Robust Efficiency With Successive Approximations

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HS

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Robust Efficiency With Successive Approximations
« on: September 25, 2019, 01:57:02 am »
Estimations could be a useful material for constructing enduring actions. There is a feeling you get when reaching for an object with your hand, It's not some high level exact mathematical formula, it's a very simple operation, which only features the fluid repetition of a sub process; observe adjust observe adjust... If things are a little off that's fine, just keep repeating the process. Each step magnifies your reference frame, creating a better environment for estimating your second step.

Now this mechanism can be used to run higher level codified thought patterns. The cycles of the world acting on you, and you acting on the world in turn, as well as the interplay of various hierarchies contained within the brain itself.

I've found these so far:

GCRERAC: Goal Conflict Result Emotion Reason Anticipation Choice (The narrative intelligence process.)

BEAR: Body Emotion Awareness Reason (The direction of flow, and order of acting, of the rapid information pyramid.)

DIKW: Data Information Knowledge Wisdom (The order of consolidation of the commonplace.)

I'm not sure whether they should be separate, or if there is a way to combine them, and create an omega process (evil laugh). If anyone has some suggestions for other patterns of intelligence that would be great.

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AndyGoode

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Re: Robust Efficiency With Successive Approximations
« Reply #1 on: September 25, 2019, 03:45:04 am »
Pretty good, though you might need to describe your mechanisms more so that people know exactly what you mean. For example, I've noticed the human brain uses variations of the scientific method in many ways, from sensing to logical reasoning, all of which involves posing a hypothesis and then testing it and maybe then refining it. Is that one of your listed mechanisms? Also, it sounds like your mechanism called BEAR is the same as Paul MacLean's Triune Brain Hypothesis:

https://en.wikipedia.org/wiki/Triune_brain



There's another mechanism I've noticed but I want to write an article about it before disclosing it. It might not be relevant to your list, anyway. I'm sure all these mechanisms fit together nicely, but I'd have to think about how to do that, and I'm not up to intense thought at the moment. When all pieced together these mechanisms will make a nice infrastructure for modeling the human brain, but the devil is in the details, especially in some of those longstanding mysteries like representation, learning, commonsense reasoning, temporal reasoning, associative memory, and self-awareness, which such a high level view won't resolve.

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MikeB

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Re: Robust Efficiency With Successive Approximations
« Reply #2 on: September 25, 2019, 06:27:08 am »
In my opinion processing should always be goal orientated, otherwise what is it really doing...?

For the human mind, what is the absolute goal...? Surviving easily (not under constant stress)? A lot of actions including work-relax cycles can come from that alone.

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Re: Robust Efficiency With Successive Approximations
« Reply #3 on: September 25, 2019, 06:31:25 am »
As Open AI showed on their website, our lives are ever challenging goals, because of competition with multi-agents, you'll never be at true equalibrium peace, you'll be forced every day to hide, work, and worry how to survive and persist. There's never any relaxing just yet really, we all have new issues once the old ones are dealt with.
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HS

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Re: Robust Efficiency With Successive Approximations
« Reply #4 on: September 26, 2019, 04:47:37 am »
Thanks for the suggestions. I'm experimenting with how these processes could fit together, and tried describing that in a paragraph, but it ended up being hopelessly complicated.  So here's a picture:

IPM" border="0


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LOCKSUIT

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Re: Robust Efficiency With Successive Approximations
« Reply #5 on: September 26, 2019, 11:37:54 am »
@HS what is that? Does it build words? Rhyme? Why so complex looking? Explain the goal it solves, an what high level ideas of it are.
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AndyGoode

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Re: Robust Efficiency With Successive Approximations
« Reply #6 on: September 26, 2019, 11:19:28 pm »
Ditto to Locksuit's questions. I'm waiting for an explanation of your original mechanisms. Also, any architecture created by fitting together components should be justified as to why the components chosen belong where they were put, and why they are connected as they were connected. I'm already satisfied as to its general goal, though--to stay alive like any living organism.
« Last Edit: September 27, 2019, 12:18:11 am by AndyGoode »

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HS

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Re: Robust Efficiency With Successive Approximations
« Reply #7 on: September 27, 2019, 03:02:19 am »
I'm trying to describe the possible interplay of the major currents flowing through the mind of a general intelligence. So that eventually, I will be able to model a neural net on the diagram. It's like a game-plan to tell me where and why to connect things.

