A Novel Methodology For Crowdsourcing AI Models in an Enterprise

• URL: http://arxiv.org/abs/2103.14033v1
• Date: Mon, 22 Mar 2021 18:27:51 GMT
• Title: A Novel Methodology For Crowdsourcing AI Models in an Enterprise
• Authors: Parthasarathy Suryanarayanan, Sundar Saranathan, Shilpa Mahatma, Divya Pathak
• Abstract summary: We present a novel methodology aiming to facilitate this collaboration through crowdsourcing of AI models. We have implemented a system and a process that any organization can easily adopt to host AI competitions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The evolution of AI is advancing rapidly, creating both challenges and opportunities for industry-community collaboration. In this work, we present a novel methodology aiming to facilitate this collaboration through crowdsourcing of AI models. Concretely, we have implemented a system and a process that any organization can easily adopt to host AI competitions. The system allows them to automatically harvest and evaluate the submitted models against in-house proprietary data and also to incorporate them as reusable services in a product.

Related papers

• A Formal Model for Artificial Intelligence Applications in Automation Systems [41.19948826527649]
  This paper proposes a formal model using standards to provide clear and structured documentation of AI applications in automation systems. The proposed information model for artificial intelligence in automation systems (AIAS) utilizes design patterns to map and link various aspects of automation systems and AI software.
  arXiv  Detail & Related papers  (2024-07-03T15:05:32Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Choosing the Right Path for AI Integration in Engineering Companies: A Strategic Guide [4.327763441385369]
  The paper covers the entire life cycle of building AI solutions, from initial business understanding to deployment and further evolution. The framework might also help engineering companies choose the optimum AI approach to create business value.
  arXiv  Detail & Related papers  (2023-12-25T11:58:37Z)
• Generative AI [20.57872238271025]
  "generative AI" refers to computational techniques that are capable of generating seemingly new, meaningful content. The widespread diffusion of this technology with examples such as Dall-E 2, GPT-4, and Copilot is currently revolutionizing the way we work and communicate with each other.
  arXiv  Detail & Related papers  (2023-09-13T08:21:59Z)
• Unlocking the Potential of Collaborative AI -- On the Socio-technical Challenges of Federated Machine Learning [0.6767885381740952]
  Federated Machine Learning is a novel AI paradigm enabling the creation of AI models from decentralized, potentially siloed data. This research investigates the challenges of prevailing collaborative business models and distinct aspects of Federated Machine Learning.
  arXiv  Detail & Related papers  (2023-04-26T17:14:44Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)
• Human-Centered AI for Data Science: A Systematic Approach [48.71756559152512]
  Human-Centered AI (HCAI) refers to the research effort that aims to design and implement AI techniques to support various human tasks. We illustrate how we approach HCAI using a series of research projects around Data Science (DS) works as a case study.
  arXiv  Detail & Related papers  (2021-10-03T21:47:13Z)
• Validate and Enable Machine Learning in Industrial AI [47.20869253934116]
  Industrial AI promises more efficient future industrial control systems. The Petuum Optimum system is used as an example to showcase the challenges in making and testing AI models.
  arXiv  Detail & Related papers  (2020-10-30T20:33:05Z)
• Distributed and Democratized Learning: Philosophy and Research Challenges [80.39805582015133]
  We propose a novel design philosophy called democratized learning (Dem-AI) Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently. We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
  arXiv  Detail & Related papers  (2020-03-18T08:45:10Z)
• Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systems [8.27310353898034]
  This work-in-progress paper aims to close the gap between the development and operation of AI-based AS. We propose a novel, holistic DevOps approach to put it into practice.
  arXiv  Detail & Related papers  (2020-03-11T17:52:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.