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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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    David Chapman‏ @Meaningness 17 Feb 2019
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    Artificial intelligence for better p-hacking! AI has serious bad-science problems of its own. I hadn’t thought about how that would slop over onto other fields, but inevitable in retrospect.https://www.bbc.co.uk/news/science-environment-47267081 …

    8:42 AM - 17 Feb 2019
    • 16 Retweets
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    • Elizabeth DuPre Colby Wright *Netsplit* Foggy Anabasis Haig Շ Full MeTAL linguiste Raymond lesbiangunshow is in quarantine Hermes Caduceus
    5 replies 16 retweets 34 likes
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      2. Rob MacLachlan‏ @robamacl 19 Feb 2019
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        Replying to @Meaningness

        If ppl are really publishing results using just evaluation on the training data then they should stop. In the (mostly genetics) papers I read they do at least some testing on other data, perhaps smaller. Best practices, especially if you have lots of tuning

        1 reply 0 retweets 1 like
      3. Rob MacLachlan‏ @robamacl 19 Feb 2019
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        Replying to @robamacl @Meaningness

        parameters or forking paths in your implementation decisions (as is usually the case in ai research) you need reserve evaluation data that you don't touch until you are ready for publication results.

        1 reply 0 retweets 0 likes
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      2. Raymond‏ @rfinz 17 Feb 2019
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        Replying to @Meaningness

        oh no

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      3. Raymond‏ @rfinz 17 Feb 2019
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        Replying to @rfinz @Meaningness

        on the other side of things, labeled data is often now partially labeled by the machine, with corrections done by a human, potentially biasing gold standards towards what ML is good at anyway

        1 reply 0 retweets 0 likes
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      1. 𝕭𝖊𝖑𝖑𝖉𝖆𝖓𝖉𝖞 𝕾𝖍𝖗𝖎𝖓𝖊‏ @smart_intellecc 17 Feb 2019
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        Replying to @Meaningness @Aelkus

        Fake science

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      1. Thad Kerosky‏ @thadk 17 Feb 2019
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        Replying to @Meaningness

        I liked how 50 yrs of data science (2017) broke out traditional academic science generative/causal methods vs (CompSci) predictive statistics and pointed at the Kaggle-like Common Task Framework as root of the ML ratcheting progress. Related?https://www.tandfonline.com/doi/full/10.1080/10618600.2017.1384734 …

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      1. NickFP‏ @CitronFurieux 20 Feb 2019
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        Replying to @Meaningness @drwjharvey

        Journalists found that on their own long time ago :) Not the first winter since 50 years ago this happens !

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