Friday, January 22, 2021
Book Reviewed: What Is Thought? by Eric B. Baum
The essence of life
Author Eric Baum proposes an interesting theory that the mind is the result of evolution, and thought processes is rooted in DNA that represents a natural algorithm. DNA code programmed the mind to construct few meaningful possibilities among countless of possibilities. The nature of thought and consciousness is built on this compact code.
DNA is a language connecting two parts of the cosmos, the matter (non-living) and life (living) in a circle. The initial time-forward process of cosmos corresponds to disorder and entropy driven physical system, and the second part is conscious awareness, the semantics of DNA information of the living cell, which corresponds to increasing order and lowering of entropy. This classical projection of the semantics of DNA encryption is analogous to the famous Double-Slit Experiment that demonstrates the quantum nature of existence, the wave-particle duality of matter and the probabilistic nature of quantum reality. In this experiment when each photon hits the screen, its location in classical space appears random and disorderly. But the wave interference pattern leads to a conclusion that the random outcome of photon hits in classical space becomes an ordered image on the screen through conscious interaction. Indeed, life which reflects a transition from disorder (matter) to order was termed as negative entropy by physicist Erwin Schrodinger.
the Informational model of consciousness is the acquisition and transmission mechanisms of certain traits to the future generations without affecting the DNA sequences. These epigenetic mechanisms are described as signal transmission agents embodying or disembodying information. Mechanisms of epigenetic inheritance could include DNA methylation, histone modification and small RNA transmission. The epigenetic mechanisms allow body adaptation in terms of the computation informational system.
The author is a developer of algorithms based on machine learning and Bayesian reasoning. Some chapters are technically more challenging than others, the overall read is a little bumpy, but the take-home message is well described.
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