Thursday, 16 August 2012

New paper Modality Independent Neuro Development Substrate for Artificial General Intelligence “MINDS for AGi”

Here is my application of the dipole / multipole framework to AGi for AGI-12, 5th Conference on AGI @ Oxford, Dec 8-11 2012  -- POSTER LINK

Its called The Integration of a Deep Structure Neuromorphic Framework for AGi: Modality Independent Neuro Developmental Substrate for Artificial General Intelligence “MINDS for AGi” - LINK


2b edited 30 pages! 


Whats it about  ? If you have the entire structure principles, you have generalized map for the entire computational principles. When filled out with low level detail its perfect for the highest level modality free neuro models for AGi. Needs refinement to specific in depth computation, but a start towards high level principles. 

Also brief 2 page outline for application of Dipole expansion framework to brain emulation / copies - LINK


Felix Lanzalaco (in picture) presents his long awaited abstract to a paid audience outside in the cold scotland climate.  Some youtube levity was needed after writing such a heavy going paper, but is she on to something ? Even more to the point, am I on to something, OR did I slip into groupthink and/or pseudoscience (yes the video was improvised).



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UPDATE 16/09/2012 : As predicted by all the paper was way too long for AGI-12 as its a Springer proceedings Journal with fixed length.. The content itself passed review on all 4 measures used (That surprised me !), but both reviewers rejected on the editorial issue of length. It has not been possible for me to reduce it to 10 pages right now for these reasons (aside from my final neuroscience exams for this year also).

1. Since producing it I have realized the concept still needs fleshed out further, i.e. Its still an over-simplification too far. 

2. To shorten to a core would need reference to its original larger work, and this has not been submitted to a repository.

To my surprise one of the reviewers proposes high level architecture is harder to get right than sub system components. Thats what I advocate also, but other AGi programmers think differently on their quest for simple principles. They haven't experienced enough neuroscience in my view. This review point is the primary reason I could not compress the conceptual framework right now. The unique insight I propose to posses into the highest level structure is not enough. There has to be more work to produce consistency with both known principles and known unknowns in neuroscience at all key levels of scale before a compression to high level principles. I already had the plan, and carried out groundwork in how to go about this, thats what the PhD is about. Its no easy task and requires a thesis just to cover the projects scope. 

Sorry to say but my current opinion is if the brain is anything to go by even with a full understanding at all levels the final principles for general intelligence are going to have to include the integration of a lot of sub principles, and that would be after such principles have been well untangled from the biological substrate. Maybe there is an E=mc2 for the brain, but I have doubts it can capture the true systems functionality. My hope is the neuro-computation aspect of the biophysical understanding I bring will be similar to the resultant success I experienced with the developmental predictions made almost 10 years ago (from the proposed top down models here).  That is that having the final high level insight integrating with the lower-level work of todays neuroscience can make predictions which can inform us in our AGi (brain derived) system research regarding both known/unknowns and the hardest of all, unknown/unknowns.
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