Nick, You have inadvertently hit upon a pet peeve.
For fifty plus years, Software Engineering — both theory and practice — has engaged in building models/simulacrum that are orders of magnitude more complicated, and those same orders of magnitude less understandable, than the business systems that are ostensibly modeled/simulated. The ultimate absurdity is that business continues to pay for what they KNOW will not work. davew On Mon, Aug 10, 2020, at 12:20 PM, [email protected] wrote: > 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 > [email protected] > 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]> 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 >> >> - .... . -..-. . -. -.. -..-. .. ... -..-. .... . .-. . >> FRIAM Applied Complexity Group listserv >> Zoom Fridays 9:30a-12p Mtn GMT-6 bit.ly/virtualfriam >> un/subscribe http://redfish.com/mailman/listinfo/friam_redfish.com >> archives: http://friam.471366.n2.nabble.com/ >> FRIAM-COMIC http://friam-comic.blogspot.com/ > - .... . -..-. . -. -.. -..-. .. ... -..-. .... . .-. . > FRIAM Applied Complexity Group listserv > Zoom Fridays 9:30a-12p Mtn GMT-6 bit.ly/virtualfriam > un/subscribe http://redfish.com/mailman/listinfo/friam_redfish.com > archives: http://friam.471366.n2.nabble.com/ > FRIAM-COMIC http://friam-comic.blogspot.com/ >
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