Natural Language Interface for Prescriptive AI Solutions
in Enterprise
Name
orderique-porderiq-meng-eecs-2024-thesis.pdf
Description
Thesis PDF
Size
5.16 MB
Format
Adobe PDF
Checksum (MD5)
e064c0efa9c358dd46b83618da1ebd97
Author(s)
Orderique, Piero
Advisor(s)
Greenewald, Kristjan
Shah, Devavrat
Date Issued
September 2024
Publisher
Massachusetts Institute of Technology
Abstract
Despite advancements in causal inference and prescriptive AI, its adoption in enterprise settings remains hindered primarily due to its complexity and lack of interpretability. This work at the MIT-IBM Watson AI Lab focuses on extending upon the proof-of-concept agent, PrecAIse, by designing a domain-adaptable conversational agent equipped with a suite of causal and prescriptive tools. The objective is to make advanced, novel causal inference and prescriptive tools widely accessible through natural language interactions. The presented Natural Language User Interface (NLUI) enables users with limited expertise in machine learning and data science to harness prescriptive analytics in their decision-making processes without requiring intensive compute. We present an agent capable of function calling, maintaining faithful, interactive, and dynamic conversations, and supporting new domains.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
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