Stop treating `AGI' as the north-star goal of AI research

• URL: http://arxiv.org/abs/2502.03689v2
• Date: Fri, 07 Feb 2025 04:07:16 GMT
• Title: Stop treating `AGI' as the north-star goal of AI research
• Authors: Borhane Blili-Hamelin, Christopher Graziul, Leif Hancox-Li, Hananel Hazan, El-Mahdi El-Mhamdi, Avijit Ghosh, Katherine Heller, Jacob Metcalf, Fabricio Murai, Eryk Salvaggio, Andrew Smart, Todd Snider, Mariame Tighanimine, Talia Ringer, Margaret Mitchell, Shiri Dori-Hacohen,
• Abstract summary: We argue that focusing on the topic of artificial general intelligence' (AGI') undermines our ability to choose effective goals.<n>We identify six key traps -- obstacles to productive goal setting -- that are aggravated by AGI discourse.
• Score: 7.292737756666293
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The AI research community plays a vital role in shaping the scientific, engineering, and societal goals of AI research. In this position paper, we argue that focusing on the highly contested topic of `artificial general intelligence' (`AGI') undermines our ability to choose effective goals. We identify six key traps -- obstacles to productive goal setting -- that are aggravated by AGI discourse: Illusion of Consensus, Supercharging Bad Science, Presuming Value-Neutrality, Goal Lottery, Generality Debt, and Normalized Exclusion. To avoid these traps, we argue that the AI research community needs to (1) prioritize specificity in engineering and societal goals, (2) center pluralism about multiple worthwhile approaches to multiple valuable goals, and (3) foster innovation through greater inclusion of disciplines and communities. Therefore, the AI research community needs to stop treating `AGI' as the north-star goal of AI research.

Related papers

• Societal AI Research Has Become Less Interdisciplinary [3.9599054392856483]
  This study analyzes over 100,000 AI-related papers published on ArXiv between 2014 and 2024.<n>Computer science-only teams now account for a growing share of the field's overall societal output.<n>These findings challenge common assumptions about the drivers of societal AI.
  arXiv  Detail & Related papers  (2025-06-10T12:34:53Z)
• Exploring Societal Concerns and Perceptions of AI: A Thematic Analysis through the Lens of Problem-Seeking [0.0]
  This study introduces a novel conceptual framework distinguishing problem-seeking from problem-solving to clarify the unique features of human intelligence in contrast to AI.<n>The framework emphasizes that while AI excels at efficiency and optimization, it lacks the orientation derived from grounding and the embodiment flexibility intrinsic to human cognition.
  arXiv  Detail & Related papers  (2025-05-29T18:24:34Z)
• Position Paper: Bounded Alignment: What (Not) To Expect From AGI Agents [0.0]
  The goal of this position paper is to argue that the currently dominant vision of AGI in the AI and machine learning (AI/ML) community needs to evolve.<n>This change in perspective will lead to a more realistic view of the technology, and allow for better policy decisions.
  arXiv  Detail & Related papers  (2025-05-17T06:17:57Z)
• Position: We Need Responsible, Application-Driven (RAD) AI Research [5.472297350890938]
  This position paper argues that achieving meaningful scientific and societal advances with artificial intelligence (AI) requires a responsible, application-driven approach (RAD) to AI research.<n>We present a vision for the future of application-driven AI research to unlock new value through technically feasible methods.
  arXiv  Detail & Related papers  (2025-05-07T03:43:52Z)
• Aligning Generalisation Between Humans and Machines [74.120848518198]
  Recent advances in AI have resulted in technology that can support humans in scientific discovery and decision support but may also disrupt democracies and target individuals. The responsible use of AI increasingly shows the need for human-AI teaming. A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise.
  arXiv  Detail & Related papers  (2024-11-23T18:36:07Z)
• Now, Later, and Lasting: Ten Priorities for AI Research, Policy, and Practice [63.20307830884542]
  Next several decades may well be a turning point for humanity, comparable to the industrial revolution. Launched a decade ago, the project is committed to a perpetual series of studies by multidisciplinary experts. We offer ten recommendations for action that collectively address both the short- and long-term potential impacts of AI technologies.
  arXiv  Detail & Related papers  (2024-04-06T22:18:31Z)
• Ten Hard Problems in Artificial Intelligence We Must Get Right [72.99597122935903]
  We explore the AI2050 "hard problems" that block the promise of AI and cause AI risks. For each problem, we outline the area, identify significant recent work, and suggest ways forward.
  arXiv  Detail & Related papers  (2024-02-06T23:16:41Z)
• The AI Incident Database as an Educational Tool to Raise Awareness of AI Harms: A Classroom Exploration of Efficacy, Limitations, & Future Improvements [14.393183391019292]
  The AI Incident Database (AIID) is one of the few attempts at offering a relatively comprehensive database indexing prior instances of harms or near harms stemming from the deployment of AI technologies in the real world. This study assesses the effectiveness of AIID as an educational tool to raise awareness regarding the prevalence and severity of AI harms in socially high-stakes domains.
  arXiv  Detail & Related papers  (2023-10-10T02:55:09Z)
• A Game-Theoretic Framework for AI Governance [8.658519485150423]
  We show that the strategic interaction between the regulatory agencies and AI firms has an intrinsic structure reminiscent of a Stackelberg game. We propose a game-theoretic modeling framework for AI governance. To the best of our knowledge, this work is the first to use game theory for analyzing and structuring AI governance.
  arXiv  Detail & Related papers  (2023-05-24T08:18:42Z)
• FATE in AI: Towards Algorithmic Inclusivity and Accessibility [0.0]
  To prevent algorithmic disparities, fairness, accountability, transparency, and ethics (FATE) in AI are being implemented. This study examines FATE-related desiderata, particularly transparency and ethics, in areas of the global South that are underserved by AI. To promote inclusivity, a community-led strategy is proposed to collect and curate representative data for responsible AI design.
  arXiv  Detail & Related papers  (2023-01-03T15:08:10Z)
• Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation [61.77881142275982]
  This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them. We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives. We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
  arXiv  Detail & Related papers  (2022-06-08T12:32:08Z)
• Metaethical Perspectives on 'Benchmarking' AI Ethics [81.65697003067841]
  Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research. An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system. We argue that it makes more sense to talk about 'values' rather than 'ethics' when considering the possible actions of present and future AI systems.
  arXiv  Detail & Related papers  (2022-04-11T14:36:39Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• Ethics and Governance of Artificial Intelligence: Evidence from a Survey of Machine Learning Researchers [0.0]
  Machine learning (ML) and artificial intelligence (AI) researchers play an important role in the ethics and governance of AI. We conducted a survey of those who published in the top AI/ML conferences. We find that AI/ML researchers place high levels of trust in international organizations and scientific organizations.
  arXiv  Detail & Related papers  (2021-05-05T15:23:12Z)
• Socially Responsible AI Algorithms: Issues, Purposes, and Challenges [31.382000425295885]
  Technologists and AI researchers have a responsibility to develop trustworthy AI systems. To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness.
  arXiv  Detail & Related papers  (2021-01-01T17:34:42Z)

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.