Sunday, March 12, 2017

A rather extreme example of privacy on the Internet of Things?  Similar to children’s toys and other devices connected to the Internet.  Note what you have to disclose to get the full $10,000
Sex Toy Maker Pays $3.75 Million to Settle ‘Smart’ Vibrator Lawsuit
Think twice about connecting those sex toys to the Internet: A vibrator company has agreed to pay up to $10,000 to U.S. customers who used a smartphone app that relayed their data to the firm’s server.
   The toys in question, which include the We-Vibe Classic and Rave by We-Vibe, are designed to be used by couples, allowing one partner to control the devices via Bluetooth and a smartphone app.
Security researchers, however, discovered the company was also using the smart phone app to harvest data about how customers used the vibrators.  The apps collected information such as what temperature and intensity settings the owners used, as well as how often they used the toys.
   In a curious twist to the settlement, the process creates two potentials ways for customers to collect: those who attest they used the app to control the vibrator, and who provide their name and phone number and other details as part of the claim process, will get up to $10,000.  Meanwhile, those who simply purchased a We-Vibe connected device will receive up to $199.

Interesting.  Very expensive and other barriers to entry.  Who documents a system that changes as it learns?  (My guess: the AI itself!) 
4 ways Google Cloud will bring AI, machine learning to the enterprise
Last November, when Google announced that machine learning research luminary Fei-Fei Li, Ph.D. would join Google’s Cloud Group Platform group, a lot was known about her academic work.  But Google revealed little about why she was joining the company except she would lead machine learning for the Google Cloud business.
After five months of suspense, yesterday Li revealed the focus of her new role during her keynote address at Google’s cloud developer conference, Cloud Next 2017. She will apply her experience to democratize machine learning to the enterprise.  Her task: Study the problems that machine learning could solve in a wide variety of industries and enable enterprises to adopt machine learning.
   Earlier in her keynote, Li began to describe some of the enterprise applications that interested her by saying: “So much more is waiting to be done.”
  • Retail: Google’s Adsense can be extended by retailers to serve the best ad to the individual consumer.
  • Supply chain: Optimize routes and inventory, predict changes in demand, drone and autonomous vehicle deliveries.
  • News content: Individually personalize news and, presumably, screen fake news.
  • Financial services: Predict credit card risk, manage an individual’s finances, flag criminal activity like money laundering and fraud, and automate processes, such as replacing call centers and processing insurance claims with trained AI agents.
  • Healthcare: Li said the implications of AI in healthcare were profound—automated visual diagnosis, reduced overhead, fewer errors, extending healthcare to the underserved, augmented surgical practices, and improved administration in areas such as scribing electronic medical records (EMR) during doctor’s visits and management of chronic conditions. 

A guide for all my students. 
A Quick Guide to Email Etiquette (Infographic)

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