it has democratized if developers the data scientist, to enable them.
Pahal Patangia: To build applications on their own, they can pick the custom applications which are open source and start building on top of that or use it as is for their use case, so this has this has really.
Pahal Patangia: Put put out and bds position as an accelerated computing platform leader, which is an enabler for data, scientists and developers in the fintech ecosystem.
Rudi Falat: All right, thank you for this now let's be critical, sometimes a little bit as well right when you look at a lot of the startups out of the fin techs.
Rudi Falat: Many of them, they use phrase Ai enabled or Ai powered and there is research out there by or you know, there was published by venture capitalists.
Rudi Falat: can't remember the name now but or put it in the podcast notes, but basically looking at the startups in Europe in a particular sector, and he would say that look.
Rudi Falat: Out of those more than half of this they don't use anything that he would classify as artificial intelligence.
Rudi Falat: So sometimes people overuse it right same thing is with blockchain at some point right that we was blockchain enabled then it was Ai enabled so let's.
Rudi Falat: let's get real what is Ai when it comes to fintech and what is not right and is this maybe just basic statistics or you know some.
Rudi Falat: Some sort of a macro Is this still an Ai you know, maybe we are further than that right, so when do people actually start using something where Maybe you can call it machine learning and then go too deep learning, etc, but how to cut through the noise.
Pahal Patangia: yeah exactly and you bring an interesting viewpoint, I interact with a lot of startups within nvidia's fintech inception ecosystem and what I see them as they are present at different stages and.
Pahal Patangia: They are all more or less as startups now what happens with the startups is that at different stages.
Pahal Patangia: When you are in an early stage, you may not have the data, you may not have the talent, you will not have the resources and the compute to do, Ai at scale.
Pahal Patangia: But may have the vision to do that in the future so yea year they may sound, to be a company's now but might not be implementing it.
Pahal Patangia: But would definitely be on the roadmap to bring Ai applications in the future and be fully Ai enabled so that could be the difference between.
Pahal Patangia: An early stage company and a company which is in the later stage of the growth cycle, and that is where nvidia's inception program kind of helps because.
Pahal Patangia: We are there to provide the startups with the resources with compute and with the required partnerships in the industry to help them grow and get to a point where they are more or less self sufficient and.
Pahal Patangia: move into the next stages of their car fundraising and building Ai capabilities now coming to the point of differentiation between what's Ai and what's not a i'm fintech.
Pahal Patangia: I come from a creditors background, so I take a very simple example of a digital fintech which is underwriting loans so, for example, if this fintech is.
Pahal Patangia: Just using rules for underwriting like say this customers ages greater than X, then we would give loan or the.
Pahal Patangia: debt to income ratio for this customer is less than Why then I would give the loan by X and by these values are more more or less intuition Beige or heuristics based, then it is not Ai.
Pahal Patangia: And on the other hand, if these companies are using machine learning models, if they are using anything mathematical which has, which is backed by existing data.
Pahal Patangia: That gets them onto the journey of Ai depending on the complexity which they are adopting it so yeah, that is, that is a key difference which which will allow you to differentiate were a starter base with respect to the journey and cut out of fluff.
Rudi Falat: All right, very kind and.
Rudi Falat: Great point still so now turning into an optimistic outlook right So what are the most promising use cases in fintech that you see you know where Ai can be deployed.
Rudi Falat: Something obviously beyond maybe automation maybe it's insights in wealth thick maybe it's insights in investment management all kinds of other things would do you see from from you know virtue of your role as the most promising use cases that are out there.
Pahal Patangia: Exactly, so in my conversations with index variety of use cases emerge but i'll just pinpoint of you might have for use cases, if you will, so the first would be fraud detection.
Pahal Patangia: Companies are using fraud detection deploying Ai to use to do fraud detection at scale be doing your know your customer checks or anti money laundering checks or.
Pahal Patangia: Even detecting credit card transaction fraud banks and financial institutions and fintech companies are using deep learning models to detect anomalies in customer behavior and we know the fraudulent candidates.
Pahal Patangia: We had a very good example with PayPal presenting in video conference last bond with this nvidia gtc they are using graph neural networks to.
Pahal Patangia: To find out a bad actors and understand their payment patterns are spending patterns, so that they can quickly detect.
Pahal Patangia: If the person is fraudulent or the merchant is fraudulent or not, the other example for the use case which is very prominent is personalization.
Pahal Patangia: Now there's a there's a big race for fintech companies and big tech companies to be the super APP so you would see square aka block or pay Pal or.
