Ray Summit 2020

Machine learning can be viewed as heralding the emergence of a new field of engineering, in a manner similar to the emergence of chemical engineering from the chemistry, thermodynamics, and fluid mechanics, or to the emergence of electrical engineering from electromagnetism. In both cases, new mathematical and conceptual tools were needed to support the ambitions of the new engineering field, allowing real-world systems to be built at scale.

In the case of machine learning, the subject matter is the blending of data, inference, and computing that we see in emerging systems and business models that we find in areas such as commerce, transportation, entertainment, and health care. The underlying scientific foundations are provided by the computational and inferential disciplines. But new mathematical and conceptual tools are needed to allow this field to emerge, and to provide support for building large-scale systems that are understandable, robust, transparent, safe, and useful. Most notably, while earlier engineering fields focused on physical materials, the new field focuses on data, decisions, and human context.