Introducing ambiguity: I draw a distinction between knowledge and intelligence, as the two are similarly different. Not all knowledge is regarded as being intelligence, but all intelligence is regarded as being knowledge. On an existential scale, that would place intelligence evolutionary higher than knowledge. Abstracting this further, all understanding (in the sense of a basis for AGI reasoning) may be regarded as being both intelligence and knowledge, but not all knowledge and/or intelligence may be regarded as being understanding. Suddenly, emerges a knowledge branch, perhaps as a genetic step?
What I just said may not be true at all, but in context of placing the three concepts (knowledge, intelligence, understanding) in a rule-based framework and justifying these statements, the stated associative "relationships" may be true enough to proceed with a reliable-enough AGI model, as a testable version of AGI, as a basis for learning. The moment relationships of any functional value (associations), and any framework of hierarchy (systems) can be established and tested against all known (domain) knowledge, and even changed if the rules driving such a hierarchy should change (adapted), it may be regarded as a concrete version of a probabilistic framework. Enters the old data, information, knowledge, wisdom argument. Some of the bits, when classified in terms of systemic value, are probably absolutely true, but which ones are they? Again, using the same reasoning framework as in the previous paragraphs, the machine could probably figure this out. It all depends on what framework of measurement, scale, reference point, etc. is being used. More on this point later. Agreed, hard facts (i.e., biological age, etc.) may differ in their intrinsic absoluteness and processed accordingly by using computational models, but even so, the very notion of AGI would not have existed, unless we had humans and computational frameworks to compare it against. To contend: Probability may not be a "good" basis for AGI, similarly as love may not be a good basis for marriage, but what might just be a "good" basis is a reliable engine (reasoning and unreasoning computational framework) for managing relativity with. This is where philosophy started from, unraveling a reasoning ontology. Herein lies the deabstraction logic, in the ability to reliably and consistently make sense of how things systemize at any class of abstraction (conceptual, and/or logical and /or physical), then to adaptively progress (read evolutionary step) to another level of abstraction thereafter (repeat: conceptual, and/or logical, and /or physical) on the basis of such sense-making, but not on that basis alone. It is simple logic really, provided methodology (method and management system) of abstraction/deabstraction existed. Accepted, there may be other methodologies at work as well in any single, instantiation of an AGI outcome, not just one of abstraction/deabstraction. The collective singularity of the overall AGI system falls within the domain of "unreason" (not knowing why we know - for now), but reason (knowing why we now know) persists as a realistic, constructivist platform. Reason is the boundary. I think, for all of "this" to succeed functionally, a reliable method for "most-true-for-now" classification is key. ________________________________ From: Jim Bromer <[email protected]> Sent: 12 April 2017 12:59 AM To: AGI Subject: Re: [agi] I Still Do Not Believe That Probability Is a Good Basis for AGI >>Closing with a lingering afterthought; If all intelligence was relative, >>surely all intelligence must be probable.<< Not necessarily. This is a case where a conclusion that can be interpreted using different kinds of abstractions is assigned one particular abstraction. It is a little like an exaggeration. You can use probable methods on relative knowledge (or knowledge that can be seen as relativistic), but that is not the only abstraction (abstract process) that would be needed by a would-be AGI program to 'understand' that knowledge. Jim Bromer On Tue, Apr 11, 2017 at 10:07 AM, Nanograte Knowledge Technologies <[email protected]<mailto:[email protected]>> wrote: The purpose of specification is to unify the design. It is not up to programmers to re-invent the design, but to apply themselves fully to realizing the functional objectives they are assigned to. Thus, the issue should not be one of managing programmers, but specification and programming competency. Nothing new here, except for as you correctly pointed out, the level of competency to both specify and translate an AGI design into pseudo code (in the sense of programmable logic) and for programmers to be able to translate that into machine code. I agree with the frustration to specify what exactly would constitute AGI at a logical and physical level. The knowhow you are referring to in terms of which knowledge schema to use is most valid. Further, your point on the physical constraints of computing platforms is generally well noted internationally. Obvious room for improvement. However, technically, it is now possible for a workable hardware/software platform to be assembled to test AGI components with. Further, new programming tool(s) exist to code AGI logic with. Practically, the AGI logic is missing. It is this logic, which I assert to be available in a distributed form throughout the world. Irrespective if one considers this from a programming or logic perspective, the pseudo code still has to be written, coded, and tested. We have reached a tangible point in AGI, which is: "Show us the pseudo code." And the response to that: "Pseudo code for what? " should become most relevant. It is that "what", which would ultimately define AGI. Let me ask it in this way then: "Is there somewhere int he world today, a center or institution, where the passionate few could go to in order to collaborative specify this pseudo code for a version of AGI, where programmers and tools and a test platform is made ready to test this logic? I am not aware of such a place. Should such pseudo code be written for free, programmed for free, and tested for free? Never. Someone has to fund it, and fund it properly. Unless, we pitted our design and programming competencies against AGI (which is the challenge before us) within a suitable SDLC, we would not know whether or not yours, mine, or anyone else's version, or collaborative versions, of approaching AGI would ever work. I am not smart enough to program this logic, but I may have been smart enough to co-write the pseudo code. In the absence of the collaborative laboratory, would we ever know? If only you were proven correct, this AGI question may be put to bed. Closing with a lingering afterthought; If all intelligence was relative, surely all intelligence must be probable. ________________________________ From: Jim Bromer <[email protected]<mailto:[email protected]>> Sent: 11 April 2017 11:58 AM To: AGI Subject: Re: [agi] I Still Do Not Believe That Probability Is a Good Basis for AGI Co operation is impossible because people have different ideas about how it should be done and as problems are noticed (management for example), the tasks that need to be done become diversified in a non-focused way. So we are now talking about managing people. I could turn this back to the essence of what we were talking about before by mentioning the programmed management that would be needed for a complicated AGI program. I think relatively simple guidelines about abstractions could be easily automated. So if my theory about abstraction is valid, then they could lead to some simple programming design that would incorporate them. But the problem is that the design I have in mind would not (for example) run as a neural network. Continuing with refocusing your ideas about management back onto a discussion about programming AGI (as if you were subconsciously talking about programming rather than managing programmers) I would point out that most AGI paradigms do not produce results that can be efficiently used by competing paradigms. So there would be a serious management issue there. For example a neural network cannot be examined (by the program) in order (for the program) to determine what abstractions it had used to come to a conclusion. A weighted graph (a probability network) should be better at this but here the problem is that the stages of the process have to be saved in order for an advancement like this to work. The efficiency the method would then be lost because it would become memory exhaustive. If a system incorporated (more) discrete abstractions a trace of a decision process could be made based on the abstracting principles that were discovered to be useful to examine the process. (This is a function of meta-analysis or meta-awareness.) Management of people is largely based on a predetermination that some focused goals are reasonable. Even if creativity is emphasized, the push is to creatively solve the narrow tasks that you are assigned. As the workers are given more autonomy to reach for a relatively more general goal, the coordination of the methodologies and goals will be lost. 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