How might global companies like Google and Nvidia utilize the concept of artificial neurons in AI to enhance their products and services?

In the context of AI, 'weights' and 'biases' are parameters of the artificial neurons in a neural network. 'Weights' are the strength or amplitude of the input signal, which are adjusted during the learning process to minimize the error in the network's output. 'Biases', on the other hand, are additional parameters that allow the activation function (the function that determines whether a neuron should be activated or not) to be shifted to the left or right, which can make the neuron more flexible in fitting the data. Both 'weights' and 'biases' are crucial in the learning process as they are continuously updated and optimized to improve the network's predictions.

Some alternative methods to using artificial neurons in AI for learning and computation include decision trees, support vector machines, and Bayesian networks. Decision trees are a type of model used for classification and regression tasks. Support vector machines are supervised learning models used for classification and regression analysis. Bayesian networks are a type of statistical model that represents a set of variables and their conditional dependencies via a directed acyclic graph. Genetic algorithms and swarm intelligence are also alternative methods used in AI.