On Sun, Aug 6, 2023 at 2:51 PM Matt Mahoney <[email protected]> wrote:

> On Sun, Aug 6, 2023 at 11:28 AM James Bowery <[email protected]> wrote:
> > On Sun, Aug 6, 2023 at 9:53 AM Matt Mahoney <[email protected]>
> wrote:
> >>
> >> ... In the US, racial discrimination has been illegal since the 1960's
> and television has been portraying a colorblind world since the 1970's with
> no effect....
> > This is the kind of thing that would be verified or debunked by Hume's
> Guillotine:
> > https://github.com/jabowery/HumesGuillotine
>
> 27,077,896 LaboratoryOfTheCountiesUncompressed.csv-8.paq8o
> ...
> 91,360,518 LaboratoryOfTheCountiesUncompressed.csv
>
> ...
> I am pretty sure that a program that found correlations in the data,
> such as between population, race, age, income, and crime, would
> achieve better compression. How would we use this information to set
> policy?
>

Better compression requires not just correlation but causation, which is
the entire point of going beyond statistics/Shannon Information criteria to
dynamics/Algorithmic information criterion.

Regardless of your values, if you can't converge on a global dynamical
model of causation you are merely tinkering with subsystems in an
incoherent fashion.  You'll end up robbing Peter to pay Paul -- having
unintended consequences affecting your human ecologies -- etc.

That's why engineers need scientists -- why OUGHT needs IS -- why SDT needs
AIT -- etc.

The social sciences haven't yet come to terms with causality in a
*principled* manner.  This is also at the root of AGI's troubles.  Even
Turing Award winners specializing in AI causality, such as  Judeo Pearl,
are confused about why Algorithmic Information is a superior model
selection criterion to progress toward discovering causal structures latent
in the data.

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