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  2. Winograd schema challenge - Wikipedia

    en.wikipedia.org/wiki/Winograd_schema_challenge

    The Winograd schema challenge (WSC) is a test of machine intelligence proposed in 2012 by Hector Levesque, a computer scientist at the University of Toronto.Designed to be an improvement on the Turing test, it is a multiple-choice test that employs questions of a very specific structure: they are instances of what are called Winograd schemas, named after Terry Winograd, professor of computer ...

  3. Overchoice - Wikipedia

    en.wikipedia.org/wiki/Overchoice

    Despite this, more choices result with more dissatisfaction and regret in decisions. [3] The feeling of responsibility causes cognitive dissonance when presented with large array situations. [5] In this situation, cognitive dissonance results when there is a mental difference between the choice made and the choice that should have been made.

  4. Decision-making - Wikipedia

    en.wikipedia.org/wiki/Decision-making

    List pros and cons of each alternative. Make the decision. Immediately take action to implement it. Learn from and reflect on the decision. In 2008, Kristina Guo published the DECIDE model of decision-making, which has six parts: [51] Define the problem; Establish or Enumerate all the criteria (constraints) Consider or Collect all the alternatives

  5. Backronym - Wikipedia

    en.wikipedia.org/wiki/Backronym

    A normal acronym is a word derived from the initial letters of the words of a phrase, [2] such as radar from "radio detection and ranging". [3] By contrast, a backronym is "an acronym deliberately formed from a phrase whose initial letters spell out a particular word or words, either to create a memorable name or as a fanciful explanation of a ...

  6. Automated decision-making - Wikipedia

    en.wikipedia.org/wiki/Automated_decision-making

    Automated decision-making involves using data as input to be analyzed within a process, model, or algorithm or for learning and generating new models. [7] ADM systems may use and connect a wide range of data types and sources depending on the goals and contexts of the system, for example, sensor data for self-driving cars and robotics, identity data for security systems, demographic and ...

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  8. Predictive analytics - Wikipedia

    en.wikipedia.org/wiki/Predictive_analytics

    Then, analyze the source data to determine the most appropriate data and model building approach (models are only as useful as the applicable data used to build them). Select and transform the data in order to create models. Create and test models in order to evaluate if they are valid and will be able to meet project goals and metrics.

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