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Integrated A.I. systems

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Abstract

The broad range of capabilities exhibited by humans and animals is achieved through a large set of heterogeneous, tightly integrated cognitive mechanisms. To move artificial systems closer to such general-purpose intelligence we cannot avoid replicating some subset—quite possibly a substantial portion—of this large set. Progress in this direction requires that systems integration be taken more seriously as a fundamental research problem. In this paper I make the argument that intelligence must be studied holistically. I present key issues that must be addressed in the area of integration and propose solutions for speeding up rate of progress towards more powerful, integrated A.I. systems, including (a) tools for building large, complex architectures, (b) a design methodology for building realtime A.I. systems and (c) methods for facilitating code sharing at the community level.

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Notes

  1. This analogy from carpentry, although having a certain intuitive appeal, has the obvious drawback of implying that the integration in question can be done as soon as we find the “right kind of glue”. This is clearly not so, as in the case of mental faculties, to continue with the analogy, the situation is more like gluing together n numbers of different kinds of materials, where n may lie somewhere between 100 and 100 million, depending on which level of abstraction we are working at.

  2. In some cases there may be gains in quality as the same software gets re-implemented, but by and large only when the original author is involved with the rewrite. There are various ways to calculate these numbers.

  3. Exceptions exist, of course. The open-sourcing of Cyc is a shining example (http://www.opencyc.org).

  4. For the purposes of this modeling approach, a pair of components that have a permanent coupling of 1:1 can be considered a single component.

  5. My intention is not to change the minds of those already convinced that A.I. can proceed without any recourse or reference to naturally intelligent systems. There is, however, good reason to look to natural intelligence for inspiration and examples. Solutions in A.I. come in many forms, as many examples show, and one would be foolish to ignore progress made in related research fields.

  6. One could argue that the fields and topics that we first succeeded in understanding were in fact (a) well-suited to our methodological approach and (b) among the simplest of natural phenomena; after all, it is unlikely that we would succeed in understanding highly complex natural phenomena before we understood the simpler ones. It can be argued that humans have only understood a small percentage or perhaps even just a fraction of all the phenomena nature encompasses. Thus, while physics does perhaps not address the simplest natural phenomena that humans have ever attempted to understand, it is likely that they are among the simpler ones.

  7. http://www.sourceforge.org

  8. Algorithms that improve their solution linearly (or semi-linearly) over time.

  9. http://www.mindmakers.org/openair/

  10. The full technical specification can be found at http://www.mindmakers.org/openair/airSpecPage.jsp

  11. http://www.cs.umbc.edu/kqml/

  12. The initial OpenAIR specification was done by Communicative Machines and is now managed by the Mindmakers consortium.

  13. http://www.mindmakers.org/openair/download/downloadPage.jsp

  14. CMLabs (2006). The Psyclone Manual.

  15. http://java.sun.com/products/ejb/

  16. Psyclone can be downloaded for free from http://www.mindmakers.org/projects/Psyclone.

  17. http://www.pairprogramming.com/

  18. CDM is in active use and continues to be improved. The latest version can be found at http://www.mindmakers.org/projects/CDM

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Acknowledgments

I would like to thank my collaborators Thor List, John DiPirro, Chris Pennock and the many others who have worked with me on the projects described (too numerous to list—you know who you are!). Thanks to the pioneers who have already joined the Mindmakers effort. Thanks to Hrafn Th. Thórisson, Hannes Högni Vilhjálmsson and Bjorn Thor Jonsson for excellent comments on this paper. Psyclone is a trademark of Communicative Machines Inc. This work was supported in part by a Rannis grant and a Marie Curie European Reintegration Grant within the 6th European Community Framework Programme.

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  1. Center for Analysis & Design of Intelligent Agents, Department of Computer Science, Reykjavik University, Kringlan 1, 103, Reykjavik, Iceland

    Kristinn R. Thórisson

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Thórisson, K.R. Integrated A.I. systems. Minds & Machines 17, 11–25 (2007). https://doi.org/10.1007/s11023-007-9055-5

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