I recently read God, Human, Animal, Machine: Technology, Metaphor, and the Search for Meaning by Meghan O’Gieblyn (2021).My top takeaways:
· Science presumes a third person perspective, yet consciousness is a first-person experience.
· We have yet to understand the phenomenological experience—the entirely subjective world (color, sensations, thoughts, ideas, beliefs)
· The “hard problem,” as described by Chalmers, Chopra, and others, is understanding consciousness.
· Kurzwell predicts that Singularity – the merging humans with technologies – will happen by the year 2045.
· Nature can be thought of metaphorically as a huge river, constantly flowing, changing, evolving.
· Are “atheists” and “believers” simply choosing different metaphors for understanding the transcendent?
· Self-organizing emergent systems occur at ALL levels of life – from tiniest organisms to humans – and perhaps beyond.
· Emergence is the ontological opposite of reductive materialism.
· Our mind is not simply a physical object to be examined, but rather, a structural pattern that emerges from the complexity of an entire internal-external network.
· Claude Shannon, the father of information theory, defined information as “the resolution of uncertainty.”
· A current theory of consciousness is called Integrated Information Theory (IIT).
· Panpsychism envisions all of nature—plants, animals, humans, angels, and God himself—existing within a continuum of consciousness. It’s sort of an all-centric vs human-centric (anthropomorphic) view of the universe.
· Metonymy is the belief that the mind serves as a microcosm of the world’s macroscopic consciousness.
· There is an irrefutable connection between “influencer” and “influenza.”
My favorite quotes:
“All perception is metaphor—as Wittgenstein put it, we never merely see, we always ‘see as.’” (p. 11)
“In the lecture rooms and the laboratory, the only value that should hold is intellectual integrity.” (p. 47)
“Bohr believed that whenever we encountered a paradox, it was a sign that we were hitting on something true and real.” (p. 130)
“In the year 2001 alone, the amount of information generated doubled that of all information produced in human history. In 2002 it doubled again, and this trend has continued every year since.” (p. 194)
Many researchers in the AI field suggests that the WHY really no longer matters. They argue that if the data show us the WHAT and the HOW, then we know all we need to know. I still have a burning question: Can justice, and morality, and quality of life be reduced to an algorithm?
I picked this book per my current interest in AI. Learning is sense-making. If you’re interested in trying to make a little better sense of things, this book will aid your journey.
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