Augmented Programming
Augmented Programming


Over the past decade, deep learning research has led to significant advances on perceptual tasks, such as object detection, image understanding or speech recognition, where there is a need to translate what is perceived into some form of 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 to the problem of software development. Traditionally, software development infrastructure leverages heuristics or arbitrary decisions to optimize its performance. However, research is increasingly identifying areas where machine learning can help achieve better-than-human performance in the software domain.

In this talk, Gideon Mann, Head of Data Science in the Office of the CTO at Bloomberg, will look at some areas of the Code-Build-Test lifecycle where machine learning research has been applied, at Bloomberg and elsewhere. In the development of software code, potential machine learning applications include neural program synthesis (where code is generated automatically) or neural decomposition (where compiled code is reverse-engineered and re-written automatically in another programming language). During the build process, machine learning can be used to automatically optimize code or to perform fuzz testing, an automated testing approach to identify program exceptions like crashes or memory leaks. And finally, once code has been deployed, machine learning can be used for automated trace debugging and/or configuration management.


Gideon Mann is the head of Data Science at Bloomberg, guiding the strategic direction for machine learning, natural language processing, and search on the core terminal. At Bloomberg, his team has worked on the company-wide data science platform, natural language question answering, and deep learning text processing, among other products. He also founded and leads the Data for Good Exchange (, an annual conference on data science applications for social good and is a core member of the Shift Commission on Work, Workers and Technology ( Mann graduated Brown University in 1999 and subsequently received a Ph.D. from The Johns Hopkins University in 2006. His focus at Hopkins was natural language processing with a dissertation on multi-document fact extraction and fusion. After a short post-doc at UMass-Amherst working on problems in weakly supervised machine learning, he moved to Google Research in NYC in 2007. In addition to academic research, his team at Google built core internal machine learning libraries, and publicly released the Google Prediction API and coLaboratory, a collaborative iPython application. He joined Bloomberg's leadership team in the CTO department in 2014.

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from - Youtube
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google