Yikes. Found another big one! Will do inline comments. On Mon, May 4, 2015 at 6:48 PM, Steve Richfield <[email protected]> wrote:
> Colin, > > It is curious that you are defensive in your beliefs. Everyone here has > their own beliefs - most of which need more defense than yours. Everyone > starts with SOME assumptions. Your primary assumptions are wrapped up in EM > fields. So be it - if it leads you to a solution to AGI, then GREAT. > You have to make allowances. I have spent an entire decade telling computer scientists computers can't do one particular thing that has been computers' assigned job for half a century. . It has taught me a lot. One of those things is that the kind and number of ways for misunderstanding things is vast. As is the number of people willing to ignore what I propose. Perhaps, like your work, this idea is ready to be heard. If so then hallelujah! What's the old joke? Takes 15 years to become an overnight success. :-) > > OK, so let's list the points of disconnections: > > 1. Real-world neurons and synapses are VERY complex, with > non-linearities, integration, differentiation, etc. Presumably these things > MUST be right for them to properly adapt to control systems. Your reliance > on EM fields will give you NO help in working out these sorts of "little" > details. What is your plan to avoid being sunk by a lack of these details? > Integration, differentiation and all the other nonlinearities get naturally approximated by the loops satisfying the overall needs of timing coincidence. The entire thing is about timing. In my case many orders of magnitude faster than the brain. So I hold out hope for solving the nastier aspects of nonlinearity with simplicity and brute force. This is a huge area and I am well aware of it. As I am aware of the field system produced by a post synaptic ion channel plaque. Just because a solution with chemical synapses has complexity X in it does not mean that its function/role needs all the apparent complexities in the synapse. All I want to do is retain the signalling (both AP and EM). This is probably something I'll only ever prove by demonstrating it in action. It suffices to say that I am well aware of all the complexities you mention and they are intended to be addressed by the elemental loop unit and its adaptation behaviour. I expect to go through some kind of hell with that in the initial zombie version that does not have the field system. The field system will then later make it hell-squared. Urghhh. > > 2. The #1 above details are probably the result of adaptation, but so far > there has been no guidance regarding how to adaptively select > functionality. Did you have a plan to do this? > The initial hardware will be far more flexible than needed for a target application. I have a whole parameter space of adaptation to explore. I just need to find the ranges to deploy for a practical learning system. Trajectories through that parameter space would be what I call adaptation. Empirically explored. This is a dynamical system that has an operational state trajectory and an adaptation state trajectory both. The adaptation is a dynamical system too. I do not think any tractable analytic mathematical account of nonlinear control systems will ever be constructed except in the sense of broad-brush properties like stability, convergence expectations and observability and so forth. I think this will be a science that will flourish through practice that is, the solution *is the practice. * > > 3. Residual imperfections in #1 above will doom testing UNLESS there is > SOME sort of mathematical or other basis to debug the implementation and > fix its problems. You will never be able to build something, turn it on, > and have it work, at least not in the next thousand years, without SOME way > of debugging it. This would seem to require a solid mathematical > understanding of adaptive control systems - which does NOT now exist. How > were you planning to debug such a thing? > I debug by playing with a purposefully hyper-flexible adaptation parameter space. These are all just numbers. The idea is that you never design any final solution. You evolve it. Like evolution there will be many deaths and dead ends. So ... We embody the brain and then teach it. We are not teaching it any particular thing X. We are teaching it to learn to learn any X. I think I will have to do it for different modalities of sensory-motor behaviour and then integrate them by literally placing them on the same substrate manually and resonating them with each other and the outside world. This would be the experiment that nature did as evolution. Except manual intervention and inorganic chips speeds the whole thing up. Figuring out how this works in practice is also part of the project. To be perfectly frank I have no idea how this will look in the end. I will build it to find out how to do it. Imagine over time I deliberately evolve sub-brains with very simple functionality and then I merge them on a single substrate and then tune them (by training, not by design) into a target niche. If you don't need audition then you don't put it in. You