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Speaker 1: Hey everyone, it's Robert and Joe here. Today we've got

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Speaker 1: something a little bit different to share with you. It

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Speaker 1: is a new edition of the Smart Talks podcast series,

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Speaker 1: which is produced in partnership with IBM. This season of

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Speaker 1: Smart Talks with IBM is all about new creators, the developers,

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Speaker 1: data scientists, c t o s, and other visionaries creatively

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Speaker 1: applying technology and business to drive change. They use their

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Speaker 1: knowledge and creativity to develop better ways of working, no

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Speaker 1: matter the industry. Join hosts from your favorite Pushkin Industries

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Speaker 1: podcast as they use their expertise to deepen these conversations.

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Speaker 1: Malcolm Gladwell will guide you through this season as your

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Speaker 1: host to provide his thoughts and analysis along the way.

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Speaker 1: Look out for new episodes of Smart Talks with IBM

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Speaker 1: every month on the I Heart Radio app, Apple Podcasts,

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Speaker 1: or wherever you get your podcasts. And learn more at

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Speaker 1: IBM dot com slash smart Talks. Hello, Hello, Welcome to

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

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Speaker 1: Heart Radio and IBM. I'm Malcolm Gladmo. This season, we're

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Speaker 1: talking to new creators, the developers, data scientists, ct o s,

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

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

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

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

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

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

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

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

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

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Speaker 1: thousand subscribers. His videos explain computer science incepts in a

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

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Speaker 1: 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 MPR, 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 piqued your interest in data.

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

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Speaker 1: really when I was around about eleven years old. So

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Speaker 1: this was really just getting started with just looking at spreadsheets.

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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: a coding or business intelligence or data is 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 they start ups or YouTube or or

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

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

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Speaker 1: to 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 it. Really,

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

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Speaker 1: 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 day to focus. So I saw

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Speaker 1: that these numbers ultimately fed into a significantly bigger picture,

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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 raw 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 gonna grab this data from

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Speaker 1: over here, grab that data from over here for a

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Speaker 1: measured together. Hey, look, these two lines follow the same trend.

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Speaker 1: They must be related. Do you find yourself ever looking

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Speaker 1: at data points and saying those the how do I

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Speaker 1: don't understand this chart? Why did they Where did they

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Speaker 1: pull this from? Do you find yourself doing that a

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Speaker 1: lot of your regular life? Oh? Yeah, that There's there's

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Speaker 1: some great charts out there as well that you always see,

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Speaker 1: and they plut like the number of Nicolas Cage movies

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Speaker 1: against the g d P of Bolivia or something, and

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Speaker 1: it's like, well, they're going in the same direction. They

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Speaker 1: must have some relationship. But people can really quickly look

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Speaker 1: at a picture and go and make an assumption about

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Speaker 1: what that is saying without actually interpreting. Hey, are these

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Speaker 1: on the same scales? The what time period is being displayed?

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Speaker 1: What am I actually looking at here? And I find

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Speaker 1: myself doing this more and more often when I just

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Speaker 1: see a child on my hold on, Let's just not

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Speaker 1: make any assumptions. What is this chart actually trying to say?

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Speaker 1: What is it actually trying to portray? Because you can

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Speaker 1: lie with statistics if you know what you're doing. It

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Speaker 1: is they're so powerful and people can gloss over them

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Speaker 1: so quickly. We've got attention spends that is so much

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Speaker 1: shorter of these days that it can be very very

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Speaker 1: easy to take away the wrong message. So you also

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Speaker 1: produce content across various platforms, including YouTube and your personal blog.

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Speaker 1: Uh as a content creator, how did you get started

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Speaker 1: in that 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 people were 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 called or this service

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

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

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Speaker 1: from that piece of text, it would analyze that particular

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Speaker 1: person's personality based on the Big five personality traits. And

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

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

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

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

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Speaker 1: so cool. It was nuts, and I was like, and

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Speaker 1: a lot of people don't know how to use this.

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Speaker 1: So that was quite possibly one of the first two

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Speaker 1: toils that I made on YouTube, and I actually used

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Speaker 1: a bunch of videos that I made following after that too.

