Author advocates for building general purpose systems that can accomplish goals within some causal boundary given relevant constraints, versus highly deterministic logical flows that are created from priors like intuition or user research.
Parallel with the bitter lesson being that general purpose algorithms that use search and learning leveraged by increasing computational capacity tend to beat out specialized methods that exploit intuition about how cognitive processes work.
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Author advocates for building general purpose systems that can accomplish goals within some causal boundary given relevant constraints, versus highly deterministic logical flows that are created from priors like intuition or user research.
Parallel with the bitter lesson being that general purpose algorithms that use search and learning leveraged by increasing computational capacity tend to beat out specialized methods that exploit intuition about how cognitive processes work.