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Meaningness's profile
David Chapman
David Chapman
David Chapman
@Meaningness

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David Chapman

@Meaningness

Better ways of thinking, feeling, and acting—around problems of meaning and meaninglessness; self and society; ethics, purpose, and value.

meaningness.com/about-my-sites
Joined September 2010

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    1. David Chapman‏ @Meaningness 18 May 2019
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      Park Doing’s PhD thesis book, on his learning to run the X-ray beam line of a football-field-sized synchrotron:pic.twitter.com/F31m4B3qbS

      1 reply 3 retweets 18 likes
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    2. David Chapman‏ @Meaningness 18 May 2019
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      I seriously considered becoming a molecular biologist, and did all the graduate-level coursework needed for a PhD. It was stuff like this that convinced me not to. [“Hanging around in labs,” p. 11.]pic.twitter.com/EgsNphhoqR

      4 replies 5 retweets 30 likes
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    3. David Chapman‏ @Meaningness 19 May 2019
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      Garfinkel's "Hanging around in labs" paper features a discussion he had with Phil Agre about Phil's and my joint work, and how the contingencies of artificial intelligence research shaped the AI architecture we developed.pic.twitter.com/2NHABbXEzx

      2 replies 1 retweet 8 likes
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    4. Sarah Constantin‏ @s_r_constantin 19 May 2019
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      Replying to @Meaningness

      I'm genuinely confused by this -- how does it refute "the scientific method"? Yes, most of the time you're trying to fix broken shit, and labs are more like workshops than anybody acknowledges, but don't lab scientists "try changing one thing and see if that fixes it"?

      1 reply 0 retweets 0 likes
    5. David Chapman‏ @Meaningness 19 May 2019
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      Replying to @s_r_constantin

      Ah, hmm, may be multiple disjunctures here… The point of the first tweet in the thread is not to *refute* “the scientific method” (it’s “more or less right, as far as it goes”) but to point out that there’s no overall formulation that is both nontrivial and empirically accurate.

      2 replies 0 retweets 5 likes
    6. David Chapman‏ @Meaningness 19 May 2019
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      Replying to @Meaningness @s_r_constantin

      So the question is “How actually do scientists gain knowledge, once we admit that there is no concise a priori answer? And how can we find that out?”

      1 reply 0 retweets 0 likes
    7. David Chapman‏ @Meaningness 19 May 2019
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      Replying to @Meaningness @s_r_constantin

      One obvious approach is to ask them “how did you determine this specific fact yesterday,” and then they launch into a story about chromatography columns and ethidium bromide or whatever.

      1 reply 0 retweets 3 likes
    8. David Chapman‏ @Meaningness 19 May 2019
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      Replying to @Meaningness @s_r_constantin

      Then instead of trying to turn that story into a tidy morality fable about The Scientific Method, you can take it seriously in its own terms. What specifically *is* the logic whereby that experiment shows protein A regulates protein B.

      1 reply 0 retweets 1 like
    9. David Chapman‏ @Meaningness 19 May 2019
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      Replying to @Meaningness @s_r_constantin

      Another thing you can do is to hang out in labs watching scientists do science. Then what you see is “shop work” that is almost perfectly dissimilar to the fables you are taught in HS/undergrad about how science is done.

      1 reply 0 retweets 1 like
    10. David Chapman‏ @Meaningness 19 May 2019
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      Replying to @Meaningness @s_r_constantin

      The actual work is mostly improvisational futzing around with materials and equipment, trying different things out, trying to coax them to produce an answer. And when you do that, you run into the “contingencies” Garfinkel enumerates.

      2 replies 0 retweets 2 likes
      David Chapman‏ @Meaningness 19 May 2019
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      Replying to @Meaningness @s_r_constantin

      Phil’s insight was that the contingencies are constraints on the form of a cognitive architecture.

      9:47 AM - 19 May 2019
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        2. David Chapman‏ @Meaningness 19 May 2019
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          Replying to @Meaningness @s_r_constantin

          E.g. if you assume knowledge consists of datastructures representing fopc wffs, you inevitably hit a combinatorial explosion. So we applied modus tolens, and concluded that knowledge can’t be datastructures or wffs or anything like that. Our program Pengi did fine without them.

          2 replies 0 retweets 0 likes
        3. Sarah Constantin‏ @s_r_constantin 19 May 2019
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          Replying to @Meaningness

          ah, the thing where you can't proceduralize a scientist. (or an engineer or mechanic for that matter.) yes, that's quite true.

          2 replies 0 retweets 1 like
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