Summary of Domain Adaptable Prescriptive Ai Agent For Enterprise, by Piero Orderique et al.
Domain Adaptable Prescriptive AI Agent for Enterprise
by Piero Orderique, Wei Sun, Kristjan Greenewald
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces PrecAIse, a proof-of-concept conversational agent developed at the MIT-IBM Watson AI Lab, designed to make advanced causal inference and prescriptive analytics accessible to enterprise users. The agent, equipped with a suite of tools, enables natural language interactions for decision-making without requiring extensive ML or DS expertise. PrecAIse’s NLUI allows users to harness prescriptive analytics through conversational interfaces, eliminating the need for intensive computing resources. This technology aims to bridge the gap between technical complexity and practical adoption in enterprise settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special kind of computer program called an “agent” that helps people make better business decisions. The agent is designed to be easy to use, even if you don’t have advanced knowledge of computers or data analysis. It can understand and respond to natural language, allowing users to ask questions and get answers without needing to write complex code. The goal is to make it easier for businesses to adopt new technologies that help them make informed decisions. |
Keywords
» Artificial intelligence » Inference