You were saying life is a series of consecutive goals, so the central through-line of my imaginary mind is a goal based narrative progression (GCRERAC), which writers use to create realistic characters. The letters represent the consecutive states (Goal, Conflict, Result, Emotion, Reason, Anticipation, Choice). As you can see by the big arrow going around the top, this process is self perpetuating, because when you make a choice, you automatically create a new goal for yourself.

In yellow (BEAR), stands for (Body, Emotion, Awareness, Reason), is the fast data pyramid. First, the various nerves and receptors of your body (B) receive all stimuli. This input can go three ways. Depending on what the input is, it can trigger a reflex action (thin arrow going to choice C), it directly effects emotion (yellow arrow B to E) because the state of your body should influence your mood (be it a vague background mood), the body would also supply the result (R) of conflict (C) stemming from goal (G).

In order to resolve conflict (C), you need to employ the scientific method (SCI METHOD) by generating hypotheses and performing experiments. The hypothesis should be informed by knowledge (K), I'll draw that in now. Then the result of the conflict gets recorded by the body and resulting data (D) is fed into (DIKW) the slow (Data, Information, Knowledge, Wisdom) consolidation pyramid. Which itself feeds backwards into (ERAC) or (Emotion, Reason, Anticipation, Choice). Data should be the final reality check for C (Choice) which has been funneled through E (Emotion). Information (I) is like playing with arithmetic. So it's used to make predictions about the future, therefore an arrow to A (Anticipation). Knowledge lets you make an fast and accurate representation of the current situation, so it is used for reasoning (R) about the present. Wisdom (W) is a broad brush and gets you going in the right direction with an emotion (E). Then you get an ever narrower focus going backwards through (DIKW), following the theme of successive approximations, as you narrow in on a choice (C).

What else? The small arrow going from yellow (E) emotion to (C) choice is the stress response pathway, for when a tiger shows up etc. Reason is effected by a bunch of stuff. It is most directly guided by (E) emotion, but emotion also bounces off (A) Awareness, which is a sense of doubt due to knowledge of the existence of possible unknowns. The signal from (E) emotion usually needs to be given the all clear by Awareness before Reason can be engaged, unless it's a stress response. 

I'll post the diagram again so you don't have to scroll.

IPM2" border="0

I hope it won't get as complicated as the metabolic pathway diagram: http://biochemical-pathways.com/#/map/1

...But were talking about the brain here, so its a faint hope.  :)

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goaty

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Re: Robust Efficiency With Successive Approximations
« Reply #8 on: September 27, 2019, 08:04:25 am »
Its simple and possibly no help,   but the way one of the little critters moves around with a neural network controlling them is successive correction of an approximation too,   if it ever mis-moves, it gets to continually recorrect it as it mosies along the ground. :)

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Re: Robust Efficiency With Successive Approximations
« Reply #9 on: September 27, 2019, 08:25:57 am »
series of goals...
(Goal, Conflict, Result, Emotion, Reason, Anticipation, Choice)...
(Body, Emotion, Awareness, Reason), is the fast data pyramid...
scientific method (SCI METHOD) by generating hypotheses and performing experiments...
(Data, Information, Knowledge, Wisdom) consolidation pyramid
Haha that metabolic system is huge and i expected to see that when you mentioned it.

