Speaker 1: Hey everyone, it's Robert and Joe here. Today we've got
Speaker 1: something a little bit different to share with you. It
Speaker 1: is a new edition of the Smart Talks podcast series,
Speaker 1: which is produced in partnership with IBM. This season of
Speaker 1: Smart Talks with IBM is all about new creators, the developers,
Speaker 1: data scientists, c t o s, and other visionaries creatively
Speaker 1: applying technology and business to drive change. They use their
Speaker 1: knowledge and creativity to develop better ways of working, no
Speaker 1: matter the industry. Join hosts from your favorite Pushkin Industries
Speaker 1: podcast as they use their expertise to deepen these conversations.
Speaker 1: Malcolm Gladwell will guide you through this season as your
Speaker 1: host to provide his thoughts and analysis along the way.
Speaker 1: Look out for new episodes of Smart Talks with IBM
Speaker 1: every month on the I Heart Radio app, Apple Podcasts,
Speaker 1: or wherever you get your podcasts. And learn more at
Speaker 1: IBM dot com slash smart Talks. Hello, Hello, Welcome to
Speaker 1: Smart Talks with IBM, a podcast from Pushkin Industries, I
Speaker 1: Heart Radio and IBM. I'm Malcolm Gladmo. This season, we're
Speaker 1: talking to new creators, the developers, data scientists, ct o s,
Speaker 1: and other visionaries who are creatively applying technology in business
Speaker 1: to drive change. Channeling their knowledge and expertise, they're developing
Speaker 1: more creative and effective solutions, no matter the industry. Our
Speaker 1: guest today is Nicholas Renaut, Senior Data science and AI
Speaker 1: technical specialist at IBM. Nicholas's job is to help companies
Speaker 1: formulate a data strategy that streamlines the way they do
Speaker 1: business and prepares them to use sophisticated AI technologies. But
Speaker 1: beyond his day to day, Nick is also a content
Speaker 1: creator on YouTube, where his channel has over a hundred
Speaker 1: thousand subscribers. His videos explain computer science incepts in a
Speaker 1: way beginners can understand, and he often demonstrates how to
Speaker 1: use machine learning and data science to solve novel problems.
Speaker 1: On today's show, How Nicholas learned Data science from the
Speaker 1: bottom up, the fundamentals of data management, and how an
Speaker 1: innovative data strategy can help businesses create novels solutions, Nick
Speaker 1: spoke with Ronald Young Jr. Host of the Pushkin podcast Solvable.
Speaker 1: Along with being a frequent contributor to MPR, Ronald also
Speaker 1: hosts and produces the podcast Time Well Spent and Leaving
Speaker 1: the Theater. Okay, let's get to the interview. So tell
Speaker 1: me a little bit about how you got into data
Speaker 1: and when you found out like the power that it
Speaker 1: really harnesses. Do you have a story or anything that
Speaker 1: kind of like when you first piqued your interest in data.
Speaker 1: My first interaction with data and with coding was act
Speaker 1: really when I was around about eleven years old. So
Speaker 1: this was really just getting started with just looking at spreadsheets.
Speaker 1: So my dad would come home and after working a
Speaker 1: nine or five job, he actually started working with investing
Speaker 1: in stocks and doing value based trading that way. I'll
Speaker 1: always remember I walked up to his desk one time
Speaker 1: and he said, Nick, if there's one thing that you
Speaker 1: should learn, I'm seeing all these people work on these
Speaker 1: things called macros in spreadsheets, and these people like wizards
Speaker 1: inside of my business. I know that you're still you're
Speaker 1: still in high school, but I really think you should
Speaker 1: learn this stuff. And I started doubling in some Excel
Speaker 1: spreadsheets and started just recording macros and tweaking stuff, and
Speaker 1: that that's where it all started. But from there, it's
Speaker 1: It's always been a recurring vein throughout my career that
Speaker 1: I've done some sort of wizardry with data, whether it
Speaker 1: a coding or business intelligence or data is it's it's
Speaker 1: always had a bit of a strain throughout throughout whatever
Speaker 1: I've done, whether they start ups or YouTube or or
Speaker 1: what I'm doing now at IBM. Your dad was right.
