Four short links: 1 August 2018
Data Science Ethics, Bandit Algorithms, Formal Methods, and FAST Goals
- Data’s Day of Reckoning (Loukides, Mason, Patil) — Data science, machine learning, artificial intelligence, and related technologies are now facing a day of reckoning. It is time for us to take responsibility for our creations. What does it mean to take responsibility for building, maintaining, and managing data, technologies, and services?
- Bandit Algorithms (Tor Lattimore) — A practitioner seeking to apply a bandit algorithm must understand which assumptions in the theory are important and how to modify the algorithm when the assumptions change. We hope this book can provide that understanding. Bandit algorithms make decisions with partial information, taking into account the cost of getting more information.
- Augmenting Agile with Formal Methods — The difference between writing TLA+ and just writing unit tests isn’t half an hour versus sixteen hours, it’s half an hour versus “Two weeks to realize there’s a bug, a week to find the bug, three days to understand the bug, sixteen hours to write the test, twenty minutes to run the test, and you don’t know if your fix really works.”
- FAST Goals Beat SMART Goals (MIT Sloan Review) — FAST = Frequently-discussed, Ambitious, Specific, and Transparent. (via Helen Bevan)