Ed Note: Gideon is speaking at ODSC Europe 2019, see his talk "Augmented Programming" there. Over the past decade, deep learning research has led to significant advances in perceptual tasks, such as object detection, face recognition, and speech recognition. In each of these use cases, raw real-world inputs have to be mapped into a normalized representation. Deep learning has also started to lead to significant advances in natural language processing, for example, where contextual embeddings enable multi-task transfer learning. A newer, emerging field for machine learning is in its application within the production, deployment, and maintenance of software.

There are many areas where machine learning is having a gradual impact on the software production process. One holy grail in this effort is program synthesis, where a program can be generated directly from natural language, explanations, or examples. Here, one of the most interesting avenues is the automatic creation of GUIs from sketches. Program-assisted code completion has also gotten significantly better, inspired by work related to speech recognition and machine translation -- in particular, a technology called "language modeling." With increasing research, bug fixing, and identification, applying machine learning by means of static analysis of code have also steadily improved, often from learning what bugs have been fixed in the past.