Speaker 1: Let me just say that, because as someone who's trying
Speaker 1: to put together a spreadsheet just to manage my personal finances,
Speaker 1: trying to look up the formula to actually bring a
Speaker 1: value from what what uhet to another is enough of
Speaker 1: a struggle for me. So I'm glad to do it. Really,
Speaker 1: it's like it's absolutely is uh so like knowing that
Speaker 1: you know, this was how you started getting into spreadsheets.
Speaker 1: You know you're looking at stocks and all of that. Um,
Speaker 1: can you talk to me about how you found out
Speaker 1: the importance of data literacy, how you begin to value
Speaker 1: understanding what the numbers meant and what power that could have.
Speaker 1: I got a cadet ship at one of the big
Speaker 1: four accounting firms and started out as an orditor there,
Speaker 1: which is pretty much day to focus. So I saw
Speaker 1: that these numbers ultimately fed into a significantly bigger picture,
Speaker 1: which was a formal annual report, and numbers being wrong
Speaker 1: in an annual report can move markets. Right. Those numbers
Speaker 1: need to be absolutely bang on. But I think that
Speaker 1: is sort of where it started. Where it really culminated
Speaker 1: was when I started doing some work at the Reserve
Speaker 1: Bank of Australia. And those numbers don't just impact the
Speaker 1: metrics for a particular organization, they impact the entire countries metrics.
Speaker 1: Getting those numbers wrong on a particular chart or getting
Speaker 1: them right on a particular chart can move entire organizations
Speaker 1: or can shift an entire country. It's kind of crazy
Speaker 1: what the value that doing things correctly with data has.
Speaker 1: So when you're presenting a metric, you have to ensure
Speaker 1: that you are portraying the appropriate message. It's not just
Speaker 1: about the raw number, because correlation does not necessarily imply causation.
Speaker 1: So understanding what it is that you're saying is so
Speaker 1: so important, and it is so much more powerful now
Speaker 1: that we've got so much more data available at our fingertips.
Speaker 1: It's really easy to go and grab a bunch of
Speaker 1: metrics and go, hey, I'm gonna grab this data from
Speaker 1: over here, grab that data from over here for a
Speaker 1: measured together. Hey, look, these two lines follow the same trend.
Speaker 1: They must be related. Do you find yourself ever looking
Speaker 1: at data points and saying those the how do I
Speaker 1: don't understand this chart? Why did they Where did they
Speaker 1: pull this from? Do you find yourself doing that a
Speaker 1: lot of your regular life? Oh? Yeah, that There's there's
Speaker 1: some great charts out there as well that you always see,
Speaker 1: and they plut like the number of Nicolas Cage movies
Speaker 1: against the g d P of Bolivia or something, and
Speaker 1: it's like, well, they're going in the same direction. They
Speaker 1: must have some relationship. But people can really quickly look
Speaker 1: at a picture and go and make an assumption about
Speaker 1: what that is saying without actually interpreting. Hey, are these
Speaker 1: on the same scales? The what time period is being displayed?
Speaker 1: What am I actually looking at here? And I find
Speaker 1: myself doing this more and more often when I just
Speaker 1: see a child on my hold on, Let's just not
Speaker 1: make any assumptions. What is this chart actually trying to say?
Speaker 1: What is it actually trying to portray? Because you can
Speaker 1: lie with statistics if you know what you're doing. It
Speaker 1: is they're so powerful and people can gloss over them
Speaker 1: so quickly. We've got attention spends that is so much
Speaker 1: shorter of these days that it can be very very
Speaker 1: easy to take away the wrong message. So you also
Speaker 1: produce content across various platforms, including YouTube and your personal blog.
Speaker 1: Uh as a content creator, how did you get started
Speaker 1: in that field and what type of content are you creating? Yeah,
Speaker 1: that's a crazy story, right. So I always wanted to
Speaker 1: get into tech and said, hey, I'd really really like
Speaker 1: to work for IBM. I saw what they were doing
Speaker 1: with Watson, and I'm like, why people were talking about
Speaker 1: this more? And I had no affiliation with with IBM
Speaker 1: at the time, and I'm like, well, this is so cool.