only have to do this process as many times as is needed to create a library of proto-sub-brains to choose from. Probably, in maturity, there will be one massively over-qualified over-powerful brain that can accept human instruction in X at a very high level. Once it has learned X then the mega-brain itself exports the X capacity alone to a simpler substrate that can only do X in an X niche and learn no more. This is the potential business model. Way way early for thinking about this though.... this is 'business model' detail. Notice in none of this is there any software. Everything that happens looks like what happens when humans teach other humans. > > 4. EM fields in chips are quite different than EM fields in an > electrolyte (like the CSF in the brain). Further, they re 2-D rather than > 3-D. Why do you think something that is SO different would do the same or > similar job? > The fields in chips are only different because the membrane-mimetic hardware has never been properly replicated inorganically. This is about capacitors that break down inside their dielectric. These chips are different. They will probably involve the kinds of materials that have electric field dependent conductivity like vanadium dioxide. Again waaay down the track. In relation to 2D and 3D. This will require 3D chunks (say fat layered 2D slices) of material 100% filled with active components. These can then be placed on each other ... layered like a sandwich. I expect all sorts of cooling problems! Although the vanadium dioxide materials at the moment need to be quite hot anyway. The fields simply have to extend out into the space above and below the individual slice planar surface through an insulating layer. Like the EEG and MEG fields extend mm into the space beyond the tissue. I think the fields will be way stonger and faster than in the brain. Wouldn't it be ironic if these brains needed a circulatory system of coolant and a 'heart' to pump it about? This is not standard chip tech. > > 5. As a physics-trained scientist, I still see NO reason to suspect there > is anything in nature that cannot be (slowly) simulated in a computer, > least of all anything as simple as an EM field. Can you provide SOME reason > to suspect that this might even be difficult, let alone impossible? > > The same old chestnut. "I see no reason ...". It seems several succeeding generations have been trained in such a way as to be unable to see ..... prior generations had the reverse problem. Computation wasn't a technological option. They did science on everything by actually building artificial version of the natural original :-) OK. Again. What if the EM fields acquire structure or dynamics nonlocally that encodes the outside world and that can only be accessed from the 1st person and that, by virtue of the field basis, acquires a causal impact? You cannot simulate this. Because it is an *input*. The field is somehow impacted by the distant natural world. Directly. You cant simulate inputs because they are a *measurement*. That is what I mean by 'essential' physics. I don't know if the EM fields are essential to intelligence in this way and neither does anyone else. Just because you can't see this doesn't mean it does not happen. Especially in the most complex matter in the entire universe that has a half century of evidence pointing at the field system having exactly this kind of non-simulatable input content. This is a 1st-person science activity. All preconceptions have to be left at the door. As weird as it sounds. EM fields have to be experimentally tested-away, not assumed away (can't see.....) So all I want is the latitude to regard this as a testable hypothesis and then test it. I do not know the answer and either does anyone else. This is exactly the same kind of claim as "I can't see how heavier than air flight is possible" when I am a Wright brother with my plan for a wind tunnel. Don't you think this is worth testing? If it is the case, as I suspect, then it explains a lot of the history of AI. Also ... look at the paradox this involves. If you already know what is in the external world *then you can simulate the fields. Just like a flight simulator simulates *everything -The plane *and* the environment - and you learn about flight but you are not flying. You are, instead, designing a flying thing. I hypothesise the same thing might apply to fields and AGI. That is, you can simulate everything but that simulation is not identical to an instance of intelligence in the same way the flight simulator is not an instance of flight. The lack of flight is obvious in the simulation. The intellect deficit suffered through the lack of the EM fields will be more subtle. My prediction is a deficit that only becomes obvious in the face of novelty. This is a really subtle thing deserved of a little more than summary dismissal. I can't dismiss it. Instead I will attempt to resolve this experimentally by setting up a system to reveal the nonlocal interaction. Or not. If I can't then I'll give up on the fields. Someone else can have a go. But remember what I am actually doing at the moment: the 'action