AI: the fabric of computing

During the summer, I spent a week at a summer school on deep learning (DL). There were several reasons to attend, but one was to simply learn more about this trending topic. In many ways, it was a wonderful, though humbling, experience as the field is progressing at a rate that’s simply phenomenal. There are constant breakthroughs in the area and the quality of models, in terms of accuracy and loss, is still improving rapidly with every year significant improvements being reported by various research groups (especially the industrial ones).

The ability of the prototypes presented during the sessions was amazing. For instance, machine learning (ML) now exceeds the ability of theoretical physicists to classify data in high energy physics. Deep learning models generate images that are amazingly realistic to the point of being indistinguishable from real photos. Classification in a wide range of problem areas is exceeding human ability, such as in medical images and understanding human speech.

Although machine learning traditionally was applied on data that initially was intended for human consumption, researchers and engineers are starting to realize that data at that level already has been processed and abstracted quite significantly. Consequently, there’s a tendency to move machine learning closer to the lower levels in systems where the raw data is generated. This allows ML techniques to detect features in the data that have been filtered out of higher-level data that has been processed for human consumption.