4 Matching Annotations
  1. Jul 2022
    1. von neumann was furious at him furious that he would waste precious machine time 00:04:20 doing the assembly that was clerical work that was supposed to be for people right and so we saw the same story happened just a little bit later when john backus and friends came up with us idea they called fortran this is so call high-level language where you could write out your formulas as if your writing mathmatical notation you could write out loops and this was shown to the assembly programmers and once again they just 00:04:46 they weren't interested they don't see any value in that they just didn't get it so um I want you to keep this in mind as I talk about the four big ideas that I'm going to talk about today that it's easy to think that technology technology is always getting better because Moore's law because computers are getting always more capable but ideas that require people to unlearn what they've learned and think in new ways there's often 00:05:10 enormous amount of resistance people over here they think they know what they're doing they think they know a programming is this programming that's not programming and so there's going to be a lot of resistance to adopting new ideas

      Cumulative cultural learning seems to be stuck in its own recursive loop- the developers of the old paradigm become the old "guard", resistant to any change that will disrupt their change. Paradigm shifts are resisted tooth and nail.

  2. Oct 2020
    1. Found reference to this in a review of Henry Quastler's book Information Theory in Biology.

      A more serious thing, in the reviewer's opinion, is the compIete absence of contributions deaJing with information theory and the central nervous system, which may be the field par excellence for the use of such a theory. Although no explicit reference to information theory is made in the well-known paper of W. McCulloch and W. Pitts (1943), the connection is quite obvious. This is made explicit in the systematic elaboration of the McCulloch-Pitts' approach by J. von Neumann (1952). In his interesting book J. T. Culbertson (1950) discussed possible neuraI mechanisms for recognition of visual patterns, and particularly investigated the problems of how greatly a pattern may be deformed without ceasing to be recognizable. The connection between this problem and the problem of distortion in the theory of information is obvious. The work of Anatol Rapoport and his associates on random nets, and especially on their applications to rumor spread (see the series of papers which appeared in this Journal during the past four years), is also closely connected with problems of information theory.

      Electronic copy available at: http://www.cse.chalmers.se/~coquand/AUTOMATA/mcp.pdf

  3. Mar 2019
    1. There was a catch, though: This symbolic abstraction made the world transparent but the brain opaque. Once everything had been reduced to information governed by logic, the actual mechanics ceased to matter—the tradeoff for universal computation was ontology. Von Neumann was the first to see the problem. He expressed his concern to Wiener in a letter that anticipated the coming split between artificial intelligence on one side and neuroscience on the other. “After the great positive contribution of Turing-cum-Pitts-and-McCulloch is assimilated,” he wrote, “the situation is rather worse than better than before. Indeed these authors have demonstrated in absolute and hopeless generality that anything and everything … can be done by an appropriate mechanism, and specifically by a neural mechanism—and that even one, definite mechanism can be ‘universal.’ Inverting the argument: Nothing that we may know or learn about the functioning of the organism can give, without ‘microscopic,’ cytological work any clues regarding the further details of the neural mechanism.”
    2. In the entire report, he cited only a single paper: “A Logical Calculus” by McCulloch and Pitts.

      First Draft of a Report on EDVAC by jon von Neumann