Quick comment while contemplate more... Are you familiar with electrolytic analog computers, commonly used to design magnetic systems? basically, they are a fish acquarium full of slightly salty water, in which conductive (e.g. aluminum foil) and insulating objects are submerged. Field is established with a battery. Field strength readout is by an insulated wire that is bare on its tip. This would allow you to inexpensively play with some of your ideas in a way that a supercomputer would have a hard time matching.
Steve On 11:38PM, Mon, Dec 21, 2020 Colin Hales <[email protected] wrote: > > > On Tue, Dec 22, 2020 at 1:56 PM Steve Richfield <[email protected]> > wrote: > >> Colin, >> >> On Mon, Dec 21, 2020 at 1:11 PM Colin Hales <[email protected]> wrote: >> >>> Hi Steve, >>> OK. Let's try: >>> >> >> GREAT - some text to kick back and forth. Here goes... >> >>> >>> Page 2: >>> "In scientific behavior, empirical observation and theoretical science >>> face-off normally in the following three familiar science contexts: >>> >>> (i) Observation of a natural context (*empirical >>> science*). >>> >>> (ii) Observation of artificial versions of the natural >>> context. Call this engineered or replicated nature a >>> ‘scientifically-artificial’ version of nature (*empirical science*). >>> >>> >> This was pioneered with the "Harmon Neuron", but then quickly moved into >> programmable digital computers as neural networks. >> >> Neural network practitioners are cleanly divided into THREE camps, each >> having their obvious limitations, one being MUCH larger than the other: >> 1. 99% Pure empiricists, who twiddle with characteristics and properties >> to optimize some measure of performance. >> 2. 1% Pure mathematicians, who solve for the best network to optimize >> some measure of performance, and then propose characteristics and >> properties that parallel their mathematics. I used to be in this camp, >> until I discovered that neurons do an interesting sort of highly efficient >> bidirectional computation that is VERY different than what conventional >> digital computers are good at. I tried discussing this here, but apparently >> no one was able to carry on this particular conversation. I think I see a >> way to make "general purpose" computers that can do this and MUCH more, but >> with no one else on this bandwagon, it will probably pass when I eventually >> pass. There is considerable intersection between your field-theory view and >> my bidirectional computing view, nearly two sides of the same coin. >> 3. Groups doing biological research, who attempt to as accurately as >> possible simulate neurons or parts of thereof. I was once part of such an >> effort at the University of Washington Department of Neurological Surgery. >> >> There is a computational method known as quadruple ledger accounting that >> is practiced by the World Bank and others to model the world economy, where >> people instead of neurons interact with each other in nonlinear and >> non-directional ways. It might be possible to "build out" quadruple ledger >> accounting methods to encompass both bidirectional and field computing, but >> the end result would probably be unrecognizable to everyone. >> >> I might be the only one, but I completely agree with you that fields are >> a BIG part of this. I even go a bit further, as I suspect that other field >> effects like the Hall Effect are probably also involved, which the Hall >> Effect can NOT be directly simulated, except at the same physical scale. It >> is all really complicated, but simply ignoring it can NEVER EVER lead to >> AGI as the others on this forum now hope. It appears to me that simulation >> methods CAN simulate field effects, but ONLY after they have been fully >> understood, and while I suspect your efforts won't directly lead to AGI, I >> DO suspect that your efforts might be absolutely necessary to EVER make an >> AGI. >> >> I see the path forward a little differently, but we might be converging >> on the same place: >> 1. We should publish a definition of "neurological simulation" that >> encompases both field and bidirectional effects, and "expose" efforts that >> fall short of this. >> 2. Once people see just HOW difficult it is to simulate real-world >> neurons in any useful way, people will start tackling the bidirectional >> problem. The bidirectional problem is a challenge, but doesn't look >> insurmountable. Electric circuit simulators like SPICE easily handle the >> bidirectional problem, at an *n log n* cost in time, which would be >> crushing for a large system like a brain, but which might be tolerable for >> simulating a flatworm's brain. I suspect you could simulate your theories >> on fields in SPICE. >> > > My project is prototyping the EM field signalling. Just the bare bones > physics of one patch of neuron membrane. Fully implemented (later), it will > do the dromic and antidromic propagation you mention as well as ephaptic > coupling. But I'll be focussing on the bare bones of the basic EM field > physics for now. It operates under science framework (ii). No models. No > emulation. No simulation. No software. > > I am hoping this will push the issue over the line into mainstream > thinking and correct the currently distorted use of the science framework - > where (ii) is missing. > > >> >> (iii) Creation of abstract models predictive of properties of >>> the natural context observable in (i) and (ii) (*theoretical science*)." >>> >>> This process is literally drawn in Figure 1 for 5 different science >>> contexts, all of which do exactly this (i)/(ii)/(iii) process EXCEPT in >>> (e), for the brain where: >>> >>> (A) (ii) empirical science, in neuroscience and 'artificial >>> intelligence', *is missing from the science.