How Google Uses AI and Machine Learning in the Enterprise


In this session Rich Dutton, the head of Enterprise AI at Google and an Adjunct Professor at Yeshiva University (formerly of Microsoft and Barclays), will give an overview of the work done by his team in applying AI techniques to running the business of Google or other businesses.

He'll first talk about the work done by the team through a series of examples across the areas of Support (e.g. smart ticket routing and ticket resolution as well as trends analysis), HR (e.g. performance management, career growth and space planning), Facilities Management (e.g. anomaly detection and control of HVAC units) and Communications (e.g. document classification, information extraction and document similarity and deduplication).

Having established the motivation to build such a team, Rich will then describe the team's mission, the types of work the team does and does not take on, how the team is structured (in terms of the numbers, types and locations of roles) and operates with other teams in engineering and beyond.

Finally, Rich will deep dive into a number of technical considerations, lessons learned and relevant research areas, when applying AI technologies to enterprise data. This will include ML Fairness (to ensure models aren't biased against certain categories of individuals), Interpretability (to provide insight into models' functioning and ensure they are working robustly and as desired - not by accident and unlikely to generalize to future data), Privacy-Preserving ML (including Differential Privacy, Federated Learning, Secure Computation and Remote Execution) and AutoML (automatically generating ML models for data).


Rich Dutton is the Head of Machine Learning for Corporate Engineering at Google, where he leads a team of 15 engineers and data scientists across NYC and Austin. Prior to this role, Rich was a tech lead in Bigtable at Google following a 15 year career working in data and analytics across both tech and finance in the US (New York and Seattle), Europe and Asia.

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