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Speaker 1: Welcome to Tech Stuff, a production from I Heart Radio.

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Speaker 1: This season of Smart Talks with IBM is all about

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Speaker 1: new creators, the developers, data scientists, c t o s,

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Speaker 1: and other visionaries creatively applying technology in business to drive change.

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Speaker 1: They use their knowledge and creativity to develop better ways

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Speaker 1: of working, no matter the industry. Join hosts from your

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Speaker 1: favorite Pushkin Industries podcasts as they use their expertise to

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Speaker 1: deepen these conversations, and of course Malcolm Gladwell will guide

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Speaker 1: you through the season as your host and provide his

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Speaker 1: thoughts and analysis along the way. Look out for new

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Speaker 1: episodes of Smart Talks with IBM on the I Heart

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Speaker 1: Radio app, Apple Podcasts, or wherever you get your podcasts,

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Speaker 1: and learn more at IBM dot com slash smart talks. Hello, Hello,

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Speaker 1: Welcome to Smart Talks with IBM, a podcast from Pushkin Industries,

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Speaker 1: I Heart Radio and ib M. I'm Malcolm Glamo. This season,

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Speaker 1: we're talking to new creators, the developers, data scientists, ct

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Speaker 1: os and other visionaries who are creatively applying technology in

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Speaker 1: business to drive change. Channeling their knowledge and expertise, they're

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Speaker 1: developing more creative and effective solutions, no matter the industry.

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Speaker 1: Our guest today is Nicholas Renaut, Senior Data science and

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Speaker 1: AI technical specialist at IBM. Nicholas's job is to help

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Speaker 1: companies formulate a data strategy that streamlines the way they

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Speaker 1: do business and prepares them to use sophisticated AI technologies.

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Speaker 1: But beyond his day to day, Nick is also a

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Speaker 1: content creator on YouTube, where his channel has over a

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Speaker 1: hundred thousand subscribers. His videos explain computer science concepts in

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Speaker 1: a way beginners can understand, and he often demonstrates how

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Speaker 1: to use machine learning and data science to solve novel problems.

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Speaker 1: On today's show, How Nicholas Learned Data science from the

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Speaker 1: bottom up, the fundamentals of data management, and how an

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Speaker 1: innovative data strategy can help businesses create novels solutions, Nick

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Speaker 1: spoke with Ronald Young Jr. Host of the Pushkin podcast Solvable.

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Speaker 1: Along with being a frequent contributor to NPR, Ronald also

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Speaker 1: hosts and produces the podcast Time Well Spent and Leaving

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Speaker 1: the Theater. Okay, let's get to the interview. So tell

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Speaker 1: me a little bit about how you got into data

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Speaker 1: and when you found out like the power that it

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Speaker 1: really harnesses. Do you have a story or anything that

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Speaker 1: kind of like when you first pique your interest in data.

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Speaker 1: My first interaction with data and with coding was actually

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Speaker 1: when I was around about eleven years old, So this

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Speaker 1: was really just getting started with just looking at spread sheets.

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Speaker 1: So my dad would come home and after working a

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Speaker 1: nine or five job, he actually started working with investing

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Speaker 1: in stocks and doing value based trading that way. I'll

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Speaker 1: always remember I walked up to his desk one time

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Speaker 1: and he said, Nick, if there's one thing that you

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Speaker 1: should learn, I'm seeing all these people work on these

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Speaker 1: things called macros in spreadsheets, and these people like wizards

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Speaker 1: inside of my business. I know that you're still you're

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Speaker 1: still in high school, but I really think you should

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Speaker 1: learn this stuff. And I started doubling in some Excel

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Speaker 1: spreadsheets and started just recording macros and tweaking stuff, and

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Speaker 1: that that's where it all started. But from there, it's

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Speaker 1: it's always been a recurring vein throughout my career that

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Speaker 1: I've done some sort of wizardry with data, whether it

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Speaker 1: be coding or business intelligence or data views. It's it's

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Speaker 1: always had a bit of a strain throughout throughout whatever

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Speaker 1: I've done, whether based start ups so YouTube or what

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Speaker 1: I'm doing now at IBM. Your dad was right. Let

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Speaker 1: me just say that, because that's someone who's trying to

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Speaker 1: put together a spreadsheet just to manage my personal finances.

