Have a sneer percolating in your system but not enough time/energy to make a whole post about it? Go forth and be mid!

Any awful.systems sub may be subsneered in this subthread, techtakes or no.

If your sneer seems higher quality than you thought, feel free to cutā€™nā€™paste it into its own post, thereā€™s no quota for posting and the bar really isnā€™t that high

The post Xitter web has spawned soo many ā€œesotericā€ right wing freaks, but thereā€™s no appropriate sneer-space for them. Iā€™m talking redscare-ish, reality challenged ā€œculture criticsā€ who write about everything but understand nothing. Iā€™m talking about reply-guys who make the same 6 tweets about the same 3 subjects. Theyā€™re inescapable at this point, yet I donā€™t see them mocked (as much as they should be)
Like, there was one dude a while back who insisted that women couldnā€™t be surgeons because they didnā€™t believe in the moon or in stars? I think each and every one of these guys is uniquely fucked up and if I canā€™t escape them, I would love to sneer at them.

  • self@awful.systemsEnglish
    11Ā·
    8 months ago

    404 media revisited the worthless DeepMind materials science dataset, featuring some world-class marketing gymnastics:

    Google DeepMind told me in a statement, ā€œWe stand by all claims made in Google DeepMindā€™s GNoME paper.ā€

    ā€œOur GNoME research represents orders of magnitude more candidate materials than were previously known to science, and hundreds of the materials weā€™ve predicted have already been independently synthesized by scientists around the world,ā€ it added.

    [ā€¦]

    Google said that some of the criticisms in the Chemical Materials analysis, like the fact that many of the new materials have already known structures but use different elements, were done by DeepMind by design.

    hundreds of the materials have already been independently synthesized you say?

    ā€We spent quite a lot of time on this going through a very small subset of the things that they propose and we realize not only was there no functionality, but most of them might be credible, but theyā€™re not very novel because theyā€™re simple derivatives of things that are already known.ā€

    this just in, DeepMindā€™s output is worthless by design. but about that credibility pointā€¦

    ā€œIn the DeepMind paper there are many examples of predicted materials that are clearly nonsensical. Not only to subject experts, but most high school students could say that compounds like H2O11 (which is a Deepmind prediction) do not look right,ā€ Palgrave told me.

    by far the most depressing part of this article is that all of the scientists involved go to some lengths to defend this bullshit ā€” every criticism is hedged with a ā€œbut we donā€™t hate AI and Googleā€™s technology is still probably revolutionary, we swear!ā€ and I donā€™t know if thatā€™s due to AI companies attributing the successes of machine learning in research to unrelated LLM and generative AI tech (a form of reputation laundering they do constantly) or because the scientists in question are afraid of getting their labā€™s cloud compute credits yanked if theyā€™re too critical. knowing the banality of the technofascist evil in play at Google, itā€™s probably both.

    • skillissuer@discuss.tchncs.deEnglish
      10Ā·
      8 months ago

      While the above analysis may seem to be critical, we do believe that many of our points could be adopted in the next version of this work. More scrutiny of the ā€œnewā€ materials needs to be performed prior to putting them into a database and claiming ā€œā€¦an order-of-magnitude expansion in stable materials known to humanityā€. In fact, we have yet to find any strikingly novel compounds in the GNoME and Stable Structure listings, although we anticipate that there must be some among the 384,870 compositions.

      This brings us to our final point concerning the claim of ā€œan order-of-magnitude expansion in stable materials known to humanityā€. We would respectfully suggest that the work by Merchant et al. (1) does not report any new materials but reports a list of proposed compounds. In our view, a compound can be called a material when it exhibits some functionality and, therefore, has potential utility. Since no functionality has been demonstrated for the 384,870 compositions in the Stable Structure database, they cannot yet be regarded as materials.

      then they proceed to explain how badly have they fucked up in the only one example where they tried to find some utility of ā€œnewā€ ā€œmaterialā€

      The few examples of functionality mentioned in the article are associated with LiĀ±ion conductors. While the proposed materials are encouraging, their compositions leave much to be desired since they incorporate chemically soft anions. These anions are usually associated with narrow electrochemical stability windows, which renders materials that incorporate them somewhat pointless as Li+ solid electrolytes. (29)

      this is basically closest you can get to ā€œyou fucked up, do betterā€ in a published article. saying ā€œyou fucked up, actually donā€™t even try to do better, go homeā€ is not what iā€™ve seen ever really in published piece, excluding obvious cases of cooked data, even if itā€™s warranted this time. itā€™s in conclusions section https://pubs.acs.org/doi/10.1021/acs.chemmater.4c00643

      • self@awful.systemsEnglish
        6Ā·
        8 months ago

        this is a lot more damning than the 404 media article let on, and Iā€™m very happy thatā€™s the case

        • skillissuer@discuss.tchncs.deEnglish
          6Ā·
          8 months ago

          it is really a damning with a slight hint of praise

          We also note that, while many of the new compositions are trivial adaptations of known materials, the computational approach delivers credible overall compositions, which gives us confidence that the underlying approach is sound

          In closing, we hope the comments presented here will usefully serve the large community of materials scientists and engineers in their continued quest to develop the next generation of useful materials. While we are confident that the tools of Artificial Intelligence and Machine Learning have a bright future in the field of materials discovery, more work needs to be done before that promise is fulfilled.

          the other paper cited is that preprint from el reg article from some two months ago

          also i wouldnā€™t agree that research on plutonium intermetallics is useless, itā€™s still a very useful material. granted, in civilian use itā€™s mostly in form of ceramic plutonium dioxide, and i guess that some (most?) of plutonium alloying chemistry came to be in search of something that could be called stainless plutonium, which would make nuclear weapons design much easier and more reliable. but itā€™s not completely useless and it can have actual civilian applications

          also authors note that even such noncontroversial thing as writing compound formula in standardized, conventional way and sorting them by compound class was too hard for them

    • skillissuer@discuss.tchncs.deEnglish
      9Ā·
      8 months ago

      itā€™s really the ā€œiā€™m doing 1000 calculations per second and they are all wrongā€ meme in machine form