Pahal Patangia: The Googles or the pts of the world they're all rushing to encompass.
Pahal Patangia: All of the offerings if they can into into one application now what that has enabled them is with them is that.
Pahal Patangia: They are equipped with data, where they're having a 360 degree customer view and in, and they have very deep information about what the customer behavior is.
Pahal Patangia: What is the customer spending and what the customer would like or not like, depending on their situation diamond life and the demographics so.
Pahal Patangia: sitting on this much information about the customer, then this information is fed into form of embedding to large recommender systems, these are powered by.
Pahal Patangia: gpu accelerated and video marlin to identify suitable product offerings understand what could be the next best action they can to be taken for the customer.
Pahal Patangia: They could be of great value and give a more personalized customized experience for the customer, not all it comes to my mind, which is a.
Pahal Patangia: player in San Francisco who who uses customer credit information their behavior patterns and helps them with recommendations for different credit products or financial products.
Pahal Patangia: Which which may suit the customer, so that that could be that those those use cases are very prevalent in the fintech ecosystem.
Pahal Patangia: On another one is my favorite when more.
Pahal Patangia: When more has a customized reward scheme for users, so if I would be spending a lot on gas station, but not on coffee, so I would get more rewards and gas station.
Pahal Patangia: So these are not fixed reward categories as traditionally would be in credit card so very interesting case of personalization going on there, and these are all powered by large scale recommender systems.
Pahal Patangia: Which are enabled by high high performance accelerated computing the third use case is around conversational Ai and virtual assistants.
Pahal Patangia: You have seen go away with go read the demand for Labor has increased.
Pahal Patangia: Also, not to mention about the Labor shortage all companies all over the world are facing this problem, so what they really need is to augment their existing capabilities with Ai with tools and technologies that can handle.
Pahal Patangia: Large work large volume in terms of customer queries Square, which is now a very big company listed already, but a great fintech.
Pahal Patangia: Has this has this platform where they were the service a seller inquiries so square developed a bird models, which are the models for training large language models.
Pahal Patangia: Large language information and processing it into.
Pahal Patangia: A fashion that it can answer Question it can answer the questions it can summarize customers queries so they use they use those models and trained using and videos accelerated computing platform to.
Pahal Patangia: answer and automate 75% of the queries, so this is something which ultimately.
Pahal Patangia: Smooth things out the customer experience, as well as reduces costs for the fintech we are talking about so very important.
Pahal Patangia: Use case for conversation Ai and then the last one, is one of my favorite you did mention automation, but I would want to take it towards a claims automation insurance.
Pahal Patangia: A lot of good use cases around insurance companies using computer vision to detect auto auto accidental claims so.
Pahal Patangia: If you send a picture of your car within 10 or a scratch the computer vision and guided them, which is trained on millions of images, if not billions.
Pahal Patangia: would be able to detect whether actually your claim is legit or not, and how much is that depth so.
Pahal Patangia: This much of enablement has has already happened, using technologies and the scale these use cases are actually.
Pahal Patangia: These use cases are the most prevalent ones, which we see in fintech nowadays and penn yan is a very good example where they are using nvidia gpus to train computer vision models for detecting such.
Pahal Patangia: For anomalies or understanding what is the claim actually about using customer image data and beyond this, there are many other cases use cases which I would just like to give a mention because they are hot.
Pahal Patangia: One is particularly St environmental, social governance that then that comes transaction monitoring credit plus prediction robo investing, these are all cases where and we, the os X rated computing platform comes into play an actual value.
Rudi Falat: All right, fantastic you know exciting range of opportunities, I think, if I were to summarize it in terms of activities it comes back to the book I also recommend to everyone is called prediction machine.
Rudi Falat: which basically says, you know the Ai and ml is all about making predictions, even when you talk about translation it's a prediction, how would human translate this right so prediction I think when it relates to fraud.
Rudi Falat: conversational Ai also prediction, you could also talk about insights I think that that could be quite insight exciting, but maybe much more challenging.
Rudi Falat: that's maybe to do with included in connection with the humans, and you know automation, of course, right, why would people do things that they don't need to do because.
Rudi Falat: These are routine tasks right so that's a whole different discussion about Ai impact on the jobs, but before we go there.
Rudi Falat: Again, I wanted to infuse a little bit of skepticism so it doesn't feel like we are just.
