I suspect automated translation between s/w standards that were not built with awareness of each other is harder than translating between 2 languages.
Conversation
Ie people arguing that 2 Google-duplex-class AIs talking to each other would switch to more “efficient” machine talk once they detect that the counterparty is also a machine are wrong.
This is only true of 2 agents that speak the same language and know that the other does too.
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Consider how humans communicate across a language barrier. Me and a Chinese speaker for example (I don’t know Chinese). Only 2 outcomes:
One of us asks “do you speak English?” and we switch to that if yes... OR
We do painful pointing-and-miming to develop minimum common ground.
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For dissimilar programs, common ground will likely be data at first. They might exchange dates for example, to detect each other’s time representations, and build from there.
Computer 1: dd-mm-yyyy?
Computer 2: mm-dd-yy?
Computer 1: mm-dd-yy!
(symmetry breaking etc needed)
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The machine learning angle here is orthogonal to the coordination/common ground problem. You need to solve both.
As s/w gets more embodied in robots/IoT, point-and-mimic-and-ground protocols will get much better, since physical world is much richer than shared data contexts.
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A good way to develop intuitions around this problem is to ask: which would be the easier robot to build?
R2D2 who can shove probe into any random computing system and hack it
OR
C3PO who speaks 100s of languages?
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The answer is, R2D2 is much easier if galaxy runs on a set of mutually intelligible co-evolved standards. Otherwise C3PO is easier.
So C3POa-to-C3POb comms will only default to R2D2a-R2D2b commas in former case.
Replying to
Interesting question is whether computing systems will evolve with greater or lesser mutual awareness in the gutter as they diversify and speciate. I’ll bet on less.
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This likely means they’ll likely mostly communicate through an “air gap” protocol based on point-mimic-ground mutual learning in shared rich physical contexts.
Your fridge and vacuum will learn each other the way a new cat learns to live with a resident dog.
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Conceptually my claim is that when there are no good shared maps, it’s actually easier to go to the territory to construct a new shared map in most cases than to try and merge incompatible maps.
Circumstantial evidence is human communication, though that’s not a proof.
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The only alternative I can think of is some gigantic top-down common world-modeling architecture, like a “Windows World for IoT (now with Blockchain consensus for fridges and vacuums, featuring OpenCycAlphaGoWolframAlpha!)”
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I’ll throw in a pointer to Stalnaker in case anyone actually wants to go down this bunny trail of common-grounding communication web.mit.edu/philosophy/fac
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