logicp_writings/tech/AI_Rewrite.md

Intelligence

Broadly, we wish to look at intelligence so that we can begin to realize what it is as a phenomenon, how it manifests, and the ways in which it is Universal.

Artificial Intelligence

The purpose in understanding intelligence is both to educate ourselves and to create artificial intelligence which is capable of freeing humanity.

Programming

Fuzzy

AI and Machine Learning techniques might be better thought of as a form of fuzzy programming, whereby similar techniques are used, but with a qualitatively different expectation regarding the output.

Traditional Approaches

For comparison, let's examine the traditional approach to programming:

Lambda Calculus

In traditional programming practices, the program, and the algorithms encapsulated by the program, take certain inputs and output information which is expected to be correct or incorrect based on it expressing a high degree of accuracy, even a form of absolute accuracy. Case in point, a program might take numerical input parameters, perform direct arithmetic operations on these parameters, and output the result of the operation. With such a program, its validity is based entirely on whether or not the output proves that the right operation was performed and that the result was correct.

Verification

Verification can be performed relatively easily, as it is just a matter of confirming the operation by controlling the values of the input parameters, utilizing a secondary process for determining the expected result (such as doing math on pen and paper), and then returning the program with those same input values and seeing if the program outputs the same value as that which was the observed result from the secondary process. Though arithmetic might seem like too simple of a comparison to be relevant to all programs, it is the right one, as all computed constructs, their behaviour and the expressions in which they participate can be abstracted or reduced to their numerical forms. It might even be said, to take it a step further, that all of these things are emergent and that their fundamental construction is based entirely as arithmetic. It just would have to be added that the particular arithmetic in question is binary.

Modern approaches:

Now let's take a look at the approaches utilized by Machine Learning and Artificial Intelligence.

Verification Limits

When using Machine Learning techniques, one cannot perform the same type of arithmetic verification. Though the Machine Learning techniques will make use of algorithms which may be similar, or the same, as those found in traditional computer programs, there is usually not any direct perspective on the workflow of the operational pattern wherein the inputs are taken through a neatly delineated lambda computation which outputs a single output value that can be scrutinized.

Inherent Fuzziness

Instead, what we have is a process which is often referred to as "fuzzy", in that no single output will necessarily reveal an answer which can serve as the apriori frame. Rather, a massive repetition of the procedure is sought, possibly with the same inputs, or with a set of inputs.

Limitation, Modality, or Different Beast?

With enough repetition, increasingly accurate estimates can be made as to the outcome of a given data set without necessarily performing the entire computation. That is to say, the Machine Learning algorithms can take a snapshot of the computation without having completed it, and produce estimates about each stage of the computational procedure, including the outcome.

Is this Artificial Intelligence?

Perhaps, perhaps not.

To know the answers to these questions, we need to define what it is to be intelligent.

Intelligence has been defined in many ways: the capacity for logic, understanding, self-awareness, learning, emotional knowledge, reasoning, planning, creativity, critical thinking and problem-solving.

Some arguments can be made to conflate, or at least infer an overlap, between intelligence and IQ. This is a reasonable vector of consideration and it is almost impossible to consider one from the context of a human being without also thinking of the other. Though no semantically complete representation of intelligence or cognition as expressed through human activity has been demonstrated, or even proposed in a capacity that can be scientifically scrutinized and found to be a viable mode of quantification, it is currently the most agnostic approach we've yet found and, as such, it will be considered in this essay.

Capacity for logic, understanding, self-awareness, learning, emotional knowledge, reasoning, application of logic, planning, perceiving, creativity, organizing, critical thinking, resolving, problem solving, ordering, conceptualizing, deducing, describing, elucidating, realizing, revealing, inferring, discerning, possessing knowledge, uncovering knowledge.

Acknowledging, verifying, naming, remarking, identifying, contextualizing, structuring, the ability to acquire and apply knowledge and skills. Intelligentsia, from intelligence understand - problem solving ability, spatial manipulation, language skilled use of reason and ability to apply knowledge to manipulate one's environment or to think abstractly as measured by objective criteria (such as tests)

Definitions (Intelligence)

There are two primary contexts for the definition of intelligence which are employed by humans, with one consisting of a set of definitions pertaining to the use of knowledge, and the alternative definition focusing on a domain of knowledge or information. We will be focusing on the former, as the latter would suggest that all computation is intelligence. Though a case can be made that all information is intelligence, that is beyond the scope of our essay and ventures too far into the metaphysical.

Domain of Knowledge

In the previous paragraph, it was alleged that in order to posit that intelligence and "domains of knowledge" or "information" are synonymous with one another. What follows is a deconstruction of this argument:

What is a domain of knowledge?

A domain of knowledge is the conceptualization of an expanse of information which can be demarcated in such a way as to describe the information contained within. As all known information, as communicated among humans, is necessarily structured for the capacity of humans and human thinking, it follows that the very concept of a domain of knowledge is purely a human one. If it is possible to encapsulate an expanse of information by non-human means, we are limited in that we cannot make such an argument without also conceiving of it within the human frame (though we might be able to do something about that with AI, even if it were nothing more than an approximation to have given us the excuse to produce a specification of some sort. As such, it must be assumed that a domain of knowledge is one which is necessarily delineated by human thinking.

The other alluded proposition put forward as a comparable conception is that of information as a whole. In its most complete form, information as a whole must consist of any and all matter which exists physically in the Universe. It might be possible to declare a form of information which is separate from the physical aspects of the Universe, but its communication is bound to physical properties within said Universe. Another aspect of this is that there is no explicative mechanism which illustrates the manner by which human thought is represented physically, except by incompletely understood indication patterns, such as firing of neurons and other similar artifacts of analysis which consider the nervous system, the brain and the mind in any quantifiable form.

How does it follow that either of these beliefs rae bound to the statement that "All computation is intelligence" ? Quite simply, to produce a valence which describes information, in any form, requires, in tandem, and as an essential component (or even as the component in its absolution), that a quantification is being produced. That is to say, the description is in and of itself a form of computation, and without a description, a production of valence, a communication about said information, or even by having a conception of thought which is never communicated from one being to another, there is no evidence that the information in question even exists.

Therefore, it is necessary to infer a supposition that a belief of "information" being intelligence is interchangeable with the supposition that all computation is intelligence.

Intelligence

Though there are semantic variations for the definition of intelligence, their underlying similarities allow us to genericize our approach to evaluating whether or not the toolsets utilized within the realm of AI are a cause for the genuine manifestation of intelligence.

Let us explore some of these definitions and then proceed through the methodology of factory of analysis in order to remove unnecessary components.