Rudi Falat: You know, talking about the future is bright every single day, if you are, if you are a student of economics or history, you see the excellent in economics, we always have booms and busts right so.
Rudi Falat: that's that's how it goes, and when you are in the boom you always think like this is not going to happen anymore we're not going to have a boss right, because now, the boom has lasted longer than ever.
Rudi Falat: And it's true, I think we have less of a crisis, then we then we had maybe it's every 15 years, maybe it's even even longer, but.
Rudi Falat: You know, when you look at the Ai history, we talked about Ai winters do you think we are way past it, that we will never have Ai winters because now we have so much data we don't know what to do with we have the computers.
Rudi Falat: do think that we see a continuous improvement in prediction percentages and and performance of the models so that we will not see the Ai winter, as we did see you know that 2030 years ago.
Pahal Patangia: Absolutely, and I I related from an investment perspective as well, so we conduct a survey within financial services.
Pahal Patangia: Professionals globally, where they just level set the pulse regarding Ai in their organization so recovering executives on the seesaw it on the it side on the data science side.
Pahal Patangia: and understanding their models deployment infrastructure spending, etc, and what has emerged is that.
Pahal Patangia: They are executives are very keen 83% of executives in fact we're very keen on investing in Ai and now that tells that he has become more or less mainstream.
Pahal Patangia: And again coming to fintech another nugget which I want to throw which makes me conclude that.
Pahal Patangia: Ai winter is way past beyond us is investment in fintech which has happened in the last year we we did see investment getting more than double around 130 $2 billion globally coming into fintech and.
Pahal Patangia: More or less, there is an element of Ai infused into fintech as these startups will scale at in the time to come, so if I blend these two nuggets of information to get oh.
Pahal Patangia: I see a very promising future for Ai and and that will be continuing in different sectors fintech included, and if there would be any direction where we would go it wouldn't be only onwards and upwards.
Rudi Falat: All right, let's hope so, I think, yes, absolutely you're right we've seen the record vc funding last year, despite the pandemic we and in general and we've seen it for the fintech worldwide.
Rudi Falat: Incredible numbers of course this is also driven by the global wealth rising, despite the pandemic and investors looking for yield.
Rudi Falat: Ever since the financial crisis so expanding their appetite for alternative assets like investing into the startup so.
Rudi Falat: let's hope that continues, even though the markets could be choppy from one day to another, but hopefully the long term trend is, as you, as you have an outline now again staying at the big picture view.
Rudi Falat: When it comes to Ai you know a lot of people also have concerns in terms of ethics privacy and the governance and when you are looking at the technological innovations, very often, of course, the regulation lags behind the innovation so.
Rudi Falat: What do you think that is needed now so that we get the API governance right.
Pahal Patangia: yeah so you know to say with great Ai comes great responsibility, so I would I would definitely apply to all stakeholders within fintech or.
Pahal Patangia: who are applying Ai to be at the people from the data side of the stakeholders involved from the business side it is everyone's responsibility to.
Pahal Patangia: Make sure the the results coming from their Ai products on models are being are being under the right ethics and the governance criteria.
Pahal Patangia: I particularly see two teams for data science teams, where Ai projects can go South first is on the model side, maybe the team is focusing a lot on.
Pahal Patangia: While affecting the model optimizing the metrics that it comes at a cost of say.
Pahal Patangia: Some bias in the model or it comes at the cost of explain ability, that is where a projects fail in the long run, and on the second hand if.
Pahal Patangia: On the data side when you collect the data if your collection data collection process is disproportionate and it does not represent.
Pahal Patangia: The target sample of your market then whatever you feed to the model is is will not will not be producing the right results when the model is trained so when these two things come together it.
Pahal Patangia: come together, even in their own existence, they are a problem for Ai projects and what we have been doing it and vdi is particularly on the explain ability front.
Pahal Patangia: We have been actively working into the ecosystem with partners who are prominent into explainable Ai into Ai governance model and monitoring.
Pahal Patangia: To develop to develop these platforms at partner, banks and partner fintech companies, so we work closely with companies like for Ai they specialize into explainable model monitoring and.
Pahal Patangia: are able to help banks, establish a platform where banks could monitor the effects of the Ai models and data are drifting, in the long run, during maintain inch.
Rudi Falat: But let's let's define it before we go further, so you mentioned explainable Ai or people call it X API right so.
Rudi Falat: it's a, of course, trying to tackle the challenge that sometimes people are looking at the black box and the algorithms will tell you, you know well, you did get alone or you didn't get along but.
