The Banana Data Podcast

BDN #3: Culpability in AI failures, Fooling NNs with NNs, AI for cancer screenings, and Epsilon Greedy Multi-Armed Bandits

June 07, 2019 Season 1 Episode 3
The Banana Data Podcast
BDN #3: Culpability in AI failures, Fooling NNs with NNs, AI for cancer screenings, and Epsilon Greedy Multi-Armed Bandits
Chapters
The Banana Data Podcast
BDN #3: Culpability in AI failures, Fooling NNs with NNs, AI for cancer screenings, and Epsilon Greedy Multi-Armed Bandits
Jun 07, 2019 Season 1 Episode 3
Dataiku
How can AI improve accessibility in healthcare? Who gets the blame when an algorithm fails? How can we tell the real from the hype in AI reporting? What the heck is an epsilon multi-armed bandits? This week on BDN.
Show Notes

This week we’re diving into some deeper impacts of AI’s successes and failures- asking where responsibility lies for an algorithm’s failures, and the endless benefits of accessibility and responsibility that come with AI implemented in healthcare. We’re also taking a deep dive into Epsilon greedy multi-armed bandits and how we can more accurately describe our successes (and our failures) in AI.

 When algorithms mess up, the nearest human gets the blame by Karen Hao (MIT Technology Review)
 Google Trained Its AI to Predict Lung Cancer by Christine Fisher (Engadget)
 No This AI Can’t Finish Your Sentence by Tiernan Ray (ZDNet)
 Introduction to Multi-Armed Bandits with Applications in Digital Advertising by Dave King (SpotX)

 Register for EGG NYC, Dataiku's human-centered AI conference on June 20th to hear from leaders in the AI & advanced analytics space, including WIRED, Twitter, Hinge, and more! 

 https://nyc.egg.dataiku.com/



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