It is indeed becoming increasingly rarer for astronomers to visit sites and perform actual observations, as manual control of telescopes, when necessary, is often done remotely from the comfort of one’s home; and any collaboration is simply done via zoom.

Machine learning has been used as a tool to optimize manual procedures since the 20th century, in fact, my colleagues always used to joke that the USPS mail system had been using machine learning since the 1960s to help sort the mail, whereas astronomers have just recently come to appreciate its application in our field due to the big data transition we are in.

Even if we could visualize the connections made by my machine learning engine, it would have been futile, as we ultimately would have made little sense of it.

As astronomers we use machine learning for object recognition, signal predictions and even as a tool to manage our instruments.

These developments have led to the creation of broker systems that take in telescope data, apply machine learning to distinguish certain objects, and then forward along the information to science teams interested in the particular phenomena.

Much data in the coming century will go unnoticed, even with the help of our machine learning programs; but I suppose that is a beautiful thing — anyone can do astronomy by simply downloading public data from their computer.

We need all the help we can get, because while machine learning has tremendous utility, in its current state it still cannot be compared to the eyes and brains we are gifted with, of which there are simply not enough.