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Posts Tagged ‘Vulcan’

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 [...]

Smart Machines And What They Can Still Learn From People – Gary Marcus

This is a must-watch video from the Allen Institute for AI for anyone seriously interested in artificial intelligence.  It’s 70 minutes long, but worth it.  Some of the highlights from my perspective are: 27:27 where the key reason that deep learning approaches fail at understanding language are discussed 31:30 where the inability of inductive approaches [...]

Deep Learning, Big Data and Common Sense

Thanks to John Sowa’s comment on LinkedIn for this link which, although slightly dated, contains the following: In August, I had the chance to speak with Peter Norvig, Director of Google Research, and asked him if he thought that techniques like deep learning could ever solve complicated tasks that are more characteristic of human intelligence, [...]

Suggested questions: Inquire vs. Knewton

Knewton is an interesting company providing a recommendation service for adaptive learning applications.  In a recent post, Jonathon Goldman describes an algorithmic approach to generating questions.  The approach focuses on improving the manual authoring of test questions (known in the educational realm as “assessment items“).  It references work at Microsoft Research on the problem of [...]

Natural Language Leadership at the Allen Institute for Artificial Intelligence (AI2)

Orin Etzioni is a marvelous choice to lead the Allen Institute for AI (aka AI2).  The NL/ML path is the right path for scaling up the deep knowledge that Paul Allen’s vision of a Digital Aristotle requires.  You can read more about it below and here’s more background on the change in the direction and [...]

Artificially Intelligent Educational Technology

Over the last two years, machines have demonstrated their ability to read, listen, and understand English well enough to beat the best at Jeopardy!, answer questions via iPhone, and earn college credit on college advanced placement exams.  Today, Google, Microsoft and others are rushing to respond to IBM and Apple with ever more competent artificially [...]

Deep question answering: Watson vs. Aristotle

At the SemTech conference last week, a few companies asked me how to respond to IBM’s Watson given my involvement with rapid knowledge acquisition for deep question answering at Vulcan.  My answer varies with whether there is any subject matter focus, but essentially involves extending their approach with deeper knowledge and more emphasis on logical [...]

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 [...]