BayesDB, a Bayesian database table, 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.

, which Wald saw instantly, was that the holes showed where the planes were strongest. The holes showed where a bomber could be shot and still survive the flight home, Wald explained. After all, here they were, holes and all. It was the planes that weren’t there that needed extra protection, and they had needed it in places that these planes had not. The holes in the surviving planes actually revealed the locations that needed the least additional armor. Look at where the survivors are unharmed, he said, and that’s where these bombers are most vulnerable; that’s where the planes that didn’t make it back were hit.

"The commonly held belief that entrepreneurs are young college students working out of their dorms is simply wrong," says study author Vivek Wadhwa of Duke University's Center for Entrepreneurship and Research Commercialization. "People typically come to a stage where they're tired of working for other people. They think, 'I'm 40 and I haven't made it big yet. This is my last chance.' That really spurs the entrepreneurial spirit."

People often think that the big city is a dangerous place: they worry that they might get murdered, for instance. Being killed on purpose is more likely in town, according to new research, but it is so rare compared to dying in an accident of some type that in fact you would be much more likely to die unexpectedly in the countryside - in America, anyway.

In this post I will describe one small but important part of the theory of causal inference, a causal calculus developed by Pearl. This causal calculus is a set of three simple but powerful algebraic rules which can be used to make inferences about causal relationships. In particular, I’ll explain how the causal calculus can sometimes (but not always!) be used to infer causation from a set of data, even when a randomized controlled experiment is not possible. Also in the post, I’ll describe some of the limits of the causal calculus, and some of my own speculations and questions.

Without the support of two major browsers and major websites most internet users are missing out on the security benefits of perfect forward secrecy. Without the protection of PFS, if an organisation were ever compelled — legally or otherwise — to turn over RSA private keys, all past communication over SSL is at risk. Perfect forward secrecy is no panacea, however; whilst it makes wholesale decryption of past SSL connections difficult, it does not protect against targeted attack on individual sessions. Whether or not PFS is used, SSL remains an important tool for web sites to use to secure data transmission across the internet to protect against (perhaps all but the most well-equipped) eavesdroppers.

Sitespeed.io is an open source tool that helps you analyze and optimize your website speed and performance based on performance best practices. It collects data from multiple pages on your website, analyze the pages using performance best practices rules and output the result as HTML-files or JUnit XML.

We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals. We coarsen the data spatially and temporally to find a formula for the uniqueness of human mobility traces given their resolution and the available outside information. This formula shows that the uniqueness of mobility traces decays approximately as the 1/10 power of their resolution. Hence, even coarse datasets provide little anonymity. These findings represent fundamental constraints to an individual's privacy and have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals.

Biases in how data are collected, a lack of context, gaps in what’s gathered, artifacts of how data are processed and the overall cognitive biases that lead even the best researchers to see patterns where there are none mean that “we may be getting drawn into particular kinds of algorithmic illusions,” said MIT Media Lab visiting scholar Kate Crawford. In other words, even if you have big data, it’s not something that Joe in the IT department can tackle—it may require someone with a PhD, or the equivalent amount of experience. And when they’re done, their answer to your problem might be that you don’t need “big data” at all.

For the software delivery process, the most important global metric is cycle time. This is the time between deciding that a feature needs to be implemented and having that feature released to users. As Mary Poppendieck asks, "How long would it take your organization to deploy a change that involves just one single line of code? Do you do this on a repeatable, reliable basis?"4 This metric is hard to measure because it covers many parts of the software delivery process—from analysis, through development, to release. However, it tells you more about your process than any other metric.