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!
Determinism in computational models, including binary systems, relies on the ability to reproduce results given the same initial conditions and operations. In a deterministic system, if you run the same sequence of instructions with the same inputs (including the seed for random number generation), you should expect the same output every time, assuming the system is isolated from external non-deterministic factors.
Multithreading introduces non-determinism when threads operate in a shared environment and their execution order affects the outcome. It’s the responsibility of the programmer to manage this through synchronization mechanisms to ensure deterministic behavior if required.
There is also analog and quantum computers. Analog computers work on the principle of approximation and continuous variable manipulation, which can introduce non-deterministic elements due to physical variations. Quantum computers operate on quantum bits (qubits) and can produce non-deterministic results because they exploit quantum superposition and entanglement.
In the context of machine learning models and AI, these principles apply as well. Binary-based AI will be deterministic if the conditions are controlled, while quantum and analog AI might introduce non-deterministic elements by their nature or design for efficiency.