Anand Kumar

“We are already seeing car insurance premiums linked to tracking devices in cars, and health insurance coverage that depends on people wearing a fitness tracking device. When surveillance is used to determine things that hold sway over important aspects of life, such as insurance coverage or employment, it starts to appear less benign. Moreover, data analysis can reveal surprisingly intrusive things: for example, the movement sensor in a smartwatch or fitness tracker can be used to work out what you are typing (for example, passwords) with fairly good accuracy [98]. And algorithms for analysis are only going to get better.”
― Martin Kleppmann, Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

“Any investment that has become a topic of widespread conversation is likely to be hazardous to your wealth. It was true of gold in the early 1980s and Japanese real estate and stocks in the late 1980s. It was true of Internet-related stocks in the late 1990s and condominiums in California, Nevada, and Florida in the first decade of the 2000s, as well as bitcoin in 2017.”
― Burton G. Malkiel, A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing

“14 A LIFE-CYCLE GUIDE TO INVESTING There are two times in a man’s life when he should not speculate: when he can’t afford it, and when he can. —Mark Twain, Following the Equator INVESTMENT STRATEGY NEEDS to be keyed to one’s life cycle.”
― Burton G. Malkiel, A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing

“Surveillance As a thought experiment, try replacing the word data with surveillance, and observe if common phrases still sound so good [93]. How about this: “In our surveillance-driven organization we collect real-time surveillance streams and store them in our surveillance warehouse. Our surveillance scientists use advanced analytics and surveillance processing in order to derive new insights.”
― Martin Kleppmann, Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems