Blue Day Bio

Daniel Tammet’s Born on a Blue Day is remarkably apt for me.

When thinking about something interesting, I become the questions . . .

Like Mr. Tammet, I see numbers as shapes, colours and textures and solve complex challenges by thinking them through.

I was thinking about spatial geometry/maths and patterns by age 3, and corrected my father on a tax math problem at age 5, but this was unwise, and learned to hide my skills, escaping into thought experiments, developing a terrible shyness and severe stutter at the same time. Complex problems were fun but I could not relate them to anything being taught in grade school , and could not sufficiently explain; repeatedly failing traditional arithmetic, maths and science testing, and physed practices – anything involving procedural memory.

But a complex cloud problem? Easy. Before industrial design school, I went back to adult high school to refresh my skills. In physics class, my test marks were not good, but the teacher (PhD physics) locked my paper on nuclear equations in the school safe; and in chemistry class, I figured out an unusual way to solve a hydrolysis problem, scoring 97% on the paper. In machine shop class, I created an “impossible to make” 7-sided part using what I call “anticipated angular liquid permutations ” [My terms are “Globular Liquid Cloudspace Thinking“ (“cloudspace” or “cloud” or “globular” or “liquid” — using this decades before “cloud” or “liquid computing” became fashionable terms) but you could call it “Cognitive CNC” or “Fluid Web Linkages”].

Fluid Web Linkages
See: http://davehuer.com/solving-wicked-problems/

Thomas A. Metzinger‘s “nemocentric” theory (‘the ‘view from nowhere’) is the best analog for my processing, thus far; although it ought to be extended to ”the view from everywhere and nowhere”. In large measure, my day is similar to the view of Inuit sculptors who see their work to be freeing an animal (or object or being) already inside the soapstone.

The animal must know how far it must go to allow itself to become free, and simultaneously must be aware of the uncut stone brushing against its skin. My term for the awareness of this membrane is ‘locating “the place of both sensing and noticing” ‘– “a glowing membrane of liquid data points” that is constantly in flux. My description of where I imaginatively solve problems as has evolved from:

  • ‘the inside of a dyson sphere’ (or particle accelerator) (late 1980’s), to …
  • nested inside a neural parliament > nested inside a landscape’ (early 1990s), to …
  • ‘a multi-nested set of interfluid bright soft electric blue and pale yellow network webs spanning data particle fog networks’ (2014).

When evaluating, I’m simultaneously testing a combination of experiments  against an incredibly dense meaning library: the blue is the primary bus and the yellow are the feeder nets. To learn webcoding, I had to go further, inventing “cloudboxing” to locate webcode to study and learn exactly what it is: (see Project #5: http://davehuer.com/solving-wicked-problems/). This (for me) solves an ancient locationing problem: where to locate procedural challenges and processes to study them. This is different from the ‘”fluid web linkage” cloud challenges I usually think about. Together they combine to produce “flow challenges” – the cloudflow of fluid thinking and the cloudboxed stopping off places of reflection; the calm eddies along the shore.

david huer flow challenges

 

Learning to use River Eyes

David paddling Pillow Rock on the Upper Gauley

David paddling Pillow Rock on the Upper Gauley

We all do this to varying degrees. And in this, we may have what Mihály Csíkszentmihályi  calls “flow” – in whitewater kayaking, what paddlers call “river eyes”. We become the questions, and the answers, and the clues, and the discarded ideas. We become the torrent and the freshet; the ice floes and the lazy placid days; the hydraulics and the logjams and the storks hunting in the shallows. A paddling accident, I believe, happens when we distinguish ourselves as distinct from the river. When we re-tune to the river, accept our place in the ecosystem,, we become the experience.

Paddling the Upper Gauley’s Class V’s became for me a perfect day, with Pillow Rock rapid/Room of Doom being the Ah-ha, high-point experience. Only years’ later did I come to know that every second of that day was Ah-ha, too. Iron Ring. Sweet’s Falls…The day, a perfect moment.

Think about this when studying problems.

We humans best solve problems when we become the challenge to comprehend the challenge.

On that day, I became the river.

When we take time to become the dots, we connect the dots.

We become the flow.

With flow perspective, where might you go?


Corporate Image: Karma by Jackson Kayaks: http://jacksonkayak.com/blog/kayak/karma/

Could Cloudbox Mimics improve the naturalness of machine-learning?

Creating a “Cloudbox Mimic”
to map Rhizome growth choices, as a self-comprehended ‘hypotheses testing’ learning tool of ever-enlarging complexity

Would ‘asymmetric logic’ help machine-learners practice natural learning?

dhuer-cloudboxing-aIn 2014, I developed the Cloudboxing© thinking technique. Teaching myself to stitch together a set of cognitive cloud datapoints to create a place to study the building blocks of coding language, to learn exactly what code was and where it could be located in my data set. ie. Using my first cognitive language (Liquid Membraning) to translate coding language into the “building blocks” of Liquid Membraning language. See Project #5 at http://davehuer.com/solving-wicked-problems/

Lately, in between work, consulting, and venturing, I’ve been thinking about machine learning and Google’s DeepMind project, and wondering whether the “flatness” of programmed teaching creates limits to the learning process? For example…whilst reading the Google team’s “Teaching Machines to Read and Comprehend” article http://arxiv.org/pdf/1506.03340v1.pdf

Could we enlarge the possibilities, using spatial constructs to teach multidimensional choice-making?

creating a cloud-box to mimic rhizome growth choicesThis could be a software construct, or a physical object [such as a transparent polymer block, where imaging cameras record choice-making at pre-determined XYZ coordinates to ensure the locations of choices are accurately mapped (especially helpful when there are multiple choices at one juncture)].

Encapsulating and organizing defined space for machine-learned self-comprehension. mimics the “cloudboxing” technique.

And, it mimics the natural self-programmed logic of self-learning…a novel teaching tool for the machine-learning entity:

  • Creating a set of challenges through 3dimensional terrain that mimics pre-defined/pre-mapped subterranean tunnels
  • Creating an opportunity to dimensionally map an emulated (or actual) entity growing through the tunnel system
  • Studying the polar coordinates of the entity traversing the pre-defined space(s)

1) What about using a rhizome?Jiaogulan-Rhizome

. . . Using a natural entity teaches a machine-learning entity to mimic natural learning.

Using a plant creates the possibility that we can map choice-making, using attractants such as H2O and minerals, as a mimic for conscious entities developing learned behaviour.

 

2) Once you have a defined baseline data set, could machines learn better if being blocked and shunted by an induced stutter?

using stuttering blocks to teach choice-decision-making

Perhaps learning by stuttering and non-stuttering might produce interesting data?

By creating a stuttering event as the baseline, perhaps the program will use this to overcome obstacles to the learning process as well as the object of the lesson to learn to not stutter? This could produce a host of interesting possibilities and implications.

3) Things get incredibly interesting if the program eventually attempts to produce choice options outside the available options . . .


Note: These ideas continue the conceptual work of WarriorHealth CombatCare, re-purposing the anti-stuttering Choral Speech device SpeechEasy for Combat PTSD treatment. The research proposal for that work is here: https://www.researchgate.net/profile/David_Huer


Images:

Jiaogulan-Rhizome: Own work/Eigenes Foto by Jens Rusch, 29 August 2014  CC Attribution-Share Alike 3.0 Germany license.

Drawings: David Huer © 2014-2015