Rudi Falat: No one can tell you why, because no one understand what's going on in the box, so what is explainable Ai and.
Rudi Falat: You know, we started talking about a high level governance of Ai in general, you mentioned that, of course, the X API is crucial for this So what is it and why is it so crucial that we actually understand what these algorithms do.
Pahal Patangia: actually sure yeah so explain the Ai in very simple terms, is a set of tools and techniques which help you.
Pahal Patangia: demystify a model which helped you trace trace it back to the inputs trace the results of the model back to the inputs and reverse engineer it now, this could be done via various ways, one of them is.
Pahal Patangia: Using libraries, which understand how much contribution each feature or each attribute of of a person is.
Pahal Patangia: contributing to the final result you can understand that, and there are at libraries like shapley and there are libraries like line which are.
Pahal Patangia: Which are actively being used in the industry to explain results of models which are machine learning based and also there is a work around libraries called captain.
Pahal Patangia: Which are helping people demystify deep learning models now via via as I was mentioning are working with fiddler.
Pahal Patangia: To implement these explainable Ai techniques at partner banks also what we have done is we have produced developer samples around these libraries, so that we.
Pahal Patangia: We educate the ecosystem or using these libraries around expanding ability and create that create that evangelizing effect around explainable Ai when when using at scale also we have been taking position positions in.
Pahal Patangia: In forums like ti X in Europe, and you fintech 2020 project of one of my colleagues is an active member of those those committees, where they are where they are actively working on defining what would be the right framework around Ai model governance and.
Pahal Patangia: Best Practices around sustainable Ai.
Rudi Falat: And what's the ambition there in terms of I wouldn't want to say dumbing down those models, but basically you know can business executives understand.
Rudi Falat: Models if you use the principles of X API or will it be something like Okay, you can use a third party company like some of your partners to verify to audited, just like the auditors would.
Rudi Falat: order to the financial statements of big banks or big companies so or will it be so easy that anybody can read through it, what do you think is the level of ambition here.
Pahal Patangia: yeah so the primary objective lies around building a set of frameworks, building a set of guidelines and policies which would.
Pahal Patangia: which would be agnostic to a company or an organization, but be applicable to anyone who was implementing Ai at scale.
Pahal Patangia: Now, this has three facets, I would describe the first passage to be around defining the policies, what are the rules defining the legislation around it.
Pahal Patangia: So this revolves around collecting how you're collecting data, how you are building models, what are the security aspects around it and how you are.
Pahal Patangia: Making sure that you are D biasing your data and models.
Pahal Patangia: The second aspect of it is around defining kpis how are you monitoring drift, how are you monitoring how your data on models are changing, with time.
Pahal Patangia: How are you monitoring your model or time what are those metrics which you are tracking actually to make sure that you are in alignment with the framework of these guidelines defined in the step first step.
Pahal Patangia: And third, is around monitoring these models and also auditing so algorithmic auditing machine learning auditing is quiet.
Pahal Patangia: Quiet in trend these days and it's a big topic with the with the voices coming out from the European a AIA Act, which was, which laid down the foundation for.
Pahal Patangia: Rules around Ai so ah, so when we work with these forums, particularly the GI forum, we are helping design a framework so that we can.
Pahal Patangia: We can educate other Ai companies in the ecosystem, or what steps to take when defining on Ai Ai governance framework when it comes to regular regulating and understanding yeah models.
Rudi Falat: Great stuff understood hopefully this will you know, prevent us from all kinds of apocalyptic scenarios that you could see on the shows of big streaming services right.
Rudi Falat: Which leads me to the next topic, I wanted to talk about something quite exciting, I think this year, when you so a lot of tech companies.
Rudi Falat: Engaging in big m&a going into metaverse others renaming themselves, etc, potentially, this was obviously related to the pandemic, where the pandemic.
Rudi Falat: impacted many sectors very, very negatively, for example, retail or certain services where physical presence is needed, like live events like the artists and.
Rudi Falat: People like that, but it probably benefited the gamers and the gaming sector right so, given the nvidia's history and the relation relations to the gaming Community where do you see as the biggest opportunities for fin techs.
Rudi Falat: Expanding into let's call it metaverse or crossing over with gaming because there were so many innovations that are happening, you know the last.
Rudi Falat: Two years so maybe some of those will fall away after Hopefully, this is over, but some of them, probably will stay right there were live events within the games.