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Speaker 1: Finally land a job at IBM. I actually spammed a

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Speaker 1: bunch of links in my resume and my couple that

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Speaker 1: I was like, Hey, I'm already working with this stuff

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Speaker 1: and I could do it. And the person that hired me,

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Speaker 1: she actually said that that was like such an amazing

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Speaker 1: way to portray what what you love about what you do.

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Speaker 1: That that that had such an influencing factor in actually

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Speaker 1: getting the job. But yeah, I did it because one

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Speaker 1: the tech was so cool and I thought it was

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Speaker 1: so interesting and so powerful, and yeah, eventually that helped

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Speaker 1: me land that job. So you do a lot of

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Speaker 1: tutorials where you're you're breaking down complex topics to kind

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Speaker 1: of a wider audience. Why is that important for you

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Speaker 1: to do? Yeah? I think one of the amazing things

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Speaker 1: about knowledge is it's one of the things that you

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Speaker 1: can give away and never lose, right. And I think

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Speaker 1: one of the trickiest things about the whole data science

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Speaker 1: and machine learning field is that it can be pretty

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Speaker 1: tricky to get started, and sometimes we get hung up

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Speaker 1: with learning from the bottom up right and there's nothing

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Speaker 1: wrong with learning fundamentals and learning foundations and really getting

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Speaker 1: stuck in. But in order to stick with something, you

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Speaker 1: have to find it interesting. So if you can see

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Speaker 1: the end result and then work your way back up

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Speaker 1: and work out how that's worked, then it is so

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Speaker 1: much more attractive because you get that instant gratification and go, hey,

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Speaker 1: I've just built this machine learning app that is able

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Speaker 1: to decode sign language. It's so cool. Now I'm going

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Speaker 1: to go and work out the tech behind it. Admittedly,

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

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Speaker 1: but what I'm trying to do is make it so

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Speaker 1: that more people can get involved and get started with it. Lately,

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Speaker 1: I've been doing these things called code that challenges, and

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Speaker 1: they're kind of crazy, right, but I love doing them.

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Speaker 1: So I have to build entire machine learning or data

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Speaker 1: science applications without looking at any reference code, stack over

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Speaker 1: a flow, or looking at any documentation within fifteen minutes.

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Speaker 1: So it is literally just like a trial by fire.

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Speaker 1: I'll have my phone, I'll set a time, and I'm like,

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Speaker 1: all right, guys, we're on. Like the edit is literally

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Speaker 1: just coding NonStop and me explaining on the go. But

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Speaker 1: it allows people to see and explain my thought process

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Speaker 1: as I'm developing it. UM, that's obviously super fun, right,

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Speaker 1: because it's highly engaging and it shows people that, hey,

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Speaker 1: you can get started in this relatively quickly. Nicholas is

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Speaker 1: a kind of person whose passion for data science is

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Speaker 1: so great it spills over from his professional life onto

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Speaker 1: his YouTube channel. But when he's not making videos, he's

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Speaker 1: using that same expertise to help his clients make their

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Speaker 1: businesses work better. At IBM, Nicholas works with businesses to

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Speaker 1: formulate a data strategy, preparing them to get the most

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Speaker 1: out of technology like machine learning or deep learning. He

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Speaker 1: explained to Ronald Wife, thinking critically about the data it

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Speaker 1: generates can help a company run more efficiently. So there's

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Speaker 1: a quote that you've used in your presentations say their

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Speaker 1: firms are trying to become insights driven, but only one

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Speaker 1: third report succeeding. What is the role of creativity in

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Speaker 1: the successful one third and how are you at IBM

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Speaker 1: helping to increase that number. I remember going to a

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Speaker 1: talk by our previous CEO, and she said that there's

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Speaker 1: a large number of organizations that are just experimenting with

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Speaker 1: random acts of digital so they're just testing out some

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Speaker 1: of these news technologies are saying kind of what's possible.

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Speaker 1: But the ones that are truly being successful are the

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Speaker 1: ones that are getting there, that data ready, that data

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Speaker 1: strategy in play. They're the ones that are starting to

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Speaker 1: collect their data. They're starting to get it ready and organized.

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Speaker 1: They're starting to take a look at it and starting

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Speaker 1: to iterate and prototype and in a st ructured manner,

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Speaker 1: they're starting to roll this stuff out. The journey to

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Speaker 1: get something as sophisticated as machine learning into production is

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Speaker 1: a lot more difficult than I think people realize because

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Speaker 1: you're now building a box that has its own rules.

