What does back propagation of errors indicate about an artificial neural network's predictions?

Back propagation of errors is a fundamental algorithm used in training artificial neural networks. It takes the output of the network and compares it to the actual target values, thereby identifying the difference, or "error." This process helps in adjusting the weights of the connections in the network to minimize the error, enabling the network to make better predictions in subsequent iterations.

When back propagation reveals significant errors in the network's predictions, it indicates that the current weights and biases in the model are not aligned well with the data. This is essential for refining the model because it shows that adjustments need to be made based on these discrepancies to improve accuracy. Hence, the assertion that the network is making predictions that are turning out to be very wrong is correct, as back propagation is a response to the observed inaccuracies in the predictions.

Other options do not accurately capture the implication of back propagation. A perfectly accurate network would not need to adjust its weights, which is contrary to the purpose of back propagation. While a lack of sufficient data can lead to poor predictions, back propagation itself does not directly indicate data scarcity. Additionally, resetting parameters might be a superficial fix that does not inherently address the underlying errors, whereas back propagation focuses on learning from mistakes.