potential only' (Zombie) version does not replicate the fields. It only has the simpler loop adaptation mechanisms and is what I want to do first. That is hard enough! So I don't intend to promulgate the field idea any more until I have the zombie version in a state that can be modified to create the fields for the nonlocality experiment. The zombie chip, I predict, will underperform in the face of novelty like all prior AI art. But that's OK because I know why and I know how to intervene and compensate for it. > Maybe in answering the above 5 questions we can find our common ground. > > Steve > =============== > > Common ground or not .... we still have established a 'beach-head' for the adaptive-control method of AGI. The more folk pile into this approach the better. I hope your 'low hanging fruit', as a first-entrant, is ripe and nearby. Mine's going to be quite a reach. I think I've captured all the basics in today's 2 emails. I hope that's enough. I have to get back to it.. cheers Colin > On Sun, May 3, 2015 at 11:34 PM, Colin Hales <[email protected]> wrote: > >> >> >> On Mon, May 4, 2015 at 1:41 AM, Steve Richfield < >> [email protected]> wrote: >> >>> Colin, >>> >>> WOW, there is more than one of us!!! >>> >>> This means we can actually have a conversation about this stuff. >>> >>> I will now sprinkle some comments in with your reply to get this >>> conversation going. I suggest addressing the several issues one-at-a-time >>> in separate threads. >>> >> >> 3 deep is enough! >> This is a massive missive posting. >> >> >>> On Sun, May 3, 2015 at 12:25 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 appears to be some confusion between form and function. A >>> computation is a computation regardless of how it is performed, e.g. EM >>> fields, electrolytic tanks, analog computation, electromechanically, etc. >>> >>> "The differences between electrical, chemical, and mechanical processes >>> disappear when the scale becomes small enough" approximate quote by John >>> von Neumann at a neuroscience conference. >>> >>> There's no programming. >>>> >>> >>> I presume you mean high-level programming. Of course there would be >>> SOMETHING (firmware, wetware) at a low level to make things work. >>> >>> No software. >>>> >>> >> No software. I mean it. Like there's no software or computing in a brain. >> I get to configure adaptation parameters. That's it. Everything else is >> emergent. Like the brain. And when we observe it .... it will appear to be >> doing computations just like the brain. The apparent computations are >> merely emergent regularities in its operation that can be described as >> computation. Not to be confused with a computer computing an abstraction of >> the observe/documented regularities. Of course the sticking point here will >> be comprehending how these two things differ and is central to my approach. >> >> Von Neumann was 100% right but, with respect, 100% irrelevant. If you >> want to study X you can compute abstractions of it to death. However, if >> you want to actually perform the role X performs in the natural context >> (build an X), and there is *essential* physics involved, then no >> essential physics = no X. >> >> Take flight. No amount of simulating flight physics actually flies. In >> the case of a robot then no amount of simulation of an arm can be an arm >> (that actually picks up the coffee cup for real). Normally we consider the >> essential physics takes over in the embodiment (input/output). The >> abstract model pulls the strings of the real world embodiment through the >> essential physics of arms and legs, motors and transducers. E.g. Like wings >> on an aircraft are not optional. E.g. 'software radio' measures an antenna >> - real essential physics. No EM field, no radio. No amount of software >> changes this. >> >> So what happened with me? I realised that the essential physics of the >> brain may extend deeper into the tissue than mere peripheral >> (affect/effect, motor/sensory) hardware. Or, alternatively, you can imagine >> that there is deeper physics that has the same input/output role that can't >> be simulated in practice. This 'deeper I/O'? For me: the EM field system >> produced by the brain. The biggest single entity in the organ. Indeed in >> *any* organ. It is a single entity (by vector field superposition) being >> blasted out the scalp (magnetic and electric). I say you can't simulate the >> fields because they have content that you cant simulate: Information from >> the outside world that is physical/causally only accessible via the field >> system. And that it has a real physical influence in the form of a >> 'virtual' feedback loop (physically via the Lorentz force) that is only >> accessible by BEING the fields. Which is what we do as humans. It is what >> my robots will do. BE the fields like we do. >> >> *This is where the conversation usually stops*. Eyes glaze over. I get >> labelled a space cadet and get sidelined. The thing is that to prove my >> position you actually build it and do it. The flight analogy: Put the >> computed flight physics and the replicated bird physics (a