HI
Still, the relationship between prime numbers is complex, and if artificial intelligence claims to be able to understand complex relationships, it must be able to distinguish these numbers from other numbers. I did an experiment and taught artificial intelligence the prime numbers from 1 to 1000 and The answer was interesting. Almost most of the methods in MATLAB did not give any correct diagnosis of primeness (even one correct diagnosis!!) and only three methods CubicSVM, QSVM, and RUBoosted Trees were able to convert one-third of these prime numbers to recognize correctly (after training, of course)
Question: Is there a way to increase this detection rate to 90%?
Dear Seyed Hossein A,hmadi ,
Artificial Itelligence (AI) can be used to find prime numbers. There are various algorithms, both traditional and more advanced, that can be implemented using AI techniques. Here are a few methods:
While traditional methods are often sufficient for finding primes, the application of AI techniques can be useful when dealing with more complex problems or when optimizing for very large numbers. Keep in mind that the choice of method depends on the specific requirements and constraints of the problem at hand.
Thank you very much Mr. Mizrah
My intention was more than asking this question to compare different machine learning methods, some of which had zero efficiency for this question, and this was strange to me, that is, I taught prime numbers from 1 to 1000, and most of the methods could not even learn a single number for the test stage. If they guess correctly, in the next step, I want to teach the first 10,000 numbers repeat the test, and check the reason why most methods have zero accuracy. Why? And some methods were only 30% accurate. While the same methods are 60 to 70 percent accurate in other matters.
I mean, what complex relationship exists between prime numbers that AI can't figure out?
Suggested steps to improve the detection rate:
1. Training Data: Consider expanding the training dataset to include a wider range of prime numbers. Including larger primes in the training set can help the model learn more complex patterns and generalize better to unseen primes.
2. Feature Engineering: Experiment with different features or representations of numbers that could help the model better capture the characteristics of prime numbers. For example, you could explore number theory-based features, such as the presence of certain divisors or the distribution of digits.
3. Algorithm Selection: Try different machine learning algorithms or variations of the existing ones to see if they perform better on the prime number detection task. Consider exploring deep learning architectures, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), which have shown success in various pattern recognition tasks.
4. Hyperparameter Tuning: Optimize the hyperparameters of the chosen machine learning algorithms to improve their performance. This could involve adjusting parameters like learning rate, regularization strength, or network architecture to find the best settings for the specific task of prime number detection.
5. Ensemble Methods: Combine multiple machine learning models using ensemble techniques. Ensemble methods, such as bagging or boosting, can often improve classification performance by aggregating the predictions of multiple models.
6. Data Augmentation: Consider generating additional synthetic data to augment the training set. This could involve applying transformations or perturbations to existing prime numbers to create new examples that the model can learn from.
7. Model Interpretability: Analyze the misclassifications made by the model and try to understand the patterns or characteristics that lead to incorrect predictions. This insight can help refine the model or guide feature engineering efforts.
Hope it helps:credit AI
There is simple and classic method: Erathosfene sieve. It is easy to create computer program. Try it.
Work on progress...
Article Calculating prime numbers given the amount of them
a new method based on machine learning methods is used to find prime numbers. In this way, by training a regression model using the sum of the previous prime numbers, the next prime number can be calculated with a very small error. Even the next one hundred prime numbers can be guessed. The error of this calculation is very small and this error is alternating (sine or cosine) and it becomes zero in some places. Therefore, it can be said that due to zero error, the next prime number must exist and this theorem proves the infinity of prime numbers.
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