Abstract
Continuing to expand the ideas developed in the last chapter, a difference exists between what AI-driven machines as currently constituted, and what a human mind can achieve. These two entities are each limited by respective architectural/anatomical specifications. The analogy we might draw is between what we observe as between ourselves and our pets. Animals do not understand syntax and semantics. Certainly, they do not respond to the abstract ideas contained in our symbology or art. That being said, they may respond to music. So it’s not that they are limited in an absolute way. Our potential to respond depends on the underlying architecture’s (form) ability to meaningfully interact with the particular category of externalities.
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Notes
- 1.
In 1902, Bertrand Russell and Alfred North Whitehead, published Principia Mathematica, which advanced a set theory that formal languages conformed to mathematical treatment, when stated in the form of propositions. Understood this way, mathematics served to frame an observation, a problem, a function or solution in terms examinable by those capable of understanding the language. The forms utilized in conveying mathematical information include equations, algorithms, biological sequences (gene sequences) and models. As such mathematics may convey epistemic objectivity about things that are ontologically subjective.
- 2.
See, Analysis of Images, U.S. Pat. 4,060,713.
- 3.
The pattern recognition described here was reported in Scientific American, November 1970, entitled Analysis of Blood Cells (K. Preston, M. Ingram).
- 4.
This idea is akin to how Riemannian geometry, the branch of differential geometry that studies manifolds with a Riemannian metric, enabled the formulation of Einstein’s general theory of relativity, and which thereafter had a profound impact on group theory and representation theory, algebraic and differential topology.
- 5.
See, Chaitin, G.J. (2019). Unknowability in Mathematics, Biology and Physics (The New School). https://www.academia.edu/38880912/Unknowabilityin_mathematics_biology_and_physics_The_New_School_2019_; Chaitin, G.J. (1966). “On the Length of Programs for Computing Finite Sequences.” Journal of the ACM 13 (4): 547–569.
- 6.
To find the shortest path that starts as city A, visits each of the other cities only once, and then returns to A, implies a starting location A, and an ending location A.
- 7.
In computability theory, the halting problem is the problem of determining, from a description of an arbitrary computer program and an input, whether the program will finish running (i.e., halt) or continue to run forever. Alan Turing proved in 1936 that a general algorithm to solve the halting problem for all possible program-input pairs cannot exist.—Wikipedia
- 8.
This subject will be taken up later in regards to the morality of changing the form or essence of one’s anatomical construct.
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Carvalko Jr., J.R. (2020). The Aesthetic Machine. In: Conserving Humanity at the Dawn of Posthuman Technology. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-26407-9_36
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