plane) on the >> tarmac and see which one flies. I want to actually do this for the brain. >> The alternative is to assume there is no essential physics beyond >> peripherals and then never do the science to prove it. >> >> So how I see the AGI milieu is as a massive >50 year experiment that this >> assumption (the lack of any essential physics deep in the brain) applies. >> Not by actually physically implementing and testing, but by intuition .... >> in assuming that there's no essential physics deep in the physical makeup >> of the brain, and computing and computing and computing and computing and >> never realising that the wrong experiment is being carried out. >> >> This is more than a technical issue. This is science-cultural. I wrote a >> book on this. It's the real problem of AGI. The technical solution is >> actually straightforward. All we have to do is resume doing actual science >> of the pre-computer-age kind. >> >> >> >>> >>> See above. >>> >>> >>>> 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 >>>> >>> >>> I hadn't really thought about individual channels, but of course you are >>> right. >>> >>> >>>> and nest at multiple levels that then appear in tissue as neurons doing >>>> what they do, >>>> >>> >>> For the rest of our viewing audience, there is a VAST chasm between what >>> is commonly taught about neurons, and what has actually been observed in >>> the laboratory, but never captured in a reproducible form. The belief that >>> if it cannot be reproduced than it is not "science" has effectively >>> destroyed the communications channel between neuroscience labs and AGI >>> people. For example, only a tiny fraction of neurons (apparently those with >>> long axons) actually produce spikes - the rest appear to compute and >>> communicate in a continuously analog form. >>> >> >> A big yes to this. This is the low frequency 'electrotonic' control >> system such as that in invertebrates. But more important for me are >> sub-threshold oscillations. In my approach these also create a structured >> EM field interference pattern without requiring any action potentials and >> that have a potential perceptual/regulation/adaptation role to be >> discovered. There is a vast undiscovered country to be explored here. Note >> also that as time goes on all empirical work zeroes ever more closely in on >> the EM field (usually called the 'local field potential') as the most >> direct 'correlate' of consciousness. >> >> >>> >>> but need not appear like this in the inorganic version. You don't >>>> actually need cells at all. >>>> >>> >>> I presume that "cells" in an inorganic version would simply be a label >>> placed on a particular level in the hierarchy. >>> >> >> Yep. By cells I mean both neurons and astrocytes. Astrocytes are deeply >> involved in structuring the background 'blank canvas' that neurons 'write' >> their EM field interference-pattern message on. But yeah... nature has >> cells at that level in the hierarchy. It's an amazing, beautiful thing. I >> am constantly in awe of the elegance of it. >> >> >>> >>> 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. >>>> >>> >>> Why simulate fields with fields, when computers can do exactly the same >>> thing computationally? >>> >> >> Bazzinga. This is a claim that is not proved and that I claim is actually >> wrong. To prove it you need to do the experiment, not assume it is true. >> If, as I entertain as a working hypothesis, there is EM field content >> structured at the nano-scale upwards by interaction with the external world >> itself, independently of the sensory transduction, non-locally, then >> "*computers >> can do exactly the same thing computationally*" is a false claim. You >> can't compute the fields because they are an INPUT. A measurement. >> >> To prove it, what do you do? *You must do the science. Build the same >> field system and then contrast it with a computed version in an appropriate >> context*. If their behaviour diverges or is indistinguishable then you >> can say you have done the appropriate science and have answered the >> question. Not before. >> >> What this means in my approach is that the route to real AGI is actually >> by a *physics experiment*, with results contrasted with a version >> lacking the fields. That physics experiment looks nothing like a computer >> or a robot. It doesn't have to verify 'intellect'. It has to verify EM >> field behaviour does/does not non-locally couple to the external world. It >> looks more like the experiments that have currently verified non-locality >> (entanglement) over 100+ km so far. Only this time done with a chip that >> produces the field system of a neuron used in the way the brain uses it. >> There will be a verifiable coupling in the field system separable from the >> neural activity. Indeed you keep the neural activity and peripheral sensing >> activity identical and look to see if the field system responds to the >> distant natural world in repeatable ways. >> >> You may think I am off in la-la land. But I say assuming the effect away >> is in la-la land and that it can be resolved experimentally and that the >> experiment is decades overdue. If anyone says I am wrong then let me prove >> it one way or the other in a lab and let the real world determine the >> result. What I will not do is presuppose a result. I do not know the answer >> and *either does anyone else!