I love it. Perfect messy empirical work suited to man cave. Xmas chaos looms. Take care everyone. 🤞🤞🤞🤞2021 Colin
On Tue., 22 Dec. 2020, 8:00 pm Steve Richfield, <[email protected]> wrote: > 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-M9c704b455987a1382596acea> > > ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Tf319c0e4c79c9397-M0cbcc92dc9801d815c4fe0ba Delivery options: https://agi.topicbox.com/groups/agi/subscription