Rudi Falat: There were two possibilities to play and win money, and if you couldn't, then you could actually borrow certain tokens and things like this, there are startups to lend you this so that you can play make money this way similarly as in real life so.
Rudi Falat: These are one of the you know one or two random examples, but where do you see the biggest opportunities in this space.
Pahal Patangia: yeah absolutely so we have already been seeing communities and to space to spaces let's start with gaming particularly so earlier gaming was around.
Pahal Patangia: play for leisure, but now gaming is more turning into play for pay, and that is enabled by the decentralized systems, and that is what metaverse has enabled.
Pahal Patangia: If these Games are brought on to do centralized platform, you can use an fps as a mode of transaction and build build a marketplace where.
Pahal Patangia: You can bring in fintech offerings as well as gaming offerings into play, and maybe use avatars or maybe use our tokens or coins which are winning games and.
Pahal Patangia: then use it to trade or land, as you just mentioned so that's a very interesting use case which has come up the example of a game called xe infinity.
Pahal Patangia: With just emerged on the similar lines as I just mentioned, is is a good foray into.
Pahal Patangia: Seeing what's coming in the world of gaming plus fintech and metaphors in the time to come, and on the other hand, what we are also seeing some Korean banks.
Pahal Patangia: Who are venturing into metaverse and I have built virtual environments like virtual backing Center for that, for the customers to interact via.
Pahal Patangia: Via virtual platform and make sure, and make sure, make sure that customers have like a more immersive experience when interacting with.
Pahal Patangia: With the bank on a virtual platform, so I think it's korea's Min bank if i'm pronouncing it correctly, they melt like a financial business Center they will like a Teller desk the middle like a pop just like a banquet and.
Pahal Patangia: The users can interact with the bank employees via via video chats and also have an immersive experience of like being around in the bank so innovation is happening.
Pahal Patangia: fin techs are definitely watching it and there's an there's an area, particularly where Ai could be infused and we have we have been seeing conversations with customers who.
Pahal Patangia: who have been interesting in using and various accelerated computing platform to build in chat bots which could be an important part of the of the metaverse so interesting interesting interesting things to see in the time to come in that scenario.
Rudi Falat: All right, well you know I think I know the answer, but I really want to ask anyway.
Rudi Falat: Just in case, do you think that metaverse is here to stay, or is it just a fad of 2021 2022, in other words, are we going to live in game player one kind of world soon enough.
Rudi Falat: You know, when people saw the movie or the the read the book some people may be got scared some people loved it, you know leave it up to everybody to make up their own mind, but do you think this is where we headed or or or not.
Pahal Patangia: Absolutely so yeah you as you, as you said, are you know the answer, the answer is yes, the minimum is here to stay.
Pahal Patangia: it's only gonna get big it and started with it's you know huge footsteps now.
Pahal Patangia: What we are seeing is that we see metaverse as a connection of virtual worlds, which are interconnected and would enable you to do anything which you are currently doing in your physical current world.
Pahal Patangia: Now, this could be enabled by platforms, where artists can develop assets and that's where in media is very bullish on a.
Pahal Patangia: platform called universe, which has enabled artists to build assets and artifacts in objects for the for the metaverse and what is what data is in a what that has done is that it has.
Pahal Patangia: It has created a concept called digital twins so, for example, we are working with BMW where BMW has created digital factories which are like exact replica of physical factories in the real world.
Pahal Patangia: And those are into the virtual world now these these virtual factories totally mimic the laws of physics, as well as the the real life scenarios which would happen.
Pahal Patangia: So, think of think of items like stress testing or you would.
Pahal Patangia: You would crash test your car and you know all those simulations are can be done now in in in the virtual factory and the final output, the final the final model could be important could be exported out of.
Pahal Patangia: The virtual world into the real world, and this is what universe has enabled, and I mean with such kind of innovations.
Pahal Patangia: I see that Meta versus only going to create more opportunities for industries in the time to come, in fact, a wells fargo analyst recently predicted that.
Pahal Patangia: metaverse building platforms, just like only worse would would be creating market opportunities for more than $10 billion in the next five years to come, so pretty exciting Stat and the outlook, which we are hearing from the street, as well as an industry.
Rudi Falat: very interesting I glad to hear about you know your products also supporting live artists and going into the metaverse but I might have one question there which.
Rudi Falat: popped up in my mind, because I went to the digital art conference last last year in Switzerland and there was an artist there said look.
Rudi Falat: I don't use galleries nowadays, I can create everything as an nfc and go directly but.
