Post AjgbxLCIeEr45kMVhA by ak@mastodon.social
(DIR) More posts by ak@mastodon.social
(DIR) Post #AjgYOQSKnyACP3WPZI by dalias@hachyderm.io
2024-07-06T13:47:08Z
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Yesterday I encountered a "wrong-on-the-internet" rando professing his excitement for "using machine learning" in #3dprinting to throttle speeds in the right places to avoid quality loss.While completely not worth engaging with, I feel like this is a useful example to understand why this idiocy is so infuriating...
(DIR) Post #AjgYORknymX4QdAfzs by dalias@hachyderm.io
2024-07-06T13:50:49Z
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This is a problem domain where the constraints and effects are pretty much entirely comprehensible in terms of known physical models. Any suboptimal behavior is entirely a matter of nobody having spent the time to apply known models. But sure, let's instead spend the time hooking up ML, CV to evaluate results, and waste tons (literally) of plastic training a model to learn a poor approximation of what we already know.But this is a general pattern that's terrifying...
(DIR) Post #AjgYOT4L5dkgVVJn5E by dalias@hachyderm.io
2024-07-06T13:57:10Z
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The proponents of this kind of shit want to throw away the whole concept of having and using scientific knowledge obtained by experiment, with documentation of how it was obtained, evidence supporting the resulting models, falsifiability, etc., and replace it with a worse version of the way humans tens of thousands of years ago came to believe things about the world: simplistic pattern recognition.
(DIR) Post #AjgYOWATZHRY7PDEZ6 by dalias@hachyderm.io
2024-07-06T13:58:15Z
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Simplistic pattern recognition, and falling for false patterns, is the worst of human stupidity. This shit should be called artificial stupidity not artificial intelligence.
(DIR) Post #AjgYcGkPhIQwLaZxrs by futurebird@sauropods.win
2024-07-07T11:12:59Z
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@dalias Can you tell us more about why machine learning isn’t a good fit for this? Though I didn’t even know throttling speeds could possibly improve quality. Knowing little it sounds possible? But what tipped you off that it would not work?
(DIR) Post #AjgbxLCIeEr45kMVhA by ak@mastodon.social
2024-07-07T11:50:23Z
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@futurebird @dalias most of the time applying ML to physical models will produce an over-linearized black-box solution compared to using known equations to solve, simulate, and optimize whatever you’re trying to do. In situations where simulation is resource intensive to do at full fidelity we have tools like domain-specific reduced -order models.
(DIR) Post #AjghNJuqXDJFPvPD3g by dalias@hachyderm.io
2024-07-07T12:51:07Z
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@futurebird It's not that it couldn't work, but that we understand the physical reasons when/why going too fast can give poor results, and can just apply that rather than trying to coax a model into figuring out the same things without underlying models of thermal transfer, etc. It's really low hanging fruit.