Andrew Sorensen's cyberphysical music-making demonstrated programming real-time systems in real time.
Music and programming share deep mathematical roots, but have very different senses of “performance”. At OSCON, Andrew Sorensen reunited those two branches to give a live “concert” performance as a keynote. Sorensen brought his decade of “live coding musical concerts in front of an audience” to a real-time demonstration of Extempore, “a systems programming language designed to support the programming of real-time systems in real time”:
“Extempore is designed to support a style of programming dubbed ‘cyberphysical’ programming. Cyberphysical programming supports the notion of a human programmer operating as an active agent in a real-time distributed network of environmentally aware systems.”
A collection of must-see keynotes from Velocity Santa Clara, with bonus videos of some of the best sessions.
Editor’s note: this post originally appeared on Steve Souders’ blog; it is published here with permission.
Velocity Santa Clara was our biggest show to date. There was more activity across the attendees, exhibitors, and sponsors than I’d experienced at any previous Velocity. A primary measure of Velocity is the quality of the speakers. As always, the keynotes were livestreamed — the people who tuned in were not disappointed. I recommend reviewing all of the keynotes from the Velocity YouTube Playlist. All of them were great, but here’s a collection of some of my favorites.
Start. Here. Scott Hanselman’s walk through the evolution of the web and cloud computing is informative and hilarious:
From tiny satellites to young programmers to reasoned paranoia, here are key talks from OSCON 2014.
Experts and advocates from across the open source world assembled in Portland, Ore. this week for OSCON 2014. Below you’ll find a handful of keynotes and interviews from the event that we found particularly notable.
How tiny satellites and fresh imagery can help humanity
Will Marshall of Planet Labs outlines a vision for using small satellites to provide daily images of the Earth.
True artificial intelligence will require rich models that incorporate real-world phenomena.
In my last post, we saw that AI means a lot of things to a lot of people. These dueling definitions each have a deep history — ok fine, baggage — that has massed and layered over time. While they’re all legitimate, they share a common weakness: each one can apply perfectly well to a system that is not particularly intelligent. As just one example, the chatbot that was recently touted as having passed the Turing test is certainly an interlocutor (of sorts), but it was widely criticized as not containing any significant intelligence.
Let’s ask a different question instead: What criteria must any system meet in order to achieve intelligence — whether an animal, a smart robot, a big-data cruncher, or something else entirely? Read more…
Can education and peer review keep a huge open source project on track?
When does a software project grow to the point where one must explicitly think about governance? The term “governance” is stiff and gawky, but doing it well can carry a project through many a storm. Over the past couple years, the crucial OpenStack project has struggled with governance at least as much as with the technical and organizational issues of coordinating inputs from thousands of individuals and many companies.
A major milestone was the creation of the OpenStack Foundation, which I reported on in 2011. This event successfully started the participants’ engagement with the governance question, but it by no means resolved it. This past Monday, I attended some of the Open Cloud Day at O’Reilly’s Open Source convention, and talked to a lot of people working for or alongside the OpenStack Foundation about getting contributors to work together successfully in an open community. Read more…
Step-by-step instruction on training your own neural network.
When I first became interested in using deep learning for computer vision I found it hard to get started. There were only a couple of open source projects available, they had little documentation, were very experimental, and relied on a lot of tricky-to-install dependencies. A lot of new projects have appeared since, but they’re still aimed at vision researchers, so you’ll still hit a lot of the same obstacles if you’re approaching them from outside the field.
In this article — and the accompanying webcast — I’m going to show you how to run a pre-built network, and then take you through the steps of training your own. I’ve listed the steps I followed to set up everything toward the end of the article, but because the process is so involved, I recommend you download a Vagrant virtual machine that I’ve pre-loaded with everything you need. This VM lets us skip over all the installation headaches and focus on building and running the neural networks. Read more…