23 Matching Annotations
  1. Feb 2022
    1. Introduction

      Start of Turing

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  2. Jan 2022
    1. Here, the card index func-tions as a ‘thinking machine’,67 and becomes the best communication partner for learned men.68

      From a computer science perspective, isn't the index card functioning like an external memory, albeit one with somewhat pre-arranged linked paths? It's the movement through the machine's various paths that is doing the "thinking". Or the user's (active) choices that create the paths creates the impression of thinking.

      Perhaps it's the pre-arranged links where the thinking has already happened (based on "work" put into the system) and then traversing the paths gives the appearance of "new" thinking?

      How does this relate to other systems which can be thought of as thinking from a complexity perspective? Bacteria perhaps? Groups of cells acting in concert? Groups of people acting in concert? Cells seeing out food using random walks? etc?

      From this perspective, how can we break out the constituent parts of thought and thinking? Consciousness? With enough nodes and edges and choices of paths between them (or a "correct" subset of paths) could anything look like thinking or computing?

  3. Dec 2021
    1. This comparison is not to claim that the index catalog is already a Turing machine. Comparisons, transfers, and analogies are not that simple. If the elements of a universal discrete machine are present, they still lack the computational logic of an operating system, the development of which constitutes Turing ’ s foundational achievement. What is described here is merely the fact that the card catalog is liter-ally a paper machine, similar to a nontrivial Turing machine only in having similar components — no more, no less.

      I felt some of this missing piece and so included the idea of human interaction as part of the process to make up the balance.

    2. s Alan Turing proved only years later, these machines merely need (1) a (theoretically infi nite) partitioned paper tape, (2) a writing and reading head, and (3) an exact

      procedure for the writing and reading head to move over the paper segments. This book seeks to map the three basic logical components of every computer onto the card catalog as a “ paper machine,” analyzing its data processing and interfaces that may justify the claim, “Card catalogs can do anything!”

      Purpose of the book.

      A card catalog of index cards used by a human meets all the basic criteria of a Turing machine, or abstract computer, as defined by Alan Turing.

  4. Nov 2021
  5. Aug 2021
    1. A neural network with a hidden layer has universality: given enough hidden units, it can approximate any function. This is a frequently quoted – and even more frequently, misunderstood and applied – theorem. It’s true, essentially, because the hidden layer can be used as a lookup table.
  6. May 2021
    1. Turing was an exceptional mathematician with a peculiar and fascinating personality and yet he remains largely unknown. In fact, he might be considered the father of the von Neumann architecture computer and the pioneer of Artificial Intelligence. And all thanks to his machines; both those that Church called “Turing machines” and the a-, c-, o-, unorganized- and p-machines, which gave rise to evolutionary computations and genetic programming as well as connectionism and learning. This paper looks at all of these and at why he is such an often overlooked and misunderstood figure.
  7. Jan 2021
    1. チャットbotやレコメンデーション、質問への回答、検索、パーソナルアシスタント、顧客サポート自動化、コンテンツ生成など、人と機械、人と人の自然言語によるやりとりを含む幅広いシナリオを支えるためだ

      NLPの処理はいろんな領域で運用できる:レコメンデーション、パーソナルアシスタント

  8. Oct 2020
  9. Sep 2019
    1. At the moment, GPT-2 uses a binary search algorithm, which means that its output can be considered a ‘true’ set of rules. If OpenAI is right, it could eventually generate a Turing complete program, a self-improving machine that can learn (and then improve) itself from the data it encounters. And that would make OpenAI a threat to IBM’s own goals of machine learning and AI, as it could essentially make better than even humans the best possible model that the future machines can use to improve their systems. However, there’s a catch: not just any new AI will do, but a specific type; one that uses deep learning to learn the rules, algorithms, and data necessary to run the machine to any given level of AI.

      This is a machine generated response in 2019. We are clearly closer than most people realize to machines that can can pass a text-based Turing Test.

  10. Jun 2018
    1. Player A's role is to trick the interrogator into making the wrong decision, while player B attempts to assist the interrogator in making the right one.

      Why are these roles gendered, the man always dissembling, the woman assisting?

  11. Jul 2017
  12. Jan 2017
    1. Like many other AI practitioners, I’m a philosophical functionalist: I believe that a cognitive state, such as one derived from reading, should not be defined by what it is made of in terms of hardware or biology, but instead by how it functions, in relation to inputs, outputs and other cognitive states. (Opponents of functionalism include behaviourists – who insist that mental states are nothing other than dispositions to behave in certain ways – and mind-brain identity theorists – who argue that mental states are identical with particular neural states, and are tied to specific biological ‘hardware’.)

      La idea de dependencia en un "hardware" biológico es la más cercana a mi perspectiva. La funcionalista se acerca al test de Turing y proponentes similares.

  13. Jun 2016
    1. Il en appelait à des « oracles » : With the help of the oracle we could form a new kind of machine (call them o-machines), having as one of its fundamental processes that of solving a given number-theoretic problem. (Turing, 1939 : 161)

      les oracles pour répondre au non-calculable, non-computationnel.

  14. Oct 2015
    1. Nearly all ap­pli­ca­tions of prob­a­bil­ity to cryp­tog­ra­phy de­pend on the fac­tor prin­ci­ple (or Bayes’ The­o­rem).

      This is easily the most interesting sentence in the paper: Turing used Bayesian analysis for code-breaking during WWII.

  15. May 2015
    1. Dr. Lamport received a doctorate in mathematics from Brandeis University, with a dissertation on singularities in analytic partial differential equations. This, together with a complete lack of education in computer science, prepared him for a career as a computer scientist at Massachusetts Computer Associates, SRI, Digital, and Compaq. He claims that it is through no fault of his that of those four corporations, only the one that was supposed to be non-profit still exists. He joined Microsoft in 2001, but that company has not yet succumbed. Dr. Lamport's initial research in concurrent algorithms made him well-known as the author of LaTeX, a document formatting system for the ever-diminishing class of people who write formulas instead of drawing pictures. He is also known for writing A distributed system is one in which the failure of a computer you didn't even know existed can render your own computer unusable. which established him as an expert on distributed systems. His interest in Mediterranean history, including research on Byzantine generals and the mythical Greek island of Paxos, led to his receiving five honorary doctorates from European universities, and to the IEEE sending him to Italy to receive its 2004 Piore Award and to Quebec to receive its 2008 von Neumann medal. However, he has always returned to his home in California. This display of patriotism was rewarded with membership in the National Academy of Engineering, the National Academy of Sciences, and the American Academy of Arts and Sciences. More recently, Dr. Lamport has been annoying computer scientists and engineers by urging them to understand an algorithm or system before implementing it, and scaring them by saying they should use mathematics. In an attempt to get him to talk about other things, the ACM gave him the 2013 Turing Award.

      Talk about badass introductions