Also, while I'm warming up, you guys aren't the only people who read "Stranger in a Strange Land" in Grad school, mkay. I'm more of a Spider Robinson guy actually. Speaking of somebody who can use our financial support. Anyway, I liked "Friday" and "The Moon is a harsh mistress" way better but I think Commander Tom Cool really nailed it with "Infectress" speaking of a guy we should really invite to come on this listserv.
I like what I'm hearing on Colin's approach but I'm going to have to take my time and re-read through everything thus far. So, any Charles Sheffield fans? I am Rustum Battacharyia. Greg "Megachirops" Staskowski, *Lord of the puzzle network* aka Lorton Nikon On Wed, May 6, 2015 at 9:02 AM, Greg Staskowski <[email protected]> wrote: > Steve, > > Tell you what, *we are dangerously off topic*. Let me help you out. I'm > not getting into a pissing match with you on a public listserv. You want to > actually learn something about collecting physiological data and then > drawing conclusions, hey e-mail me at [email protected] and we can > spend the next two weeks going back and forth over what I see as the flaws > in your approach. You really want to go into this with an ultramarathon > runner, sure hey, no problem, but assuming a skeptic and a scientist will > never believe you based on your somewhat limited data? *Hey, bad form > guvnor.* I call shenanigans. Just sayin. > > -GJS > > On Wed, May 6, 2015 at 2:07 AM, Nanograte Knowledge Technologies < > [email protected]> wrote: > >> Hi Colin >> >> You seem to be following a similar process to AI as to what was used to >> develop the first, nuclear bomb - various approaches were used coupled with >> great experimentation. >> >> Semantically, your inclusion of the term "emergent" in your last message >> undersores this approach for me. I'd like to dwell on its relevance for a >> few seconds. Emergence is regarded as the basis for complex-systems >> engineering (Checkland). Further, Checkland asserted how the debate between >> complex and simple systems would probably give rise to what is regarded as >> systems thinking. This is ancient stuff I'm repeating only to stress the >> importance of its credibility. Thus, on the theoretically basis alone, your >> experimental approach could be deemed to be sound. >> >> Narrow AI, broad AI, AGI? All peas in the same pod of complex-systems >> thinking. The fundamentals still have no significant incentive to change. >> >> Personally, I would value such an experimental approach on the basis of >> rethinking the whole idea of developing AI. How else was the sound-barrier >> broken? In addition, if one followed the emerging trend in recent, >> adaptively-autonomous technologies, one would be hard pressed to write off >> your approach. >> >> Just one theoretically-moot point if I may, albeit a semantic one? Any >> institutionalised process effectively is a program of code. As an >> extension, any reduced process - as a procedural implementation - on a >> computer would become a computerized program. Hence, I suppose, your search >> for a generic algorithmic platform. >> >> In the sense of systemically, as soon as you'd link the "stochastic" >> environment to a computer chip in any way it should emerge as a form of >> computer program. Whilst one understands the need for research to be highly >> focussed on its objectives, one must still have a design framework that >> would not unduly restrict any design in a short-sighted >> Heisenbergian-Einstein debate. >> >> I would assume then that you do have a quantum-based design framework >> you're working from. If not though, this particular, organic approach >> would sooner or later come up against the eco-systemic realities of >> highly-abstracted implementation. This then, mainly due to the lack of >> navigational competency in the R&D framework to consistently and reliably >> perform adaptive integration. If it cannot be measured somehow, it cannot >> be reliably tested and I'm by no means suggesting this to be the case with >> your experiment. Mine are just thoughts on the interesting topic at hand. >> One day, when bootstrapping does occur, you'd be wanting to debug though. >> If only purely mathematical, then purely computational? Maybe that was how >> computer science emerged. >> >> Good luck with the experiment. >> >> Rob >> >> ------------------------------ >> To: [email protected] >> From: [email protected] >> Subject: RE: [agi] Re: Starting to Define Algorithms that are More >> Powerfulthan Narrow AI >> Date: Wed, 6 May 2015 10:01:33 +1000 >> >> Hi, >> Rather busy... Having trouble devoting time here. >> >> Jim.... You ask if I am making some kind of electric circuit. Basically >> yes. Except it's physical instantiation is important. Materials in space. I >> know you won't get why that is important. That's ok for now. Just accept >> that it's like that for the same reason the brain is like that. >> >> What it isn't is an 'Equivalent circuit' in the traditional sense of >> voltage/current replication. It is designed to produce functionally >> equivalent action potential-style signaling AND the brain-style field >> system that actually expresses the voltages. The hardware will (in the >> field version) express an EEG and MEG like brains. >> >> Having said that I am currently designing a version that doesn't express >> the fields but allows their addition later...knowing what performance >> degradation results (it will be narrow-AI not AGI). Call it a causality >> mirror with a faked image in it. >> >> It is deeply self modifying. The circuits literally