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Speaker 1: Trying to look up the formula to actually bring a

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Speaker 1: value from what what uhet to another is enough of

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Speaker 1: a struggle for me. So I'm glad to do. Really

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Speaker 1: is it's like it's absolutely is uh so like knowing

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Speaker 1: that you know, this was how you started getting into spreadsheets.

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Speaker 1: You know you're looking at stocks and all of that. Um,

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Speaker 1: can you talk to me about how you found out

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Speaker 1: the importance of data literacy, how you begin to value

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Speaker 1: understanding what the numbers meant and what power that could have.

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Speaker 1: I got a cadet ship at one of the big

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Speaker 1: four accounting firms and started out as an orditor there,

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Speaker 1: which is pretty much data focus. So I saw that

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Speaker 1: these numbers ultimately fed into a significantly big at PA Chill,

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Speaker 1: which was a formal annual report, and numbers being wrong

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Speaker 1: in an annual report can move markets, right, those numbers

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Speaker 1: need to be absolutely bang on. But I think that

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Speaker 1: is sort of where it started. Where it really culminated

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Speaker 1: was when I started doing some work at the Reserve

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Speaker 1: Bank of Australia. And those numbers don't just impact the

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Speaker 1: metrics for a particular organization, they impact the entire countries metrics.

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Speaker 1: Getting those numbers wrong on a particular chart, or getting

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Speaker 1: them right on a particular chart can move entire organizations,

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Speaker 1: or can shift an entire country. It's kind of crazy

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Speaker 1: what the value that doing things correctly with data has.

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Speaker 1: So when you're presenting a metric, you have to ensure

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Speaker 1: that you are portraying the appropriate message. It's not just

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Speaker 1: about the wrong number, because correlation does not necessarily imply causation.

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Speaker 1: So understanding what it is that you're saying is so

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Speaker 1: so important, and it is so much more powerful now

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Speaker 1: that we've got so much more data available at our fingertips.

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Speaker 1: It's really easy to go and grab a bunch of

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Speaker 1: metrics and go, hey, I'm going to grab this data

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Speaker 1: from over here, and grab that data from over here

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Speaker 1: for a measured together. Hey, look, these two lines follow

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Speaker 1: the same trend. They must be related. Do you find

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Speaker 1: yourself ever looking at data points and say those this,

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Speaker 1: How do I don't understand this chart? Why did they

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Speaker 1: where did they pull this from? Do you find yourself

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Speaker 1: doing that a lot of your regular life. Oh yeah,

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Speaker 1: that There's there's some great charts out there as well

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Speaker 1: that you always see, and they they plot like the

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Speaker 1: number of Nicolas Cage movies against the g d P

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Speaker 1: of Bolivia or something, and it's like, well, they're going

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Speaker 1: in the same direction. They must have some relationship. But

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Speaker 1: people can really quickly look at a picture and go

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Speaker 1: and make an assumption about what that is saying without

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Speaker 1: actually interpreting. Hey, are these on the same scales? Are

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Speaker 1: they what time period is being displayed? What am I

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Speaker 1: actually looking at here? And I find myself doing this

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Speaker 1: more and more often when I just see a child

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Speaker 1: them like, hold on, let's just not make any assumption.

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Speaker 1: What is this chart actually trying to say? What is

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Speaker 1: it actually trying to portray? Because you can lie with

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Speaker 1: statistics if you know what you're doing. It is they're

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Speaker 1: so powerful and people can gloss over them so quickly.

