The paper is specific to a novel and quantitative approach and method for association in general and specifically.
It emerges possible and statistical (most correct) relationships. This stands in stark contrast to the deterministic commitment to construct functional relationships. Hence, a polymorphic feature is enabled. Constructors are implied in the design. How else? Further, this paper opens the door to computational entanglement and auto optimization. Mathematically, a control hierarchy could relatively simply be set at any order of logic. Thus, it has a scalar feature (deabstraction readiness). Rather than classification dependent, categorization may become fully enabled. Probably, information and semantics would be contextually enabled across any number of universes. This paper doesn't venture into all the implications, which in all fairness justifies scepticism. The persistence that pattern should be somehow decoupled doesn't make much sense to me. Information itself is as a result of pattern. Pattern is everything. Light itself is a pattern, so are the four forces. Ergo. I suppose, it depends on how you view it. Here, we have a "language" in which to emerge and initiate any pattern, to bring form2function2form (circular, yet progressive chain reactions). I think it qualifies the design as having the potential to become fully recursive. We'll have to wait and see. For now, I'll contend that 'Design' (as pattern application/architectural principles) remains key. On Wed, May 22, 2024, 15:01 John Rose <johnr...@polyplexic.com> wrote: > On Tuesday, May 21, 2024, at 10:34 PM, Rob Freeman wrote: > > Unless I've missed something in that presentation. Is there anywhere in > the hour long presentation where they address a decoupling of category from > pattern, and the implications of this for novelty of structure? > > > I didn’t watch the video but isn’t this just morphisms and functors so you > can map ML between knowledge domains. Some may need to be fuzzy and the > best structure I’ve found is Smarandache’s neutrosphic...So a generalized > intelligence will manage sets of various morphisms across N domains. For > example, if an AI that knows how to drive a car attempts to build a > birdhouse it takes a small subset of morphisms between the two but grows > more towards the birdhouse. As it attempts to build the birdhouse there > actually may be some morphismic structure that apply to driving a car but > most will be utilized and grow one way… N morphisms for example epi, mono, > homo, homeo, endo, auto, zero, etc. and most obvious iso. Another mapping > from car driving to motorcycle driving would have more utilizable > morphisms… like steering wheel to handlebars… there is some symmetry > mapping between group operations but they are not full iso. The pattern > recognition is morphism recognition and novelty is created from > mathematical structure manipulation across knowledge domains. This works > very well when building new molecules since there are tight, almost > lossless IOW iso morphismic relationships. > > *Artificial General Intelligence List <https://agi.topicbox.com/latest>* > / AGI / see discussions <https://agi.topicbox.com/groups/agi> + > participants <https://agi.topicbox.com/groups/agi/members> + > delivery options <https://agi.topicbox.com/groups/agi/subscription> > Permalink > <https://agi.topicbox.com/groups/agi/T682a307a763c1ced-Me455a509be8e5e3671c3b5e0> > ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T682a307a763c1ced-M34b70925a493b96ae5ccdf6f Delivery options: https://agi.topicbox.com/groups/agi/subscription
