Subject: RE: StepEncog|
From: "Bill Howell. Hussar. Alberta. Canada" <>
Date: Fri, 31 May 2019 17:23:26 -0600
To: "John Hilton O'Brien. Co-Owner. Hobbs Hobbies. Strathmore. Alberta"
StepEncog - I don't think you'll realize how much, but I really appreciate your detailed comments, valuable to me as you are from outside of the Artificial Neural Network community, and you have put thought into this. I rarely get responses from scientists like this - of a critical [insight, viewpoint].
Value is that your perspective is VERY different, which I retain in the context of "Multiple Conflicting Hypothesis". I have no interest in "changing your mind", as you way of thinking is intrinsically valuable, and while [blending, hybridization] is normal in CI, it is important to retain independent streams of concepts.
You are very astute at picking out [flaws, limitation], and seem to have a good sense of how primitive Computational Intelligence (CI) is, in spite of its oft-wickedly [complex, rich, creative] mathematical basis. Mathematics is like the Greek sirens - so alluring as to be dangerous?
Plato's solids -> Philospher's stone -> crystal structures -> alternative theory to quantum mechanics for [atoms, molecules]
I was at Sunridge Mall yesterday buying a pair of running shoes when I saw a ~4 or 5 3D printers at a games store :
Alchemy 403-457-5244 (hard to read phone #!)They produce snap-together components of Dungeons and Dragons on site (no inventory). I don't play games, but the idea of producing 3D molecules based on a theory that could replace quantum mechanics (for some things) might be of interest, although my budget puts this into the future. The trick would be to find the designs online.
https://www.design-point.com/blog/3d-software/cad/platonic-solids-modeling-a-dodecahedron-using-3d-sketches/Geometrical structure of the atom :
/media/bill/VIDEOS/Electric Universe/EU2017 Future Science conf/Kaal, Edwin 170820 The proton-electron atom - a proposal for a structured atomic model.ogv
In closing : I promise not to bombard you with huge piles more of stuff. But this was interesting to me.
By the way, I lost my last email to you, possibly due to a regular series of crashes with my newly upgraded operating system (Linux mint Debian Edition 3). That's very worrisome, as I don't have much of a memory, and I need past emails for conference work etc! Oh well, comes with the territory...
Mr. Bill Howell
P.O. Box 299, Hussar, Alberta, T0J1S0
member - International Neural Network Society (INNS), IEEE Computational Intelligence Society (IEEE-CIS),
IJCNN2019 Budapest, Authors' Guide, Sponsors & Exhibits Chair, https://www.ijcnn.org/organizing-committee
WCCI2020 Glasgow, Publicity Chair mass emails, http://wcci2020.org/
Retired: Science Research Manager (SE-REM-01) at Natural Resources Canada, CanmetMINING, Ottawa
Note - my comments in [blue, italics] font below are too long-winded, perhaps of more interest to me than you!
-------- Forwarded Message --------
I found this paper interesting - but I also see why it might generate relatively little interest.
For me personally, it was an interesting first view into where AI studies currently are.
To me, Oota etal's work falls between Computational Intelligence (CI) and Artificial Intelligence (AI), which makes it interesting to me. The work cannot be seen as even being known so much in the community, other than perhaps being yet another series of studies of fMRI data by those in that field.But when I consider it from the perspective of a fellow researcher, already deep in the field, I think it is disappointing. Nobody’s methods are called into question. No concepts are being tested out: it is making use of existing modalities, which it applies more broadly to emulate a larger part of the brain.
Perhaps the assemblage and adaptation of [concepts, tools] will be of central interest, but that is not what I see from the reactions of others at this time. What strikes me is that I am not aware of anything that can touch their results, and though I don't normally look at linguistics and resoning in the traditional sense, this kind of bridges two disparate worlds.Disappointingly, the authors do not actually give a complete picture of brain activity. No attempt is made to cover reasoning activity.
