Cell Language Theory, The: Connecting Mind And Matter

The Philosophical Implications of the Cell Language Theory 437

“6x9” b2861 The Cell Language Theory: Connecting Mind and Matter

in physics, computer science, and statistics are viewed as “subjective” and

hence grouped together as members of I3.

On the basis of Table 10.8, we can make the following deductions:

1. There are three kinds of entropies, belonging to I2 (i.e., Boltzmann
   entropy), E2 (Clausius entropy), and I3 groups (e.g., Shannon
   entropy), which can be identified with what were previously referred
   to as “statistical mechanical”, “thermodynamic”, and “mathematical”
   entropies, respectively [432].


2. There are two kinds of information, belonging to the I2 group (e.g.,
   DNA sequence Information, Bohm’s vector potentials, also called
   quantum potentials, and Laszlo’s scalar potentials of cosmic plenum)
   or to the I3 group (e.g., Fisher information). I2 is objective and I3 sub-
   jective, in the sense that the former is independent of, and the latter
   dependent on, the emergence of Homo sapience on this planet.


3. I2 and I3 entropies can increase with time, but these increases are not
   the physical consequence of the Second Law but merely its simulations
   or descriptions. Therefore, there can be many mathematical expres-
   sions that can simulate the Second Law as long as they show a mono-
   tonic increase with time. In contrast, the increase of E2 entropy e.g.,
   S in Gibbs free energy with time is a direct physical consequence of
   the Second Law. In other words, there are two kinds of entropies — the
   Second Law-obeying and the Second Law-independent entropies, the
   E2 entropy belonging to the former and I2 and I3 entropies belong to
   the latter. This conclusion seems at odd with NPI (Negentropy
   Principle of Information) of Brillouin [414, 416].

10.15 The Information–Energy–Entropy Relation:

The “NewJerseyator”

Klir [189] distinguished two kinds of information — (a) algorithmic or

descriptive information and (b) uncertainty-based information. According

to Klir,

Algorithmic information represented by an object is measured by the

length of the shortest possible program written in some standard language

by which the object is described in the sense that it can be computed.

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