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Prikvačeni tweet
June 28, 2019 - As expected, joined words are prevalent in vocabulary. But a framework for non-deterministic search makes such problems a lot easier to solve: e.g. ethylenediaminetetraacetic => ethylene diamine tetraacetic (https://metarecursive.neocities.org/Intelligence-Amplification-Journal/Database-and-PDFs.html#header-n637 …)pic.twitter.com/BW8TKV5lGP
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Especially for dresses with complex shapes. This stereo-typically feminine role requires no less spatial reasoning than STEM (certainly more than for many types of programming). I got called an idiot spouting rubbish and told I had no idea what I was talking about :(
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I remember once, when someone claimed women are worse at math because of differences in spatial reasoning ability... I pointed out that anyone's brain while sewing is at least consciously implicitly & subconsciously explicitly computing/applying differential geometry of surfaces https://twitter.com/Alliterativity/status/1220098418187460608 …pic.twitter.com/SNjaIO2lDQ
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There is an odd phenomenon in trying to remember something which resists recall. The oddness comes from the fact that, even though I apparently do not remember the thing, there is something "helpfully" rejecting all my generated options as wrong. If it knows, why not just tell?
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I also dislike the new changes and find them disorienting. I'm uncertain whether it's because it's objectively bad or because it was unexpected jarring & requires relearning before it's automatic again.https://twitter.com/jeffseibert/status/1218059136694284288 …
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The justification should be more of a clear boundary of limitations than a means to convince. For example, if I'm modeling traffic or crowds as gas particles, I have to justify why this is okay for my purposes even though it's clearly wrong in general.
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A theory (which can/should make use of math & computation) should at least contain a useful coarsening of the relevant slice of dynamics, a path/mechanisms from coarse to observed (eg by commutative diagrams) & justification for why the coarsening is contextually valid
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Theory is important in biology as it serves as a navigational aid for maintaining bearings in a multi-dimensional labyrinth (more-so than a method for correctness) In physics, math also plays a major role in aiding coherent reasoning, but biology resists such simple treatment.
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Also relevant: 1) [Between the devil and the deep blue sea](https://osf.io/39q8y/download/?format=pdf …)** 2) [A note on the evidence and Bayesian Occam’s razor](https://homepages.inf.ed.ac.uk/imurray2/pub/05occam/occam.pdf …), Image from: http://www.gatsby.ucl.ac.uk/~turner/TeaTalks/bayes-model-comp/bayes-model-comp.pdf … --- ** Highly recommend https://twitter.com/cian_neuro/status/1217427347495948289 …pic.twitter.com/lPFbu9uIkd
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People buy into this; applying the trappings of excessive notation & obtuse rhetoric to appear clever. Doesn't help that once they've understood, many want to use math to appear clever too, rather than clearer. They end up participating in & perpetuating obscured approaches.
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Math is harder to learn than it is to use. Something like: simple ideas hard to incorporate There's a feedback loop where, only people good at puzzles, abstraction+great WM write stuff for each other. It is, in a sense, optimizing for those attributes far beyond what is required
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7) Realizing that quantitative working is not always more complex & careful than qualitative. Using math doesn't mean you're smarter, it is best considered a tool for making you smarter. Currently too often used as decoration to appear clever.
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5) Erasing working behind you. Thought processes which lead to the work carry most of the value but are often erased. Often to feed genius image 6) Emphasize progress more than endpoints. People shouldn't feel embarrassed because they worked on something "trivial" Related to (2)
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Presentation should be in terms of paths which maximize understanding. E.g. more worked examples early, reduces the conceptualization burden. This goes beyond visualization, which only works for low dimensions. Rather, examples which deconstruct interactions with notation.
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Clearer more consistent notation would help a lot of people. Right now,researchers are working against comprehensibility or at best, have no empathy for people who have not being studying their problem for months 4) Presentation in terms of the logical order of concepts is wrong
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Saying "that's just linear algebra" or even "that is just arithmetic" isn't helpful. Every subject level has its own internal difficulty scaling 3) Math notation, with its tower of ambiguous superscripts & subscript tunnels is optimized for people with excellent working memory
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Some ideas: 1) There should be no cost for frequently making stupid & minor costs for careless mistakes. This will help people with attention issues stay on longer. 2) Drop the concept of real math as being more complex. Real math as being more interesting is ok. That is,https://twitter.com/Category_Fury/status/1214635038051983360 …
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deen-chan proslijedio/la je Tweet
I guess slight delay in being uploaded, but here's
@math3ma in the MIT category seminar last Octoberhttps://www.youtube.com/watch?v=12j8OV-ptC4 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
deen-chan proslijedio/la je Tweet
You can say "evolutionary algorithms" and it sounds all mystical and such, or you can say "particle filter with resampling" and it's pretty much the same except now it's boring stuffy numerical analysis for doing integration faster
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Way more is needed before a diagnosis approach of questionable utility (independent of ML, see https://ebm.bmj.com/content/23/1/1 ). Such as developing human capital (thus educational tools), basic biotech & chemistry equipment, more affordable basic healthcare and so on.
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in the first place & what the desired end goal truly is. Do we want better face recognition or less face recognition? Crime prediction or neighborhood health etc? Back to topic at hand. Some said this is useful for developing economies; which couldn't be further from the truth.
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