* >> >> Note that you do NOT need to sum the products of voltages times the >>> inverse squares of the distances to every other point in the system, only >>> the inverse squares to the NEAREST points, which themselves form a sort of >>> "shield" from more distant points, and which themselves already contain the >>> effects of the more distant points. >>> >>> However, I wonder what the computationally OPTIMAL thing to do might be, >>> e.g. limit the radius of consideration, etc? You are proposing to simulate >>> 3-D fields in 2-D, but maybe 3-D fields are used only because that is what >>> can be done in wetware. Here, a better grasp of the math involved seems to >>> be in order. >>> >> >> The early field implementations will be more 2D/planar. Layering makes it >> more a 3D field system. Yes, in simulating the way the fields are produced >> by the membrane, the restriction to a cutoff radius is a valuable way to >> improve computing loads. >> >> Once again: The final device is not simulating anything. >> >> >>> >>> Control systems are well understood. However, no one seems to have >>> worked (much) on *adaptive* control systems theory. >>> >>>> >>>> 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. >>>> >>> >>> This sounds considerably MORE difficult than simply putting a 'droid >>> into a real physical environment. >>> >> >> Ultimately I *am* 'putting the droid in a real physical environment'. >> With a complete kit of I/O. What's missing is the EM field system. Yes the >> trainer/puppeteer is a difficulty. But it's something we can engineer. This >> aspect probably needs more elaboration another day (maybe later after the >> zombie hardware is built). >> >> >>> >>> >>>> 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. >>>> >>> >>> Yes. >>> >>>> >>>> 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. >>>> >>> >>> My failure/epiphany is that I don't see how cognitive is anything but >>> higher-level computation that considers the prospective effects of >>> potential actions taken to control the system. >>> >>> Remember, "consciousness" probably bares NO resemblance to what is >>> actually happening in our brains that appears to up to have the >>> characteristics we refer to as consciousness. We have dysfunctional models >>> of consciousness that should NOT be carried over into designing future >>> conscious systems. >>> >> >> You have a perspective on consciousness that I am having trouble >> fathoming. I suspect the reverse is also the case! >> >> All the evidence so far points to the brain being 'like something' from a >> 1st person perspective of 'being' the brain. Consciousness. All I am >> hypothesising that EM fields of the brain (electromagnetism) are what >> consciousness looks like to a conscious scientist using consciousness to >> observe it. Why it should be 'like something' from a 1st person perspective >> of *being* the fields is our problem as scientists. It's a unique >> problem in science and its a problem with *science, not with nature. *This >> is what my book is about. >> >> OK. >> Let's say your failure/epiphany is X. >> My logical equivalent failure/epiphany is Y. >> >> You can't see how X {etc etc} cannot be the case in the brain. >> I cannot see how anyone can miss the obvious presence/role of Y in the >> brain. >> >> So there we sit. >> >> I can't help this. And I don't know what to do about it except an >> experimental verification Y is the case or not. >> >> All I ask of this forum is that my hypothesis Y be treated the same as X. >> Not dismissed out of hand for reasons of culture. I ask that the idea of an >> experiment applied to Y be given the same respect as the decades of >> inconclusive experiments on X. >> >> I am still OK with Y being a failure! What I am not OK with is Y being >> sidelined by preference of X, not by science. >> >> >> >>> >>> 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. >>>> >>> >>> You are either seeing something here that I haven't yet grasped, or >>> seeing something here that I have long ago rejected.. >>> >>> >>>> I suppose it'd be an 'adaptive control loop' philosophy for cognition >>>> and 'EM field theory of consciousness' combined. No computing needed >>>> whatever. >>>> >>> >>> This is ALL simply 3-D analog computing implemented in wetware. >>> >> >> You can name it a computation if you like. You can even abstract it >> and write it down. That naming/abstraction does not entitle anyone to >> assume that what is described is not essential physics. That is the notion >> I have rejected long ago. I found what looks like essential physics. >> Proving it means building it, not computing models of it. >> >> Note that when I build an artificial version of the original natural >> physics, that too will look like a regularity that could be classified as >> 'computation' of an apparently identical kind to the original