"Majority vote" is not what I mean.  You can have a biased measurement
instrument report a gazillion off-by-one measurements, but when its
measurements are added to the global database, the algorithmic information
content will, with certainty approaching 1 with the diversity of sources,
simply adjust all of its measurements by one so as to reduce their
contribution to the total complexity.

This also occurs even if you have a large number of biased measurement
instruments all reporting a gazillion off-by-one measurements.  What will
happen is that this set of measurement instruments will expose a latent
identity as a single measurement instrument so as to reduce size of the
compressed representation.

This can be taken into the political realm through a unified model of
society that exposes who is and who is not "biased" in their perceptions of
the world, where "who" becomes a latent identity of a school of thought or
viewpoint.

On Sat, Feb 15, 2020 at 4:16 PM <[email protected]> wrote:

> What JB means is, while AI intelligence is important, and while AI
> friendliness is even more important, AI bias is the most important - the
> large data we have (assuming you take enough diverse samples) has a
> majority vote on different topics like laws, what they feel, their beliefs,
> their wants, etc. Whenever you train an AI, it will become what we feed
> it...as long as you feed it diverse data then it won't fall into a
> non-majority 'taboo' that no one really actually wants (based on All their
> wants, hopes, etc). So figure out AI, make it have Good goals (majority of
> human's desires), and train it on large diverse data. And this goes for any
> system in place today, like court rooms and elections and city decisions.
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