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Speaker 1: We've got attention spans that are so much shorter these

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Speaker 1: days that it can be very very easy to take

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Speaker 1: away the wrong message. So you also produce content across

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Speaker 1: various platforms, including YouTube and your personal blog. UH as

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Speaker 1: a content creator, how did you get started in that

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Speaker 1: field and what type of content are you creating? Yeah,

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Speaker 1: that's a crazy story, right. So I always wanted to

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Speaker 1: get into tech and said, hey, I'd really really like

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Speaker 1: to work for IBM. I saw what they were doing

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Speaker 1: with Watson, and I'm like, why are people talking about

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Speaker 1: this more? And I had no affiliation with with IBM

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Speaker 1: at the time, and I'm like, well, this is so cool.

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Speaker 1: There used to be this thing or or this service

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Speaker 1: available on the cloud platform called Personality Insights, and you

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Speaker 1: could plug in a little bit of text and from

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Speaker 1: that piece of text, it would analyze that particular person's

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Speaker 1: personality based on the Big five personality traits. And there

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Speaker 1: actually used to be this demo app where you could

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Speaker 1: cook it up to a Twitter account, so I could

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Speaker 1: pass through Oprah's Twitter account or Lebron's Twitter account and

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Speaker 1: it would actually analyze their profiles. And this is so cool.

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Speaker 1: It was nuts, and I was like and a lot

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Speaker 1: of people don't know how to use this. So that

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Speaker 1: was quite possibly one of the first true toils that

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Speaker 1: I made on YouTube, and actually used a bunch of

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Speaker 1: videos that I made following after that too. Finally land

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Speaker 1: a job at IBM. I actually spammed a bunch of

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Speaker 1: links in my resume and my coverle that I was like, hey,

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Speaker 1: I'm already working with this stuff and I could do it.

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Speaker 1: And the person that hired me, she actually said that

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Speaker 1: that was like such an amazing way to portray what

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Speaker 1: what you love about what you do, that that that

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Speaker 1: had such an influencing factor in actually getting the job.

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Speaker 1: But yeah, I did it because one the tech was

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Speaker 1: so cool and I thought it was so interesting and

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Speaker 1: so powerful, and yeah, eventually that helped me land that job.

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Speaker 1: So you do a lot of tutorials where you're you're

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Speaker 1: breaking down complex topics to kind of a wider audience.

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Speaker 1: Why is that important for you to do? Yeah? I

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Speaker 1: think one of the amazing things about knowledge is it's

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Speaker 1: one of the things that you can give away and

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Speaker 1: never lose, right, And I think one of the trickiest

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Speaker 1: things about the whole data science and machine learning field

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Speaker 1: is that it can be pretty tricky to get started,

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Speaker 1: and sometimes we get hung up with learning from the

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Speaker 1: bottom up right, And there's nothing wrong with learning fundamentals

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Speaker 1: and learning foundations and really getting stuck in. But in

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Speaker 1: order to stick with something, you have to find it interesting.

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Speaker 1: So if you can see the end result and then

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Speaker 1: work your way back up and work out how that's worked.

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Speaker 1: Then it is so much more attractive because you get

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Speaker 1: that instant gratification and go, hey, I've just built this

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Speaker 1: machine learning app that is able to decode sign language.

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Speaker 1: It's so cool. Now I'm going to go and work

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Speaker 1: out the tech behind it. Admittedly, not everyone goes and

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Speaker 1: works out the tech behind it, but what I'm trying

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Speaker 1: to do is make it so that more people can

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Speaker 1: get involved and get started with it. Lately, I've been

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Speaker 1: doing these things called code that challenges, and they're kind

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Speaker 1: of crazy, right, but I love doing them. So I

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Speaker 1: have to build entire machine learning or data science applications

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Speaker 1: without looking at any reference code, stack overflow, or looking

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Speaker 1: at any documentation within fifteen minutes. So it is literally

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Speaker 1: just a trial by fire. I'll have my phone, I'll

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Speaker 1: set a time, and I'm like, all right, guys, we're on.

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Speaker 1: Like the edit is literally just cording NonStop and me

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Speaker 1: explaining on the go. But it allows people to see

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Speaker 1: and explain my thought process as I'm developing it. Um.