Speaker 1: There used to be this thing called or this service
Speaker 1: available and that the cloud platform called Personality Insights, and
Speaker 1: you could plug in a little bit of text and
Speaker 1: from that piece of text, it would analyze that particular
Speaker 1: person's personality based on the Big five personality traits. And
Speaker 1: there actually used to be this demo app where you
Speaker 1: could hook it up to a Twitter account, so I
Speaker 1: could pass through Oprah's Twitter account or Lebron's Twitter account
Speaker 1: and it would actually analyze their profiles. And this is
Speaker 1: so cool. It was nuts, and I was like, and
Speaker 1: a lot of people don't know how to use this.
Speaker 1: So that was quite possibly one of the first two
Speaker 1: toils that I made on YouTube, and I actually used
Speaker 1: a bunch of videos that I made following after that too.
Speaker 1: Finally land a job at IBM. I actually spammed a
Speaker 1: bunch of links in my resume and my couple that
Speaker 1: I was like, Hey, I'm already working with this stuff
Speaker 1: and I could do it. And the person that hired me,
Speaker 1: she actually said that that was like such an amazing
Speaker 1: way to portray what what you love about what you do.
Speaker 1: That that that had such an influencing factor in actually
Speaker 1: getting the job. But yeah, I did it because one
Speaker 1: the tech was so cool and I thought it was
Speaker 1: so interesting and so powerful, and yeah, eventually that helped
Speaker 1: me land that job. So you do a lot of
Speaker 1: tutorials where you're you're breaking down complex topics to kind
Speaker 1: of a wider audience. Why is that important for you
Speaker 1: to do? Yeah? I think one of the amazing things
Speaker 1: about knowledge is it's one of the things that you
Speaker 1: can give away and never lose, right. And I think
Speaker 1: one of the trickiest things about the whole data science
Speaker 1: and machine learning field is that it can be pretty
Speaker 1: tricky to get started, and sometimes we get hung up
Speaker 1: with learning from the bottom up right and there's nothing
Speaker 1: wrong with learning fundamentals and learning foundations and really getting
Speaker 1: stuck in. But in order to stick with something, you
Speaker 1: have to find it interesting. So if you can see
Speaker 1: the end result and then work your way back up
Speaker 1: and work out how that's worked, then it is so
Speaker 1: much more attractive because you get that instant gratification and go, hey,
Speaker 1: I've just built this machine learning app that is able
Speaker 1: to decode sign language. It's so cool. Now I'm going
Speaker 1: to go and work out the tech behind it. Admittedly,
Speaker 1: not everyone goes and works out the tech behind it,
Speaker 1: but what I'm trying to do is make it so
Speaker 1: that more people can get involved and get started with it. Lately,
Speaker 1: I've been doing these things called code that challenges, and
Speaker 1: they're kind of crazy, right, but I love doing them.
Speaker 1: So I have to build entire machine learning or data
Speaker 1: science applications without looking at any reference code, stack over
Speaker 1: a flow, or looking at any documentation within fifteen minutes.
Speaker 1: So it is literally just like a trial by fire.
Speaker 1: I'll have my phone, I'll set a time, and I'm like,
Speaker 1: all right, guys, we're on. Like the edit is literally
Speaker 1: just coding NonStop and me explaining on the go. But
Speaker 1: it allows people to see and explain my thought process
Speaker 1: as I'm developing it. UM, that's obviously super fun, right,
Speaker 1: because it's highly engaging and it shows people that, hey,
Speaker 1: you can get started in this relatively quickly. Nicholas is
Speaker 1: a kind of person whose passion for data science is
Speaker 1: so great it spills over from his professional life onto
Speaker 1: his YouTube channel. But when he's not making videos, he's
Speaker 1: using that same expertise to help his clients make their
Speaker 1: businesses work better. At IBM, Nicholas works with businesses to
Speaker 1: formulate a data strategy, preparing them to get the most
Speaker 1: out of technology like machine learning or deep learning. He
Speaker 1: explained to Ronald Wife, thinking critically about the data it
Speaker 1: generates can help a company run more efficiently. So there's
Speaker 1: a quote that you've used in your presentations say their
Speaker 1: firms are trying to become insights driven, but only one
Speaker 1: third report succeeding. What is the role of creativity in
Speaker 1: the successful one third and how are you at IBM
Speaker 1: helping to increase that number. I remember going to a
Speaker 1: talk by our previous CEO, and she said that there's
Speaker 1: a large number of organizations that are just experimenting with
Speaker 1: random acts of digital so they're just testing out some
Speaker 1: of these news technologies are saying kind of what's possible.
