That stuff is ancient history, albeit perhaps more-neglected than it should be.
Again, I refer you to Ingber as someone likely to have paid proper attention to these matters. Email him. On Tue, Dec 22, 2020 at 7:15 PM Colin Hales <[email protected]> wrote: > 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-M0cbcc92dc9801d815c4fe0ba> > ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Tf319c0e4c79c9397-M7d102d940530a5f651685e90 Delivery options: https://agi.topicbox.com/groups/agi/subscription