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Speaker 1: That's obviously super fun, right because it's highly engaging and

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Speaker 1: it shows people that, hey, you can get cited in

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Speaker 1: this relatively quickly. Nicholas is the kind of person whose

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Speaker 1: passion for data science is so great it spills over

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Speaker 1: from his professional life onto his YouTube channel. But when

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Speaker 1: he's not making videos, he's using that same expertise to

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Speaker 1: help his clients make their businesses work better. At IBM,

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Speaker 1: Nicholas works with businesses to formulate a data strategy, preparing

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Speaker 1: them to get the most out of technology like machine

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Speaker 1: learning or deep learning. He explained to Ronald why thinking

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Speaker 1: critically about the data it generates can help a company

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Speaker 1: run more efficiently. So there's a quote that you've used

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Speaker 1: in your presentation say their firms are trying to become

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Speaker 1: insights driven, but only one third report succeeding. What is

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Speaker 1: the role of creativity in the successful one third and

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Speaker 1: how are you at IBM helping to increase that number.

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Speaker 1: I remember going to a talk by our previous CEO,

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Speaker 1: and she said that there's a large number of organizations

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Speaker 1: that are just experimenting with random acts of digital So

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Speaker 1: they're just testing out some of these news technologies are

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Speaker 1: seeing kind of what's possible. But the ones that are

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Speaker 1: truly being successful are the ones that are getting there

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Speaker 1: the data ready their data strategy in play. They're the

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Speaker 1: ones that are starting to collect their data. They're starting

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Speaker 1: to get it ready and organized. They're starting to take

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Speaker 1: a look at it and starting to iterate and prototype

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Speaker 1: and in a structured manner. They're starting to roll this

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Speaker 1: stuff out. The journey to get something as sophisticated as

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Speaker 1: machine learning into production is a lot more difficult than

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Speaker 1: I think people realize because you're now building a box

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Speaker 1: that has its own rules. You haven't defined those rules yourself,

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Speaker 1: So how do you explain that when something goes right?

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Speaker 1: But how do you explain when something goes wrong? And

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Speaker 1: having governance around that is absolutely critical, which is really

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Speaker 1: whether the data strategy does come into play. So let's

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Speaker 1: let's get into more business focused data strategies. Why is

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Speaker 1: it so important to have a data strategy in place

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Speaker 1: to fuel AI modeling and how does data literacy play

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Speaker 1: a role in getting value from these models. We've got

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Speaker 1: algorithms left, right and center these days, but I think

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Speaker 1: the thing that people forget is that you can't use

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Speaker 1: any of these algorithms unless you've got data. So ensuring

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Speaker 1: that you have a structure in place too one collect

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Speaker 1: your data to organ is it three, analyze it, and

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Speaker 1: then or infused to machine learning or deep learning into it.

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Speaker 1: Is absolutely critical because if you don't collect it, you

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Speaker 1: can't do anything with it. If you don't organize it,

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Speaker 1: you can't discover what you've actually got, what the quality

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Speaker 1: looks like. You don't analyze it, you don't know whether

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Speaker 1: or not you can trust it. Um and then he

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Speaker 1: infused is always like the icing on the cake, right

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Speaker 1: to the machine learning, the deep learning, all the cool

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Speaker 1: buzzwords that people throw around. That is like the last step,

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Speaker 1: and it is always the coolest step. But you can't

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Speaker 1: ever get to that last cool step unless you've gone

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Speaker 1: through that the hard work that that's come before. Let's

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Speaker 1: like expand a little bit on the pain points for

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Speaker 1: companies when they're developing or implementing a data strategy. What

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Speaker 1: do those pain points look like? Honestly, the biggest pain

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Speaker 1: point that I see organizations actually the top two that

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Speaker 1: I see them coming back to over and over again,

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Speaker 1: and is collecting and organizing their data. So let's say,

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Speaker 1: for example, you've got a manufacturing type organization and what

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Speaker 1: they want to do is they want to improve the

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Speaker 1: production quality on a particular manufacturing line. So ideally, if

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Speaker 1: they see that they've got defective products on the manufacturing line,

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Speaker 1: they want to get rid of those sooner rather than

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Speaker 1: later because they don't want to be shipping him out

