Dear Frennemies,

 

I have had my ears boxed so often for dragging threads into my metaphor den, 
that I thought I ought to rethread this.  But the paper Glen posts and Russ 
applauds posts is really interesting, describing the manner in which implicit 
assumptions into our AI can lead it wildly astray: “There’s more than one way 
to [see] a cat.” 

 

The article had an additional lesson for me.  To the extent that you-folks will 
permit me to think of simulations as contrived metaphors, as opposed to Natural 
metaphors – ie., objects that are built solely for the purpose of being 
metaphors, as opposed to objects that are found in the world and appropriated 
for that purpose, then that reminds me of a book by Evelyn Fox Keller which 
argues that a model (i.e., a scientific metaphor) can only be useful if it is  
more easily understood than the thing it models.  Don’t use chimpanzees as 
models if you are interested in mice.   

 

Simulations would seem to me to have the same obligation.  If you write a 
simulation of a process that you don’t understand any better than the thing you 
are simulating, then you have gotten nowhere, right?  So If you are publishing 
papers in which you investigate what your AI is doing, has not the contrivance 
process gone astray? 

 

What further interested me about these models that the AI provided was that 
they were in part natural and in part contrived.  So the contrived part is 
where the investigators mimicked the hierarchical construction of the visual 
system in setting up the AI; the natural part is the focus on texture by the 
resulting simulation.  So, in the end, the metaphor generated by the AI turned 
out to be a bad one – heuristic, perhaps, but not apt.

 

Nicholas Thompson

Emeritus Professor of Ethology and Psychology

Clark University

 <mailto:[email protected]> [email protected]

 <https://wordpress.clarku.edu/nthompson/> 
https://wordpress.clarku.edu/nthompson/

 

 

From: Friam <[email protected]> On Behalf Of Russ Abbott
Sent: Monday, August 10, 2020 11:04 AM
To: The Friday Morning Applied Complexity Coffee Group <[email protected]>
Subject: Re: [FRIAM] ∄ meaning, only text

 

Independent of Kavanaugh, that was a great article. That's the first I have 
heard of this work. It begins to explain a lot about deep learning and its 
literal and figurative superficiality.

 

-- Russ Abbott                                       
Professor, Computer Science
California State University, Los Angeles

 

 

On Mon, Aug 10, 2020 at 7:02 AM uǝlƃ ↙↙↙ <[email protected] 
<mailto:[email protected]> > wrote:

And to round out another thread, wherein I proposed Brett Kavanaugh *is* 
Artificial Intelligence, this article pops up:

  Where We See Shapes, AI Sees Textures
  Jordana Cepelewicz
  https://www.quantamagazine.org/where-we-see-shapes-ai-sees-textures-20190701/

In the context of "originalism" and reading *through* the text, the question 
is: Why does Brett *seem* intelligent [‽] in a different way than your average 
zero-shot AI? I like Nick's argument that meaning is higher-order pattern. The 
results Cepelewicz cites validate that argument [⸘]. But if we continue, we'll 
fall back into the argument about high-order Markovity, free will, and 
steganographic [de]coding. And (worse) it dovetails with No Free Lunch and 
whether strict potentialists are well-justified in using higher order 
operators. Multi-objective constraint solving (aka parallax) seems to cut a 
compromise through the whole meta-thread. But, as always, the tricks lie in 
composition and modularity. How do the constraints compose? Which problems can 
be teased apart from which other problems to create cliques in the graph or 
even repurposable anatomical modules? How do we construct structured memory for 
saving snapshots of swapped out partial solutions? Etc.


[‽] If you can't tell, I'm really enjoying using a frat boy political operative 
who *pretends* to be a SCOTUS justice in the argument for strong AI. To use an 
actual justice like Gorsuch as such just isn't satisfying.

[⸘] Of course, we don't learn from confirmation. We only learn from critical 
objection. And the 2nd half of the article does that well enough, I think.

-- 
↙↙↙ uǝlƃ

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