Hmm...bit complex of a design...it's more Data=hierarchy+RL=prediction/anticipation_choice_reason_Goals/Results,
You also say Emotion twice and (Data, Information, Knowledge, Wisdom) are all the same fellow lol.
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goaty

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Re: Robust Efficiency With Successive Approximations
« Reply #10 on: September 27, 2019, 08:48:39 am »
Hmm...bit complex of a design

Its unethical to think that the solution to ai is that bloody simple,   are we even reducting the worth of what we are doing as well???

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LOCKSUIT

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Re: Robust Efficiency With Successive Approximations
« Reply #11 on: September 27, 2019, 09:11:57 am »
If you went through every atom of your house when describing how to build a house complete with 2019 dishwashers, you'd get stuck. All you gotta do is say metal bar, etc and buy me a qualified washer. Or you could just say "build home". High-level talk.

We use past experience to cover similar problems.

Use the hierarchy.
« Last Edit: September 27, 2019, 09:54:43 am by LOCKSUIT »
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AndyGoode

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Re: Robust Efficiency With Successive Approximations
« Reply #12 on: September 28, 2019, 01:24:00 am »
OK, here is as far as I got on my version of your modules. I haven't had time to touch the reasoning module. I feel like I'm wasting my time because surely most robotics fans have made such diagrams many times before.

I would still need to know exactly how your BEAR and GCRERAC modules work before I could incorporate those. Those must be your inventions since I can't find reference to them online, so unless you describe them exactly, nobody is going to know how they work. Which parts happen sequentially versus in parallel? What decisions do they make? What is the goal of each? Are they hierarchical? Can you draw a flowchart of each? Etc.

https://ibb.co/880kkP8

Some comments about my diagram...

DIKW is assumed to be the potential structure of any input or output, and maybe even used in that hierarchy throughout the system, so that's why you see those green pyramids of DIKW only on the input/output arrows.
I tried to use the same colors you did for each part, like forest green for DIKW.
PEI means physical-emotional-intellectual, which is the Triune Brain Hypothesis organization. Each of those pink pyramids is one of a person's goals, since each goal could contain any mixture of those components. The pyramid of goals is like Maslow's hierarchy, where the highest esteemed goals [like being happy] are at the top, but are overridden by higher priority survival level goals at the bottom.
https://en.wikipedia.org/wiki/Maslow's_hierarchy_of_needs
If anybody has other questions, let me know.

« Last Edit: September 28, 2019, 07:18:39 pm by AndyGoode »

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HS

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Re: Robust Efficiency With Successive Approximations
« Reply #13 on: September 28, 2019, 04:19:51 am »
Oh, I treated all inputs as (D) in DIKW. Then imagined IKW would be internally generated. How can you just absorb higher level data? Right, I need to include a goal hierarchy system. I think the obvious way to do that is (degree of positive or negative influence which goal is trying to achieve or avoid)*(1/time until event).

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AndyGoode

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Re: Robust Efficiency With Successive Approximations
« Reply #14 on: September 28, 2019, 09:39:51 pm »
Oh, I treated all inputs as (D) in DIKW. Then imagined IKW would be internally generated. How can you just absorb higher level data?

The I/O can have any combination of the DIKW components, as long as all the levels below the main intended level are present. For example, if someone gives you general advice like 'It's very risky to talk to strangers', that is wisdom that could be imparted verbally via words, and words are at the knowledge level, and the auditory data itself is structured data, also called information, so you're getting all four levels at the same time in that case. In other cases you might have to generate the higher levels yourself, say after realizing that after gaining the knowledge that several specific kids have been abducted after talking to strangers, then it must be very risky to talk to strangers. In general, any DIKW component can be present or absent in any transmission, under the constraint that since the higher levels are based on the lower levels, any level present must have all the levels below it also present. It's sort of like the Internet protocol suite used for computer communication on the Internet, except that I believe all levels must always be present in Internet communication, unlike in human communication.



More later. If I can, I'll update my diagram today, though I could put more into it if you explained those concepts I mentioned.

 


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