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Speaker 1: to the customer going through the whole warranty and claims

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Speaker 1: process that just costs a ton of money. So they're like, well,

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Speaker 1: it would be great to use some computer vision or

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Speaker 1: some deep learning to detect when we've got defects on

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Speaker 1: the product line, and then we can grab those and

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Speaker 1: rip them out. Somebody along the line is like, great,

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Speaker 1: let's go and do it. The first stumbling block that

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Speaker 1: you're going to trip up at is, hold on, do

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Speaker 1: you have any images of defective products from example cameras

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Speaker 1: that are looking at that production line. So if you

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Speaker 1: haven't gone and collected images of that or video of that,

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Speaker 1: there is no way in hell that you can actually

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Speaker 1: go and build that system to improve your organizational productivity.

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Speaker 1: So knowing well in advance what data you're likely to

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Speaker 1: need is absolutely critical. It is the first step in

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Speaker 1: the data science life cycle. So collecting, understanding, and exploring

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Speaker 1: your data is the absolute first step. The second one

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Speaker 1: is a little bit more interesting. So let's say, for example,

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Speaker 1: you sort of want to get in on the craze

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Speaker 1: that is data science or machine learning, and you bring

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Speaker 1: on a data science team. The next biggest stumbling block

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Speaker 1: that I find a lot of organizations trip up on

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Speaker 1: is discovering their data. They've got a ton of data,

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Speaker 1: but nobody knows what they've got. So being able to

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Speaker 1: find so to discover, rate, review, and rank that information

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Speaker 1: is paramount because you'll have people come in and go okay.

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Speaker 1: So a line managers approached me and said that we

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Speaker 1: want to take a look at our top performing customers

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Speaker 1: and we want to build a retention strategy so we're

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Speaker 1: not losing customers anymore. Well, your data scientists is then

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Speaker 1: going to go, well, do we have data of customers

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Speaker 1: that have left previously. If you can't easily search and

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Speaker 1: find out what you've got, that makes it pretty hard

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Speaker 1: to go and build those models. So collecting, organizing, and

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Speaker 1: discovering really absolutely critical, but that they can be a

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Speaker 1: little bit tricky to handle in a large number of organizations.

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Speaker 1: What kind of supporting technology and new solutions do we

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Speaker 1: need to meet growing data management issues? It really comes

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Speaker 1: down to two a few things. So ensuring that you

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Speaker 1: can one collect the types of data that you're looking at.

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Speaker 1: So I think when people think of data, they're always

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Speaker 1: thinking of hate it's just going to be a bunch

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Speaker 1: of spreadsheets. It might just be stuff that we can

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Speaker 1: throw into a database. But there is so much more

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Speaker 1: out there. Right, there's video, how do we store that?

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Speaker 1: How do we hold that? There is images, there's natural text.

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Speaker 1: Like we're just talking about ensuring that you've got appropriate

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Speaker 1: processes in place to be able to store holding catalog

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Speaker 1: that I think is absolutely critical. We talked a little

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Speaker 1: bit about data cataloging and the need to be able

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Speaker 1: to search and discover that data that is absolutely paramount.

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Speaker 1: Once you've got it collected, how do you find it?

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Speaker 1: What is IBM's unique approach to facilitating access to data

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Speaker 1: within companies. So one of the biggest things, and one

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Speaker 1: of the my favorite things that I get to work with,

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Speaker 1: is a particular tool set, right, and this tool set

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Speaker 1: is called cloud Path for Data. So, without getting too

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Speaker 1: pitchy that the absolutely amazing thing about this is that

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Speaker 1: those stages that I was talking about, right, so collect, organized, analyzing, infused,

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Speaker 1: it actually helps facilitate each one of those stages, right,

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Speaker 1: So you can actually collect, store, and hold your data

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Speaker 1: in a secure and government place. You've got data catalog

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Speaker 1: in capabilities which allows you to search. Like one of

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Speaker 1: my favorite things is that you might have a data set, right,

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Speaker 1: So I might be a data scientist, and then we

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Speaker 1: might have another data scientist on the team. I can

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Speaker 1: have a data set inside of there, and I can

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Speaker 1: actually rank it and add comments and go, hey, just

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Speaker 1: be wary of this column with lot certain features that

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Speaker 1: you need to be mindful of, and that provides additional

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Speaker 1: metadata understand what is what my data actually looks like

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Speaker 1: and and things that I should be mindful for. So

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Speaker 1: I'm I'm Joe employee. How can data be helpful to me?