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Speaker 1: You haven't defined those rules yourself, So how do you

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

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Speaker 1: explain when something goes wrong? And having governance around that

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Speaker 1: is absolutely critical, which is really whether the data strategy

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Speaker 1: does come into play. So let's let's get into a

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Speaker 1: more business focused data strategies. Why is it so important

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Speaker 1: to have a data strategy in place to fuel AI

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Speaker 1: modeling and how does data literacy play a role in

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Speaker 1: getting value from these models. We've got algorithms left, right

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Speaker 1: and center these days, but I think the thing that

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Speaker 1: people forget is that you can't use any of these

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Speaker 1: algorithms unless you've got data. So ensuring that you have

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Speaker 1: a structure in place too one, collect your data, to

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Speaker 1: organize it, three, analyze it, and then or infuse to

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Speaker 1: machine learning or deep learning into it is absolutely critical

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Speaker 1: because if you don't collect it, you can't do anything

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

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Speaker 1: what you've actually got, what the quality looks like. You

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Speaker 1: don't analyze it, you don't know whether or not you

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Speaker 1: can trust it. Um and then he infused is always

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Speaker 1: like the icing on the cake, right to the machine learning,

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Speaker 1: the deep learning, all the cool buzzwords that people throw around.

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Speaker 1: That is like the last step, and it is always

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Speaker 1: the coolest step. But you can't ever get to that

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Speaker 1: last cool step unless you've gone through that the hard

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Speaker 1: work that that's come before. Let's like expand a little

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Speaker 1: bit on the pain points for companies when they're developing

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Speaker 1: or implementing a data strategy. What do those pain points

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Speaker 1: look like? Honestly, the biggest pain point that I see organizations,

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Speaker 1: actually the top two that I see them coming back

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Speaker 1: to over and over again, is collecting and organizing their data.

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Speaker 1: So let's say, for example, you've got a manufacturing type organization,

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Speaker 1: and what they want to do is they want to

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

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Speaker 1: if they see that they've got defective products on the

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Speaker 1: manufacturing line, they want to get rid of those sooner

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Speaker 1: rather than later because they don't want to be shipping

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Speaker 1: him out to the customer going through the whole warranty

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Speaker 1: and claims process that just costs a ton of money.

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Speaker 1: So they're like, well, it would be great to use

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Speaker 1: some computer vision or some deep learning to detect when

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Speaker 1: we've got defects on the product line, and then we

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Speaker 1: can grab those and rip them out. Somebody along the

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Speaker 1: line is like, great, let's go and do it. The

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Speaker 1: first stumbling block that you're going to trip up at is,

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Speaker 1: hold on, do you have any images of defective products

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Speaker 1: from example cameras that are looking at that production line.

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Speaker 1: So if you haven't gone and collected images of that

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Speaker 1: or video of that, there is no way in hell

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Speaker 1: that you can actually go and build that system to

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Speaker 1: improve your organizational productivity. So knowing well in advance what

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Speaker 1: data you're likely to need is absolutely critical. It is

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Speaker 1: the first step in the data science life cycle. So collecting, understanding,

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Speaker 1: and exploring your data is the absolute first step. The

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

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Speaker 1: for example, you sort of want to get in on

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Speaker 1: the craze that is data science or machine learning, and

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Speaker 1: you bring on a data science team. The next biggest

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Speaker 1: stumbling block that I find a lot of organizations trip

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Speaker 1: up on is discovering their data. They've got a ton

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Speaker 1: of data, but nobody knows what they've got. So being

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Speaker 1: able to find, search, discover, rate, review, and rank that

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Speaker 1: information is paramount because you'll have people come in and

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Speaker 1: go okay. So a line managers approached me and said

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Speaker 1: that we want to take a look at our top

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Speaker 1: performing customers and we want to build a retention strategy

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Speaker 1: so we're not losing customers anymore. Well, your data scientists

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Speaker 1: is then going to go, well, do we have data

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Speaker 1: of customers that have left previously. If you can't easily

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Speaker 1: search and find out what you've got, that makes it

