# Being caution with New Science ML/DL/NN: Part one – Prompting Caution

According to the bureau of labor statistics Computer and Information Research Scientists jobs are projected to grow 16%. While I believe that one of the “hot words” for now is Machine Learning, the applications and use cases can only speak for themselves. This field is growing exponentially fast and I feel that its foundation is somewhat shaky. This opinion is based off the answers I have received from the following three questions:

• How does one appropriately develop a neural network and determine how good it is?
• Is there any statistical/mathematical approach that has more foundation that would be better to use than a Neural Network?
• If this product goes into production how can we guarantee that the model can be updated, built upon and changed based on the changes of the user?