Can Pi be predicted by a neural network?

In summary, a neural network could be used to calculate Pi, but it would only prove what we already know.
  • #1
arnesinnema
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Hi all,

Would it be possible to try to predict Pi by a neural net. So basically when you input 1 into the neural net you will train it to output 3. Next input 2 and train it to ouput 1. Etc... Next after training the net such that it will predict the first 1000 positions correctly would it predict the 1001th, 1002th, etc... position correctly.

If so Pi is not random. Otherwise Pi is random.

Would this be possible?
 
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  • #2
No.
 
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  • #3
Why not?
 
  • #4
arnesinnema said:
If so Pi is not random. Otherwise Pi is random.

Pi is not random.
 
  • #5
You don't understand what a neural net does. They are complex models which have many parameters and can approximate arbitrary mathematical functions. You need to train them on large amounts of data to tune the parameters and fit the model to your training data.

There are simple series expansions which can already calculate Pi accurately.
 
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  • #6
Hmm okay, so it probably would work but it would only prove what we already know.
 
  • #7
arnesinnema said:
Hmm okay, so it probably would work

I'm not sure how you got that out of the replies so far. It would not work.
 
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  • #8
Okay thanks for the explanation.
 
  • #9
arnesinnema said:
Hmm okay, so it probably would work but it would only prove what we already know.
No it wouldn't. Neural networks detect patterns. The digits of pi have no pattern.
 
  • #10
I disagree with everyone saying it wouldn't work outright. I think it might be possible, given a large enough network that has some recursion.

Basically with a large number of nodes a NN can represent any differentiable function. The Bailey–Borwein–Plouffe formula rapidly converges to pi, and is a summation. It also be used to give you the Nth hexadecimal digit of pi. So if you can accurately represent the formula by your network, it should be able to give you the Nth hexadecimal digit of pi. Bellard's formula does the same in binary digits.

Now, ASSUMING there exists a formula similar to BPP that will give you the Nth DECIMAL digit, (is there one?) If the neural network can simulate the other two accurately, I see no reason why it could not do the same for decimal digits.

I would believe that you couldn't just use some basic feed-forward NN, but would have to have something much deeper that at minimum loops backward.

This is an interesting project. If you want to work on it and write a paper it would be interesting.EDIT: I should add that I'm currently using neural networks in my research.
 
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  • #11
Oke thanks for the positive feedback that's what this world needs more.

At the moment however I have no interest and opportunity to work on this. I put this in the open so I guess it's open-source now.
 
  • #12
No problem, its actually quite an interesting problem and perhaps I'll look into it further, though I need to finish up these 3 other projects first.
 

1. How does a neural network predict Pi?

A neural network uses a series of interconnected nodes to process data and make predictions. In the case of predicting Pi, the neural network is trained on a dataset of known values of Pi and learns to recognize patterns and make accurate predictions.

2. Can a neural network accurately predict Pi?

Yes, a well-trained neural network can accurately predict Pi within a certain margin of error. However, the accuracy of the predictions depends on the quality and size of the training dataset, as well as the complexity of the neural network architecture.

3. How is the accuracy of a neural network's Pi prediction measured?

The accuracy of a neural network's Pi prediction is measured by comparing the predicted values to the actual values of Pi. This can be done by calculating the mean squared error or other metrics such as root mean squared error or mean absolute error.

4. Are there any limitations to using a neural network to predict Pi?

Yes, there are some limitations to using a neural network to predict Pi. One limitation is the availability and quality of the training data. Another limitation is the complexity of the neural network model, which can lead to overfitting if not properly optimized.

5. How can the accuracy of a neural network's Pi prediction be improved?

The accuracy of a neural network's Pi prediction can be improved by using a larger and more diverse training dataset, optimizing the neural network architecture, and fine-tuning the training process. Regular validation and testing can also help identify and address any issues with the predictions.

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