Unless you turn off the microphones and use a button or a remote, Alexa Voice Service and other IVCSs are always listening. Let me be the first to scream … "Look out!"
Shelly Palmer talks about the technology required for WestWorld to be real with Teresa Priolo and Antwan Lewin on Fox 5 NY. Original Airdate: December 6, 2016
Generally speaking, there are two kinds of companies in the world: data rich and data poor. The richest of the data rich (Google, Facebook, Amazon, Apple, etc.) are easy to name. But you don't need to be at the top of this list to use data to create value. You need to have the tools in place to turn information (data) into action -- that's what the data rich do that the data poor and the data middle class do not.
Because the velocity of data is increasing and will always increase, the need for data literacy is increasing and will always increase. This does not mean that to be successful executive you have to become a data scientist -- quite the contrary. It means that in order to be a successful executive, you need to understand how data is turned into action, be familiar with the methods of data science and data scientific research, and be able to think strategically about how to use data to create value for your business. All other things being equal, there is a significant difference between being literate and being fluent.
What made move 37 so interesting is that no one expected it. It was early in game two of the million-dollar Google DeepMind Challenge Match, and AlphaGo, an artificial intelligence (AI) system developed by Google, placed its 19th stone on a part of the game board that no human Go master would have considered. Some called it a "mistake." Others called it "creative" and "unique." But considering that AlphaGo went on to win its third game in a row against one of the strongest Go players in the world, the move should probably have been called what it really was: "intuitive."