While preparing for our next podcast recording, here are some interesting recent machine learning developments.
- Machine learning as a service. In the words of the creators:
The Protocols and Structures for Inference (PSI) project aims to develop an architecture for presenting machine learning algorithms, their inputs (data) and outputs (predictors) as resource-oriented RESTful web services in order to make machine learning technology accessible to a broader range of people than just machine learning researchers.
Currently, many machine learning implementations (e.g., in toolkits such as Weka, Orange, Elefant, Shogun, SciKit.Learn, etc.) are tied to specific choices of programming language, and data sets to particular formats (e.g., CSV, svmlight, ARFF). This limits their accessability [sic], since new users may have to learn a new programming language to run a learner or write a parser for a new data format, and their interoperability, requiring data format converters and multiple language platforms.
I think it seems promising. The specification is here.
- BigML, which has been mentioned in passing on this blog, has now published some videos of what the interface actually looks like. It seems quite nice. While watching the videos, I was thinking “OK, this looks really nice, but does it have an API?” Luckily, it turns out that it has, which is good news for us geekier people who don’t just want to use the GUI.
- Machine learning in Google Goggles. A video describing some real cutting-edge ML research in Google’s augmented reality glasses, Google Goggles. Definitely worth checking out.