Speaker 1: But the ones that are truly being successful are the
Speaker 1: ones that are getting there, that data ready, that data
Speaker 1: strategy in play. They're the ones that are starting to
Speaker 1: collect their data. They're starting to get it ready and organized.
Speaker 1: They're starting to take a look at it and starting
Speaker 1: to iterate and prototype and in a st ructured manner,
Speaker 1: they're starting to roll this stuff out. The journey to
Speaker 1: get something as sophisticated as machine learning into production is
Speaker 1: a lot more difficult than I think people realize because
Speaker 1: you're now building a box that has its own rules.
Speaker 1: You haven't defined those rules yourself, So how do you
Speaker 1: explain that when something goes right? But how do you
Speaker 1: explain when something goes wrong? And having governance around that
Speaker 1: is absolutely critical, which is really whether the data strategy
Speaker 1: does come into play. So let's let's get into a
Speaker 1: more business focused data strategies. Why is it so important
Speaker 1: to have a data strategy in place to fuel AI
Speaker 1: modeling and how does data literacy play a role in
Speaker 1: getting value from these models. We've got algorithms left, right
Speaker 1: and center these days, but I think the thing that
Speaker 1: people forget is that you can't use any of these
Speaker 1: algorithms unless you've got data. So ensuring that you have
Speaker 1: a structure in place too one, collect your data, to
Speaker 1: organize it, three, analyze it, and then or infuse to
Speaker 1: machine learning or deep learning into it is absolutely critical
Speaker 1: because if you don't collect it, you can't do anything
Speaker 1: with it. If you don't organize it, you can't discover
Speaker 1: what you've actually got, what the quality looks like. You
Speaker 1: don't analyze it, you don't know whether or not you
Speaker 1: can trust it. Um and then he infused is always
Speaker 1: like the icing on the cake, right to the machine learning,
Speaker 1: the deep learning, all the cool buzzwords that people throw around.
Speaker 1: That is like the last step, and it is always
Speaker 1: the coolest step. But you can't ever get to that
Speaker 1: last cool step unless you've gone through that the hard
Speaker 1: work that that's come before. Let's like expand a little
Speaker 1: bit on the pain points for companies when they're developing
Speaker 1: or implementing a data strategy. What do those pain points
Speaker 1: look like? Honestly, the biggest pain point that I see organizations,
Speaker 1: actually the top two that I see them coming back
Speaker 1: to over and over again, is collecting and organizing their data.
Speaker 1: So let's say, for example, you've got a manufacturing type organization,
Speaker 1: and what they want to do is they want to
Speaker 1: improve the production quality on a particular manufacturing line. So ideally,
Speaker 1: if they see that they've got defective products on the
Speaker 1: manufacturing line, they want to get rid of those sooner
Speaker 1: rather than later because they don't want to be shipping
Speaker 1: him out to the customer going through the whole warranty
Speaker 1: and claims process that just costs a ton of money.
Speaker 1: So they're like, well, it would be great to use
Speaker 1: some computer vision or some deep learning to detect when
Speaker 1: we've got defects on the product line, and then we
Speaker 1: can grab those and rip them out. Somebody along the
Speaker 1: line is like, great, let's go and do it. The
Speaker 1: first stumbling block that you're going to trip up at is,
Speaker 1: hold on, do you have any images of defective products
Speaker 1: from example cameras that are looking at that production line.
