Thinking on a Grand Scale
Lecturer:
Prof. Michael Witbrock (Cycorp Inc., USA).
About the lecturer | Course Summary | Slides | Assignment
About the lecturer:
Dr. Michael Witbrock serves as the Vice President for Research at
Cycorp, Inc. and as CEO of Cycorp Europe. At Cycorp, he has overall
responsibility for corporate research, and is particularly interested
in automating the process of knowledge acquisition and elaboration,
extending the range of knowledge representation and reasoning to mixed
logical and probabilistic representations, and in validating and
elaborating knowledge in the context of task performance, particularly
in tasks that involve understanding text and communicating with users.
Michael received his PhD in Computer Science from Carnegie Mellon in
1996 and a BSc in Psychology from Otago University, in New Zealand, in
1985. Prior to joining Cycorp, he was Principal Scientist at Terra
Lycos, working on integrating statistical and knowledge based
approaches to understanding web user behavior; a research scientist at
Just Systems Pittsburgh Research Center, working on statistical text
summarization; and a systems scientist at Carnegie Mellon on the
Informedia spoken and video document information retrieval project. He
is author of numerous publications in areas ranging across knowledge
representation and acquisition, neural networks, parallel computer
architecture, multimedia information retrieval, web browser design,
genetic design, computational linguistics and speech recognition, and
is the holder of four US patents.
Course summary:
There is currently a renaissance in the application of logic-based AI
techniques, some of it under the rubric of the "Semantic Web". But
another dark age is possible if these techniques cannot be applied at
scale, in terms of knowledge capture, breadth of coverage, and scale
of reasoning. None of these have yet been demonstrated at the
required levels, but some promising approaches exist. In the first
part of the course, we will characterise these problems more precisely
and then explore how representations from RDF to probabilistic logics
affect potential breadth, diving more deeply into the representational
capabilities of ResearchCyc. We'll also discuss and use knowledge
capture techniques that may make it possible to apply those
representations at social content (Web 2.0) scale. In the second part
of the course, we'll discuss scaling reasoning, both in terms of
inferential power and in terms of inferential bulk, concentrating on
the goals and techniques of the FP7 LarKC project, and on using
inference with Research Cyc and OpenCyc. The course will be fairly
self-contained, but some familiarity with first order logic and web
services will be helpful, as will some familiarity with Java (although
PHP, C# or Ruby should suffice).
Assignment
Assignment is here: [PDF].
Due: 24 May (please send pdfs to Michael Witbrock).
Results will be discussed during the second part of the classes.