@Brett That's interesting, I still have to go through your work BTW. I'm a
little curious though, is there anything holding you back if it isn't compute
and RAM - supposing you are not making some massive G P T beast of the openLand.
Also would you say your AI, compared to GPT, actually reasons
Hi Jim, It has been 2 years since we last communicated. The article I was
writing has now turned into a book and based on your continued AGI curiosity
and search for answers as illustrated in this thread I invite you to read the
introduction to my book. Most of the chapters are now complete and
I guess an interpretable network of (graph) components (or subcomponents) that
uses hypergraphs to refer to them could work in the way I am thinking. To use
them in a variety of what-if scenarios the lines as well as the nodes could be
varied. But I think this would still be in the Relevancy Pro
show us results first
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Concept nodesoh.
Still a messy blob of text.
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None of this makes any sense.
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As I was thinking about a few presentations I realized that my ideas about more
human-like AI must include something that acts a little like a trial and error
method even when the subject matter is well understood. I think the component
method of AI is necessary. The components would represent c
Next product is called "Gazelle V" and it will understand your screen contents
(images as well as text). Preliminary work is done. Next stream soon.
Same range as always, 1-2k €, should work. Who's up for it this time? :)
Many greetings from the post-COVID AI world (that's the best I can put it)
I was disappointed by the end of Linas's presentation because he seemed to be
promoting the method as scalable although he did not say anything about the
complexity (complicatedness) of the integration of ideas that would need to be
built on numerous and complicated references. A concept has to