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

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

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

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Speaker 1: of organizations. What kind of supporting technology and new solutions

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Speaker 1: do we need to meet growing data management issues? It

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Speaker 1: really comes down to a few things. So ensuring that

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

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Speaker 1: looking at. So I think when people think of data,

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Speaker 1: they're always thinking of hate it's just going to be

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Speaker 1: a bunch of spreadsheets. It might just be stuff that

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Speaker 1: we can throw into a database, But there is so

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Speaker 1: much more out there. Right, there's video, how do we

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Speaker 1: store that? How do we hold that? There is images,

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Speaker 1: there's natural text. Like we're just talking about ensuring that

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Speaker 1: you've got appropriate processes in place to be able to

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Speaker 1: store holding catalog that I think is absolutely critical. We

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Speaker 1: talked a little bit about data cataloging and the need

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Speaker 1: to be able to search and discover that data. That

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Speaker 1: is absolutely paramount. Once you've got it collected, how do

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Speaker 1: you find it? What is IBM's unique approach to facilitating

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Speaker 1: access to data within companies. So one of the biggest things,

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Speaker 1: and one of the my favorite things that I get

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Speaker 1: to work with, is a particular tool set, right, and

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Speaker 1: this tool set is called cloud Path for Data. So,

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Speaker 1: without getting too pitchy, that the absolutely amazing thing about

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Speaker 1: This is that those stages that I was talking about, right,

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Speaker 1: So collect, organized, analyze, and infused. It actually helps facilitate

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Speaker 1: each one of those stages. Right. So you can actually collect, store,

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Speaker 1: and hold your data in a secure and government place.

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Speaker 1: You've got data catalog in capabilities which allows you to search.

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Speaker 1: Like one of my favorite things is that you might

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Speaker 1: have a data set. Right, So I might be a

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Speaker 1: data scientist, and then we might have another data scientist

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Speaker 1: on the team. I can have a data set inside

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Speaker 1: of there, and I can actually rank it and add

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Speaker 1: comments and go, hey, just be wary of this column

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Speaker 1: with lot certain features that you need to be mindful of,

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Speaker 1: and that provides additional metadata understand what is what my

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Speaker 1: data actually looks like and and things that I should

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Speaker 1: be mindful for. So I'm I'm Joe employee. How can

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Speaker 1: data be helpful to me? Great question? So, I mean

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Speaker 1: data is impacting everyone, right, whether you you like it

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Speaker 1: or not. Um and more often than not, what you're

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Speaker 1: going to find is that you can improve whatever it

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Speaker 1: is that you do by by looking at that data,

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Speaker 1: whether it's let's take an organization out of it. If

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Speaker 1: you use sleep trackers, you can begin to see when

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Speaker 1: you're sleep, or when you're getting good quality sleep versus

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Speaker 1: when you're getting bad quality sleep. If you start to

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Speaker 1: collect additional data points like hey, am I drinking enough

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Speaker 1: water during the day? Am I doing certain things like

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Speaker 1: looking at my phone just before I go to bed?

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Speaker 1: Are these things influencing my sleep? And is that causing

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Speaker 1: a negative impact on my quality of life? So that's

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Speaker 1: taking a broader view of it. But when you step

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Speaker 1: into a team or a business view, data can can

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Speaker 1: make your life for billion times easier. If you know

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Speaker 1: that there's a particular issue in a system earlier on

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Speaker 1: in a data pipeline, before something crosses your desk, you

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Speaker 1: might go and say, hey, look, if we just changed

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

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Speaker 1: transformed what we actually did with it, this is going

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Speaker 1: to streamline my entire workflow and and help me out.

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Speaker 1: But not only that, Right, So I work a little

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Speaker 1: bit with the automation team, and they're really big on

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Speaker 1: robotic process automation. Let's say you're doing something each and

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Speaker 1: every single day. You're copying a far from here to there.

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Speaker 1: You're grabbing some information from a website, You're throwing it

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Speaker 1: into a form and you have to do that twenty

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Speaker 1: times a day. There are tools that can automate that

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Speaker 1: entire process for you, and they're smart. They're not just

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Speaker 1: looking at where you're clicking on the page. They're looking

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

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Speaker 1: you're pulling data out of. You can automate those entire workflows.

