What are weights and biases in AI?

June 12, 2023 

For the answer to this question check our AI Wiki source. 

Weights and biases are parameters added to regulate the signal from one neuron to another in artificial intelligence (AI) neural networks.

AI neural networks are meant to replicate to some extent the neural operations of the human brain. In human brains, neurons send signals to other neurons to activate mental associations that generate learning. Likewise, in AI neural networks neuromorphic chips called also "neurons" receive input and communicate signals to other layers of neurons to produce output. 

Weights regulate the importance of input variables within a signal. Important input should carry more weight into the signal than less important input. For example, let's assume that your AI model is calculating lifestyle habits that may correlate to good health. Variables include sleep, nutrition, exercise, meditation, relationships, income, etc. If sleep is more important than income, weights would be used to reflect that difference. Biases are added to influence output even in the absence of relative weights. For example, in the same health study is the person indicates yes to tobacco use, a bias may be added to reflect a health risk independent of the other input variables. 

                                                            

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Weights and biases are added to the AI models during the training sessions by which machines learn to improve the accuracy of their predictions and results. You can become an AI programmer, or an AI trainer. This is an ever growing field. Think about it.

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