Then Google Now, then Assistant - trying to make a UI that can capture people's intent and pipe it into something beyond keyword search 3/
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Replying to @benedictevans
Not sure these quite work yet - demos are always amazing but reality is always a script or a decision tree that can't quite connect 4/
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Replying to @benedictevans
Posit: NLP is a trap. You think you can answer question: really, you can capture them perfectly but can't *answer* more than a fraction 5/
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Replying to @benedictevans
Isn't the default assumption behind releases like Google Assistant that the only way to improve NLP is to gather data from end users?
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Replying to @NikhaarShah
it's not the NLP that's the structural barrier, but having something to do with the query once you've understood it
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Replying to @benedictevans
Valid; my hypothesis is that in the medium-term end-users will prefer a NLP-centric UX, thus giving a first movers adv. from investments
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Replying to @NikhaarShah
I don't think NLP is capable of being a general-purpose UI without general AI
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Replying to @benedictevans
Yeah, as Nick Bostrom says in SuperIntelligence - we can’t replicate brain intelligence w/o basing of a general-AI on biological components
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Replying to @NikhaarShah @benedictevans
Nikhaar, I agree. The foolish path is to try to replicate brain intelligence. As in humans, most “intelligence” = deep domain knowledge.
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Replying to @BrianRoemmele @benedictevans
Yeah the prize in this game is SuperIntelligence i.e. intellect that is much smarter than the best human brains in practically every field
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Bukhara, yes! Absolutely. This is why I feel @BenedictEvans keeps hitting the wall on this subject. Deep domain expertise is how humans work
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