[ExI] thawing of ai winter?

Fri Apr 17 15:39:08 UTC 2020

Stanford weekly seminars are quite good.  I have half a mind to tune in to this discussion today.

https://hai.stanford.edu/events/hai-weekly-seminar-vinay-uday-prabhu-four-horsemen-ethical-malice-peer-reviewed-machine?utm_source=Stanford+University <https://hai.stanford.edu/events/hai-weekly-seminar-vinay-uday-prabhu-four-horsemen-ethical-malice-peer-reviewed-machine?utm_source=Stanford+University&utm_campaign=0f2d627924-EMAIL_CAMPAIGN_2020_03_13_10_26_COPY_01&utm_medium=email&utm_term=0_aaf04f4a4b-0f2d627924-199837799> &utm_campaign=0f2d627924-EMAIL_CAMPAIGN_2020_03_13_10_26_COPY_01&utm_medium=email&utm_term=0_aaf04f4a4b-0f2d627924-199837799

This comment about thawing of the AI winter caught my attention.  What is that?  I didn’t realize we were in an AI winter.

AI hipsters, do offer an educational comment please.

spike

EVENT TODAY!

HAI Weekly Seminar with Vinay Uday Prabhu

On the Four Horsemen of Ethical Malice in Peer-Reviewed Machine Learning Literature

April 17, 2020

11:00AM PDT 

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Speaker Bio: Vinay Prabhu is currently the Chief Scientist at UnifyID Inc, where he leads efforts towards architecting and deploying the state-of-the-art passive mobile biometrics solution by bringing together machine learning algorithms and smart-sensor data to model the human behind the device. 

Abstract: The thawing of the AI winter and the subsequent deep learning revolution has been marked by large scale open-source-driven democratization efforts and a paper publishing frenzy. As we navigate through this massive corpus of technical literature, four categories of ethical transgressions come to fore: Dataset curation, Modeling, Problem definitions and sycophantic tech-journalism. In this talk, we will explore specific examples in each of these categories with a strong focus on computer vision. The goal of this talk is to not just demonstrate the widespread usage of these datasets and models, but to also elicit a commitment from the attending scholars to either not use these datasets or models, or to insert an ethical caveat in case of unavoidable usage…

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