Google Data

By Ben Goertzel, PhD, Director of Research, SIAI

This summer OpenCog was chosen by Google to participate in the Google Summer of Code™ project: Google funded 11 students from around the world to work under the supervision of experienced mentors associated with the OpenCog project, and the associated OpenBiomind project.

OpenCog is a large AI software project with hugely ambitious goals (you can't get much more ambitious than "creating powerful AI at the human level and beyond") and a lot of "moving parts" -- and the most successful OpenCog GSoC projects seemed to be the ones that successfully split off "summer sized chunks" from the whole project, which were meaningful and important in themselves, and yet also formed part of the larger OpenCog endeavor ... moving toward greater and greater general intelligence.

Many of the GSoC projects were outstanding but perhaps the most dramatically successful (in my own personal view) was Filip Maric's project (mentored by Predrag Janicic) which involved pioneering an entirely new approach to natural language parsing technology. The core parsing algorithm of the link parser, a popular open-source English parser (that is used within OpenCog's RelEx language processing subsystem), was replaced with a novel parsing algorithm based on a Boolean satisfaction solver: and the good news is, it actually works ... getting the best parses of a sentence faster than the old, standard parsing algorithm; and, most importantly, providing excellent avenues for future integration of NL parsing with semantic analysis and other aspects of language-utilizing AI systems. This work was very successful but needs a couple more months effort to be fully wrapped up and Filip, after a brief break, has resumed working on it recently and will continue throughout November and December.

Cesar Maracondes, working with Joel Pitt, made a lot of progress on porting the code of the Probabilistic Logic Networks (PLN) probabilistic reasoning system from a proprietary codebase to the open-source OpenCog codebase, resolving numerous software design issues along the way. This work was very important as PLN is a key aspect of OpenCog's long-term AI plans. Along the way Cesar helped with porting OpenCog to MacOS.

There were also two extremely successful projects involving OpenBiomind, a sister project to OpenCog:

* Bhavesh Sanghvi (working with Murilo Queiroz) designed and implemented a Java user interface to the OpenBiomind bioinformatics toolkit, an important step which should greatly increase the appeal of the toolkit within the biological community (not all biologists are willing to use command-line tools, no matter how powerful)
* Paul Cao (working with Lucio Coelho) implemented a new machine learning technique within OpenBiomind, in which recursive feature selection is combined with OpenBiomind's novel "model ensemble based important features analysis." The empirical results on real bio datasets seem good. This is novel scientific research embodied in working open-source code, and should be a real asset to scientists doing biological data analysis.


And the list goes on and on: in this short post I can't come close to doing justice to all that was done, but please see our site for more details!

All in all, we are very grateful to Google for creating the GSoC program and including us in it. Thanks to Google, and most of all to the students and mentors involved.