Friday, October 22, 2004

LifeNet Beta Demo

I've just added a demonstration of LifeNet to my lab page, which uses the Commonsense data from the OpenMind project. LifeNet is a temporal inference model. The underlying probabilistic model is very simple and currently only returns very fuzzy results, so the Commonsense group is looking into many different ways to gather more temporal commonsense data, including logging information from volunteer's cell phones about their daily activities. The PlaceLab, part of the House_n project, is also a possible source of learning temporal data. Highest priority at the moment though, is to make the probabilistic backend more precise without limiting the expressivity and ease of use regarding the human language interface.

Sunday, October 10, 2004

Transitive Inference

There are many probabilistic methods for performing temporal inference using models such as Dynamic Bayesian Networks and Temporal Markov Random Fields, but the patterns that are hardcoded into these models about time can be abstracted to a more general representation. More general representations have the advantage in learning problems of being able to reuse learned patterns in describing complex relationships.

The more general pattern that can be abstracted from temporal inference betworks are transitive relationships. Transitive inference networks would allow inference along any number of axes, not only the temporal axis. Obvious examples of transitive inference are relating the concepts of dimensionality, such as space and time. Thinking about moving forward in a spatial dimension is similar in many ways to thinking backwards in time and the primitive patterns used to learn these relationships could be used to distinguish differences and establish a deeper understanding of similarity within patterned data.

Traditional approaches to the temporal inference problem, such as dividing the inference space into duplicate slices of the entire state space are too naive an approach for a general transitive inference problem as the number of dimensions approaches the number of relationships within the model. Size, shape, quantity, brightness, speed, all of these share in defining transition relationships among data. The model cannot simply be naivy expanded in all of these dimensions resulting in explosive requirements for memory and computational resources.

Friday, October 08, 2004

My First Blog Post

I just created my first blog. Congratulations my self. Keep checking back for more exciting details!