Water Advisor - A Data-Driven, Multi-Modal, Contextual Assistant to Help With Water Usage Decisions

Authors

  • Jason Ellis IBM Research
  • Biplav Srivastava IBM Research
  • Rachel Bellamy IBM Research
  • Andy Aaron IBM Research

DOI:

https://doi.org/10.1609/aaai.v32i1.11374

Keywords:

Decision Support, Water Quality, NLP, Data Analysis, User Interfaces, Chatbots, Regulations, Ethics

Abstract

We demonstrate Water Advisor, a multi-modal assistant to help non-experts make sense of complex water quality data and apply it to their specific needs. A user can chat with the tool about water quality and activities of interest, and the system tries to advise using available water data for a location, applicable water regulations and relevant parameters using AI methods.

AAAI 2018 Proceedings Cover

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Published

2018-04-29

How to Cite

Ellis, J., Srivastava, B., Bellamy, R., & Aaron, A. (2018). Water Advisor - A Data-Driven, Multi-Modal, Contextual Assistant to Help With Water Usage Decisions. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11374

Issue

Vol. 32 No. 1 (2018): Thirty-Second AAAI Conference on Artificial Intelligence

Section

Demonstrations