Final answer:
Backpropagation with a steep gradient descent indicates significant errors in the neural network's predictions. These large error gradients lead to substantial adjustments in weights and biases, implying the network's predictions are far from accurate.
Step-by-step explanation:
When a data scientist mentions that the backpropagation of errors is correcting for guesses that have a steep gradient descent, it reveals something about the learning process of the neural network. This means that the network's predictions are initially far off from the expected values, leading to a large error being propagated backward through the network. The 'steep' descriptor suggests that adjustments made to the network parameters (weights and biases) are significant, which happens when the error gradient—the directional signal that tells us how to change our parameters to reduce error—is large.
In the context of an artificial neural network, a steep gradient descent indicates that the model's predictions are not yet accurate, and substantial changes are being made to improve the network's performance. A very steep gradient often signifies that the model is not close to converging to a minimum of the cost function. Hence, the correct interpretation of the statement is that the network is making predictions that are turning out to be very wrong.