Smashing Podcast Episode 33 With Charlie Gerard: What Is Machine Learning?


Photo of Charlie GerardDREW MCLELLAN: She’s a senior front-end developer at Netlify, a Google Developer expert in web technologies and a Mozilla tech speaker. In her spare time, she explores the field of human computer interaction, and builds interactive prototypes using hardware, machine learning and creative coding. She regularly speaks at conferences and writes blog posts to share the things she learns. And most recently, is the author of the book, Practical Machine Learning in JavaScript for Apress.

DREW: So we know she’s a front-end expert, but did she once to escape from jail using a metal file she’d crocheted out of dreams. My smashing friends, please welcome, Charlie Gerard. Hi Charlie. How are you?

CHARLIE GERARD: I am smashing.

DREW: I wanted to talk to you today about machine learning, which might seem like a little bit of a strange topic for a podcast that focuses mainly on the sort of browser end of web development. I tend to think of machine learning as something that happens in giant data centers or laboratories with people with white coats on. It’s definitely a bit of a sort of buzzword these days. What on earth do we actually mean when we say machine learning?

CHARLIE: So in general, the standard definition would be it’s giving the ability for computers to generate predictions without being told what to do. Hopefully, this will make sense when we keep talking about it, but that’s the kind of generic conversation definition. You don’t really tell algorithms or models to go and search for certain things. They learn through data that you give it and it can then generate predictions.

DREW: So rather than having to specifically code for certain circumstances, you kind of create a generic case where the software can learn how to do that stuff itself?

CHARLIE: Yeah, exactly.

DREW: That sounds almost a little bit creepy. It’s kind of verging on that artificial intelligence sort of side of things. Do you need to be a hardcore math nerd or a data scientist to do this? Or is there stuff out there like established algorithms and things that you can call on to get started?

CHARLIE: Yeah. So luckily you don’t need to be a hardcore math nerd or a data scientist. Otherwise, I would definitely not be talking about this. But there are algorithms that have already been figured out and tools already available that allow you to use these algorithms without having to write everything from scratch yourself. So if we use the front-end ecosystem as a comparison, you can use web APIs, like the navigator to get user media when you want to have access to the webcam or the microphone.

CHARLIE: And you don’t have to know how that API was actually implemented under the hood. What matters is that you know what this API is good for and how to use it, if you want. Then later on you can go and look into the source code of your favorite browser to know how it really works, but it’s really not useful in the first place. And it can be useful if you want to write your own algorithm later on. But to be really honest, it’s highly unlikely that you’ll want to do this.

DREW: Okay. So it’s a bit like the way you can write CSS to position an element on a page. You don’t care how the browser is actually doing that. You just write some CSS and the browser takes care of it.

CHARLIE: Yeah. When you get started, it’s mostly something like that.

DREW: That’s good. That’s more sort of my level of data science.

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