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Speaker 1: Great question? So, I mean data is impacting everyone, right,

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Speaker 1: whether you you like it or not. Um and more

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Speaker 1: often than not, what you're going to find is that

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Speaker 1: you can improve whatever it is that you do by

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Speaker 1: by looking at that data, whether it's let's take an

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Speaker 1: organization out of it. If you use sleep trackers, you

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Speaker 1: can begin to see when you're sleep or when you're

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Speaker 1: getting good quality sleep versus when you're getting bad quality sleep.

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Speaker 1: If you start to collect additional data points like hey,

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Speaker 1: am I drinking enough water during the day, am I

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Speaker 1: doing certain things like looking at my phone just before

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Speaker 1: I go to bed? Are these things influencing my sleep?

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Speaker 1: And is that causing a negative impact on my quality

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Speaker 1: of life? So that's taking a broader view of it.

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Speaker 1: But when you step into a team or a business view,

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Speaker 1: data can can make your life a billion times easier.

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Speaker 1: If you know that there's a particular issue in a

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Speaker 1: system earlier on in a data pipeline, before something crosses

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Speaker 1: your desk, you might go and say, hey, look, if

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Speaker 1: we just changed how we collected these pieces of information,

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Speaker 1: if we just transformed what we actually did with it,

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Speaker 1: this is going to streamline my entire workflow and and

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Speaker 1: help me out. But not only that. Right, So I

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Speaker 1: work a little bit with the automation team, and they're

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Speaker 1: really big on robotic process automation. Let's say you're doing

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Speaker 1: something each and every single day. You're copying a far

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Speaker 1: from here to there. You're grabbing some information from a website,

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Speaker 1: You're throwing it into a form, and you have to

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Speaker 1: do that twenty times a day. There are tools that

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Speaker 1: can automate that entire process for you, and they're smart.

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Speaker 1: They're not just looking at where you're clicking on the page.

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Speaker 1: They're looking at what applications you're opening, They're looking at

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Speaker 1: what fields you're pulling data out of. You can automate

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Speaker 1: those entire workflows. That means that you don't have to

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Speaker 1: do that repetitive, kind of boring work that you don't

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Speaker 1: really want to do. You can palm that off and

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Speaker 1: do the robot and do the stuff that you actually

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Speaker 1: really want to get involved in. As Nicholas said, the

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Speaker 1: way a company leverages this data has an impact on

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Speaker 1: every level of the business. Data informs how we do

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Speaker 1: our jobs day to day and how we plan for

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Speaker 1: the future. Having an open mindset about data makes it

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Speaker 1: easier for a business to come up with creative solutions.

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Speaker 1: In the next part of their conversation, Ronald asked Nicholas

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Speaker 1: how data science and creativity come together. So let's talk

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Speaker 1: a little bit more about creativity. We talked a little

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Speaker 1: bit about your YouTube channel, UH and how you use

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Speaker 1: that to help people get started with data science. What

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Speaker 1: does creativity mean to you and do you see your

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Speaker 1: work as creative? I definitely say my work as creative,

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Speaker 1: and I think creativity is truly thinking outside of the

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Speaker 1: box and looking at just different ways of doing things.

