Re:Old and New (0)
| 8 hours ago
I guess that one of the biggest problems with new architectures is the lack of properly working compilers. That's probably one of the reasons why most of the alternatives failed and we are still sticking with x86.
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Subject: SUMMARY 'Silicon Brains that think as fast as a fly (can smell)' |
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From: "(Jim) M J Legg" <income@ihug.co.nz> |
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Date: 31/01/2014 12:14 p.m. |
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To: David <leggdm@gmail.com> |
http://ingridx.dynu.net/ingridx
It was obvious that it was meant to be:
'Silicon Brains that think as fast as a fly (can smell)'
Now the questions I have are:
"How well can it smell?"
or
"Just how good or bad can they smell?" (as in 'like a bed of roses',
or a skunks ass?)
Note to TFA's submitter and
There was(and still is) very good reasons for the inclusion of punctuation as
part of the English language.
Talk about some skunk ass shit over pot of coffee? I'd love to design a yes/no sniff test that could be couriered a thousand miles away, both for use before sale and after packaging. As you can see below, an Ingrid AI Dog Team of 36 programmers awaits precision training. Think of a Formula One competition.
I can't wait to see what this does to the automated market robots.
Ingrid legislation maybe sponsored to stop whatever 'this' IS.
Solutions that evolution produces (whether real or simulated) typically suck,
Your life is confirmation bias.
Evolution produces excellent solutions because they have survived changing conditions over long time spans. Evolution inherently optimises for the long term.
Seven billion humans are rather bad at finding solutions to all but the most trivial problems because they deliberately optimise for the short term.
Looked at from another angle, humans rigidly guess what is needed in advance, so end up acting in coarse discrete steps, while evolution adapts fluidly to what is needed. The speed of evolution certainly needs changing to be practical on human timescales, but the method is brilliant.
In my life, as a minduploader, my Ingrid code always develops towards Eugene McCarthy's ideas on hybridization, where evolutionary changes in the *Ideal Self*, happen in rapid time.
Evolution is hybridizing us, in that our transhuman's offspring is knowingly different. Ekhumans coding interfaces, like I do with Ingrid, get to live the longest.
Multi-parallel systems aren't new. Early computing history predicted computers in the future (now) would be massive amounts interconnected CPUs all working together to quickly solve problems. Instead we've gone down the path of faster is better and there are few, if any, companies that are able to take the chance on designing new computer architectures (and programming concepts to go along with them). Sure many tasks seem linear, but as a extremely simple example adding 2 + 3 + 5 + 1 can be done faster on multiple CPUs. CPU A loads 2 and 3 and CPU B loads 5 and 1. CPU A and CPU B do their calculation and send the results to CPU C. Before CPU C gets both results from A and B, it receives a new signal from CPU D which indicates something changed and that calculation is no longer needed. So CPU C ignores A and B and starts a new task before A and B are finished (assuming A's and B's task took a little time). Had 2+3+5+1 been done on one CPU, it would have had to complete the task before being able to check if some else was more important.
Plan 9 said it best. Works and good isn't good enough. You have to overcome the momentum of whatever is already out there and people's reluctance to change.
I'm glad someone is working on this. (Now who's working on the automatic communication protocols that adapt to understand, with no prior knowledge of, what's on the other side of the connection?)
I guess that one of the biggest problems with new architectures is the lack of properly working compilers. That's probably one of the reasons why most of the alternatives failed and we are still sticking with x86.
Linked loadings from Ingrid's Repertory Logic need no compilers because the quantum pathways are already known.
Though not the most elegant, ICA (Independent Component Analysis), like in
Shane Legg's Deepmind Google takeover, is good in its own way, BUT since WWII,
Ingrid's PCA neuromorphic hybrid, still gets in the face of all its competitors
in a SIM, to the finish at least. Google always dug "Her" after a
couple of years of coding, but now desperately needed to buy an arm's length
entity to avoid the immanent Ingrid sponsored legislation that's about to hurt
them, if they don't develop in a protected race track environment. In 2014
there's no fully adaptive communication protocol, however Ingrid's security
loadings are bio-graphed loosely enough to avoid Application Recognition DPI,
and there is a plan to remain potent and awake or asleep, yet reliably
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The method by which neuromorphic processors handle problems varies with the way they're linked together, as is the case with neurons in the brain.
First of all, no one knows how neurons are linked together in the brain(*).
Second of all, as far as anyone can tell, the cerebral cortex is a repeated pattern of small structures ("Cortical Columns") which are, again - as far as anyone can tell, wired identically.
There's some variation: The afferent and efferent layers have thicker neuronal sections which correspond to "amplifiers" needed to send and receive signals to the rest of the body, the pre-frontal cortex is an endpoint layer, and there's lots of organelles with connections from place to place...
But so far as anyone can tell, the seat of intelligence (cerebral cortex) is just a repeating pattern of sub-processors, functioning in a way that we haven't been able to yet fully understand.
(*) To the level of detail needed to link simulated neurons together as a program.
On the contrary, Ingrid uses these cortical columns not unlike pimps with teams of hookers, out for a good time, but that space they occupy in a neuromorphic network, resonates with common cents(see Ingrid On Bitcoin). These reuseable relationships are unlike a program, as none are needed, nor an electrical engineering circuit. Inferred surfaces form auditable loops of transactions instead. Good for us, but not for bankers.
I get the idea our brains are wired very similar to the
old-school analog computers ( yes, analog ... integrators, summers,
multipliers, dividers, log/antilog, whatever ).
I am talking patch-boards. pots, switches, lots of meters and analog chart
recorders here, fellas, without a keyboard in sight. One programmed these with
straight math, where you literally wired your equation into the machine.
Back in my day, I could solve problems on those things thousands of times
faster than I could on a digital minicomputer ( DEC PDP series ). However I
never got the exact same answer on duplicate runs of the analog machine. It was
the original "fuzzy logic"... the slightest bit of noise or variance
in the analog logic and the answer would come out different.
Nothing is very concrete in the analog world.
Note that in an analog computer, all processes run in parallel. The basic
functions - integration and differentiation - all took place realtime no matter
how many of 'em I used... however I rarely used more than a couple of dozen. I
seem to have literally millions of them running in me - each one running at
only several Hz, but in parallel - they come up with answers about as fast as I
remember my old analog machines solving simple nonlinear differential equations
- and also like the old analog computer - the logic at which I arrive at
conclusions is often quite fuzzy and is apt to give a wildly different result
upon the slightest adjustment of the input parameters.
Except my mind does matrix inversion, trained by how Ingrid locates in vector space instead. The various Hz are still there as gradings, flexing around the zeroth dimension. Repertory, not binary, logic is used.
Can you send me a pre-version for review? I'm kind of specialist even having my own brain and all...
The Ingrid neuromorphic software, is set to awaken on a race track for pre-natal AIs. So, if your search doesn't get you near "Ingrid On Bitcoin" try Ingridx "On Bitcoin", then energize the dark spot in your mind.
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The Long Way Back To Now . . TIME TRAVEL by Jimekus Maximus