nature. That >> classification does prove the actual physics any less essential. You have >> to physically experiment to prove it can be replaced by computation. >> Remember: I am hypothesising that the EM fields are as essential to >> a potentially human level robot brain as arm and leg physics are to its >> body. >> >> >> >>> >>> >>>> Just like the brain. Most of the last ten years has been spent figuring >>>> out the EM field bits! >>>> >>> >>> What have you figured out? >>> >> >> How the field is produced by aggregates of ion channels in plaque form. >> Both the electric and the magnetic field. How it superposes from multiple >> sources to causally effect neuron behaviour. I attach a video of the >> electric field system produced by an artificially (randomised) set of ion >> channels arranged in the membrane (>19,000 of them a rat CA1 pyramidal >> cell). It operates like a lighthouse centred on the smoa. Sweeping in a >> loop like a searchlight over its neighbours (that also do it, all >> superposing on each other). The total field is an emergent property of >> thousands of little transmembrane dipoles that dynamically coordinate to >> create a single field system attributable to the whole cell. That field is >> produced by a single action potential and does not include any contribution >> from synapses. It is proof-of-principle only. >> >> The physics I used was that of inorganic conduction. I am currently >> setting up to produce the same kind of transmembrane dipoles using >> convection (electrolyte ions). The result is the same (I expect) but its >> expression depends on a lot more details like ion concentrations, >> diffusivity, mobility and the like. Nernst Planck equations. >> >> When I have shown how the convection-based fields originate, including >> the enormous transmembrane electric field, the whole argument will be >> better founded and I will expect less resistance. That should be done this >> year along with the beginnings of the zombie chip without the fields. >> >> I have done COMSOL simulation that shows, at macroscopic scales, the same >> dipole field produced by a capacitor that discharges at a highly localised >> spot in its dielectric. COMSOL turned out to be incapable of also producing >> the magnetic field (which operates in the plane of the membrane, >> circulating around the transmembrane current). >> >> So I have learned a lot. >> >> >>> >>> >>>> That I am now omitting, knowing what I lose when I do that (i.e. >>>> consciousness). >>>> >>> >>> I don't (yet) see the connection between between EM fields and >>> consciousness. If this is truly a hierarchical system, then either EM >>> fields are needed for everything, or for nothing. >>> >> >> The EM fields ARE consciousness! You have to BE them, like we do. We are >> the fields. To be us is to be the fields. There's nothing else there!. It's >> just that the only fields that add up to anything complex and large and >> causally efficacious and informative are in the brain. >> >> EM fields are a description, by a conscious entity (us), of what >> consciousness looks like to a consciousness made of EM fields. Think of it >> this way: there is literally nothing there but electromagnetism. Holding EM >> fields accountable for consciousness is a no-brainer (so to speak[?]). A >> choice from a list of 1 is easy. What is not easy is seeing how the >> fields arise. You can have action potentials without a significant >> aggregate EM field depending on ion channel co-location and density. You >> can have action potential and synaptic activity-based significant EM fields >> that have no causal impact on other cells, depending on cell morphology and >> cell spacing. Higher cell density means fields have more causal impact. It >> all requires energy and nature doesn't waste that. >> >> So I think it not 'all or nothing' for the brain's particular EM fields. >> You can have a completely different physical basis for action potential >> signalling (inorganic, say based on specialised capacitors) that properly >> configured, produces the same kind of aggregate field system as tissue with >> the same functional role. The field produced by the ion channels is >> designed (by nature) to dominate all the chemistry EM field 'noise'. You >> can make the same dominant field system with organics or inorganics. The >> total field system is a single unified emergent entity with a life of its >> own, independent of the underlying neurons. An infinity of different >> neurons can produce the same field system. A single neuron can produce an >> infinity of different field systems. Its a vast new axis of tissue >> operation that I have only barely touched with my work. >> >> >> >>> >>>> 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. >>>> >>> >>> ... or as in my recent patent, a clever algorithm that can be >>> efficiently implemented in available hardware. >>> >> >> Perhaps one day your zombie chip and mine can be compared >> (architecture-wise)? Later this year maybe. Mine will be studied/designed >> using software and maybe the software version will be useful. Don't know >> yet. I suspect it will