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More Than An Algorithm: Exploring the gap between natural evolution and digitally computed artificial intelligence
A palette of intelligence explored. In this book, the author invites you to the musings of master algorithms, brain fractals and Chinese rooms. This book, aimed at science-savvy readers, makes you question the validity of the Many-Worlds Interpretation of quantum mechanics, tries to reconcile the paradoxes of idealistic panpsychism and shows you the parallels between the codes of evolution and the routines of the human mind. Countering the Computational Hypothesis, according to which our minds are computational in nature, the philosopher skillfully presents his case that our knowledge, and more specifically intelligence, well may be more than a mere algorithm.
135 pages, Kindle Edition
Published June 19, 2019
7 people want to read
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August 16, 2019
The beauty of Antonin Tuynman's work is the ease with which he explains invaluable concepts. Perhaps even better than having "a way with words" is having "a way with ideas" (as Tuynman assuredly does). Amongst the tidbits dropped here is one which, until I read this work, I had not realized -- that hermeneutics was named for the Greek God Hermes, conceptually connecting theological interpretation with its technological counterpart -- fitting for a book keenly raising questions at the now quickly evolving technotheological level.
There are many such points and revelations across this fine work. And with his powers of description, he aptly indicates the problems with perceiving the human mind -- both as an evolutionary and a spiritual vessel -- as simply a progression upon what can be constructed artificially, noting for example that the lauded artificial neural network, though teachable, lacks many substantial capacities of even the most infantile human mind (and perhaps especially of the human mind in infancy, when self-absorption and development of understanding must occur substantially without instruction of the type available to artifices). The human mind, in contrast, exercises an ability to release itself from algorithmic patterns and make nonlinear and even entirely intuitive connections and decisions which solve quandaries without revealing their thought path -- a product of billions of years of binary trees making (and occasionally breaking) a vital symmetry on the way to a pancomputational consciousness.
Interestingly, Tuynman's ultimate conclusion is something of a hedge (and a quite fair one) as to whether we might find some other way to pass upon the question of artificially generating a human-like thought process. If we can teach a machine to learn, after all, can we teach one to learn how to learn in intuitive ways (or to learn to build a machine which can)? And so the most optimistic reading of the ultimate end of this book is not to foreclose possibilities, but to open up their doors.
There are many such points and revelations across this fine work. And with his powers of description, he aptly indicates the problems with perceiving the human mind -- both as an evolutionary and a spiritual vessel -- as simply a progression upon what can be constructed artificially, noting for example that the lauded artificial neural network, though teachable, lacks many substantial capacities of even the most infantile human mind (and perhaps especially of the human mind in infancy, when self-absorption and development of understanding must occur substantially without instruction of the type available to artifices). The human mind, in contrast, exercises an ability to release itself from algorithmic patterns and make nonlinear and even entirely intuitive connections and decisions which solve quandaries without revealing their thought path -- a product of billions of years of binary trees making (and occasionally breaking) a vital symmetry on the way to a pancomputational consciousness.
Interestingly, Tuynman's ultimate conclusion is something of a hedge (and a quite fair one) as to whether we might find some other way to pass upon the question of artificially generating a human-like thought process. If we can teach a machine to learn, after all, can we teach one to learn how to learn in intuitive ways (or to learn to build a machine which can)? And so the most optimistic reading of the ultimate end of this book is not to foreclose possibilities, but to open up their doors.
Displaying 1 of 1 review