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
