Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systems

• URL: http://arxiv.org/abs/2003.05434v2
• Date: Fri, 23 Apr 2021 09:21:38 GMT
• Title: Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systems
• Authors: Silverio Mart\'inez-Fern\'andez, Xavier Franch, Andreas Jedlitschka, Marc Oriol, and Adam Trendowicz
• Abstract summary: 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.
• Score: 8.27310353898034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Companies dealing with Artificial Intelligence (AI) models in Autonomous Systems (AS) face several problems, such as users' lack of trust in adverse or unknown conditions, gaps between software engineering and AI model development, and operation in a continuously changing operational environment. This work-in-progress paper aims to close the gap between the development and operation of trustworthy AI-based AS by defining an approach that coordinates both activities. We synthesize the main challenges of AI-based AS in industrial settings. We reflect on the research efforts required to overcome these challenges and propose a novel, holistic DevOps approach to put it into practice. We elaborate on four research directions: (a) increased users' trust by monitoring operational AI-based AS and identifying self-adaptation needs in critical situations; (b) integrated agile process for the development and evolution of AI models and AS; (c) continuous deployment of different context-specific instances of AI models in a distributed setting of AS; and (d) holistic DevOps-based lifecycle for AI-based AS.

Related papers

• Transforming the Hybrid Cloud for Emerging AI Workloads [81.15269563290326]
  This white paper envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads. The proposed framework addresses critical challenges in energy efficiency, performance, and cost-effectiveness. This joint initiative aims to establish hybrid clouds as secure, efficient, and sustainable platforms.
  arXiv  Detail & Related papers  (2024-11-20T11:57:43Z)
• Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization [53.80919781981027]
  Key requirements for trustworthy AI can be translated into design choices for the components of empirical risk minimization. We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.
  arXiv  Detail & Related papers  (2024-10-25T07:53:32Z)
• Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies [3.3374611485861116]
  Large language model (LLM) based artificial intelligence technologies have been a game-changer, particularly in sentiment analysis. However, integrating diverse AI models for processing complex multimodal data and the associated high costs of feature extraction presents significant challenges. This study introduces a collaborative AI framework designed to efficiently distribute and resolve tasks across various AI systems.
  arXiv  Detail & Related papers  (2024-10-17T06:14:34Z)
• Science based AI model certification for new operational environments with application in traffic state estimation [1.2186759689780324]
  The expanding role of Artificial Intelligence (AI) in diverse engineering domains highlights the challenges associated with deploying AI models in new operational environments. This paper proposes a science-based certification methodology to assess the viability of employing pre-trained data-driven models in new operational environments.
  arXiv  Detail & Related papers  (2024-05-13T16:28:00Z)
• Science based AI model certification for untrained operational environments with application in traffic state estimation [1.2186759689780324]
  The expanding role of Artificial Intelligence (AI) in diverse engineering domains highlights the challenges associated with deploying AI models in new operational environments. This paper proposes a science-based certification methodology to assess the viability of employing pre-trained data-driven models in untrained operational environments.
  arXiv  Detail & Related papers  (2024-03-21T03:01:25Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
  The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs) It examines the challenges introduced by AI components and the impact on testing procedures. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
  arXiv  Detail & Related papers  (2024-02-21T08:29:42Z)
• A Vision for Operationalising Diversity and Inclusion in AI [5.4897262701261225]
  This study seeks to envision the operationalization of the ethical imperatives of diversity and inclusion (D&I) within AI ecosystems. A significant challenge in AI development is the effective operationalization of D&I principles. This paper proposes a vision of a framework for developing a tool utilizing persona-based simulation by Generative AI (GenAI)
  arXiv  Detail & Related papers  (2023-12-11T02:44:39Z)
• 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)
• Proceedings of the Robust Artificial Intelligence System Assurance (RAISA) Workshop 2022 [0.0]
  The RAISA workshop will focus on research, development and application of robust artificial intelligence (AI) and machine learning (ML) systems. Rather than studying robustness with respect to particular ML algorithms, our approach will be to explore robustness assurance at the system architecture level.
  arXiv  Detail & Related papers  (2022-02-10T01:15:50Z)
• Data-Driven and SE-assisted AI Model Signal-Awareness Enhancement and Introspection [61.571331422347875]
  We propose a data-driven approach to enhance models' signal-awareness. We combine the SE concept of code complexity with the AI technique of curriculum learning. We achieve up to 4.8x improvement in model signal awareness.
  arXiv  Detail & Related papers  (2021-11-10T17:58:18Z)

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.