The Brains Behind the Operation
A blog about hiddenMind products and engineering
While the human brain contains roughly 86 billion neurons of staggering complexity, artificial intelligence has conquered numerous cognitive tasks using a dramatically simplified version.
An artificial neuron, often referred to as a “perceptron,” is a fundamental computational unit inspired by (but much simpler than) the way biological neurons work in the human brain. It takes multiple input values, applies individual weights to them, sums up these weighted inputs, and then passes the result through an activation function.
The process can be broken down into several steps:
Input values: An artificial neuron receives input values, which represent various features or characteristics of the data being processed.
Weights: Each input is associated with a weight, which indicates the significance or influence of that input on the neuron’s output.
Weighted sum: The neuron calculates the weighted sum of its inputs by multiplying each input by its corresponding weight and then summing up these products.
Activation function: The weighted sum is then passed through an activation function. This function introduces non-linearity into the neuron’s behavior and determines whether the neuron “fires” (activates) or remains dormant based on the calculated result.
Artificial neurons are the foundation of neural networks, which consist of layers of interconnected neurons. The connections between neurons, characterized by the weights, are learned during the training process using optimization techniques. Through this process, neural networks can learn to perform tasks such as image recognition, natural language processing, and more, by adjusting the weights and activation functions to minimize prediction errors.
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