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  "documentTitle": "2022 Air Street Capital The State of AI Report 2022",
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      "text": "One of the decisive moments in mathematics is formulating a conjecture, or a hypothesis, on the relationship between variables of interest. This is often done by observing a large number of instances of the values of these variables, and potentially using data-driven conjecture generation methods. But these are limited to low-dimensional, linear, and generally simple mathematical objects.",
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      "text": "In a Nature article, DeepMind researchers proposed an iterative workflow involving mathematicians and a supervised ML model (typically a NN). Mathematicians hypothesize a function relating two variables (input X(z) and output Y(z)). A computer generates a large number of instances of the variables and a NN is fit to the data. Gradient saliency methods are used to determine the most relevant inputs in X(z). Mathematicians can turn refine their hypothesis and/or generate more data until the conjecture holds on a large amount of data.",
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