Intelligence Seminar: Action Perception, June 23, 2009

Intelligence Seminar

June 23, 2009 (note special place)
3:30 pm
NSH 1507
Host: Jaime Carbonell
For meetings, contact Michelle Pagnani (pagnani@cs.cmu.edu).

Action Perception

Robert Thibadeau
Seagate Research

Abstract:
The human perception of actions has barely been studied, but this study of action perception promises to provide a wealth of interesting hypotheses regarding cognitive processing. Action perception is distinct from motion perception in that the direct perception of causation is central to the percept. Among the interesting hypotheses is that it can be hypothesized that what we know as thought and reasoning is where we perceive and plan actions. Another hypothesis is that what we know as logic and mathematics derives from our direct perceptions of causation in the actions we perceive and think about.

I will present a study that attempts to estimate the scale of computation needed to implement a system for visually perceiving meaningful actions and non-trivially producing an English narration of what is being visually perceived, as well as answering questions about what is visually perceived. The scale of the computation for learning could easily reach exaflops over distributed datasets (HADOOP or MapReduce style).

This study is partly based on my work (Thibadeau, 1986), and Doug Rohde's 2002 dissertation (http://tedlab.mit.edu:16080/~dr/Thesis/), as well as Simon and Rescher (1966 see summary below). The study includes an explicit proposal for extending Rohde's work to multimodal, multisensory, processing.

(Simon and Rescher 1966 From Wikipedia, Causality)
Derivation theories

The Nobel Prize holder Herbert Simon and Philosopher Nicholas Rescher[20] claim that the asymmetry of the causal relation is unrelated to the asymmetry of any mode of implication that
contraposes. Rather, a causal relation is not a relation between values of variables, but a function of one variable (the cause) on to another (the effect). So, given a system of equations, and a set of
variables appearing in these equations, we can introduce an asymmetricrelation among individual equations and variables that corresponds perfectly to our commonsense notion of a causal ordering. The system of equations must have certain properties, most importantly, if some values are chosen arbitrarily, the remaining values will be determined uniquely through a path of serial discovery that is perfectly causal. They postulate the inherent serialization of such a system of equations may correctly capture causation in all empirical fields, including physics and economics.