Kim Zetter, Wired
Nicholas Weaver, a senior researcher at the International Computer Science Institute at UC Berkeley, has detailed the basic steps that it would take to create your own mass-surveillance system like the NSA employs. While he points out that you would need massive hardware to actually pull off the same things that the largest spy agencies do, the technology is mainly based on off-the-shelf software, including open source system, Vortex, created by Lockheed Martin.
The point of his presentation, which he gave at the Enigma Security Conference this week, is that bad actors don’t have to be governments, but can be anyone with enough technical skill to setup these systems, as the cost is so dramatically low.
Another visitor to Enigma? NSA’s head of Tailored Access Operations, Rob Joyce, who spoke about common exploit techniques that they use, and how to guard against them.
Mika McKinnon, Gizmodo
Yesterday was the thirtieth anniversary of the Challenger Shuttle tragedy, which remains ever large in mind when human space flight is brought up. Mika McKinnon, who writes extensively on space for io9 and Gizmodo, created a timeline of events in pictures that describes the disaster and it’s aftermath.
She also wrote an article a year ago about NASA’s Day of Remembrance, which coincides with the Challenger explosion, and is near the Apollo 1 and Columbia incidents as well. It’s good to remember that incidents such as these are bound to happen with progress, and that as a group, humanity is interested in overcoming these hurdles to accept the challenges of exploration beyond our planet bring. Matt Damon and ‘The Martian’ deserve to be rewarded critically as well as commercially for reigniting a passion for human expansion onto other worlds.
Much of the code in Linux is written by employees paid to do this work, but significant parts of both Linux and the huge range of software that it depends on are written by community members who now have no representation in the Linux Foundation.
Matthew Garrett has noticed that the bylaws of the Linux foundation have changed as of last week to no longer allow associate members of the foundation (individuals who are not members as part of a large sponsor business) to be involved in the election process for directors of the organization. This seems like a pretty clear indication to me where decision making in the foundation is going, and it’s not surprising, but also not very welcome to the community at large.
Elliot Harmon, Electronic Frontier Foundation
I tweeted about this on Thursday, but it bears repeating: there should be no reason that research done using federal grants at public universities isn’t made public and accessible to all. This gets even worse when it comes to journal publishers who claim copyright on work that they had no involvement in until the point of publication, but any and all copyrightable work can still grant rights and recognition to the actual researchers and assistants while still using share-alike creative commons licensing.
Software patents are already dangerous (after all, should loading-screen games really be a patentable idea?), but they get worse when made with public dollars.
Cade Metz, Wired
Go is one of those games that has long been touted as a high-water mark for AI developers. After the success of Deep Blue in 1997 and Watson in 2011, there were few areas that were considered as hard for computers to make progress as the ancient board game. While they wouldn’t call it a solved problem set, a Google team in the UK has created a system that has bested one of the best Go players in the world, and will be iterated upon for a public exhibition against the most awarded human player in the world in Korea this March.
The accomplishment is historic in how it defied estimates as to how long this would take. Even a year ago, one of the developers of the project anticipated at least a decade of work before real progress was made, and some researches were expecting that it would never happen. The researchers behind the win also point out that it’s not just optimizing a game playing machine that they’ve built, but working on AI systems that can teach and reinforce themselves, as well as make headway on more practical issues of robotics and signals analysis.