Abstract
Semantic systems are sytems with an inherent semantics. An example would be systems showing intrinsic intentionality: if a system is genuinely intentional, it must be able to define its own meanings. Searle was a forerunner of the modern idea of semantic systems in his oft-cited “Chinese Room” paper in 1980. The current author has approached the problem from a different angle 30 years ago in his book Self-Modifying Systems (Pergamon), claiming that minds can define their own meanings by virtue of being “material”, in the sense of freely generating new properties on the fly, as do material objects. In the course of the process, meaning should arise because syntax (and therefore any computation that is entirely syntactical) does not fully describe the rich functioning of such systems, so goes the argument. While still attractive, the author has in the meantime abandoned the idea, for at least two reasons. One, fundamentals-seeking has itself been largely abandoned, because the study of questions like “is the mind computational”? has turned out to be non-productive. Two, brute force approaches such as ANN-based deep learning have been “unreasonably” successful, and this fact puts aside arguments about the limits of computational systems. The paper provides an overview of these developments.
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Notes
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- 2.
see https://en.wikipedia.org/wiki/Chinese_room or the above cited Stanford Encyclopedia entry.
- 3.
or , or , etc.....
- 4.
- 5.
This is not meant to be provocative. In a narrow tradition of CogSci—and philosophy of mind—concepts like functionalism, the Physical Symbol Hypothesis, and the like are meant to characterize both CogSci and AI. The author and the present paper follows this tradition. There exists however, also a more “liberal” tradition to CogSci, one based e.g. on continuity with Cognitive Psychology and its several varieties.
- 6.
A historical account: https://towardsdatascience.com/an-intro-to-deep-learning-for-face-recognition-aa8dfbbc51fb and a more technical-minded introduction is given in https://missinglink.ai/guides/tensorflow/tensorflow-face-recognition-three-quick-tutorials/.
- 7.
Some of them are notorious (Dreyfus, 1972) and have started an industry of their own.
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The author has a double affiliation. Generous support of both institutions is acknowledged. The two affiliations have each contributed 50–50% to the end product.
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Kampis, G. (2022). Semantic Systems After 30 Years. In: Gervain, J., Csibra, G., Kovács, K. (eds) A Life in Cognition. Language, Cognition, and Mind, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-66175-5_15
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