∄ meaning, only text

[gepr]

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.

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[Russ Abbott]

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.

On Mon, Aug 10, 2020 at 7:02 AM uǝlƃ ↙↙↙ <[hidden email]> 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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- .... . -..-. . -. -.. -..-. .. ... -..-. .... . .-. .
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