17 Matching Annotations
  1. Jun 2024
    1. TensionThe ability to see like a data structure afforded us the technology we have today. But it was built for and within a set of societal systems—and stories—that can’t cope with nebulosity. Worse still is the transitional era we’ve entered, in which overwhelming complexity leads more and more people to believe in nothing. That way lies madness. Seeing is a choice, and we need to reclaim that choice. However, we need to see things and do things differently, and build sociotechnical systems that embody this difference.This is best seen through a small example. In our jobs, many of us deal with interpersonal dynamics that sometimes overwhelm the rules. The rules are still there—those that the company operates by and laws that it follows—meaning there are limits to how those interpersonal dynamics can play out. But those rules are rigid and bureaucratic, and most of the time they are irrelevant to what you’re dealing with. People learn to work with and around the rules rather than follow them to the letter. Some of these might be deliberate hacks, ones that are known, and passed down, by an organization’s workers. A work-to-rule strike, or quiet quitting for that matter, is effective at slowing a company to a halt because work is never as routine as schedules, processes, leadership principles, or any other codified rules might allow management to believe.The tension we face is that on an everyday basis, we want things to be simple and certain. But that means ignoring the messiness of reality. And when we delegate that simplicity and certainty to systems—either to institutions or increasingly to software—they feel impersonal and oppressive. People used to say that they felt like large institutions were treating them like a number. For decades, we have literally been numbers in government and corporate data structures. BreakdownAs historian Jill Lepore wrote, we used to be in a world of mystery. Then we began to understand those mysteries and use science to turn them into facts. And then we quantified and operationalized those facts through numbers. We’re currently in a world of data—overwhelming, human-incomprehensible amounts of data—that we use to make predictions even though that data isn’t enough to fully grapple with the complexity of reality.How do we move past this era of breakdown? It’s not by eschewing technology. We need our complex socio-technical systems. We need mental models to make sense of the complexities of our world. But we also need to understand and accept their inherent imperfections. We need to make sure we’re avoiding static and biased patterns—of the sort that a state functionary or a rigid algorithm might produce—while leaving room for the messiness inherent in human interactions. Chapman calls this balance “fluidity,” where society (and really, the tech we use every day) gives us the disparate things we need to be happy while also enabling the complex global society we have today.
  2. May 2024
  3. Feb 2024
    1. Eine neue Studie der Universität für Bodenkultur beziffert erstmals, wieviel Kohlenstoff zwischen 1900 und 2015 langfristig oder kurzfristig in menschlichen Artefakten wie Gebäuden gespeichert wurde. Die Menge des dauerhaft gespeicherten Kohlenstoffs hat sich seit 1900 versechzehnfacht. Sie reicht aber bei weitem nicht aus, um die globale Erhitzung wirksam zu beeinflussen. Die Möglichkeiten, Boot in Gebäuden zu nutzen, um der Atmosphäre CO2 zu entziehen, werden bisher nicht genutzt. https://www.derstandard.at/story/3000000208522/co2-entnahme-durch-holzbau-ist-bisher-nicht-relevant-fuer-den-klimaschutz

      Studie: https://iopscience.iop.org/article/10.1088/1748-9326/ad236b

  4. Jun 2022
    1. Jesse Stommel and I wrote once that, In the room with our students, we can know if they’re engaged and participating, even as each of them participates in his or her own unique fashion. In an online discussion forum, it’s difficult to observe such nuance, and impossible to quantitatively evaluate it.

      The answer shouldn't necessarily be to figure out how to quantify the online unseen portions of the learning process.

      Similarly how might one assess the end results of things which are non-literate?

  5. Apr 2021
  6. Aug 2020
    1. Now it is much clearer that id is really a family of infinitely many functions. It is fair to say that it is an abstract function (as opposed to a concrete one), because its type abstracts over the type variable a. The common and proper mathematical wording is that the type is universally quantified (or often just quantified) over a.

      This was very neatly put, and forall above is also spot on.

    1. Quantified Types

      My main issue with this book is that the difficulty is exponentially increasing, and by "keeping it simple" (i.e., trying to use simple terms) it is even harder to do a proper research.

      For example:

      1. The name of this chapter

      This chapter should have been called Explicitly quantified type or Explicit universal quantification as it is too general as is, and doing a search to get to know more when someone has no formal/previous functional programming background, makes very hard.

      Most importantly though, even if Haskell not mentioned, the word "explicit" would have been important.

      It is also more about generic parameters than about quantification itself, and forall is kind of introduced but it is totally misleading.

      2. forall

      The post “forall” is the type-level “lambda” (saved) is the best, most succinct explanation of forall that I ever found. Unfortunately not before going down the rabbit hole.. (See links below.) One still needs to know about

      • typeclasses
      • generic parameters
      • constraints
      • what pragmas are but after that, it is straightforward.

      (Jordan's Reference section on forall also doesn't help much.)

      forall is also mandatory in PureScript (which is also not mentioned when introducing it), and I believe a comparison (the way the above post did) with Haskell is important, but at the right time. At least Jordan's Reference tries to put it off until later, but still before explaining concepts required to understand it.

      3. The "rabbit hole" links

      These are all good resources, but not for uninitiated mortals, and at a lower level (such as where I am now) they raise more questions than answers.

  7. Jul 2020
    1. There is a simple mathematical relationship between the fraction of droplets that are unoccupied (black bar) and the concentration of target molecules.
  8. Jan 2020
    1. The concentration of GFP in the sample had been measured using a nanodrop and was ~120μM

      How was the protein quantified? I assume UV absorption or Bradford assay was used.

  9. Oct 2017