no code implementations • 23 Feb 2024 • Kinjal Basu, Ibrahim Abdelaziz, Subhajit Chaudhury, Soham Dan, Maxwell Crouse, Asim Munawar, Sadhana Kumaravel, Vinod Muthusamy, Pavan Kapanipathi, Luis A. Lastras
There is a growing need for Large Language Models (LLMs) to effectively use tools and external Application Programming Interfaces (APIs) to plan and complete tasks.
Existing federated learning models that follow the standard risk minimization paradigm of machine learning often fail to generalize in the presence of spurious correlations in the training data.
The inception of large language models has helped advance state-of-the-art performance on numerous natural language tasks.
We propose a paradigm for the construction of business automations using natural language.
APIs are everywhere; they provide access to automation solutions that could help businesses automate some of their tasks.
AI business process applications automate high-stakes business decisions where there is an increasing demand to justify or explain the rationale behind algorithmic decisions.
Robotic process automation (RPA) and its next evolutionary stage, intelligent process automation, promise to drive improvements in efficiencies and process outcomes.
Robotic process automation (RPA) has emerged as the leading approach to automate tasks in business processes.
In this survey, we study how recent advances in machine intelligence are disrupting the world of business processes.
To optimize operations of production-grade AI workflow platforms we can leverage existing scheduling approaches, yet it is challenging to fine-tune operational strategies that achieve application-specific cost-benefit tradeoffs while catering to the specific domain characteristics of machine learning (ML) models, such as accuracy, robustness, or fairness.
There is a large opportunity for infusing AI to reduce cost or provide better customer experience, and the business process management (BPM) literature is rich in machine learning solutions including unsupervised learning to gain insights on clusters of process traces, classification models to predict the outcomes, duration, or paths of partial process traces, extracting business process from documents, and models to recommend how to optimize a business process or navigate decision points.
no code implementations • 14 Sep 2019 • K. R. Jayaram, Vinod Muthusamy, Parijat Dube, Vatche Ishakian, Chen Wang, Benjamin Herta, Scott Boag, Diana Arroyo, Asser Tantawi, Archit Verma, Falk Pollok, Rania Khalaf
This paper describes the design, implementation and our experiences with FfDL, a DL platform used at IBM.
no code implementations • 16 Nov 2017 • Tejas Dharamsi, Payel Das, Tejaswini Pedapati, Gregory Bramble, Vinod Muthusamy, Horst Samulowitz, Kush R. Varshney, Yuvaraj Rajamanickam, John Thomas, Justin Dauwels
In this work, we present a cloud-based deep neural network approach to provide decision support for non-specialist physicians in EEG analysis and interpretation.
2 code implementations • 18 Sep 2017 • Bishwaranjan Bhattacharjee, Scott Boag, Chandani Doshi, Parijat Dube, Ben Herta, Vatche Ishakian, K. R. Jayaram, Rania Khalaf, Avesh Krishna, Yu Bo Li, Vinod Muthusamy, Ruchir Puri, Yufei Ren, Florian Rosenberg, Seetharami R. Seelam, Yandong Wang, Jian Ming Zhang, Li Zhang
Deep learning driven by large neural network models is overtaking traditional machine learning methods for understanding unstructured and perceptual data domains such as speech, text, and vision.
Distributed, Parallel, and Cluster Computing