@korrelan<mailto:[email protected]>

>From your deterministic view of the brain as a soft machine, it should follow 
>how the functional dependencies you mentioned in a causal relationship are 
>being employed in a supervariable sense. Meaning, coding a system of variables 
>where N => N.

In other words, meta function (in the sense of RNA) would be employed in 
context where the Outcome value = <1>. If your theory resembles what I just 
said, then I get that. Such logic may depict a perfect-state model, which to my 
mind serves as essential component of logic for the construction of AGI.

What I don't get is your implication how an open- and closed-loop system would 
provide functional generics, yet serve as an auto-limiting function on the 
quest for context-independent discovery. Surely, in any super-generic system, 
the objective for attaining optimal efficiency within any context is a given 
state of knowledge-at-work? My view being where optimal efficiency (in a 
classical sense) => regenerative competency.

Robert Benjamin

________________________________
From: korrelan <[email protected]>
Sent: Thursday, 04 July 2019 22:35
To: AGI
Subject: Re: [agi] ARGH!!!


@Robert

Taking dementia as a single problem, there can be a myriad of theories that can 
describe its function.  Taken in the context of a system that can also simulate 
many other human mental conditions you start to limit the possibilities, which 
is the point of the exercise.

The destination is unknown; you have to feel your way. I’m not building a human 
consciousness, we have plenty of them and there easy to make. I'm building a 
scalable intelligence, based on the mechanisms that allow humans to do what we 
do. And to do that I need to consider and simulate anything and everything, all 
the information is useful for cross referencing and gaining insights.

I’m currently a self-employed software engineer; my vocational career has 
included vehicle mechanic, teaching/ lecturing, precision engineer, 
electronics/ design engineer, corporate software designer/ programmer, I think 
you get the gist. Everyone has a skill set and a ‘mind type’, I tend to fall 
onto the practical side of the spectrum. Hence I always approach a problem 
space from this point of view; I see the brain as a complex deterministic bio 
electrochemical machine and nothing more, cause and effect. There is no special 
magic, soul or other paranormal forces at work, it’s not derived from quantum 
effects and rocks aren't conscious, and neither is the universe.

@Jim

I’m sure your speculations as to what the Wright brothers were thinking are on 
par, though the relevance of my reference seems to have gone a miss. Perhaps I 
should have used the term ‘mind-set’ rather than methodology, apologies, my bad.

@Alan

>How much hardware do you need?

 I wish it was a matter of throwing computing resources at the problem to 
expedite progress. I’m currently running a 12 x 4 core PC cluster which is 
sufficient at the moment.  My one saving grace regarding computing power has 
been that the GTP complexity is roughly linearly proportional to the maturity/ 
experience of the connectome, so the older it gets the slower it gets (in 
simulation).  The generality of how the knowledge is stored also helps to a 
degree; many diverse concepts can be learned using the same knowledge facets, 
just recombined.

And just for the record, although I didn’t think it would be a necessary thing 
to state, when I mention consciousness or self-awareness I’m not referring to 
the human level phenomena, I’m referring to the mechanisms/ phenomena within my 
model that I construe as the equivalent phenomena.

The key point is I’m not building a human mind… it’s a machine/ alien 
equivalent, a scalable improvement leveraging what I believe to be the essence/ 
seat of our intelligence.

:)
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