never operate in real time. The only thing I know >> for sure is that my architecture will not function at all without I/O and >> the real world being attached to it. What I hope to do is commercialise >> some version of the zombie chip to fund the future non-zombie version (with >> the fields). I may yet live long enough for that to happen! >> >> >>> >>> It's chip-foundry stuff. >>>> >>> >>> ANYTHING you can make on a chip can be simulated in a computer, albeit >>> VERY slowly. No foundry would even think of building anything in silicon >>> that had NOT first been simulated. >>> >> >> Yes! Likewise no airplane manufacturer would build a plane without >> simulations to validate the design. But that design is not flight. Making >> artificial brain tissue is the same. Not being able to simulate the actual >> field system produced by the chip during operation is not a failure to >> design the chip ..... any more than the failure of the flight simulator to >> actually fly is a failure of a design of a flying thing. >> >> >>> 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. >>>> >>> >>> If these guys are ever to build anything that actually does what they >>> are hoping for, then they simply MUST connect with the computational >>> processes needed to implement such things. If someone here seeks to do >>> something ELSE that is unable to do what adaptive control systems can do, >>> then obviously, it can never ever do what they are hoping for. So, unless >>> Ben, et a., thinks it is out of order here, I think we should look deeper >>> into adaptive control implementations. >>> >>> HOWEVER, remember that what you are talking about IS computation, it CAN >>> be simulated on a digital computer, and the computations can probably be >>> done MUCH more efficiently by divorcing the implementation from the >>> physicality of wetware, e.g. EM fields, etc. >>> >>> The challenge comes in finding mathematical expression of the task at >>> hand, while would (hopefully) lead to a (more) optimal solution (than in >>> wetware). >>> >>> I wonder... If as some here have suggested our computational "goal" is >>> to "understand" things well enough to reduce the information content of >>> what we see to as little as possible, then what better thing to sense than >>> the "noise" in the EM field?!!! If every neuron successfully did this, they >>> would function in the channel-reducing form hypothesized on various past >>> postings. Perhaps the thing that has so limited retrograde propagation NN >>> implementations is that they have lacked this "world view" of their impact >>> on the entire system?!!! >>> >> >> The information content in the field system, independent of everything >> else, is vast by my estimation. When I look at memory and CPU power >> estimates needed to do real AGI I see it many orders of magnitude >> underestimated. Take the Kurzweil estimates and put 10 orders of magnitude >> on it. And it's right there in front of everybody, screaming its presence >> at us. It doesn't make AGI impossible! It just means you don't base it >> entirely on computers. Just make the fields the same way... as an adaptive >> control system ..... voila. >> >> >> I haven't mentioned it much over the years because it seems that most of >>>> you aren't interested in my approach. >>>> >>> >>> There are many talkers but few doers here. The doers WILL carefully read >>> our postings and accept what makes sense (to them). Our challenge is to >>> "translate" our POV enough so that they can grok what we say from their POV. >>> >>> For reference and for the record.... I am *the* 'AGI as adaptive >>>> control' guy. >>>> >>> >>> Correction: Change "the" above to "an". >>> >>> Steve >>> >> >> A small and select group. >> >> So I guess your adaptive control loops are in software and mine in >> hardware for reasons of later addition of the field system. So we have that >> kind of common ground. >> >> Am very pleased to not be quite so alone, even tho there are a bunch of >> conceptual disconnects operating at multiple levels that might separate us. >> Maybe in time that gulf may be better understood and speak to the wider >> community in helpful ways. We'll see! I'll report in as results happen. >> >> cheers >> >> Colin >> >> >> >> >>> >>>> 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> >>>>>> <https://www.listbox.com/member/archive/rss/303/24379807-653794b5> | >>>>>> 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/303/11721311-f886df0a> | >>>>> 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/303/10443978-6f4c28ac> | >>>> 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/303/11721311-f886df0a> | >>> 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/303/10443978-6f4c28ac> | >> Modify <https://www.listbox.com/member/?&;> Your Subscription >> <http://www.listbox.com> >> > > > > -- > Full employment can be had with the stoke of a pen. Simply institute a six > hour workday. That will easily create enough new jobs to bring back full > employment. > > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/11721311-f886df0a> | > 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
