Traditionally, when we've depended upon software to make a decision with real-world implications,

that software was deterministic.

It had some inputs, a few if statements, and we could point to the exact line of code

where the decision was made, and the same inputs would lead to the same decisions.

Nowtays, with the rise of machine learning and neural networks, this is much more blurry.

How did the model decide?

Has the model inputs drifted apart so the decisions are outside what it was designed for?

These are just some of the questions discussed with our guest, Andrew Clark, on this episode

261 of Talk Python to Me, recorded April 17th, 2020.

Thank you. 2020. Welcome to Talk Python to me, a weekly podcast on Python, the language, the libraries,

the ecosystem, and the personalities. This is your host, Michael Kennedy.

Follow me on Twitter where I'm at M. Kennedy.

Keep up with a show and listen to past episodes at talk python.fm.

And follow the show on Twitter via at Talk Python.

This episode is sponsored by Linode and Ruvan Learners.

Please check out what they're both offering during their segments.

It really helps support the show. Andrew Clark, welcome to Talk Python to Me. I'm Michael. Glad to be here.

It's always a pleasure listening to your podcast and really excited to get to talk to you today.

Well, thank you so much. I'm excited to talk to you as well. It's going to be really fun to talk

about machine learning. And I think of all of the programming type of stuff out there

more than most machine learning is truly a black box for a lot of people the decisions that it makes

is sort of unclear it's sort of statistical I don't know to me it feels a little bit like quantum

...