<s>[INST] Why do artificial neurons use a bias term? Shouldn't only using weights be enough? [/INST] Artificial neurons use bias terms to allow them to represent an intercept or an offset in the output they produce. In simple linear regression models, the bias term can be thought of as a y-intercept, which is a constant value that is added to the sum of the weighted inputs to produce the output.

In neural networks, the bias term allows neurons to shift the activation function to the left or right, even if all of the input values are zero. This is important because it helps to prevent the network from always outputting the same value (e.g. 0) and allows the network to model more complex relationships between the inputs and outputs.

In summary, while it is possible to model complex relationships using only weights, the bias term provides additional flexibility to the model and helps to prevent certain types of limitations or restrictions in the output that would otherwise occur if only weights were used.</s>