Speaker 1: So if you haven't gone and collected images of that
Speaker 1: or video of that, there is no way in hell
Speaker 1: that you can actually go and build that system to
Speaker 1: improve your organizational productivity. So knowing well in advance what
Speaker 1: data you're likely to need is absolutely critical. It is
Speaker 1: the first step in the data science life cycle. So collecting, understanding,
Speaker 1: and exploring your data is the absolute first step. The
Speaker 1: second one is a little bit more interesting. So let's say,
Speaker 1: for example, you sort of want to get in on
Speaker 1: the craze that is data science or machine learning, and
Speaker 1: you bring on a data science team. The next biggest
Speaker 1: stumbling block that I find a lot of organizations trip
Speaker 1: up on is discovering their data. They've got a ton
Speaker 1: of data, but nobody knows what they've got. So being
Speaker 1: able to find, search, discover, rate, review, and rank that
Speaker 1: information is paramount because you'll have people come in and
Speaker 1: go okay. So a line managers approached me and said
Speaker 1: that we want to take a look at our top
Speaker 1: performing customers and we want to build a retention strategy
Speaker 1: so we're not losing customers anymore. Well, your data scientists
Speaker 1: is then going to go, well, do we have data
Speaker 1: of customers that have left previously. If you can't easily
Speaker 1: search and find out what you've got, that makes it
Speaker 1: pretty hard to go and build those models. So collecting, organizing,
Speaker 1: and discovering really absolutely critical, but that they can be
Speaker 1: a little bit tricky to handle in a large number
Speaker 1: of organizations. What kind of supporting technology and new solutions
Speaker 1: do we need to meet growing data management issues? It
Speaker 1: really comes down to a few things. So ensuring that
Speaker 1: you can one collect the types of data that you're
Speaker 1: looking at. So I think when people think of data,
Speaker 1: they're always thinking of hate it's just going to be
Speaker 1: a bunch of spreadsheets. It might just be stuff that
Speaker 1: we can throw into a database, But there is so
Speaker 1: much more out there. Right, there's video, how do we
Speaker 1: store that? How do we hold that? There is images,
Speaker 1: there's natural text. Like we're just talking about ensuring that
Speaker 1: you've got appropriate processes in place to be able to
Speaker 1: store holding catalog that I think is absolutely critical. We
Speaker 1: talked a little bit about data cataloging and the need
Speaker 1: to be able to search and discover that data. That
Speaker 1: is absolutely paramount. Once you've got it collected, how do
Speaker 1: you find it? What is IBM's unique approach to facilitating
Speaker 1: access to data within companies. So one of the biggest things,
Speaker 1: and one of the my favorite things that I get
Speaker 1: to work with, is a particular tool set, right, and
Speaker 1: this tool set is called cloud Path for Data. So,
Speaker 1: without getting too pitchy, that the absolutely amazing thing about
Speaker 1: This is that those stages that I was talking about, right,
Speaker 1: So collect, organized, analyze, and infused. It actually helps facilitate
Speaker 1: each one of those stages. Right. So you can actually collect, store,
Speaker 1: and hold your data in a secure and government place.
Speaker 1: You've got data catalog in capabilities which allows you to search.
Speaker 1: Like one of my favorite things is that you might
Speaker 1: have a data set. Right, So I might be a
Speaker 1: data scientist, and then we might have another data scientist
Speaker 1: on the team. I can have a data set inside
Speaker 1: of there, and I can actually rank it and add
Speaker 1: comments and go, hey, just be wary of this column
Speaker 1: with lot certain features that you need to be mindful of,
Speaker 1: and that provides additional metadata understand what is what my
Speaker 1: data actually looks like and and things that I should
Speaker 1: be mindful for. So I'm I'm Joe employee. How can
Speaker 1: data be helpful to me? Great question? So, I mean
Speaker 1: data is impacting everyone, right, whether you you like it
Speaker 1: or not. Um and more often than not, what you're
Speaker 1: going to find is that you can improve whatever it
Speaker 1: is that you do by by looking at that data,
Speaker 1: whether it's let's take an organization out of it. If
Speaker 1: you use sleep trackers, you can begin to see when
Speaker 1: you're sleep, or when you're getting good quality sleep versus
Speaker 1: when you're getting bad quality sleep. If you start to
Speaker 1: collect additional data points like hey, am I drinking enough
Speaker 1: water during the day? Am I doing certain things like
Speaker 1: looking at my phone just before I go to bed?
Speaker 1: Are these things influencing my sleep? And is that causing
Speaker 1: a negative impact on my quality of life? So that's
Speaker 1: taking a broader view of it. But when you step
Speaker 1: into a team or a business view, data can can
Speaker 1: make your life for billion times easier. If you know
Speaker 1: that there's a particular issue in a system earlier on
Speaker 1: in a data pipeline, before something crosses your desk, you
Speaker 1: might go and say, hey, look, if we just changed
Speaker 1: how we collected these pieces of information, if we just
Speaker 1: transformed what we actually did with it, this is going
Speaker 1: to streamline my entire workflow and and help me out.
