6 Matching Annotations
  1. Feb 2024
    1. Dr Minor would read a text not for its meaning but for its words. It wasa novel approach to the task – the equivalent of cutting up a book word byword, and then placing each in an alphabetical list which helped the editorsquickly find quotations. Just as Google today ‘reads’ text as a series of wordsor symbols that are searchable and discoverable, so with Dr Minor. A manualundertaking of this kind was laborious – he was basically working as acomputer would work – but it probably resulted in a higher percentage of hisquotations making it to the Dictionary page than those of other contributors.
  2. Nov 2023
    1. How will people build professional callouses if the early work that may be viewed as mundane essentials are taken over by AI systems? Do we risk living in the age of the last masters, the age of the last experts?

      Professional callouses

      This is a paragraph too far. There are many unnecessary "callouses" that have been removed from work, and we are better for it. Should we go back to the "computers" of the 1950s and 1960s...women whose jobs were to make mathematical calculations?

      As technology advances, there are actions that are "pushed down the complexity stack" of what is assumed to exist and can be counted on.

  3. Feb 2021
    1. It turns out that, given a set of constraints defining a particular problem, deriving an efficient algorithm to solve it is a very difficult problem in itself. This crucial step cannot yet be automated and still requires the insight of a human programmer.
  4. Dec 2019
    1. With a computer manipulating our symbols and generating their portrayals to us on a display, we no longer need think of our looking at the symbol structure which is stored—as we think of looking at the symbol structures stored in notebooks, memos, and books. What the computer actually stores need be none of our concern, assuming that it can portray symbol structures to us that are consistent with the form in which we think our information is structured.

      Separation of model and view

  5. Jul 2019
    1. In 1996 and 1998, a pair of workshops at the University of Glasgow on information retrieval and human–computer interaction sought to address the overlap between these two fields. Marchionini notes the impact of the World Wide Web and the sudden increase in information literacy – changes that were only embryonic in the late 1990s.

      it took a half a century for these disciplines to discern their complementarity!

  6. Oct 2018
    1. As the power is unleashed, computers on the Semantic Web achieve at first the ability to describe, then to infer, and then to reason. The schema is a huge step, and one that will enable a vast amount of interoperability and extra functionality. However, it still only categorizes data. It says nothing about meaning or understanding.

      The author presents an interesting progression for the Web to eventually learn to reason. The picture he paints of more accessible content on the internet hinges on the internet learning to reason, which is a human characteristic. It seems we need to apply human characteristics to all of our mechanics for them to progress in their usefulness.