Ben Conceptually, I like where you are going with this. Your team's work with human-language-based robotic communication is astounding.
I think your idea of a universal attractor has merit. I suppose, in the end, when matter exists, it generates an elcetro-magnetic field. In a genetic sense, the flux of such a field would act as an open and closed-loop communications network. In this sense, the relevant data, information, and a relative perspective of knowledge, would all be packaged within relative, genomic code. In other words, we are imagining a relative system of relative systems from which reality would functionally emerge. Given my systems methodology, what remains to be done in order to visualize a model of human-like machine reasoning, is to be able to link your "attractor" value to the information, from which it should become possible to systematically emerge any informational concept at any level of abstraction within any, dimension of reasoning. The genetics of resultant information would in theory make forward and backchaining possible, and much more. The completeness schema of functional, attractor values seems to be a critical machine-reasoning component to pursue. It would probably also assume the role of a priority systems constraint. I've been doing much thinking about this as a next-step for my own research. I think you've got this. Keep up the great work. Rob ________________________________ From: Ben Goertzel <[email protected]> Sent: 27 July 2017 04:57 AM To: AGI Subject: Re: [agi] Neuroscience-Inspired AI Well I would say that none of the work done at Deep Mind and also none of the ideas in Demis etc.'s paper address the questions I raised in this paper http://ieeexplore.ieee.org/document/6889662/ How might the brain represent complex symbolic knowledge? - IEEE Xplore Document<http://ieeexplore.ieee.org/document/6889662/> ieeexplore.ieee.org A novel category of theories is proposed, providing a potential explanation for the representation of complex knowledge in the human (and, more generally, (sorry for the paywall ... use sci-hub.cc ...) So there is no real plan for how to achieve abstract symbolic reasoning as needed for human level general intelligence within a purely formal-NN type approach Obviously in opencog we are taking more of a symbolic-neural approach so we don't have issues with abstraction Also if you look at the recent Markram et al paper on algebraic topology and mesoscopic brain structure, there is nothing in the Hassabis etc. universe that seems to address how such structures would be learned or would emerge But sure in a big-picture historical sense the progress happening these days on "narrow AI verging toward AGI" and on "making complex cognitive architectures finally do stuff" is super exciting. We are on the verge of multiple breakthroughs within the next few years. Woo hoo !! - -Ben On Thu, Jul 27, 2017 at 5:55 AM, EdFromNH . <[email protected]<mailto:[email protected]>> wrote: About the above linked Hassabis paper, Ben said, "It's sort of a high level inspirational paper... it does lay down pretty clearly what sort of thinking and approach Deep Mind is likely to be taking in the next years ... there are no big surprises here though as this has been Demis's approach, bias and interest all along, right?" >From my knowledge of several articles and videos by, or about, Hassabis -- I >totally agree. But I am a little less ho-hum than Ben, perhaps because I'm >not as up on the current state of AGI as Ben. Reading Hassabis's paper makes me bullish about how close we are to powerful, if not fully human-level AGI, within 5 years. Why? Because all of the unsolved challenges Hassabis discusses seem like they could be easily solved if enough engineering and programming talent was thrown at them. I feel like I could relatively easily -- within a few months -- weave plausible high level architectural descriptions for solving all of these problems, as, presumably, people like Demis and Ben could do even better. (Perhaps that is why Ben is so ho-hum about the paper.) With the money that's being thrown into AGI, and the much greater ease of doing cognitive architectural experiments made possible with Neural Turing Machines -- which allow programmable, modular plug-and-play with pre-designed and pre-trained neural net modules -- the world is going to get weird fast. Tell me why I am wrong. On Sun, Jul 23, 2017 at 8:29 PM, Ed Pell <[email protected]<mailto:[email protected]>> wrote: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5467749/ On 7/23/2017 4:18 PM, Giacomo Spigler wrote: An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/8630185-a57a74e1 Modify Your Subscription: https://www.listbox.com/member/?&; Powered by Listbox: http://www.listbox.com AGI | Archives<https://www.listbox.com/member/archive/303/=now> [https://www.listbox.com/images/feed-icon-10x10.jpgecd5649.jpg?uri=aHR0cHM6Ly93d3cubGlzdGJveC5jb20vaW1hZ2VzL2ZlZWQtaWNvbi0xMHgxMC5qcGc] <https://www.listbox.com/member/archive/rss/303/19237892-5029d625> | Modify<https://www.listbox.com/member/?&;> Your Subscription [https://www.listbox.com/images/listbox-logo-small.pngecd5649.png?uri=aHR0cHM6Ly93d3cubGlzdGJveC5jb20vaW1hZ2VzL2xpc3Rib3gtbG9nby1zbWFsbC5wbmc] <http://www.listbox.com> -- Ben Goertzel, PhD http://goertzel.org "I am God! 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