Speaker 1: But not only that, Right, So I work a little
Speaker 1: bit with the automation team, and they're really big on
Speaker 1: robotic process automation. Let's say you're doing something each and
Speaker 1: every single day. You're copying a far from here to there.
Speaker 1: You're grabbing some information from a website, You're throwing it
Speaker 1: into a form and you have to do that twenty
Speaker 1: times a day. There are tools that can automate that
Speaker 1: entire process for you, and they're smart. They're not just
Speaker 1: looking at where you're clicking on the page. They're looking
Speaker 1: at what applications you're opening. They're looking at what fields
Speaker 1: you're pulling data out of. You can automate those entire workflows.
Speaker 1: That means that you don't have to do that repetitive
Speaker 1: kind of boring work that you don't really want to.
Speaker 1: You can palm that off and do the very bot
Speaker 1: and do the stuff that you actually really want to
Speaker 1: get involved in. As Nicholas said, the way a company
Speaker 1: leverages this data has an impact on every level of
Speaker 1: the business. Data informs how we do our jobs day
Speaker 1: to day and how we plan for the future. Having
Speaker 1: an open mindset about data makes it easier for a
Speaker 1: business to come up with creative solutions. In the next
Speaker 1: part of their conversation, Ronald asked Nicholas how data science
Speaker 1: and creativity come together. So let's talk a little bit
Speaker 1: more about creativity. We talked a little bit about your
Speaker 1: YouTube channel, UH and how you use that to help
Speaker 1: people get started with data science. What does creativity mean
Speaker 1: to you? And do you see your work as creative.
Speaker 1: I definitely say my work as creative, and I think
Speaker 1: creativity is truly thinking outside of the box and looking
Speaker 1: at just different ways of doing things. I think the
Speaker 1: biggest thing that I try to embody is having an
Speaker 1: open mindset and really never being willing to shut something
Speaker 1: down or not look at a particular solution or option,
Speaker 1: because you really never know where a particular solution might
Speaker 1: come from. If you look at where some of the
Speaker 1: advancements in that the medical field are coming from, it's
Speaker 1: because they're being open to new ideas, new materials, new ingredients,
Speaker 1: new recipes, new technologies. Having an open mindset really helps
Speaker 1: improve that that that ability to solve complex problems. And
Speaker 1: I think for me, creativity is really just having that
Speaker 1: that open mindset. Tell me a little bit about how
Speaker 1: you approach novel problems. What do you do when you
Speaker 1: get stuck? I think the most important thing I really
Speaker 1: like when I push myself to do something that I've
Speaker 1: personally never done before, and a lot of the time
Speaker 1: that yields new solutions to problems that that that might
Speaker 1: be really difficult to solve. It doesn't necessarily need to
Speaker 1: be using this particular set of techniques. It's what else
Speaker 1: can we do to solve this problem? And sometimes like
Speaker 1: it'll be staring you in the face and you'll just
Speaker 1: have no idea until you go, hey, I'm going to
Speaker 1: throw everything out of the box and just give it
Speaker 1: a crack and see what is possible. Um. But sometimes
Speaker 1: it does require that that little bit of grit to
Speaker 1: to push yourself to see just what is possible. And
Speaker 1: I think that's when I've come up with some of
Speaker 1: my favorite things that I've ever done, so something that
Speaker 1: I'm trying to adopt in my in my daily life.
Speaker 1: And I'm reading a lot more about stoicism and philosophy,
Speaker 1: and I'm seeing that you kind of really just got
Speaker 1: to push through sometimes to to see what what's on
Speaker 1: the other side. We talked a little bit earlier about
Speaker 1: how um folks can take bits of data and kind
Speaker 1: of tell their own story with it, especially if they
Speaker 1: if they know the story that they're trying to tell.
