|
|
|
|
|
|
by yogthos
2912 days ago
|
|
|
Neural networks are graphs that evolve at runtime by balancing their weights based on reinforcement, and as far as I know there hasn't been much success in using formal methods for AI. I do think theorem provers can be useful in certain contexts, and I can see AI using these tools to solve problems. |
|
|
This is not correct in the current state of tech. Neural networks are parametrized equations systems. You train the parameters on a dataset in a training phase, then freeze the result, then distribute the model to devices. Once distributed, the "neural network" can't be modified, and stop to "learn" new cases.
Edit : I mean, you are not completely wrong, you described the training phase of the neural network. That's only half of the story tho