Abstract
According to how AI has defined itself from its beginning, thinking in non-living matter, i.e., without life, is possible. The premise of symbolic AI is that operating on representations of reality machines can understand it. When this assumption did not work as expected, the mathematical model of the neuron became the engine of artificial “brains.” Connectionism followed. Currently, in the context of Machine Learning success, attempts are made at integrating the symbolic and connectionist paths. There is hope that Artificial General Intelligence (AGI) performance can be achieved. As encouraging as neuro-symbolic AI seems to be, it remains unclear whether AGI is actually a moving target as long as AI itself remains ambiguously defined. This paper makes the argument that the intelligence of machines, expressed in their performance, reflects how adequate the means used for achieving it are. Therefore, energy use and the amount of data necessary qualify as a good metric for comparing natural and artificial performance.
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Notes
Diderot: s’il se trouvait un perroquet qui répondit à tout, je prononcerais sans balancer que c’est un être pensant (cf. Pensées Philosophique), translated as..if there was a parrot which could answer every question, I should say at once that it was a thinking being (cf. Philosophic thoughts, page 37).
Occam (thought that it is vain to do with more than can be done with less (Frustra fit per plura quod potest fieri per pauciora). Summa Totius Logicus, Loux 1974).
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Nadin, M. Intelligence at any price? A criterion for defining AI. AI & Soc 38, 1813–1817 (2023). https://doi.org/10.1007/s00146-023-01695-0
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DOI: https://doi.org/10.1007/s00146-023-01695-0