Not clear why one excludes the other. Experience helps to estimate whether a
particular system will better yield to careful study or to chaotic
perturbation. Witness the success of stochastic gradient descent in machine
learning.
On 7/29/19, 11:57 PM, "Friam on behalf of glen∈ℂ" <friam-boun...@redfish.com
on behalf of geprope...@gmail.com> wrote:
It's in these crevices of the discussion that it becomes obvious that the
"painted surface" analogy [†] fails completely. This is why Hoffman's
"interface" theory is so attractive. The same core point is made, just with
more explanatory power. It's analogous to how we play video games. Good
examples are fighting games where you push buttons in different sequences to
make the (physics-based) avatar do things in its environment. Our mental map of
the controller interface has literally no similarity to the "physical"
realities inside the game. But it *works*. You can control the avatar even
though the control surface is nothing like the physics it's controlling.
As Hoffman points out in one of his papers, it can even be *bad* to have an
accurate understanding of the controlled system. Competent players don't get
hung up on, e.g. whether a rapier or a broadsword being wielded by their avatar
reflects the real thing. They simply (randomly?) try lots of game play
techniques and "git gud". What's being selected for is not a good/true/real
mapping, but an effective mapping.
cf. Here's a video I haven't had the chance to watch yet:
https://iai.tv/video/the-reality-illusion
FWIW, I find Hoffman's style and attitude in both his writing and
presentation very off-putting. But I really like the fundamental idea.
[†] Which I first heard of by D. Dennett, I think, where our mind is
projecting a movie onto an opaque screen. The world is projecting an image onto
the other side of the screen. And there's some functionality to the screen so
that when the two projections *match*, there's some feedback to both. When the
projections are too different, then perhaps there's negative feedback.
On 7/29/19 11:28 AM, Marcus Daniels 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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