Sketchnotes from TWiML&AI #121: Reproducibility and the Philosophy of Data with Clare Gollnick

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These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Reproducibility and the Philosophy of Data with Clare Gollnick:

Sketchnotes from TWiMLAI talk #121: Reproducibility and the Philosophy of Data with Clare Gollnick

You can listen to the podcast here.

In this episode, i’m joined by Clare Gollnick, CTO of Terbium Labs, to discuss her thoughts on the “reproducibility crisis” currently haunting the scientific landscape. For a little background, a “Nature” survey in 2016 showed that more than 70% of researchers have tried and failed to reproduce another scientist’s experiments, and more than half have failed to reproduce their own experiments. Clare gives us her take on the situation, and how it applies to data science, along with some great nuggets about the philosophy of data and a few interesting use cases as well. We also cover her thoughts on Bayesian vs Frequentist techniques and while we’re at it, the Vim vs Emacs debate. No, actually I’m just kidding on that last one. But this was indeed a very fun conversation that I think you’ll enjoy! https://twimlai.com/twiml-talk-121-reproducibility-philosophy-data-clare-gollnick/

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