rewire themselves. >> Circuit loops duplicate/diverge and switch out/off. It accounts for the >> process of brain development as a kind of learning. I.e. I don't even have >> to design the 'brain'. It will self configure based on being in the world. >> Because it's not using neurons it won't automatically mimic brains in >> structure. I have no idea what a brain will look like. Physically its a >> crystalline rock. No actual material growth. Functionally it will stabilize >> in ways I can't know except by experiment. It means that it must be >> permanently juvenile.. Overexpressed neurons and overexpressed synapses >> culled back. Lots of wastage. But so what? >> >> Not one line of software anywhere. Any 'algorithm' it has is in the >> adaptation mechanisms. But they are in hardware. The state of the chip's >> self configuration is the only actual data involved. Yet, when you look at >> it there will be deep regularities in its behaviour. You could write them >> down. However they are all emergent. >> >> You know what the hardest part of this is? ... Giving it goals. A reason >> to bother. A reason for it to sustain the quasi-stable resonances that >> signify its functioning. I have to think of something akin to homeostasis >> to keep it going! ROBEOSTASIS. You know what might happen? It possibly >> self-sustain without human intervention or some kind of hardwiring until >> the fields are added. Unsure. Answering that is an experimental goal. Steve >> seems to be deeply inside homeostatic concerns. So that's good. >> >> I'm not here to justify anything. Experimental proof will speak for me. >> And if I can't get the version with and without the fields to be different >> in predicted ways then I will grovel at the feet of the great god >> computationalism. Not before.[image: Smiling face with smiling eyes] >> >> >> I think the approach is a reversion to 'natural cybernetics' that had a >> brief life in the 1950s and then was lost in a tsunami called computer >> science. I bring it back for an upgrade. Notice that AGI failure started >> the moment cybernetics stopped. The actual science of artificial >> intelligence stopped then, too...IMO. >> >> Enough poking the bear. Gotta get back to it. >> >> I really appreciate the interest in this 'adaptive control' approach. >> >> Cheers >> >> Colin >> >> >> ------------------------------ >> From: Jim Bromer <[email protected]> >> Sent: 4/05/2015 12:42 AM >> To: AGI <[email protected]> >> Subject: Re: [agi] Re: Starting to Define Algorithms that are More >> Powerfulthan Narrow AI >> >> I thought the ideas are interesting and Colin's description was more >> readable than usual but the arguments supporting the method weren't >> very powerful. I am curious about how Colin is implementing the >> method. Could you give me a little more about that? Are you designing >> some kind of electrical circuit? >> >> What I was trying to say in this thread is that you have to supply a >> little more insight about why you think that the methods that you are >> designing and will be implementing would rise above being 'narrow ai'. >> For instance, Colin's honest report on how far he has actually gotten >> so far sounds like it is on par with simple narrow AI. As I reread >> your messages I keep finding a little more in it. But back to my >> point. Since I can rough out the algorithms that I would use as if >> they were abstractions, or as if they could exist within an abstract >> world, it would seem that I should be able to conduct simple tests to >> show that they could diversify in some way that is: 1. at least better >> than narrow ai, and 2. useful in some way. So perhaps I should add >> that. I would say, for example, that artificial neural networks would >> pass this kind of test. However, the criticism then is, ironically >> given our use of the narrow ai term, that they lack efficient means to >> focus and they cannot be efficiently used as componential objects. >> >> So, can you guys define some abstract or simple tests that could show >> that your ideas would become able to adapt to the more complicated >> demands of actual tests? The value of the simple test is that once you >> can get your algorithms to pass the first test you might come up with >> ways to design a slightly more aggressive test. So if I could test my >> ideas to,say, try to learn to recognize some simple classifications >> then I might try to see if I can get it to try to get it to learn to >> utilize systems of classifications effectively and efficiently >> (without redesigning the program only for that specific kind of test.) >> So then I would have to design some other kind of test to make sure >> that it is somewhat general. >> Jim Bromer >> >> On Sun, May 3, 2015 at 3:25 AM, Colin Hales <[email protected]> wrote: >> > >> > >> >> On Sat, May 2, 2015 at 2:50 AM, Steve Richfield < >> [email protected]> wrote: >> >>> >> >>> Jim, >> >>> >> >>> Again, I think I see the POV to solve this. All animals, from single >> cells to us, are fundamentally adaptive process control systems. We use our >> intelligence to live better and more reliably, procreate, etc., much as >> single-celled animals, only with MUCH richer functionality. Everything fits >> this hierarchy of function leading to intelligence. >> >>> >> >>> Then, people like those on this forum start by ignoring this and >> trying to create intelligence from whole cloth. This may be possible, but >> there is NO existence proof for this, no data to guide the effort, etc. In >> short, there is NO reason to expect a whole-cloth approach to work