*Synergy, Reduction, and Saliency Are Paramount to General AI* * https://www.facebook.com/notes/juan-carlos-kuri-pinto/synergy-reduction-and-saliency-are-paramount-to-general-ai/10151442948752712 * In my AI systems I never preprogram preexisting AI algorithms. I rather let the machine learn the causal geometries of Reality:
Reduction is a proactive and unconscious exploration of the whole space of mental resources, mind patterns, and hypotheses. It is not a straightforward and preprogrammed recipe to solve a problem. It is not a reductionist system. It is rather an inverse problem in which the mind holistically tries to find the recipe. If the mind cannot match mental patterns of thinking to solve problems or to explain phenomena, the mind tries to learn or to create the key elements and the missing pieces in the mind puzzles. Thus, both the time of the reduction algorithm and the resulting recipes are totally unpredictable, imperfect, and non-guaranteed. Successful recipes are always stored. Therefore "fully functional minds" always seek to maximize utility functions which are recursively products of reductions. The evolution of sane minds always seeks sophistication, welfare, and improvement. That's the basis of the scientific method. This conversation is also relevant: Juan Carlos Kuri: "In case of pattern recognition, salient features are the ones that are critical and crucial to recognize the pattern. Remove them from your pattern representation, and your pattern recognizer will start to fail. ... The same applies to model thinking: If you forgot to include a crucial and critical feature, the behavior of your abstraction will completely diverge from the real entity." Monica Anderson: "Saliency is the key to AI. And to Models. One could say that the goal of AI is to create a machine capable of Autonomous Reduction - that Understands the World and creates useful Models for it and/or our use. ... An AI is a Model Making Machine. It has to be implemented without Models of the World. It has to experience, learn, abstract, and determine saliency of its input data and it has to Understand the World. Only when that Understanding exists and operates do we expect it to generate Models for us." Paraphrasing Dr. Joaquín Fuster: "Intelligence is within the brain network. Trying to understand intelligence by studying neurotransmitters is like trying to understand written language by studying the chemical composition of the ink. It's simply not the right level of complexity. Language lies within the relationships between words." ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