Correct - they are very focused and limited at this stage. Furthermore, I suspect that very different approaches may be needed for each different type of brain processing. Even for the same type of [function, processing], there is a "No free lunch" theory that is taken quite seriously, that no [technique, math, approach] is best for all cases. And of course, best-performing toolsets for [vision, sound, language, control] are very different.The idea that perception and processing and combination of sensory stimuli was enunciated by Aristotle 24 centuries ago, and had general acceptance of every scholar who paid attention to the study of people. This study doesn’t go into sense such as smell, taste, and touch, either, sticking with “safe” subjects already covered by other researchers. This isn’t groundbreaking: it's the work of some bright grad students, not someone accepted as Doctoris in the sense that a PhD implies.
Here are some ways that the paper might be improved, or future research might be done. I think that a
1. Is an established model actually inferior in predictive ability? That claim and its demonstration will generate controversy - the inevitable counter-studies and other responses that it generates are exactly what the writers need to get a career boost.
Yes - and that is normal in Computational Intelligence where scientists are often trying to break through "performance barriers" of previous work, either with [same, similar, different] conceptual basis or instrumentation. A HUGE issue is to try and handle vastly large amounts of data to get the breakthroughs, and that is NOT usually a simple issue of scale-up. It often requires fundamentally new concepts and adaptations.
2. Previous models appear to only have covered words and images. What about smell, taste, and touch? Are these also predictable?
There has been a fair amount of work on artificial [nose, taste], whereby actual [instrumentation, computational systems] have been built to try to replicate human-like classification using "artificial sensory systems" with some success. A particularly [fun, dangerous] project was a wine-tasting system for French wines. Talk about looking for trouble! <grin> Chaos theory was applied to EEG-based olfacory information with some success, and was expanded [Walter Freeman, Robert Kozma] as proposing that chaos is a basis for understanding whole-brain processing. Can't say that's strongly supported by the broader community.3. Could this model predict the brain activity of other species, such as dogs? Aristotle suggests that their processing must be similar, and the learning systems in use here suggest that it could measure such activity.
I don't know, but I suspect that it could, except the funding support would be biased to human studies with fMRI information. However, rat studies in particular are important given the relative [ease, ?ethical acceptability?] of such studies, and perhaps mostly where actual hardware implants are developed. Here the best example I am thinking of is a hypocampal prothesis (navigation, but the current project is short-term to long-term memory conversion, with Alzheimers as the target), which apparently has undergone human trials. But I am a little unsure of the details and I don't have great confidence in those claims as I haven't seen the actual papers.
4. If the model COULD model the brain activity of animals analyzing images, and showed that it was remarkably different, it might controvert the work of a bunch of contemporary biologists and philosophers such as Martha Nussbaum, and even have consequence for ethics. It could potentially cause ripples throughout academia, and generate a ton of replies from other disciplines.
We'll have to wait and see.
5. It would be useful to see what someone learning something NEW looks like, rather than recognizing already-known. Would it be different than simply processing an image? If so, its noteworthy.
An earlier paper by Oota and colleauges dealt with predicting "where" a new word would be stored in the brain of a test subject. "Where" is very fuzzy, as fMRI isn't very exact in terms of location, and that is a limit of the techniques they are using. However, the fMRI data was still image-sequence (time dependent) based, the results do not arise from a static image.
6. A big question here is whether kinesthetic learning is substantially different than other forms, since it involves a multi-step process. Can they model it? Is it materially different than simply analyzing an image?
I had to look up the definion (wikipedia, to which I donate a small sum annually) "... Kinesthetic learning (American English), kinaesthetic learning (British English), or tactile learning is a learning style in which learning takes place by the students carrying out physical activities, rather than listening to a lecture or watching demonstrations. As cited by Favre (2009), Dunn and Dunn define kinesthetic learners as students who require whole-body movement to process new and difficult information. ..."
Anyhow, thanks very much for sharing this with me!