anytime >> during the next century (or two). >> >>> >> >>> However, some of the mathematics of adaptive process control is >> known, and I suspect the rest wouldn't be all that tough - if only SOMEONE >> were working on it. >> > >> > >> > Erm.... guys. This would be me. >> > >> > I am working on it. For well over a decade now. Cognition and >> intelligence is implemented as an adaptive control system replicating, >> inorganically, the natural original called the human (mammal) nervous >> system. I simply replicate it inorganically. Tough job but I am getting >> there. There's no programming. No software. Just radically adaptively >> nested looping processes. In control strategy terms it is a non-stationary >> system (architecture itself is adaptive). Control loops come into existence >> and bifurcate and vanish adaptively. The architecture commences at the >> level of single ion channels and nest at multiple levels that then appear >> in tissue as neurons doing what they do, but need not appear like this in >> the inorganic version. You don't actually need cells at all. These then >> nest at increasing spatiotemporal scales forming coalitions, layers, >> columns and finally whole tissue. All inorganically. All the same at all >> scales from an adaptive control perspective. Power-law scalable. Physically >> and logically. >> > >> > In my case, for the conscious version the hardware includes the >> field-superposing, active additional feedback in the wave mechanics of the >> EM field system produced by brain cells at specific points. The fields form >> an addition/secondary loop modulation that operates orthogonally, >> outside/through the space occupied by the chip substrate. >> > >> > What I am starting with is the 'zombie' or symbolically ungrounded >> version. It doesn't produce the active field system (missing a whole >> control system feedback mechanism) and uses supervised learning >> (externalised by a conscious human trainer) to compensate for the loss of >> the natural role consciousness has as an endogenous supervisor. It will, in >> the zombie form, underperform in precisely the way all computer AGI >> underperforms. This is what is missing when you use computers to do it all. >> You end up with a recipe (software) for pulling Pinocchio's strings. >> Whereas my system bypasses the puppetry altogether. It makes the little >> boy, not the puppet. >> > >> > However you view it, there's nothing else there in a brain except >> nested loops that have power-law responses in two orthogonal axes: sensory >> and cognitive. Adding the field system to the sensory axis (e.g. visual >> experience) or part of the cognitive axis (e.g. emotional experience) >> provide the active role for consciousness implemented through the causal >> impact of the Lorentz force within the hardware. I suppose it'd be an >> 'adaptive control loop' philosophy for cognition and 'EM field theory of >> consciousness' combined. No computing needed whatever. Just like the brain. >> Most of the last ten years has been spent figuring out the EM field bits! >> That I am now omitting, knowing what I lose when I do that (i.e. >> consciousness). >> > >> > Teeny weeny Zombie version 0.0 this year I hope. No EM field >> generation. I call it the 'circular causality controller'. I aim to add the >> EM fields later. That part requires $millions. It's chip-foundry stuff. >> > >> > So chalk me in under this 'adaptive control loop' category for AGI >> implementation please. I know this forum is a 'using computers to do AGI' >> forum so I'll just continue to zip it. I haven't mentioned it much over the >> years because it seems that most of you aren't interested in my approach. >> For reference and for the record.... I am the 'AGI as adaptive control' guy. >> > >> > cheers >> > colin >> > >> >>> >> >>> >> >>> I suspect that when the answers are known, it will be a bit like >> spread spectrum communications, where there is a payoff for complexity, but >> where ultimately there is a substitute for designed-in complexity, e.g. >> like the pseudo-random operation of spread spectrum systems. Genetics seems >> to prefer designed-in complexity (like our brains) but there is NO need for >> computers to have such limitations. >> >>> >> >>> Whatever path you take, you must "see a path" to have ANY chance of >> succeeding. You must have a POV that helps you to "cut the crap" in pursuit >> of your goal. Others here are working on whole-cloth approaches, yet >> bristle when challenged for lacking a guiding POV. I see some hope in >> adaptive control math. Perhaps you see something else, but it MUST have an >> associated guiding POV for you to have any hope of succeeding - more than a >> simple list of what it does NOT have. >> >>> >> >>> Steve >> >> >> ------------------------------------------- >> AGI >> Archives: https://www.listbox.com/member/archive/303/=now >> RSS Feed: >> https://www.listbox.com/member/archive/rss/303/11721311-f886df0a >> Modify Your Subscription: https://www.listbox.com/member/?&; >> Powered by Listbox: http://www.listbox.com >> *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> >> <https://www.listbox.com/member/archive/rss/303/26941503-0abb15dc> | >> Modify <https://www.listbox.com/member/?&;> Your Subscription >> <http://www.listbox.com> >> *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> >> <https://www.listbox.com/member/archive/rss/303/27055757-c218d4f9> | >> Modify >> <https://www.listbox.com/member/?&;> >> Your Subscription <http://www.listbox.com> >> > > ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
