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Artificial Intelligence

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    $\begingroup$ The short answer is "No." :) $\endgroup$ Commented Sep 1, 2022 at 21:12
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    $\begingroup$ Could you give more details? Do you have any information about the process of the event before it starts that could feasibly be used to make a prediction, as opposed to simply knowing that the event has been observed in the past (or is expected due to its nature) to occur with $p=0.5$? For instance, are sequential events not independent (a theoretical coin toss is independent of previous coin tosses)? $\endgroup$ Commented Sep 1, 2022 at 22:37
  • $\begingroup$ @NeilSlater Knowing more information also does not prove that the additional information is related to the probability of the event occurring, as I know the quality of the dice, the material from which it is made, etc. I put this information into the neural network and it doesn't increase the accuracy either. $\endgroup$ Commented Sep 1, 2022 at 22:44
  • $\begingroup$ If you're evaluating your neural network on the same set of data used to train the model, you could definitely get higher than 50% "accuracy". But, the accuracy would go down to 50% once evaluated on unseen data if the event is truly random (i.e. truly unrelated to any features fed into the neural network). $\endgroup$ Commented Sep 2, 2022 at 14:25
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    $\begingroup$ @clementzach True randomness may be a false proposition, since we can't find features that accurately predict it, it's true randomness, and once we find features that accurately predict it, it's no longer true randomness. $\endgroup$ Commented Sep 2, 2022 at 14:31