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Posts Tagged ‘SILK’

Confessions of a production rule vendor (part 2)

Going on 5 years ago, I wrote part 1.  Now, finally, it’s time for the rest of the story.

“Only full page color ads can run on the back cover of the New York Times Magazine.”

A decade or so ago, we were debating how to educate Paul Allen’s artificial intelligence in a meeting at Vulcan headquarters in Seattle with researchers from IBM, Cycorp, SRI,  and other places. We were talking about how to “engineer knowledge” from textbooks into formal systems like Cyc or Vulcan’s SILK inference engine (which we were [...]

Requirements for Logical Reasoning

Here is a graphic on how various reasoning technologies fit the practical requirements for reasoning discussed below: This proved surprisingly controversial during correspondence with colleagues from the Vulcan work on SILK and its evolution at The requirements that motivated this were the following:

Financial industry to define standards using defeasible logic and semantic web technologies

Last week, I attended the FIBO (Financial Business Industry Ontology) Technology Summit along with 60 others. The effort is building an ontology of fundamental concepts in the financial services. As part of the effort, there is surprisingly clear understanding that for the resulting representation to be useful, there is a need for logical and rule-based [...]

Pedagogical applications of proofs of answers to questions

In Vulcan’s Project Halo, we developed means of extracting the structure of logical proofs that answer advanced placement (AP) questions in biology.  For example, the following shows a proof that separation of chromatids occurs during prophase. This explanation was generated using capabilities of SILK built on those described in A SILK Graphical UI for Defeasible [...]

Background for our Semantic Technology 2013 presentation

In the spring of 2012, Vulcan engaged Automata for a knowledge acquisition (KA) experiment.  This article provides background on the context of that experiment and what the results portend for artificial intelligence applications, especially in the areas of education.  Vulcan presented some of the award-winning work referenced here at an AI conference, including a demonstration [...]

Semantic Technology & Business Conference (SemTechBiz)

Benjamin Grosof and I will be presenting the following review of recent work at Vulcan towards Digital Aristotle as part of Project Halo at SemTechBiz in San Francisco the first week of June. Acquiring deep knowledge from text We show how users can rapidly specify large bodies of deep logical knowledge starting from practically unconstrained [...]

Deep QA

Our efforts at acquiring deep knowledge from a college biology text have enabled us to answer a number of questions that are beyond what has been previously demonstrated. For example, we’re answering questions like: Are the passage ways provided by channel proteins hydrophilic or hydrophobic? Will a blood cell in a hypertonic environment burst? If [...]

Logic from the English of Science, Government, and Business

Our software is translating even long and complicated sentences from regulations to textbooks into formal logic (i.e,, not necessarily first-order logic, but more general predicate calculus).   As you can see below, we can translate this understanding into various logical formalisms including defeasible first-order logic, which we are applying in Vulcan’s Project Halo.  This includes classical [...]

Project Sherlock

Working as part of Vulcan’s Project Halo[1], Automata is applying a natural language understanding system that translates carefully formulated sentences into formal logic so as to answer questions that typically require deeper knowledge and inference than demonstrated by Watson. The objective over the next three quarters is to acquire enough knowledge from the 9th edition [...]