Information Theory 1.1

January 25, 2016 Leave a comment Go to comments

1/25/2016 Information Theory Update

Here are some notes which I hope will provide a more concise understanding about the nature of computation, logic, and mathematics.

Information theories such as those offered by Shannon and Turing give us cause to see an underlying universality of information which is rooted in simple Arithmetic truths such as addition, multiplication, and integers. These arithmetic truths are theories with can be applied successfully to computing machines without regard to their physical substrate*. While this offers a method to deploy universal principles to the control of a specific mechanism, the control which is offered is different in kind from the literal (motor) control of the hardware. Motor control of computer hardware can be accomplished electromagnetically or classically (as with analog clocks with gears powered by spring tension or a gravity pendulum), and now quantum-mechanically to some extent, but not directly by math. Mathematics cannot turn a computer on or keep it running, it can only provide a non-local set of rules which can be localized through motor control.

This is critically important to understand when considering the possibility of Artificial Intelligence: Computation can only be absolutely general or absolutely specific. When we implement a logic circuit, we are not literally imposing philosophical logic on a circuit, rather we are only interpreting the physical changes of a device metaphorically. In short, a logic circuit cannot literally represent a state of 1/0 or True/False, it can only literally present a concrete state of being switched to Stop (Off) or Go (On). This is the territory of computation – what is known as Layer 1 in the seven layer OSI network model**. All higher layers are not physical territories but logical maps – human abstractions projected by software engineers and application users.


For this reason, no computing machine can represent the middle ranges between the absolute generality of mathematical theory and the absolute specificity of a machine’s physical condition. It’s all above-the-line of personal awareness (oceanic metaphor) or below-the-line (granular semaphores). We can get a lot of utility out of these devices, however we can’t get any empathy from them. They can’t care about anything or anyone, since ‘they’ are purely in our imagination.

The philosophically relevant part of what I’m proposing applies to the prospects for generating natural intelligence artificially. AGI that feels as well as thinks is not necessarily desirable, but if my view is on the right track, computers becoming sentient is not something that we need to worry about. It won’t happen. Why? Because mathematics is not accessing the Physical layer from the top down but from the beneath the bottom layer. This means that even though we can use a computing device to validate truth conditions, we can only validate those truths with refer literally to the concrete states of the machine, and those truths which refer figuratively to the universal arithmetic relations. Nothing that a computer does needs to be *about* anything beyond the machine’s physical state, and so any appearance of emotion, intention, sensitivity, etc are purely hypothetical and would violate parsimony. Church-Turing Thesis lays out the framework for universal computing, but in saying that all functions of calculation can be reduced to a-signifying digital steps, we are also saying that all semantic meanings shall be reduced to blind syntax. It cuts both ways.

Isn’t the brain just a biological computer?

No. This is an obsolete idea, for a lot of reasons which I won’t get into here, but suffice it to say, the brain is an organ within a living body which developed organically from a single self-replicating, self-modifying cell. Machines, by contrast, are assembled artificially from naturally unaffiliated substances and parts. That’s not a reason to discount the possibility of sentience through silicon, but it is a reason to go beyond knee-jerk presumptions that continue to dominate thinking about AI. While Turing’s genius is only now beginning to receive the appreciation it deserves, the shortcomings of his Imitation Game approach have not yet been widely understood.

Alan Turing can be pardoned for his reliance on mid-century Behaviorism as a psychological model, since it was very popular at the time and also because, along with others, I suspect that his natural instincts were quite systemizing/autistic. This carries over in modern populations, with autistic-masculine influences far overwhelming the psychotic-feminine influences in computer science and engineering fields. As a result, we have a lot of strong, controlling voices which insist upon reducing psychology to mechanistic terms, and all dimensions of consciousness to processing of logical information. This is so pervasive that any casual conversation online which challenges the supremacy of first-order logic will tend to erupt into a firestorm that ends with something like “Yeah I’m done here. You’re just spouting nonsense“.

To this end, I find this pyramid model for debate at least as important as the other models of information networking:


My call for civility in discussion is not mere political correctness or over-sensitivity, but rather a purely pragmatic consideration. Unlike a computer, the human mind loses its capacity for curiosity and fairness when it falls into aggression. People talk over each other and assert their opinions ever more rigidly and repetitively rather than thinking creatively. This mirrors the action of computation itself – recursive enumeration masquerading as communication.

A great many people think they are thinking when they are merely rearranging their prejudices. – William James

*Not entirely true. The physical substrate of a machine requires precision and solidity. We cannot build a computer out of clouds or fog, it needs to be made of something physical which stays put and which has at least one absolutely persistent read/write capacity. Traditional logic circuits must be implemented physically through a rigid skeleton of readable coordinates.

**It has been popular in recent years to proclaim that the OSI Model is dead. The feeling is that TCP/IP is the predominant protocol suite being used in the real world, and it doesn’t match up with OSI, so we should dump OSI in favor of something like this:


I do see the appeal of this, however, agree with this author that “OSI teaches more of the reasoning behind making multiple layers and what they do. Collapsing the traditional model for the sake of making it look like TCP/IP is going to cause more harm than good.” – Tom Hillingsworth

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