PresAIse, A Prescriptive AI Solution for Enterprises
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916123157463040 |
|---|---|
| author | Sun, Wei McFaddin, Scott Tran, Linh Ha Subramanian, Shivaram Greenewald, Kristjan Tenzin, Yeshi Xue, Zack Drissi, Youssef Ettl, Markus |
| author_facet | Sun, Wei McFaddin, Scott Tran, Linh Ha Subramanian, Shivaram Greenewald, Kristjan Tenzin, Yeshi Xue, Zack Drissi, Youssef Ettl, Markus |
| contents | Prescriptive AI represents a transformative shift in decision-making, offering causal insights and actionable recommendations. Despite its huge potential, enterprise adoption often faces several challenges. The first challenge is caused by the limitations of observational data for accurate causal inference which is typically a prerequisite for good decision-making. The second pertains to the interpretability of recommendations, which is crucial for enterprise decision-making settings. The third challenge is the silos between data scientists and business users, hindering effective collaboration. This paper outlines an initiative from IBM Research, aiming to address some of these challenges by offering a suite of prescriptive AI solutions. Leveraging insights from various research papers, the solution suite includes scalable causal inference methods, interpretable decision-making approaches, and the integration of large language models (LLMs) to bridge communication gaps via a conversation agent. A proof-of-concept, PresAIse, demonstrates the solutions' potential by enabling non-ML experts to interact with prescriptive AI models via a natural language interface, democratizing advanced analytics for strategic decision-making. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02006 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PresAIse, A Prescriptive AI Solution for Enterprises Sun, Wei McFaddin, Scott Tran, Linh Ha Subramanian, Shivaram Greenewald, Kristjan Tenzin, Yeshi Xue, Zack Drissi, Youssef Ettl, Markus Machine Learning Prescriptive AI represents a transformative shift in decision-making, offering causal insights and actionable recommendations. Despite its huge potential, enterprise adoption often faces several challenges. The first challenge is caused by the limitations of observational data for accurate causal inference which is typically a prerequisite for good decision-making. The second pertains to the interpretability of recommendations, which is crucial for enterprise decision-making settings. The third challenge is the silos between data scientists and business users, hindering effective collaboration. This paper outlines an initiative from IBM Research, aiming to address some of these challenges by offering a suite of prescriptive AI solutions. Leveraging insights from various research papers, the solution suite includes scalable causal inference methods, interpretable decision-making approaches, and the integration of large language models (LLMs) to bridge communication gaps via a conversation agent. A proof-of-concept, PresAIse, demonstrates the solutions' potential by enabling non-ML experts to interact with prescriptive AI models via a natural language interface, democratizing advanced analytics for strategic decision-making. |
| title | PresAIse, A Prescriptive AI Solution for Enterprises |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.02006 |