Rudi Falat: let's be honest, because everybody spoke about nfc selling for 60 million, etc, most of those they they obviously costly be 500 $700.
Rudi Falat: In gas fees and the theorem etc, so of course we focusing today on Ai well if you compare it to the the assets that you can create in metaverse using your product.
Rudi Falat: Is there a cost there as well, because you know it's very nice to talk about democratization on a very high level.
Rudi Falat: But then, if 70% of the artists, you know they wouldn't create an FT that would cover their costs they don't make any profit either, so how expensive, is it, for example, to create any sort of assets for metaverse using what you just mentioned.
Pahal Patangia: yeah so in videos one philosophy is devon's before revs, which means that we are always in the interest of developers and, in this case designers and artists.
Pahal Patangia: So, in the recent announcement and vidya open source and media only worse platform for individuals to create and build objects for the metaverse so.
Pahal Patangia: Our which otherwise would have been, as you mentioned expensive for an individual creator.
Pahal Patangia: But we recognize that artists are are the living life of metaverse they are the they will be the building blocks, they will develop the building blocks for metaverse.
Pahal Patangia: And that is why we want to enable every one of them, we want to get everyone on board and make sure that they are working in and virtual interconnected world it's like a Google drive for designing with virtual collaboration so.
Pahal Patangia: Why not will be are we are ready to invest on developers and help them create and build something which is beneficial to the entire universe.
Rudi Falat: Alright, so it looks like you are interested in bringing more players into this world.
Rudi Falat: And while, in any case later down the line you are supporting the infrastructure, so why not right it's not about okay charging every minute for for every little thing you seems like you are building a big ecosystem and there is a longer play it.
Rudi Falat: You know insight Is that correct.
Pahal Patangia: yeah absolutely, it is about enabling the ecosystem between our partners or be via building the right tools for.
Pahal Patangia: Helping designers and artists create.
Rudi Falat: Okay, so, and you know let's maybe finish off on metaverse and fintech any other words or use cases that you you've seen already where.
Rudi Falat: There is a crossover which is tangible, you know it's all very nice to repaint the company headquarters and put the new logo there but what's what's tangible that you see in fintech and metaverse.
Pahal Patangia: yeah absolutely the first is the advent of Ai that will also infuse in the metaverse I mentioned the example of chalkboards I also mentioned.
Pahal Patangia: The kind of data that will be flowing in when people when they interacting with the metaverse so a lot of interesting information to come out of that and using an accelerated computing platform to.
Pahal Patangia: derive insights out of it or to make use of it in the end, business cases will be crucial so we'll see a lot of investments on that end as well.
Pahal Patangia: I also see a new trend around fraud detection coming into play, we, and this is this is beyond medallia's, this is about real world as well, like we saw we really simply hired a financial services survey where 83% were.
Pahal Patangia: 83% of the executives 80% of the executives mentioned that fraud detection is a bigger problem we also saw pay Pal coming in with their earnings yesterday.
Pahal Patangia: Where they were affected by 4.3 million accounts which are fraudulent so fraud is a growing problem within fintech as well as that that foot, that would be a panel case when it comes to crypto currencies and.
Pahal Patangia: And the blockchain universe, so this is a problem to invest in will see more investments coming from.
Pahal Patangia: Coming from a executives around accelerated computing around having an enterprise Ai platform having having a place where they can develop.
Pahal Patangia: and build a full stack and have applications which serve the end business use cases So these are these other two takeaways which I would like to.
Pahal Patangia: I would like to have the audience with with respect to what's coming out in the future, and also as as as as we wrap up, we know there are different.
Pahal Patangia: There are different people on this call on the in the audience So if you are a developer, you should definitely visit and bds github to see what we are doing in the field of.
Pahal Patangia: Accelerated computing go to Doc start nvidia.com or github on nvidia and if you are a fintech executives if if you are a business person within fintech definitely visit the nvidia finance page on the website and.
Pahal Patangia: You would get the latest and best of what's hot and happening in fintech and Ai.
Rudi Falat: What a great stuff but how Thank you so much, I think you wrapped it up for me as well, so that's great and now over to Murray, to close out the session thanks so much.
Thanks.
Marie Mize (she/her) | Moderator: Yes, and i'll just echo that gratitude, thank you to you both for being here and for taking us through this important content.
Marie Mize (she/her) | Moderator: um it's been an absolute honor learning from both of you, and over the course of today's session event.
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Rudi Falat: Thank you.
Pahal Patangia: Thanks everyone.