[FRIAM] Predictive coding basedon deep learning

Marcus Daniels marcus at snoutfarm.com
Mon Jul 29 15:44:41 EDT 2019

There is a sense in which even professionals can become helpless to the state-of-the-art in any field.   For example, if Matlab or Mathematica don’t make it easy, then don’t even try.   Or if a compiler produces slow code, change the source code of the program until it does produce optimal code rather than fixing the compiler.    When there is no technology, people accept that they have to figure things in out.   A problem has to be very serious to motivate the investment in literacy to get over an energy barrier introduced by tried-and-true but never perfect technology, and increasingly, only specialists make that investment.   That said, having no technology is a ridiculous solution to the problem.

From: Friam <friam-bounces at redfish.com> on behalf of Gary Schiltz <gary at naturesvisualarts.com>
Reply-To: The Friday Morning Applied Complexity Coffee Group <friam at redfish.com>
Date: Monday, July 29, 2019 at 12:33 PM
To: The Friday Morning Applied Complexity Coffee Group <friam at redfish.com>
Subject: Re: [FRIAM] Predictive coding basedon deep learning

Boy howdy (I've always wanted to say that) does that ring true here in Ecuador. There are just so many things that I took for granted back in the USA that I can't get here, be it technology, tools, food ingredients. If something I brought from there breaks, I make sure to tear it apart to see if it is something I can fabricate from what I can get here. It really pays to be a Jack/Jill of all trades. I've come to appreciate the value of knowing a little of everything, and not much of anything in particular.

On Mon, Jul 29, 2019 at 1:29 PM Marcus Daniels <marcus at snoutfarm.com<mailto:marcus at snoutfarm.com>> wrote:

Steve writes:

< What I'm trying to expose is the meta-heuristic of being a facile model builder/adopter/fitter... and how our technological prosthetics (precut colored plexiglass and stain-by-number patterns or GPS/routing systems that present opaque-to-the-user preferences or predictive SDE programming environments).  >

When technology doesn’t work, take it apart and figure out what is wrong with it or how it could be improved.    Human experts, or skilled practitioners, can hurt more they help because they have no incentive to unpack their expertise into reusable automated systems.   The trick is to look at skills as technology and to be facile evolving the technology.

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