Teresa Tung
Teresa Tung is a Managing Director in Accenture Labs responsible for its global Software & Platforms R&D group whose projects include semantic modeling, edge analytics, and robotics. Teresa leads her team in evaluating best-of-breed next-generation software architecture solutions from industry, start-ups and academia for relevance to Accenture and its clients, then building experimental prototypes and delivering pioneering pilot projects.   Accenture’s most active inventor with over 150 patents filed or granted, Teresa has had several leadership roles in her career with Accenture. In previous roles, she led Accenture Labs research and development activities for Cloud, Big Data, the Internet of Things and APIs, creating a wide range of key assets that are still in use across Accenture and with clients today.   Teresa holds a Ph.D. in Electrical Engineering and Computer Science from the University of California at Berkeley. She is based in Accenture’s San Francisco Innovation Hub.

Using a Domain Knowledge Graph to Manage AI at Scale

Most of todays AI is narrow and fit-for-purpose, and we do not reuse the learnings across use cases. Even where we might re-apply the same techniques, in each implementation AI practitioners re-apply and re-tune those techniques anew.   Indeed, much of the work is specific to the particulars of the available data and use case. But many times, the questions and the techniques are the same e.g., to predict failure, to prescribe bill of materials or workplan, or to advise users via virtual agents.   We describe the technology and organizational ways of working being applied across Accenture that creates a framework for the reapplication of learnings, automated application of the techniques, and then self-learning to optimize meaning we can reuse AI learnings and configurations across use cases and across clients   At its heart is a domain knowledge the same technology proven to scale with Internet search—to capture domain-specific particulars. The knowledge graph is the structure that encapsulates the human knowledge of an industry domain and captures the learnings.   This knowledge allows for bootstrapping the AI system to reason about what can be reused and how and for self-learning what works and what doesn't.   We will walk through real case studies that show its impact in Oil & Gas, Financial Services, and Enterprise IT.