Prioritizing training data, model interpretability, and dodging an AI Winter

This episode, Triveni and Will tackle the value, ethics, and methods for good labeled data, while also weighing the need for model interpretability and the possibility of an impending AI winter.  Triveni will also take us through a step-by-step of the decisions made by a Random Forest algorith

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  Be sure to check out the articles we mentioned this week:

 The Side of Machine Learning You’re Undervaluing and How to Fix it by Matt Wilder (LabelBox)

 The Hidden Costs of Automated Thinking by Jonathan Zittrain (The New Yorker)

 Another AI Winter Could Usher in a Dark Period for Artificial Intelligence by Eleanor Cummins (PopSci)