Improvements to virtual world based NLP functionality

Here is an update on some recent OpenCog work that may be relevant to some of you… (Email samir@vettalabs.com if you have detailed questions) The main update is that the “virtual pet QA system” now answers questions regarding many more spatial relationships; for the full list see http://www.opencog.org/wiki/EmbodimentLanguageComprehension_Questions (The work here was in creating rules to identify when [...]

Meaning-Text Theory

During some recent reading, it struck me that a useful framework for thinking about and talking about sentence generation is the MTT or “meaning-text theory” of Igor Mel’cuk, et al Here is one readable reference: Igor A. Mel’čuk and Alain Polguère, (1987) “A Formal Lexicon in Meaning-Text Theory”, Computational Linguistics, vol. 13, pp. 261-275. portal.acm.org/citation.cfm?id=48160.48166 www.aclweb.org/anthology/J/J87/J87-3006.pdf Within the [...]

An Update

Time that we post a status update! OpenCog has been a little more quiet than usual over the last couple of months. The developers list is still sporadically active, but some of the main developers are having to spend time on other work related projects meaning less AGI-driven focus (want to change that? donate here). We’re [...]

Semantic dependency relations

I spent the weekend comparing the Stanford parser to RelEx, and learned a lot. RelEx really does deserve to be called a “semantic relation extractor”, and not just a “dependency relation extractor”. It provides a more abstract, more semantic output than the Stanford parser, which sticks very narrowly to the syntactic structure of [...]

Sentence Patterns

I’ve recently resumed work on the question-answering chatbot, and am trying to get it to comprehend a broader range of questions and statements. The “big idea” is to create a number of “sentence patterns” that the pattern matcher can recognize and respond to. The reason this is a “big” idea is because I am trying [...]

Why an AI-based singularity?

A friend of mine, JMM knew that I’ve been funded in the past by SIAI to work on OpenCog, so he asked the following question: “The Singularity Institutes “main purpose” is meant to be to investigate whether a recursively improving intelligence can maintain “friendliness” towards human kind. “ Okay, but my standpoint is: Why does the recursively [...]

Frequency of grammatical disjuncts

The link-grammar parser uses labeled links to connect together pairs of words. In order to capture the idea of proper grammatical construction, any given word is only allowed to have very specific links to its right or left: for example, verbs have their subject on the left, and an object on the right. Link-grammar defines [...]

Visualizing PLN inference

Recently Jared Wigmore, a student of Waikato University, New Zealand, created a tool for visualizing PLN as part of a visualisation project. In my opinion, the BIT visualiser shows great promise as a tool for understanding the complexities of BIT expansion. In particular, the cross joins between sub-trees make it much clearer how sharing of sub-trees [...]

Notes on PLN

Kaj Sotala has been making his notes on PLN available on LJ as he reads through the Probabilistic Logic Networks book.

proto-chatbot at last!

A prototype chatbot demonstrates the OpenCog NLP pipeline by parsing simple statements and answering simple questions.

GSoC 2009 project list

The decision process for the 2009 GSoC projects has been completed. You can read Ben’s announcement on the opencog-soc Google group. The accepted projects are: Joel Lehman – Extending MOSES to evolve Recurrent Neural Networks David Kilgore – Python Interfaces For OpenCog Framework API Ruiting Lian – Natural Language Generation using RelEx and the Link Parser Rui Liu – Application [...]

Telling stories and priming the mind

As a kid, and even in the first few years of University, I used to have trouble understanding why things needed to be explained in detail. Essays were difficult because I’d take the point I was trying to make and think of it like a logic problem: This interesting fact and this analysis, thus this is [...]

OpenCog and Google Summer of Code 2009

We are happy to announce that the SIAI has been selected again this year to participate in the Google Summer of Code program as a mentoring organization. GSoC is an annual program that awards successful student contributors a 4500 USD summer stipend to work on open source and free software projects for three months. Around [...]

OpenCog and GSoC

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.

Off to the land of Oz

I’m not a particular regular updater with this particular blog (too many things have been demanding my attention lately), but I thought I’d drop a note to say I’ll be off the radar for a week or so… I’ll be attending Burning man. I’m immensely looking forward to this as this is the first year in [...]

Complete Pipeline

I implemented the Complete Pipeline task for OpenBiomind-GUI. Following is the snapshot of the wizard:

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Converting Graphviz dot file to image file using dot utility

OpenBiomind generates a Graphviz dot file as an output of GraphFeatures task. In the GUI, we thought to provide a image of the graph too. This was simply achieved using the dot utility provided by Graphviz.

Following snippet shows a sample usage (working example can be seen in this snippet was modified from revision 46 of GraphFeaturesWizard):

Following is the snapshot of a workbench after opening the graph (image):
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Graph Features

I implemented the Graph Features task for OpenBiomind-GUI. Following is the snapshot of the wizard:
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Preference Dialog

Here is the preference dialog of the application:
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Image Editor

I implemented an Image Editor to display the generated image. I needed that for View Clusters task. Found a good amount of help from Snippet 48 of SWT Snippets. Following is the snapshot of the workbench after opening the image file:
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