AI Competitions and Benchmarks, Practical issues: Proposals, grant money, sponsors, prizes, dissemination, publicity

• URL: http://arxiv.org/abs/2401.04452v1
• Date: Tue, 9 Jan 2024 09:33:59 GMT
• Title: AI Competitions and Benchmarks, Practical issues: Proposals, grant money, sponsors, prizes, dissemination, publicity
• Authors: Magali Richard (TIMC-MAGe), Yuna Blum (IGDR), Justin Guinney, Gustavo Stolovitzky, Adrien Pav\~ao (LRI)
• Abstract summary: This chapter provides a comprehensive overview of the pragmatic aspects involved in organizing AI competitions. We begin by discussing strategies to incentivize participation, touching upon effective communication techniques. We then shift to the essence of community engagement, and into organizational best practices and effective means of disseminating challenge outputs.
• Score: 0.21098119797209025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This chapter provides a comprehensive overview of the pragmatic aspects involved in organizing AI competitions. We begin by discussing strategies to incentivize participation, touching upon effective communication techniques, aligning with trending topics in the field, structuring awards, potential recruitment opportunities, and more. We then shift to the essence of community engagement, and into organizational best practices and effective means of disseminating challenge outputs. Lastly, the chapter addresses the logistics, exposing on costs, required manpower, and resource allocation for effectively managing and executing a challenge. By examining these practical problems, readers will gain actionable insights to navigate the multifaceted landscape of AI competition organization, from inception to completion.

Related papers

• The Transformative Impact of AI and Deep Learning in Business: A Literature Review [0.0]
  This paper aims to review the radical role of AI and deep learning in various functional areas of the business. It covers material applications in the healthcare sector, the retail and manufacturing industry, agriculture and farming, and finance.
  arXiv  Detail & Related papers  (2024-10-30T20:35:03Z)
• The Imperative of Conversation Analysis in the Era of LLMs: A Survey of Tasks, Techniques, and Trends [64.99423243200296]
  Conversation Analysis (CA) strives to uncover and analyze critical information from conversation data. In this paper, we perform a thorough review and systematize CA task to summarize the existing related work. We derive four key steps of CA from conversation scene reconstruction, to in-depth attribution analysis, and then to performing targeted training, finally generating conversations.
  arXiv  Detail & Related papers  (2024-09-21T16:52:43Z)
• From Stem to Stern: Contestability Along AI Value Chains [21.781422547251676]
  This workshop will grow and consolidate a community of interdisciplinary CSCW researchers focusing on the topic of contestable AI. As an outcome of the workshop, we will synthesize the most pressing opportunities and challenges for contestability along AI value chains in the form of a research roadmap. Considering the length and depth of AI value chains, it will especially spur discussions around the contestability of AI systems along various sites of such chains.
  arXiv  Detail & Related papers  (2024-08-02T06:57:52Z)
• CompeteSMoE -- Effective Training of Sparse Mixture of Experts via Competition [52.2034494666179]
  Sparse mixture of experts (SMoE) offers an appealing solution to scale up the model complexity beyond the mean of increasing the network's depth or width. We propose a competition mechanism to address this fundamental challenge of representation collapse. By routing inputs only to experts with the highest neural response, we show that, under mild assumptions, competition enjoys the same convergence rate as the optimal estimator.
  arXiv  Detail & Related papers  (2024-02-04T15:17:09Z)
• Responsible AI Considerations in Text Summarization Research: A Review of Current Practices [89.85174013619883]
  We focus on text summarization, a common NLP task largely overlooked by the responsible AI community. We conduct a multi-round qualitative analysis of 333 summarization papers from the ACL Anthology published between 2020-2022. We focus on how, which, and when responsible AI issues are covered, which relevant stakeholders are considered, and mismatches between stated and realized research goals.
  arXiv  Detail & Related papers  (2023-11-18T15:35:36Z)
• The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice [64.29355073494125]
  This article aims to ground what we dub the "participatory turn" in AI design by synthesizing existing theoretical literature on participation. We articulate empirical findings concerning the current state of participatory practice in AI design based on an analysis of recently published research and semi-structured interviews with 12 AI researchers and practitioners.
  arXiv  Detail & Related papers  (2023-10-02T05:30:42Z)
• The Robust Semantic Segmentation UNCV2023 Challenge Results [99.97867942388486]
  This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segmentation in urban environments, with a particular focus on natural adversarial scenarios. The report presents the results of 19 submitted entries, with numerous techniques drawing inspiration from cutting-edge uncertainty quantification methodologies.
  arXiv  Detail & Related papers  (2023-09-27T08:20:03Z)
• Power to the People? Opportunities and Challenges for Participatory AI [9.504176941117493]
  Participatory approaches to artificial intelligence (AI) and machine learning (ML) are gaining momentum. This paper reviews participatory approaches as situated in historical contexts as well as participatory methods and practices within the AI and ML pipeline. We argue that as participatory AI/ML becomes in vogue, a contextual and nuanced understanding of the term as well as consideration of who the primary beneficiaries of participatory activities ought to be constitute crucial factors to realizing the benefits and opportunities that participation brings.
  arXiv  Detail & Related papers  (2022-09-15T19:20:13Z)
• Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir" [76.44130385507894]
  This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices. Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
  arXiv  Detail & Related papers  (2021-11-01T17:57:04Z)
• Institutionalising Ethics in AI through Broader Impact Requirements [8.793651996676095]
  We reflect on a novel governance initiative by one of the world's largest AI conferences. NeurIPS introduced a requirement for submitting authors to include a statement on the broader societal impacts of their research. We investigate the risks, challenges and potential benefits of such an initiative.
  arXiv  Detail & Related papers  (2021-05-30T12:36:43Z)
• Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practices [3.119859292303396]
  This paper examines and seeks to offer a framework for analyzing how organizational culture and structure impact the effectiveness of responsible AI initiatives in practice. We present the results of semi-structured qualitative interviews with practitioners working in industry, investigating common challenges, ethical tensions, and effective enablers for responsible AI initiatives.
  arXiv  Detail & Related papers  (2020-06-22T15:57:30Z)

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.