- Reset (Rowan Simpson) — It was a bit chilling to go back over a whole years worth of tweets and discover how many of them were just junk. Visiting the water cooler is fine, but somebody who spends all day there has no right to talk of being full.
- Google’s AI Brain — on the subject of Google’s AI ethics committee … Q: Will you eventually release the names? A: Potentially. That’s something also to be discussed. Q: Transparency is important in this too. A: Sure, sure. Such reassuring.
- AVA is now Open Source (Laura Bell) — Assessment, Visualization and Analysis of human organisational information security risk. AVA maps the realities of your organisation, its structures and behaviors. This map of people and interconnected entities can then be tested using a unique suite of customisable, on-demand, and scheduled information security awareness tests.
- Deep Learning for Torch (Facebook) — Facebook AI Research open sources faster deep learning modules for Torch, a scientific computing framework with wide support for machine learning algorithms.
"deep learning" entries
The history of computing has been a constant pendulum — that pendulum is now swinging back toward distribution.
The trifecta of cheap sensors, fast networks, and distributing computing are changing how we work with data. But making sense of all that data takes help, which is arriving in the form of machine learning. Here’s one view of how that might play out.
Clouds, edges, fog, and the pendulum of distributed computingThe history of computing has been a constant pendulum, swinging between centralization and distribution.
The first computers filled rooms, and operators were physically within them, switching toggles and turning wheels. Then came mainframes, which were centralized, with dumb terminals.
As the cost of computing dropped and the applications became more democratized, user interfaces mattered more. The smarter clients at the edge became the first personal computers; many broke free of the network entirely. The client got the glory; the server merely handled queries.
Once the web arrived, we centralized again. LAMP (Linux, Apache, MySQL, PHP) buried deep inside data centers, with the computer at the other end of the connection relegated to little more than a smart terminal rendering HTML. Load-balancers sprayed traffic across thousands of cheap machines. Eventually, the web turned from static sites to complex software as a service (SaaS) applications.
Then the pendulum swung back to the edge, and the clients got smart again. First with AJAX, Java, and Flash; then in the form of mobile apps, where the smartphone or tablet did most of the hard work and the back end was a communications channel for reporting the results of local action. 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…