While waiting for day 2 of the Bay Area Deep Learning school in three hours, here are the ten videos of presentations made at the HORSE2016 workshop (On “Horses” and “Potemkin Villages” in Applied Machine Learning) organized by Bob Sturm. Bob came to present something around that theme at the 9th meetup of Season 3 of the Paris Machine Learning meetup. With this workshop, the field and attendant issues are becoming more visible: this is outstanding! as this has bearing on algorithm bias and explainability. To make the video-watching more targeted, Bob even included commentaries with embedded videos in his blog post, here is the beginning of the whole blog entry, you should go there:
September 19, 2016 saw the successful premier edition of HORSE2016: On “Horses” and “Potemkin Villages” in Applied Machine Learning. I have now uploaded videos to the HORSE2016 YouTube channel, and posted slides to the HORSE2016 webpage. I embed the videos below with some commentary.
HORSE2016 had 10 speakers expound on a variety of interesting topics, and about 60 people in the audience. I am extremely pleased that the audience included several people from outside academia, including industry, government employees and artists. This shows how many have recognised the extent to which machine learning and artificial intelligence are impacting our daily lives. The issues explored at HORSE2016 are essential to ensuring this impact remains beneficial and not detrimental.
Here is my introductory presentation, “On Horse Taxonomy and Taxidermy”. This talk is all about “horses” in applied machine learning: what are they? Why is this important and relevant today? Why the metaphor, and why is it appropriate? I present an example “horse,” uncovered using an intervention experiment and a generation experiment. Finally, I discuss what a researcher should do if someone demonstrates their system is a “horse”.
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