Thanks Rob, I do have a scale: representational value = (sum of all variables) / redundancy. This is computed per level of pattern ) whole pattern ) level of search ) system. That's a general criterion, not scheme- or task-specific.
There is no separate mapping, just multi-level search: cross-comparison among patterns. And there is no need for randomizing, this search is driven by inputs and feedback. I guess your "orientation" is my "motor feedback": speed-up | slow-down | direction-reversal for new inputs, in each dimension of the input flow. So, yes, this hierarchy should be fluid / dynamic. But this dynamics is defined by the most general principles, not some extraneous schema. Appreciate your interest! https://github.com/boris-kz/CogAlg On Mon, Jun 11, 2018 at 3:11 PM Nanograte Knowledge Technologies via AGI < [email protected]> wrote: > 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]> wrote: > > On Mon, Jun 11, 2018 at 2:55 PM, MP via AGI <[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-M4da0db38a479166b632b946b> > ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T731509cdd81e3f5f-M1306bed8c1f5b8ab6b0646b9 Delivery options: https://agi.topicbox.com/groups