Speaker 1: But let's talk about using that for good. How does
Speaker 1: creativity play a role in data storytelling. I think there's
Speaker 1: just so much good that you can do with data
Speaker 1: that if you have that in your core ethos then
Speaker 1: the world's your oyster, right. I always come back to
Speaker 1: my favorite project that I've ever done, and that was
Speaker 1: using computer vision to try to decode sign language. It
Speaker 1: is by no means a state of the art model,
Speaker 1: but I forget hold on why is never nobody ever
Speaker 1: approached this or at least shared how they've tried to
Speaker 1: do it. And I've kind of just had to get
Speaker 1: real creative and trying to build that I had. I
Speaker 1: literally spent weeks just trying to install stuff, then trying
Speaker 1: to get it writting on my computer before I even
Speaker 1: got anywhere near building that particular model, And and it's
Speaker 1: super hard grow in terms of trying to get it
Speaker 1: set up. But there's so many opportunities for good, whether
Speaker 1: that's improving accessibility to certain technologies, improving the quality of
Speaker 1: life for people that could benefit from us using data
Speaker 1: a little bit better. There's a large body of work
Speaker 1: with a bunch of different data scientists where they're actually
Speaker 1: building language translation models for languages which aren't hyper popular
Speaker 1: or aren't as widely spread as we might see in
Speaker 1: our day to day lives. If you look at India,
Speaker 1: there are a turn of dialects. If you look at
Speaker 1: even where my parents from Mauritius. There's there's a whole,
Speaker 1: completely separate dialect where if you've never heard it before,
Speaker 1: you were like, it's just slang French, but no, it's it.
Speaker 1: It's like um, it's its whole separate language. That obviously
Speaker 1: allows or improves the ability for people to to to
Speaker 1: tap into data and do a little bit of good.
Speaker 1: But there's so much I mean, people are using medical
Speaker 1: image data to improve medical segmentation and improve diagnoses that
Speaker 1: there's just so much amazing work that that's happening in
Speaker 1: that space. There is obviously the temptation or used data
Speaker 1: for bad, but I'd like to think that the large
Speaker 1: majority of the community are really trying to use it
Speaker 1: for good. You started talking about a little bit just now,
Speaker 1: but what are some future trends and challenges and future
Speaker 1: topics or projects you're excited about, anything in particular looking
Speaker 1: real further forward. What I'm super excited about and I
Speaker 1: still don't know how it's necessarily going to impact me,
Speaker 1: whether or not that's going to change my experience as
Speaker 1: a developer or not. That we've got quantum computers coming right,
Speaker 1: there's a ton of work that's happening in that space.
Speaker 1: It's going to radically shift how large a machine learning
Speaker 1: model we're able to create, how fast we're able to
Speaker 1: train them. I'm just excited to see what happens in
Speaker 1: that space. I'm not a quantum physicist by any means,
Speaker 1: but I'm still excited to see what I'll be able
Speaker 1: to do with him in the future. I love that,
Speaker 1: as you'll continued belt this technology, you're excited to play
Speaker 1: with it after it's built, which I'm I'm totally bored
Speaker 1: that I don't want to have to build it, Nicholas
Speaker 1: or not. Thank you so much for a talk with
Speaker 1: me today. It's been an absolute pleasure. Thank you so
Speaker 1: much for your insightful questions. It's it's been awesome. Ronald
Speaker 1: Nick made a point that I think is important to
Speaker 1: remember when it comes to technologies ability to improve our businesses,
Speaker 1: or make our jobs easier, or even do social good,
Speaker 1: a thoughtful data strategy is always the first stepping stone.
Speaker 1: Without good data, using machine learning or artificial intelligence to
Speaker 1: create in a sative solutions becomes much much harder. Our
Speaker 1: technology gets more sophisticated every day, but that doesn't mean
Speaker 1: we should lose sight of the fundamentals. If we want
Speaker 1: to get the most out of smarter technologies, better business decisions,
Speaker 1: more optimized technology, fresh and unexpected insights, we're going to
Speaker 1: need smarter data strategy. On the next episode of Smart
Speaker 1: Talks with IBM, the Power of Salesforce to transform the
Speaker 1: customer experience, we talked with Phil Weinmeister had a product
Speaker 1: for Salesforce America's at IBM consulting about transforming digital experiences
Speaker 1: with the Power of Salesforce and IBM. Smart Talks with
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