Episode 31: Bill Thompson, UC Berkeley, on how cultural evolution shapes knowledge acquisition
Generally Intelligent
Bayesian Inference in a Distributed, Sample-Based Way
The analogy there is that suppose you think about each person in a group as a sample. And through this process of keeping and throwing away, you end up approximating the posterior much more efficient. One consequence of thinking about distributed systems of knowledge accumulation in that way is that once you think about them in computational terms, you can start to think about auxiliary processes that support or inhibit learning in the distributed computation.
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