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Speaker 1: That means that you don't have to do that repetitive

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Speaker 1: kind of boring work that you don't really want to.

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Speaker 1: You can palm that off and do the very bot

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Speaker 1: and do the stuff that you actually really want to

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Speaker 1: get involved in. As Nicholas said, the way a company

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Speaker 1: leverages this data has an impact on every level of

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Speaker 1: the business. Data informs how we do our jobs day

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Speaker 1: to day and how we plan for the future. Having

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Speaker 1: an open mindset about data makes it easier for a

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Speaker 1: business to come up with creative solutions. In the next

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Speaker 1: part of their conversation, Ronald asked Nicholas how data science

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Speaker 1: and creativity come together. So let's talk a little bit

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

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Speaker 1: YouTube channel, UH and how you use that to help

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Speaker 1: people get started with data science. What does creativity mean

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Speaker 1: to you? And do you see your work as creative.

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Speaker 1: I definitely say my work as creative, and I think

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Speaker 1: creativity is truly thinking outside of the box and looking

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Speaker 1: at just different ways of doing things. I think the

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Speaker 1: biggest thing that I try to embody is having an

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Speaker 1: open mindset and really never being willing to shut something

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Speaker 1: down or not look at a particular solution or option,

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Speaker 1: because you really never know where a particular solution might

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Speaker 1: come from. If you look at where some of the

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Speaker 1: advancements in that the medical field are coming from, it's

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Speaker 1: because they're being open to new ideas, new materials, new ingredients,

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Speaker 1: new recipes, new technologies. Having an open mindset really helps

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Speaker 1: improve that that that ability to solve complex problems. And

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Speaker 1: I think for me, creativity is really just having that

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Speaker 1: that open mindset. Tell me a little bit about how

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Speaker 1: you approach novel problems. What do you do when you

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Speaker 1: get stuck? I think the most important thing I really

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Speaker 1: like when I push myself to do something that I've

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Speaker 1: personally never done before, and a lot of the time

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Speaker 1: that yields new solutions to problems that that that might

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Speaker 1: be really difficult to solve. It doesn't necessarily need to

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Speaker 1: be using this particular set of techniques. It's what else

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Speaker 1: can we do to solve this problem? And sometimes like

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Speaker 1: it'll be staring you in the face and you'll just

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Speaker 1: have no idea until you go, hey, I'm going to

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Speaker 1: throw everything out of the box and just give it

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Speaker 1: a crack and see what is possible. Um. But sometimes

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Speaker 1: it does require that that little bit of grit to

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

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

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

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

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

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

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

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

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

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

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

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

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

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Speaker 1: is by no means a state of the art model,

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

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Speaker 1: approached this or at least shared how they've tried to

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Speaker 1: do it. And I've kind of just had to get

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Speaker 1: real creative and trying to build that I had. I

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Speaker 1: literally spent weeks just trying to install stuff, then trying

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Speaker 1: to get it writting on my computer before I even

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Speaker 1: got anywhere near building that particular model, And and it's

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Speaker 1: super hard grow in terms of trying to get it

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Speaker 1: set up. But there's so many opportunities for good, whether

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Speaker 1: that's improving accessibility to certain technologies, improving the quality of

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Speaker 1: life for people that could benefit from us using data

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Speaker 1: a little bit better. There's a large body of work

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Speaker 1: with a bunch of different data scientists where they're actually

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Speaker 1: building language translation models for languages which aren't hyper popular

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Speaker 1: or aren't as widely spread as we might see in

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Speaker 1: our day to day lives. If you look at India,

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

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Speaker 1: even where my parents from Mauritius. There's there's a whole,

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Speaker 1: completely separate dialect where if you've never heard it before,

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00:26:50,320 --> 00:26:54,119
Speaker 1: you were like, it's just slang French, but no, it's it.

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Speaker 1: It's like um, it's its whole separate language. That obviously

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00:26:58,680 --> 00:27:02,399
Speaker 1: allows or improves the ability for people to to to

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Speaker 1: tap into data and do a little bit of good.

