"Okay, but this begs the question of how you define AGI. Domain knowledge is the distinguishing point of what might be called regular AI. It is the General part of AGI that doesn't allow a domain intense approach."
I do not have my own definition of AGI. Any accepted definition is fine by me, but I understand AGI to mean that a computerized machine would be able to exhibit human functionality via human-like brain functionality, as sentient intelligence. In the main, domain knowledge pertains to knowledge about any domain. Knowledge to me is not AI, but it could be argued to be so. To me, AI is reasoning towards knowledge. On the contrary, I would contend that it is exactly the General part of AGI, which most allows for a domain intense approach. If we replaced the broader term 'domain', with a more specialized term, 'schema', and expanded it to specifically mean 'contextual schema', would your argument still hold equally strongly? ________________________________ From: Stanley Nilsen <[email protected]> Sent: 12 April 2017 05:16 PM To: AGI Subject: Re: [agi] I Still Do Not Believe That Probability Is a Good Basis for AGI On 04/11/2017 10:00 PM, Nanograte Knowledge Technologies wrote: 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. Okay, but this begs the question of how you define AGI. Domain knowledge is the distinguishing point of what might be called regular AI. It is the General part of AGI that doesn't allow a domain intense approach. Is it accepted that the "general" indicates that we are looking across domains into the realm of all domains? And, we have to choose between actions coming from multiple domains. One might call this "meta-domain" knowledge. Such knowledge, I believe, would require abstraction. That is the abstraction problem of AGI. For example, is health a more significant domain than finance? Is public service better for the AGI than bettering the skills of the AGI? Choices, choices, choices... 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. I don't think probability is a problem. A piece of knowledge may increase the chance that we see the situation accurately, and accuracy will help us be more specific about our response. That said, it is the way we put assumptions together that will determine our final action. Probability has been used in that we think our assumptions are "probably" right. It is the qualifying of our assumptions that distinguishes the quality of our actions. Adopt sloppy assumptions and your results will probably not always be appropriate or best - not super intelligent. An "advanced" system will have some mechanism for adopting assumptions (most currently rely on the judgment of the programmer.) It is this process of evaluating assumptions that we tend to get abstract. Since we are calling these "heuristics" assumptions, there is an implication that we can't prove this premise that we are adopting. Most likely we can't prove because the premise we choose to build on is abstract - at least has elements of abstraction that won't allow a clear logical conclusion. The issue is, where will AGI get the assumptions? And, how rigorous will the process be for accepting a new assumption? 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