* >>> (B) It just so happens that if you decide to do (ii), brain EM is the >>> thing that has been lost and that you replicate for the purposes. If you do >>> the science to explore that, then you are not using a general purpose >>> computer. You are exploring actual EM physics. It is empirical science. >>> (C) if you claim (iii) is all you need then you are distorting the >>> science in one place: *a unique, anomalous and unprecedented lack for >>> which empirical proof is required*. That proof arises through using >>> (ii) and (iii) *together*. >>> >> >> It looks to me like some of (iii) absolutely MUST precede (ii), or at >> least be intertwined with (ii), to provide enough guidance to ever make and >> debug anything that actually works. The last decade of AI "research" has >> absolutely PROVEN (at least to me) that even highly intelligent people >> can't blindly stumble onto the secret sauce for AGI. >> > > I don't think we're quite there yet .... I am talking about getting the > neuroscience established properly in *all three* traditional areas by > restoring (ii) so that neuroscience/AI operates like a normal science > with normal empirical work. It currently does not do that. To clarify this, > let me cite a more completed definition of science from the paper. Page 2 > again: > > "In scientific behavior, empirical observation and theoretical science > face-off normally in the following three familiar science contexts: > > (i) Observation of a natural context (empirical science). > > (ii) Observation of artificial versions of the natural > context. Call this engineered or replicated nature a > ‘scientifically-artificial’ version of nature (empirical science). > > (iii) Creation of abstract models predictive of properties of > the natural context observable in (i) and (ii) (theoretical > science).*Activities > (i)-(iii) meet each other in a mutual, reciprocating distillation that > converges on empirically proved ‘laws of nature’ that are then published in > the literature* (Rosenblueth and Wiener, 1945;Hales, 2014)." > > It is likely that most of the people on the AGI forum have never > encountered (ii). (i) and (ii) provide empirical evidence for comparison > with (iii) predictions. (iii) provides theoretical model predictions tested > under (i) and (ii). It reciprocates. This is how science works everywhere > *except > in neuroscience/AI.* We do not do (ii) in neuroscience/AI for no reason. > It is an accident/cultural habit handed down from the 1950s and > industrialised. Mistaking (iii) activities for (ii) is what the paper is > all about. Everything described with abstract equivalent circuits > (neuromorphic chips) and symbolic models (software) fits under (iii). The > natural (i)/(ii) physics is gone under (iii). In (iii) theoretical science > is emulation, simulation, models, software. In (ii) there is only (i) > physics and no models/software/emulation/simulation. I describe how the > (i)/(ii)/(iii) framework operates in great detail in Supplementary 2. > > The proposed neuromimetic Xchip is the first time such a proposition for > (ii) has been proposed in the literature. It retains the (likely) > critically necessary natural (EM) physics of (i) for the purposes of > scientific characterisation of the brain under (ii) and so that > neuroscience/AI is normalised. Then and only then can the science properly > examine the anomalous, unique and unprecedented equivalence of (i) and > (iii), an unproved assumption only made in neuroscience/AI that may > actually be true. But we can't test it without (ii). Which we have never > done. > > There is a professional obligation on all of us to recognise and accept a > flaw in our science conduct when we find it. The article details such a > situation. Can I suggest reading the conclusion? I can cite again: > > Page 17. The way we conduct the science without (ii) ... > > "... is methodologically equivalent to expecting to fly while never > actually using any flight physics and assuming, without any principled > reason explored by experimentation with flight physics, that flight can be > achieved by disposing of flight physics through completely replacing it > with the physics of a general-purpose computer, a state of > ‘physics-independence’ not found in any other physics context. This sounds > like a harsh depiction of the science. It is merely a realistic description > of the situation. " > > OK. Over the word limit we go. Turns out it takes many words to fix the > most complicated science mess in the history of science messes. > > cheers, > colin > > > > > > > > > > > > *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/agi/subscription> Permalink > <https://agi.topicbox.com/groups/agi/Tf319c0e4c79c9397-M9f0d615ec03ab86b5b7474c1> > ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Tf319c0e4c79c9397-M9c704b455987a1382596acea Delivery options: https://agi.topicbox.com/groups/agi/subscription
