AI_Rewrite.md 6.7 KB
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.
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 taht no single output will nceessarily reveal an answer which can serve an a-priori purpose. Rather, a massive repetition of the procedure is sought, possibly wiht 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.
For us to know the answers to these questions, we need to define what it is to be intelligent.
Intellicen 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 ina 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 essage.
Capacity for logic, understanding, self-awareness, learning, emotional knowledge, reasoning, application of logic, planning, perceiving, creativity, organizing, critical thinking, resolving, problem solving, ordering.
Acknowledging, verifying, naming, remarking, identifying, contextualizing, structuring, the ability to acquire and apply knowledge and skills. Intelligentia, from intelligere 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 deifnitions pertaining to the use of knowledge, and the alternative definition focusing on a domain of knowledge or information. We will be fcusing 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 essage and ventures too far into the metaphysical.
DOMAIN OF KNOWLEDGE/INFORMATION
In the previous paragraph it is alleged that in order to posit that intelligence and domains of knowledge or information are synonymous with one another. What follows ia deconstruction of this argument.
What exactly is a domain of knowledge? A domain of knowledge is the conception of encapsulating an expanse of information through arbitrary demarcation. As all known information, as communicated amongst humans, is necessarily structured by a human capacity, it follows that the very concept of a domain of knowledge is purely 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 wihtin the human frame. 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 comparable conception is taht of information as awhole. In its most complete orm, information as awhole must consiste of any and all matter which exists physically int he Universe. It might be possible to declare a form of information which is separate from the phsyical 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 or other similar artifacts of analysis which consider the nervous system, the brain and the mind in any quantifiable form.