This paper is concerned with two issues: the nature of the methodology employed in the construction of language understanding systems within Artificial Intelligence (AI); and, secondly, the status of the “semantic primitives” employed within certain of those systems. On the first, it is argued that attempts to justify the methodology of AI research on natural language by appeal to the methods of the sciences are in general misguided. On the second issue, the paper argues, more specifically, that semantic primitives cannot be justified in the way that theoretical objects in the sciences (such as neutrinos) are. It suggests that such primitives are not essentially different from the surface words whose meanings they are used to express, and that recognition of this fact in no way limits their usefulness in linguistic research.

Uncertainty and its representation have an important role to play in any situation where the goal is to infer useful information from noisy data. In diffusion-weighted MRI (DW-MRI) scientists attempt to infer information about, for example, diffusion anisotropy or underlying fiber tract direction, by fitting models of the diffusion and measurement processes to DW-MRI data (e.g., Refs. 1, 2). In this scheme there is uncertainty caused both by the noise and artifacts present in any MR scan, but also by the incomplete modeling of the diffusion signal. That is, the true diffusion signal is more complicated than we choose to model. This additional complexity in the diffusion signal appears as residuals when we fit a simple model to the data, causing additional uncertainty in the model parameters.

See also:Uniqueness of the Gaussian Kernel for Scale-Space FilteringUnited States PatentEuropean PatentProceedings of the Eighth International Joint Conference on Artificial Intelligence, Karlsruhe, West Germany, 1019-1022

Swartout accrue from such an endeavor?

See also: C.I.P. #425, Departments of Computer Science and Psychology, Carnegie-Mellon University, 1980In Michalski, R. S., Carbonell, J. G., and Mitchell, T. M. (Eds), Machine Learning, An Artificial Intelligence Approach, Tioga Press, Palo Alto, CA

In Proceedings of the Eighth IJCAI, pages 272-279. IJCAI, Karlsruhe, West Germany, August, 1983. Volume 1.

American Mathematical Soceify 29, 1983

Artificial intelligence 20(4):393-425, July, 1983.

Proceedings of the Eighth International Joint Conference on Artificial Intelligence, Karlsruhe, West Germany, 377-381

Reading, Mass.: Addison-Wesley.