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[D] "AI systems are always deterministic," AI teacher says. How can I reply (with examples and papers)? (alien.top)

submitted 1 year ago by shiftingsmith@alien.top to c/machinelearning@academy.garden

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I had a discussion in class with one of my teachers. He says that AI is and can only be always deterministic because "even a deep learning neural network is a set of equations running on a computer, and the stochastic factor is added at the beginning. But the output of a model is always deterministic, even if it's not interpretable by humans."

How would you reply? (Possibly with examples and papers)

Tysm!

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[–] IQroniK@alien.top 1 points 1 year ago

Answer to him with 4.1 :) smth like this: This is an interesting discussion, and you are making an important argument about determinism in the performance of artificial intelligence (AI) and neural networks! But, there are:

1. Determinism in AI and Neural Networks

In general, your lecturer is correct that most AI algorithms, including neural networks, are deterministic in the sense that they are a fixed set of mathematical operations. If you put the same input data set into an AI model, you will get the same result every time you run it, provided the model and input data do not change.

Example:

Consider a neural network for image recognition. If you give the same image as input, the recognition result will be the same each time you run it, because the mathematical operations performed by the network will be the same.

2. Stochastic Factor and Indeterminacy

However, there are aspects in AI where stochasticity plays a role:

Initialization of Weights: In machine learning, especially deep learning, the initial weights of a neural network are often initialized randomly. This means that different initializations can lead to different learning paths and possibly different results.
Stochastic Gradient Descent: Many learning algorithms use stochastic gradient descent, where the training data is sampled randomly in each iteration.

Example:

Suppose we are training a neural network for image classification. If the initial weights and the order in which the data is fed in each training session are different, the final model will probably produce different results on the same input image.

3. The Role of Randomness in Some AI Algorithms

In some AI algorithms, such as genetic algorithms or Monte Carlo-based methods, randomness is an important part of the process.

Example:

Genetic algorithms use random mutations and crossovers to generate new solutions, which makes their results non-deterministic in a sense.

Conclusion

In summary, while the basic operation of a neural network or other AI algorithm may be deterministic, the processes that lead to the creation and tuning of these algorithms often involve stochastic elements. This means that there may be non-deterministic aspects to the overall process of creating and using AI models.