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I suspect automated translation between s/w standards that were not built with awareness of each other is harder than translating between 2 languages.
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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.
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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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