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Speaker 1: But there's so much I mean, people are using medical

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Speaker 1: image data to improve medical segmentation and improve diagnoses that

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Speaker 1: there's just so much amazing work that that's happening in

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Speaker 1: that space. There is obviously the temptation or used data

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Speaker 1: for bad, but I'd like to think that the large

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Speaker 1: majority of the community are really trying to use it

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Speaker 1: for good. You started talking about a little bit just now,

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Speaker 1: but what are some future trends and challenges and future

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Speaker 1: topics or projects you're excited about, anything in particular looking

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Speaker 1: real further forward. What I'm super excited about and I

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Speaker 1: still don't know how it's necessarily going to impact me,

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00:27:47,040 --> 00:27:48,960
Speaker 1: whether or not that's going to change my experience as

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00:27:48,960 --> 00:27:52,679
Speaker 1: a developer or not. That we've got quantum computers coming right,

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00:27:52,760 --> 00:27:55,280
Speaker 1: there's a ton of work that's happening in that space.

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Speaker 1: It's going to radically shift how large a machine learning

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Speaker 1: model we're able to create, how fast we're able to

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Speaker 1: train them. I'm just excited to see what happens in

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Speaker 1: that space. I'm not a quantum physicist by any means,

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Speaker 1: but I'm still excited to see what I'll be able

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Speaker 1: to do with him in the future. I love that,

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Speaker 1: as you'll continued belt this technology, you're excited to play

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Speaker 1: with it after it's built, which I'm I'm totally bored

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Speaker 1: that I don't want to have to build it, Nicholas

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Speaker 1: or not. Thank you so much for a talk with

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Speaker 1: me today. It's been an absolute pleasure. Thank you so

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00:28:33,280 --> 00:28:36,879
Speaker 1: much for your insightful questions. It's it's been awesome. Ronald

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Speaker 1: Nick made a point that I think is important to

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

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00:28:46,680 --> 00:28:49,400
Speaker 1: or make our jobs easier, or even do social good,

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00:28:49,960 --> 00:28:54,160
Speaker 1: a thoughtful data strategy is always the first stepping stone.

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00:28:54,920 --> 00:28:59,000
Speaker 1: Without good data, using machine learning or artificial intelligence to

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00:28:59,160 --> 00:29:04,960
Speaker 1: create in a sative solutions becomes much much harder. Our

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00:29:05,120 --> 00:29:09,640
Speaker 1: technology gets more sophisticated every day, but that doesn't mean

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Speaker 1: we should lose sight of the fundamentals. If we want

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Speaker 1: to get the most out of smarter technologies, better business decisions,

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00:29:17,280 --> 00:29:22,320
Speaker 1: more optimized technology, fresh and unexpected insights, we're going to

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00:29:22,400 --> 00:29:28,040
Speaker 1: need smarter data strategy. On the next episode of Smart

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00:29:28,080 --> 00:29:32,080
Speaker 1: Talks with IBM, the Power of Salesforce to transform the

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00:29:32,160 --> 00:29:36,479
Speaker 1: customer experience, we talked with Phil Weinmeister had a product

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00:29:36,520 --> 00:29:42,360
Speaker 1: for Salesforce America's at IBM consulting about transforming digital experiences

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00:29:42,360 --> 00:29:46,920
Speaker 1: with the Power of Salesforce and IBM. Smart Talks with

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Speaker 1: IBM is produced by Matt Romano, David jaw Roist and

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00:29:50,600 --> 00:29:55,600
Speaker 1: Deserve and Edith Rousselo with Jacob Goldstein were edited by

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00:29:55,640 --> 00:30:00,280
Speaker 1: Sophie Crane. Our engineers are Jason Gambrel, Sarah brug Air,

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00:30:00,520 --> 00:30:05,280
Speaker 1: and Ben Holliday. Theme song by Granmoscope. Special thanks to

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Speaker 1: Carli Migliori, Andy Kelly, Kathy Callaghan and the eight Bar

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Speaker 1: and IBM teams, as well as the Pushkin marketing team.

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00:30:13,800 --> 00:30:16,520
Speaker 1: Smart Talks with IBM is a production of Pushkin Industries

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00:30:16,760 --> 00:30:20,760
Speaker 1: and I Heart Media. To find more Pushkin podcasts, listen

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Speaker 1: on the I Heart Radio app, Apple Podcasts, or wherever

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Speaker 1: you listen to podcasts. I'm Malcolm Gladwell. This is a

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