Proposes five layers of abstraction for AI with complexity-performance and control-complexity trade-offs as a conceptual framework for organizing subfields and targeting innovation.
<Internal signals> are interpreted as <internal data structures>, and many of those together give rise to <data output>
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A system of different layers of abstraction for artificial intelligence
Proposes five layers of abstraction for AI with complexity-performance and control-complexity trade-offs as a conceptual framework for organizing subfields and targeting innovation.