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. (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 have simply said what the paper says. > YES, this conversation is ~2 screenfuls. Please edit out ~half of it (probably most of my stuff) and we should be there. *Steve* > > cheers > colin > > > > > > On Tue, Dec 22, 2020 at 6:01 AM Steve Richfield <[email protected]> > wrote: > >> Hi Colin, >> >> Most of the people on this list, including you and me, are each doing >> their own thing, while reviewing each other for mutual benefit. NOW, I >> FINALLY understand other people's objections to some of my earlier >> postings, namely, I was exposing them to my evolving view of the world, and >> each exposure was 95% the same as the previous exposure, and I wasn't >> announcing what was new with this version. Instead of continually writing >> anew, perhaps I should have included change bars, or encapsulated the >> changing theory into a one-screen abstract, or ??? >> >> Most people here feel they see a fatal flaw in your work, but different >> people see different apparent flaws, so it is difficult to carry on a group >> conversation. Without addressing the apparent flaws, even though they might >> not be real flaws, you are chasing your audience away. >> >> As for me, understanding and models are two sides of the same coin. >> Ordinary explanations of everything center around models of their operation >> or lack thereof. "Claiming to operate in the absence of a model seems to be >> either >> 1. a simple declaration of abandoning science - which I think I know you >> enough to KNOW you aren't intending, or >> 2. part of the first step in the Scientific Method - looking for >> interesting things to study further - but you apparently disclaim this by >> claiming to be able to magically jump to useful hardware/wetware/AI WITHOUT >> creating a model upon which to build an explanation. >> 3. that something useful can come of systems without need for the >> functional complexity of synapses, that commonly have non-linearities, >> integrate, differentiate, etc. >> >> I'm not sure whether I just don't see a pot of gold at the end of your >> rainbow, or I just don't see your particular rainbow. >> >> Perhaps you could write a screenful of words that advance your central >> theses? I might even take a shot at what I understand, for you to edit to >> correct my errors: >> >> *The physical arrangement of neurons in brains strongly suggests that >> field considerations might predominate over detailed wiring considerations. >> Indeed, some of the more inexplicable computational abilities of neurons, >> like mutual inhibition, are difficult to explain based on connections, but >> easier to explain based on fields.* >> >> *Colin (you) proposes that computational analogues to the operation of >> these fields might turn out to be adequate to explain VERY complex behavior >> - like the operation of our brains.* >> >> *Steve (me) believes fields are just another component of normal neural >> operation, that MUST be factored in for neuroscience and AI to ever >> advance. However, fields are linear, so ignoring the non-linear components >> like synapses would be like leaving the transistors out of an IC and >> expecting it to do something useful.* >> >> >> OK. Can you correct the errors in the above to match your view of reality? >> >> Thanks again for all of your efforts. >> >> *Steve Richfield* >> >> On Fri, Dec 18, 2020 at 8:28 PM Colin Hales <[email protected]> wrote: >> >>> Hi, >>> For a very long time I have been trying to articulate a fundamental >>> issue in the conduct science of AI (AGI). The issue is the proper conduct >>> of the science such that we can know, with empirical certainty, whether and >>> under what circumstances, a general-purpose computed abstract model of >>> nature (the brain) has functional equivalence with the nature (the brain). >>> >>> It's taken 10 years of brutal grind, but I think I have found the >>> mature/accurate shape of the argument, the proper nature of the problem, >>> and the way forward. >>> >>> I have completed the paper to preprint stage before I go to a journal >>> for the final peer review meat-grinder. >>> >>> So for a bit of a quiet read while the world self-immolates over the >>> next couple of weeks: >>> >>> Hales, C.G. (2020). The Model-less Neuromimetic Chip and its >>> Normalization of Neuroscience and Artificial Intelligence. >>> https://doi.org/10.36227/techrxiv.13298750.v2 >>> >>> 1 main article. >>> 2 supplementary supporting articles. >>> 4 videos from a computational EM study. >>> >>> Many of you will find previous discussions here remain part of it. It's >>> been quite a job to get to the bottom of the matter. >>> >>> I hope it makes sense of a difficult issue. >>> >>> Take care out there, >>> >>> cheers, >>> Colin >>> >> >> >> -- >> 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. >> >> *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-Mff5a457d3291043724f78a0f> > -- 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. ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Tf319c0e4c79c9397-M19d2733404fb8393d7f371b8 Delivery options: https://agi.topicbox.com/groups/agi/subscription
