A comparison of AI and the human brain

When AI was first thought of, it was meant to replicate how the human brain works. In our brain, we have something called neurons, which are the basic building block of the brain. What they do is receive input from the outside world and transfer it into messages the rest of the body can understand. This may sound familiar to how machine learning works since a model is given input and then produces an output. This is also why when referencing the anatomy of the machine learning model we talk about individual neurons because the math behind these neurons is meant to a simple replica of a human brain neuron.

In a better understanding of the calculation performed inside a neuron in a machine learning model, the following functions are performed:

Givens: x = input, y = output, w = weight, b = bias, g = activation function

Finding(s): y = output

Equations/Math: z = w*x + b

y = g(z)

ex of sigmoid: g(z) = 1/(1 + e^(-z))

Answer: y = activation function(sum of all(wx+b))