[FRIAM] Celeste Kidd - How to Know

[Steven A Smith]

REC -

Good find!

I am not closely following the development and results of GAN work, but
it seems like this kind of study explicates at least ONE GOOD REASON for
worrying about AI changing the nature of the world as we know it (even
if it isn't a precise existential threat).   Convolved with Carl's
offering around "weaponizing complexity", it feels more and more
believable (recursion unintended) that the wielders of strong AI/ML will
have the upper hand in any tactical and possibly strategic domain
(warfare, public opinion, markets, etc.).   

I don't know how deeply technical the presumed election-manipulation of
2016 (now 2020) is, but it *does* seem like the work you reference here
implies that with the information venues/vectors like streaming video
(TV, Movies, Clips, attendant advertising) and social media
(FB/Insta/Twit...) the understanding and tools are already in place to
significantly manipulate public opinion.  Based on my anecdotal
experience about people's *certainty*, this article is very on-point.  
And this doesn't even reference the technology of "deep fakes".   

- Steve


On 12/27/19 8:21 PM, Roger Critchlow wrote:
> This talk was mentioned on hacker news this week and inspired my
> babbling at Saveur this
> morning.  https://slideslive.com/38921495/how-to-know.  The talk was
> delivered at Neural IPS on December 9 and discusses recent research on
> how people come to believe they know something.
>
> This
> paper https://www.mitpressjournals.org/doi/full/10.1162/opmi_a_00017 describes
> the Amazon Mechanical Turk experiment on people becoming certain they
> understood the boolean rule they were being taught by examples.
>
> -- rec --
>
>
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