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

vs. the apparently-most-possible of the allegedly impossible

Colossal_octopus_by_Pierre_Denys_de_MontfortYesterday, one of my friends (founder) asked why I don’t start easier ventures? The reason relates to the framing (and re-framing) of the challenge: No matter what I think about, it has been my experience that the leap-frogging steps of a disruption are nearly always seen as impossible/hard to comprehend . . . 

. . . (and in this, it appears not so much that the barrier is a belief in the “impossibility”, but that the person cannot intellectually follow or comprehend how I got there and ego seems to get in the way).

So, if whatever I do is seen this way, then (perhaps paradoxically) it makes more sense to me to do the biggest scariest monster.

Because this takes exactly the same effort as the apparently-most-possible of the allegedly impossible.

Not to mention massively satisfying.


Image: Pen and wash drawing by malacologist Pierre Dénys de Montfort, 1801, from the descriptions of French sailors reportedly attacked by such a creature off the coast of Angola. Public Domain, https://en.wikipedia.org/wiki/Kraken

Will Angel List post Impact ROI Target scores?

I posted my profile at Angel List yesterday
https://angel.co/david-huer-01

It’s a neat site, but I noticed something funny. The job board breaks out hundreds of jobs the usual way, by location, job title, compensation…

But no Impact ROI Target Scores.

This may be restricted to US site visitors. But if not, I want more. When looking for contractors and employees, or a team to join, I want something that matters, to my definition of what “matters” for me. Angel List, the go-to site for startups offering job opportunities across the English-speaking world, could provide that data. Here’s what I am asking for: a Balanced Scorecard filter that lays out key impact targets determined by the founders, team and investors.

As a yard/meterstick, all measures are good.
david-huer-angellist-benchmark

 

 

 

 

 

If the target is to . . .

  • Maximize investor returns as a pure fintech money machine play…that’s great.
  • Create a gene-splicing tool for skeletal dysplasia…that’s also great.
  • Create incentives to stop the bear paws trade…again, that’s great, too.

Crunching Impact ROI targets into a neutral benchmark, and job-comparing tool. That’s dollarable value-add.

Will Angel List invite startups to post targets?