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Speaker 1: I think the biggest thing that I try to embody

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Speaker 1: is having an open mindset and really in never being

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Speaker 1: willing to shut something down or not look at a

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Speaker 1: particular solution or option, because you really never know where

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Speaker 1: a particular solution might come from. If you look at

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Speaker 1: where some of the advancements in that the medical field

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Speaker 1: are coming from, it's because they're being open to new ideas,

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Speaker 1: new materials, new ingredients, new recipes, new technologies. Having an

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Speaker 1: open mindset really helps improve that that that ability to

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Speaker 1: solve complex problems. And I think for me, creativity is

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Speaker 1: really just having that that open mindset. Tell me a

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Speaker 1: little bit about how you approach novel problems. What do

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Speaker 1: you do when you get stuck? I think the most

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Speaker 1: important thing I really like when I push myself to

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Speaker 1: do something that I've personally never done before, and a

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Speaker 1: lot of the time that yields new solutions to problems

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Speaker 1: that that that might be really difficult to solve. It

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Speaker 1: doesn't necessarily need to be using this particular set of techniques.

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Speaker 1: It's what else can we do to solve this problem?

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Speaker 1: And sometimes like it'll be staring you in the face

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Speaker 1: and you'll just have no idea until you go, hey,

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Speaker 1: I'm going to throw everything out of the box and

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Speaker 1: just give it a crack and see what is possible. Um.

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Speaker 1: But sometimes it does require that that little bit of

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Speaker 1: grit to to push yourself to see just what is possible.

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Speaker 1: And I think that's when I've come up with some

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Speaker 1: of my favorite things that I've ever done, so something

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Speaker 1: that I'm trying to adopt in my in my daily life.

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Speaker 1: And I'm reading a lot more about stoicism and philosophy,

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Speaker 1: and I'm seeing that you kind of really just got

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Speaker 1: to push through sometimes to to see what what's on

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Speaker 1: the other side. We talked a little bit earlier about

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Speaker 1: how folks can take bits of data and kind of

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Speaker 1: tell their own story with it, especially if they if

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Speaker 1: they know the story that they're trying to tell. But

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Speaker 1: let's talk about using that for good. How does creativity

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Speaker 1: play a role in data storytelling. I think there's just

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Speaker 1: so much good that you can do with data that

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Speaker 1: if you have that in your core ethos, then the

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Speaker 1: world's your oyster, right. I always come back to my

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Speaker 1: favorite project that I've ever done, and that was using

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Speaker 1: computer vision to try to decode sign language. It is

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Speaker 1: by no means a state of the art model. But

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Speaker 1: I forget hold on why is never nobody ever approached

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Speaker 1: this or at least shared how they've tried to do it.

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Speaker 1: And I've kind of just had to get real creative

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Speaker 1: and trying to build that I had. I literally spent

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Speaker 1: weeks just trying to install stuff then trying to get

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Speaker 1: it running on my computer before I even got anywhere

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Speaker 1: near building that particular model. And and it's super hardcore

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Speaker 1: in terms of trying to get it set up. But

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Speaker 1: there's so many opportunities for good, whether that's improve accessibility

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Speaker 1: to certain technologies, improving the quality of life for people

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Speaker 1: that could benefit from us using data a little bit better.

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Speaker 1: There's a large body of work with a bunch of

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Speaker 1: different data scientists where they're actually building language translation models

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Speaker 1: for languages which aren't hyper popular or aren't as widely

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Speaker 1: spread as we might see in our day to day lives.

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Speaker 1: If you look at India, there are a turn of dialects.

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Speaker 1: If you look at even where my parents from Mauritius,

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Speaker 1: there's there's a whole completely separate dialect where if you've

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Speaker 1: never heard it before, you were like this just slang French,

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Speaker 1: but no it's it. It's like um, it's its whole

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Speaker 1: separate language that obviously allows or improves the ability for

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Speaker 1: people to to tap into data and do a little

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Speaker 1: bit of good. But there's so much I mean, people

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Speaker 1: are using medical image data to improve medical segmentation and

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Speaker 1: improve diagnoses. There's just so much amazing work that that's

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Speaker 1: happening in that space. There is obviously the temptation or

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Speaker 1: used data for bad, but I'd like to think that

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Speaker 1: the large majority of the community are really trying to

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Speaker 1: use it for good. You started talking about a little

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Speaker 1: bit just now, But what are some future trends and

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Speaker 1: challenges and future topics or projects you're excited about anything

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Speaker 1: in particular looking real further forward, what I'm super excited

