The six answers you want to have about Machine Learning, all in one place. The founder of Pinboard, Maciej Cegłowski, has just published his statement about "Privacy Rights and Data Collection in a Digital Economy". The addendum on Machine Learning of that document is a great, simple explanation of its intrinsical limits, and the risks coming from them. To make them even simpler to understand, I took the liberty of synthesizing it in an easier Q&A format. Questions and parts in italic are my own additions, and any error is mine only.

Part II by the inquisitive lawyer: AI Camp Next Con event takeaways: 1. AI and Health Care: I enjoyed this session a lot by Anitha Kannan. I learned about recency bias amongst doctors: this is where a doctor who has dealt with 10 patients with similar symptoms assumes patient 11 has the same ailment. Anitha's work with Curai aids these doctors to give diagnosis on a particular ailment avoiding bias. However ML is required to ensure that the AI program can detect the right ailment or have the ability to respond "I don't know" when it can't. Jay Baxter explains the modeling engagements in creating & ranking your feed and bias in candidate generation. It looks at 3 things: all tweets, the impressions(likes, RTs) and engagement(how often you have engaged with the user in the past) 3 Testing AI & Bias- this was another interesting one on search queries by Jason Arbon.