- Reform Government Surveillance — hard not to view this as a demarcation dispute. “Ruthlessly collecting every detail of online behaviour is something we do clandestinely for advertising purposes, it shouldn’t be corrupted because of your obsession over national security!”
- Brian Abelson — Data Scientist at the New York Times, blogging what he finds. He tackles questions like what makes a news app “successful” and how might we measure it. Found via this engaging interview at the quease-makingly named Content Strategist.
- StageXL — Flash-like 2D package for Dart.
- BayesDB — lets users query the probable implications of their data as easily as a SQL database lets them query the data itself. Using the built-in Bayesian Query Language (BQL), users with no statistics training can solve basic data science problems, such as detecting predictive relationships between variables, inferring missing values, simulating probable observations, and identifying statistically similar database entries. Open source.
Surveillance Demarcation, NYT Data Scientist, 2D Dart, and Bayesian Database
Exploiting Glass, Teaching Probability, Product Design, and Subgraph Matching
- Exploiting a Bug in Google Glass — unbelievably detailed and yet easy-to-follow explanation of how the bug works, how the author found it, and how you can exploit it too. The second guide was slightly more technical, so when he returned a little later I asked him about the Debug Mode option. The reaction was interesting: he kind of looked at me, somewhat confused, and asked “wait, what version of the software does it report in Settings”? When I told him “XE4” he clarified “XE4, not XE3”, which I verified. He had thought this feature had been removed from the production units.
- Probability Through Problems — motivating problems to hook students on probability questions, structured to cover high-school probability material.
- Connbox — love the section “The importance of legible products” where the physical UI interacts seamless with the digital device … it’s glorious. Three amazing videos.
- The Index-Based Subgraph Matching Algorithm (ISMA): Fast Subgraph Enumeration in Large Networks Using Optimized Search Trees (PLoSONE) — The central question in all these fields is to understand behavior at the level of the whole system from the topology of interactions between its individual constituents. In this respect, the existence of network motifs, small subgraph patterns which occur more often in a network than expected by chance, has turned out to be one of the defining properties of real-world complex networks, in particular biological networks. […] An implementation of ISMA in Java is freely available.
- An Intuitive Guide to Linear Algebra — Here’s the linear algebra introduction I wish I had. I wish I’d had it, too. (via Hacker News)
- Think Bayes — an introduction to Bayesian statistics using computational methods.
- Divshot — a startup turning mockups into web apps, built on top of the Bootstrap front-end framework. I feel momentum and a tipping point approaching, where building things on the web is about to get easier again (the way it did with Ruby on Rails). cf Jetstrap.
A review of "The Drunkard's Walk: How Randomness Rules Our Lives."
While Leonard Mlodinow's book offers a good introduction to probabilistic thinking, it carries two problems: First, it doesn't uniformly account for skill. Second, when we're talking probability and statistics, we're talking about interchangeable events.