Boris I'd like to throw a few ideas around to see if they gel.
From my perspective, what your hierarchical pattern-management process is describing reminded me of flow-characteristics of a researched, meta model for sustainable competency I once spent time on. I think this has significance for intelligence-based systems, as my view would be to aim for a system that exhibits optimal efficiency (effective complexity). For such a system to constantly orientate itself, you'll have to be able to provide a standard intelligence scale and a pseudo-random driven, dynamic, mapping process. I think, such a scale and map would have relevance for accuracy in situational decision making. [I'm slowly pulling the thread through to a conclusion here.] You may elect to position scaling and mapping schemas as clusters of higher-strategic management functionality. Again, hierarchy and levels go hand in glove. However, hierarchies may also become relative, depending on the schema employed. Up can be done, and down can be up and everything may happen in between. In this sense, hierarchy could become a gateway to state and state the point of departure for every, single moment, as timespace continuum, as systems event. To my mind, such a system would be able to perform self adaptation aka autonomous behavior. If my view should have merit, I would suggest that there are a number of essential components still missing from your design schema. Such system components may be related to your thinking on levels of pattern recognition, and suitable to your notion of hierarchical increments from the lowest (meta) level. The wood for the trees, and the RNA for the fruit, kinda thing. Best Rob ________________________________ From: Boris Kazachenko via AGI <[email protected]> Sent: Monday, 11 June 2018 2:41 PM To: [email protected] Subject: Re: [agi] Anyone interested in sharing your projects / data models AGI's bottleneck must be in learning, anyone who focuses on something else is barking under the wrong tree... Not just a bottleneck, it's the very definition of GI, the fitness / objective function of intelligence. Specifically, unsupervised / value-free learning, AKA pattern recognition. Supervision and reinforcement are simple add-ons. Anything else is a distraction. "Problem solving" is meaningless: there is no such thing as "problem" in general, except as defined above. Now think about this: we already have the weapon (deep learning) which is capable of learning arbitrary function mappings. Yeah, we have some random hammer, so every problem looks like a nail. We are facing a learning problem which we already know the formal definition of. No, you don't. There no constructive theory behind ANN, it's just a hack vaguely inspired by another hack: human brain. Which is a misshapen kludge, and this list makes it perfectly obvious. Sorry, can't help it. Anyway, this is my alternative: https://github.com/boris-kz/CogAlg On Mon, Jun 11, 2018 at 3:41 AM YKY via AGI <[email protected]<mailto:[email protected]>> wrote: On Mon, Jun 11, 2018 at 2:55 PM, MP via AGI <[email protected]<mailto:[email protected]>> wrote: Right. Most of them work off of a variant of depth first search which would usually lead to a combinatorial explosion, or some kind of heuristic to cut down on search time at some other cognitive expense... Not to mention most of them run off human made rules, rather than learning it for themselves through subjective experience. I highly doubt even Murray’s bizarre system subjectively learns. There are hand coded concepts in the beginning of his trippy source code. How can your system overcome this? How can it subjectively learn without human intervention? AGI's bottleneck must be in learning, anyone who focuses on something else is barking under the wrong tree... Now think about this: we already have the weapon (deep learning) which is capable of learning arbitrary function mappings. We are facing a learning problem which we already know the formal definition of. So we just need to apply that weapon to the problem. How hard can that be? Well, it turns out it's very hard to understand the abstract (algebraic) structure of logic, that took me a long time to master, but now I have a pretty clear view of its structure. Inductive learning in logic is done via some kind of depth-first search in the space of logic formulas, as you described. The neural network can also perform a search in the weight space, maximizing some objective functions. So the weight space must somehow correspond to the space of logic formulas. In my proposal (that has just freshly failed), I encoded the formulas as the output of the neural network. That is an example, albeit I neglected the first-order logic aspects. Does this answer your question? And thanks for asking, because that helps me to clarify my thinking as well... ☺ 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> Permalink<https://agi.topicbox.com/groups/agi/T731509cdd81e3f5f-M85c87b4f6aeac8dd7343b99c> ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T731509cdd81e3f5f-M4da0db38a479166b632b946b Delivery options: https://agi.topicbox.com/groups