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Speaker 1: about And I still don't know how it's necessarily going

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Speaker 1: to impact me, whether or not that's going to change

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00:27:40,000 --> 00:27:42,919
Speaker 1: my experience as a developer or not. That we've got

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00:27:43,000 --> 00:27:45,880
Speaker 1: quantum computers coming right, there's a ton of work that's

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00:27:45,920 --> 00:27:50,960
Speaker 1: happening in that space. It's going to radically shift how

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Speaker 1: large a machine learning model we're able to create, how

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Speaker 1: fast we're able to train them. I'm just excited to

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00:27:58,400 --> 00:28:01,800
Speaker 1: see what happens in that space. I'm not a quantum

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00:28:01,800 --> 00:28:05,440
Speaker 1: physicist by any means, but I'm still excited to see

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Speaker 1: what I'll be able to do with him in the future.

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Speaker 1: I love that, as you'all contingent filt this technology, you're

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Speaker 1: excited to play with it after it's built, which I'm

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Speaker 1: I don't want to have to build it. Nicholas for

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Speaker 1: not thank you so much for a talk of me today.

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Speaker 1: It's been an absolute pleasure. Thank you so much for

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00:28:25,520 --> 00:28:31,360
Speaker 1: your insightful questions. It's it's been awesome. Ronald Nick made

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Speaker 1: a point that I think is important to remember when

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Speaker 1: it comes to technologies ability to improve our businesses, or

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00:28:38,560 --> 00:28:41,800
Speaker 1: make our jobs easier, or even do social good, A

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Speaker 1: thoughtful data strategy is always the first stepping stone. Without

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Speaker 1: good data, using machine learning or artificial intelligence to create

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00:28:51,440 --> 00:28:58,160
Speaker 1: innovative solutions becomes much much harder. Our technology gets more

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Speaker 1: sophisticated every day, but that doesn't mean we should lose

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Speaker 1: sight of the fundamentals. If we want to get the

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Speaker 1: most out of smarter technologies, better business decisions, more optimized technology,

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00:29:10,760 --> 00:29:16,760
Speaker 1: fresh and unexpected insights, we're going to need smarter data strategy.

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Speaker 1: On the next episode of Smart Talks, with IBM the

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00:29:21,120 --> 00:29:26,200
Speaker 1: power of Salesforce to transform the customer experience. We talked

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00:29:26,200 --> 00:29:30,320
Speaker 1: with Phil Weinmeister had a product for Salesforce America's at IBM,

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00:29:30,360 --> 00:29:35,560
Speaker 1: consulting about transforming digital experiences with the power of Salesforce

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00:29:36,040 --> 00:29:40,520
Speaker 1: and IBM. Smart Talks with IBM is produced by Matt Romano,

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00:29:40,960 --> 00:29:45,920
Speaker 1: David jaw, Royston Deserve, and Edith Rousselo with Jacob Goldstein.

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00:29:46,480 --> 00:29:50,720
Speaker 1: Were edited by Sophie crane Are. Engineers are Jason Gambrel,

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00:29:51,200 --> 00:29:56,680
Speaker 1: Sarah Brugare and Ben Holliday. Theme song by Granmascope. Special

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00:29:56,680 --> 00:30:00,720
Speaker 1: thanks to Carlie mcglory, Andy Kelly, Kathy cal Hand and

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00:30:00,840 --> 00:30:03,520
Speaker 1: the eight Bar and IBM teams, as well as the

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00:30:03,520 --> 00:30:07,280
Speaker 1: Pushkin marketing team. Smart Talks with IBM is a production

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Speaker 1: of Pushkin Industries and I Heart Media. To find more

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00:30:10,800 --> 00:30:15,240
Speaker 1: Pushkin podcasts, listen on the I Heart Radio app, Apple Podcasts,

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00:30:15,560 --> 00:30:20,360
Speaker 1: or wherever you listen to podcasts. Hi'm Malcolm Gladwell. This

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00:30:20,440 --> 00:30:29,960
Speaker 1: is a paid advertisement from IBM.