BS-meter: Difference between revisions
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(Created page with "Client: Christopher Newfield <chris.newfield@isrf.org> Recent research has used machine learning methods to apply the language philosophy of Wittgenstein, in a way that can quantify the likelihood of any particular text being bulls**t. These results have extraordinarily exciting implications for political discussion, journalism, corporate press releases, even the content of Facebook or eX-Twitter. Your task is to create a BS-meter that uses these methods to produce an i...") |
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Client: Christopher Newfield <chris.newfield@isrf.org> | Client: Christopher Newfield, Independent Social Research Foundation <chris.newfield@isrf.org> | ||
Recent research has used machine learning methods to apply the language philosophy of Wittgenstein, in a way that can quantify the likelihood of any particular text being bulls**t. These results have extraordinarily exciting implications for political discussion, journalism, corporate press releases, even the content of Facebook or eX-Twitter. Your task is to create a BS-meter that uses these methods to produce an intuitive test device accessible to anyone, perhaps with an international authentication body that can apply validated BS stamps to any text that deserves it. | Recent research has used machine learning methods to apply the language philosophy of Wittgenstein, in a way that can quantify the likelihood of any particular text being bulls**t. These results have extraordinarily exciting implications for political discussion, journalism, corporate press releases, even the content of Facebook or eX-Twitter. Your task is to create a BS-meter that uses these methods to produce an intuitive test device accessible to anyone, perhaps with an international authentication body that can apply validated BS stamps to any text that deserves it. | ||
Latest revision as of 06:54, 18 October 2024
Client: Christopher Newfield, Independent Social Research Foundation <chris.newfield@isrf.org>
Recent research has used machine learning methods to apply the language philosophy of Wittgenstein, in a way that can quantify the likelihood of any particular text being bulls**t. These results have extraordinarily exciting implications for political discussion, journalism, corporate press releases, even the content of Facebook or eX-Twitter. Your task is to create a BS-meter that uses these methods to produce an intuitive test device accessible to anyone, perhaps with an international authentication body that can apply validated BS stamps to any text that deserves it.
