Linux users familiar with other filesystems or ZFS users from other platforms will often ask whether ZFS on Linux (ZoL) is “stable”. The short answer is yes, depending on your definition of stable. The term stable itself is somewhat ambiguous.Oh dear. that’s not a good start. Good reference page, though
“This is rule No. 1: There are no screens in the bedroom. Period. Ever.”
How can we measure the number of additional clicks or sales that an AdWords campaign generated? How can we estimate the impact of a new feature on app downloads? How do we compare the effectiveness of publicity across countries? In principle, all of these questions can be answered through causal inference. In practice, estimating a causal effect accurately is hard, especially when a randomised experiment is not available. One approach we’ve been developing at Google is based on Bayesian structural time-series models. We use these models to construct a synthetic control — what would have happened to our outcome metric in the absence of the intervention. This approach makes it possible to estimate the causal effect that can be attributed to the intervention, as well as its evolution over time. We’ve been testing and applying structural time-series models for some time at Google. For example, we’ve used them to better understand the effectiveness of advertising campaigns and work out their return on investment. We’ve also applied the models to settings where a randomised experiment was available, to check how similar our effect estimates would have been without an experimental control. Today, we’re excited to announce the release of CausalImpact, an open-source R package that makes causal analyses simple and fast. With its release, all of our advertisers and users will be able to use the same powerful methods for estimating causal effects that we’ve been using ourselves. Our main motivation behind creating the package has been to find a better way of measuring the impact of ad campaigns on outcomes. However, the CausalImpact package could be used for many other applications involving causal inference. Examples include problems found in economics, epidemiology, or the political and social sciences.
Shamefully, I haven’t visited most of these!
Now a series of decisions from lower courts is starting to bring the ruling’s practical consequences into focus. And the results have been ugly for fans of software patents. By my count there have been 11 court rulings on the patentability of software since the Supreme Court’s decision — including six that were decided this month. Every single one of them has led to the patent being invalidated. This doesn’t necessarily mean that all software patents are in danger — these are mostly patents that are particularly vulnerable to challenge under the new Alice precedent. But it does mean that the pendulum of patent law is now clearly swinging in an anti-patent direction. Every time a patent gets invalidated, it strengthens the bargaining position of every defendant facing a lawsuit from a patent troll.
A practical demo of “differential privacy” — allowing public data dumps to happen without leaking privacy, using Laplace noise addition