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Jackson - Postscript for Human-Level Models of Minds - 20180130
Abstract
This TalaMind White Paper further discusses some topics in (Jackson 2017): reasoning with natural language syntax; interlinguas and generalized societies of mind; self-talk; artificial consciousness and the Hard Problem of consciousness.
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Content uploaded by Philip C. Jackson, Jr., Ph.D.
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A comparison of Laird, Lebiere, and Rosenbloom's Standard Model of the Mind with the 'TalaMind' approach suggests some implications for computational structure and function of human-like minds, which may contribute to a community consensus about architectures of the mind.
Is synthetic phenomenology a valid concept? In approaching consciousness from a computational point of view, the question of phenomenology is not often explicitly addressed. In this paper we re- view the use of phenomenology as a philosophical and a cognitive construct in order to have a meaningful transfer of the concept into the computational domain. Two architectures are discussed with respect to these definitions: our 'kernel, axiomatic' structure and the widely quoted 'Global Workspace' scheme. The conclusion suggests that architectures with phenomenal properties genu- inely address the issue of modelling consciousness and indicate and the way that a machine with synthetic phenomenology may benefit from the property
Little is clearly understood about the similarities, differences, and comparative computational and representational advantages of the many proposals extant for organizing minds into collections of "mental subagents." Using a new mathematical framework for exactly specifying the structure of mental organizations, we formulate separately the ideas of multiple perspectives, reasoned assumptions, and virtual copies. When combined, these notions form a common backbone for systems as diverse as CONLAN, NETL, and FOL, and show many particular characteristics of those systems results of the "language of thought" adopted for representing the contents of mental subagents. The framework also suggests connections between the "strengths" of mental attitudes, the ambiguity of "self," and the possibilities for self-omniscience.
To make progress on the problem of consciousness, we have to confront it directly. In this paper, I first isolate the truly hard part of the problem, separating it from more tractable parts and giving an account of why it is so difficult to explain. I critique some recent work that uses reductive methods to address consciousness, and argue that these methods inevitably fail to come to grips with the hardest part of the problem. Once this failure is recognized, the door to further progress is opened. In the second half of the paper, I argue that if we move to a new kind of nonreductive explanation, a naturalistic account of consciousness can be given. I put forward my own candidate for such an account: a nonreductive theory based on principles of structural coherence and organizational invariance and a double-aspect view of information.
Mental Spaces is the classic introduction to the study of mental spaces and conceptual projection, as revealed through the structure and use of language. It examines in detail the dynamic construction of connected domains as discourse unfolds. The discovery of mental space organization has modified our conception of language and thought: powerful and uniform accounts of superficially disparate phenomena have become available in the areas of reference, presupposition projection, counterfactual and analogical reasoning, metaphor and metonymy, and time and aspect in discourse. The present work lays the foundation for this research. It uncovers simple and general principles that lie behind the awesome complexity of everyday logic.
In order to understand and create human-level intelligence I have developed the Polyscheme cognitive architecture to build systems that combine several representation and inference schemes when they think. Polyscheme is based on three principles. First, different (aspects of) situations that intelligent systems must deal with are best modeled with different schemes for representing knowledge and making inferences. Polyscheme includes several "specialists" such that each models a particular aspect of the world with its own (possibly unique) representation and inference techniques. Second, specialists must communicate with other specialists frequently so that each specialist uses the most complete, accurate and relevant information when it deals with a situation. Specialists in Polyscheme communicate and combine information by simultaneously concentrating on the same focus of attention. Finally, because information about some aspects of a situation is more important than information about others and because the order that specialists focus on those aspects is important, a system of focused specialists must have mechanisms that decide where to focus. Polyscheme's specialists, especially the reflective specialist, guide the focus of attention and thereby implement inference schemes using an "attraction" mechanism to specify their preferred foci. Polyscheme enables multiple inference techniques to be integrated in dealing with a situation because each inference technique can be implemented with one or more focus schemes. I describe how to implement several important inference techniques (e.g., script matching, backtracking search, reason maintenance, stochastic simulation and counterfactual reasoning) as focus schemes.
The well-designed child. Artificial Intelligence
- J Mccarthy
McCarthy, J. 2008. The well-designed child. Artificial Intelligence, 172, 18, 2003-2014.