Imagining and building wise machines: The centrality of AI metacognition

• URL: http://arxiv.org/abs/2411.02478v2
• Date: Wed, 07 May 2025 21:18:20 GMT
• Title: Imagining and building wise machines: The centrality of AI metacognition
• Authors: Samuel G. B. Johnson, Amir-Hossein Karimi, Yoshua Bengio, Nick Chater, Tobias Gerstenberg, Kate Larson, Sydney Levine, Melanie Mitchell, Iyad Rahwan, Bernhard Schölkopf, Igor Grossmann,
• Abstract summary: We examine what is known about human wisdom and sketch a vision of its AI counterpart.<n>We argue that AI systems particularly struggle with metacognition.<n>We discuss how wise AI might be benchmarked, trained, and implemented.
• Score: 78.76893632793497
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although AI has become increasingly smart, its wisdom has not kept pace. In this article, we examine what is known about human wisdom and sketch a vision of its AI counterpart. We analyze human wisdom as a set of strategies for solving intractable problems-those outside the scope of analytic techniques-including both object-level strategies like heuristics [for managing problems] and metacognitive strategies like intellectual humility, perspective-taking, or context-adaptability [for managing object-level strategies]. We argue that AI systems particularly struggle with metacognition; improved metacognition would lead to AI more robust to novel environments, explainable to users, cooperative with others, and safer in risking fewer misaligned goals with human users. We discuss how wise AI might be benchmarked, trained, and implemented.

Related papers

• 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)
• AI Awareness [8.537898577659401]
  We explore the emerging landscape of AI awareness, which includes meta-cognition, self-awareness, social awareness, and situational awareness. We examine how AI awareness is closely linked to AI capabilities, demonstrating that more aware AI agents tend to exhibit higher levels of intelligent behaviors. We discuss the risks associated with AI awareness, including key topics in AI safety, alignment, and broader ethical concerns.
  arXiv  Detail & Related papers  (2025-04-25T16:03:50Z)
• Creating 'Full-Stack' Hybrid Reasoning Systems that Prioritize and Enhance Human Intelligence [0.0]
  The paper proposes the development of generative AI-based tools that enhance the human ability to reflect upon a problem.<n>A high-level model is also described for integrating AI and human capabilities in a way that centralizes human participation and control.
  arXiv  Detail & Related papers  (2025-04-18T05:38:21Z)
• Aligning Generalisation Between Humans and Machines [74.120848518198]
  AI technology can support humans in scientific discovery and forming decisions, but may also disrupt democracies and target individuals.<n>The responsible use of AI and its participation in human-AI teams increasingly shows the need for AI alignment.<n>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)
• Combining AI Control Systems and Human Decision Support via Robustness and Criticality [53.10194953873209]
  We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks. We show that the learned AI control system demonstrates robustness against adversarial tampering. In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction.
  arXiv  Detail & Related papers  (2024-07-03T15:38:57Z)
• Advancing Explainable AI Toward Human-Like Intelligence: Forging the Path to Artificial Brain [0.7770029179741429]
  The intersection of Artificial Intelligence (AI) and neuroscience in Explainable AI (XAI) is pivotal for enhancing transparency and interpretability in complex decision-making processes. This paper explores the evolution of XAI methodologies, ranging from feature-based to human-centric approaches. The challenges in achieving explainability in generative models, ensuring responsible AI practices, and addressing ethical implications are discussed.
  arXiv  Detail & Related papers  (2024-02-07T14:09:11Z)
• Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems [67.01132165581667]
  We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components. We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
  arXiv  Detail & Related papers  (2023-11-13T21:20:17Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)
• Advancing Perception in Artificial Intelligence through Principles of Cognitive Science [6.637438611344584]
  We focus on the cognitive functions of perception, which is the process of taking signals from one's surroundings as input, and processing them to understand the environment. We present a collection of methods in AI for researchers to build AI systems inspired by cognitive science.
  arXiv  Detail & Related papers  (2023-10-13T01:21:55Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• Trustworthy AI: A Computational Perspective [54.80482955088197]
  We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
  arXiv  Detail & Related papers  (2021-07-12T14:21:46Z)
• 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)
• 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)
• Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles [95.58955174499371]
  We describe various aspects of multiple human intelligences and learning styles, which may impact on a variety of AI problem domains. Future AI systems will be able not only to communicate with human users and each other, but also to efficiently exchange knowledge and wisdom.
  arXiv  Detail & Related papers  (2020-08-07T21:00:13Z)
• Modelos din\^amicos aplicados \`a aprendizagem de valores em intelig\^encia artificial [0.0]
  Several researchers in the area have developed a robust, beneficial, and safe concept of AI for the preservation of humanity and the environment. It is utmost importance that artificial intelligent agents have their values aligned with human values. Perhaps this difficulty comes from the way we are addressing the problem of expressing values using cognitive methods.
  arXiv  Detail & Related papers  (2020-07-30T00:56:11Z)
• Dynamic Cognition Applied to Value Learning in Artificial Intelligence [0.0]
  Several researchers in the area are trying to develop a robust, beneficial, and safe concept of artificial intelligence. It is of utmost importance that artificial intelligent agents have their values aligned with human values. A possible approach to this problem would be to use theoretical models such as SED.
  arXiv  Detail & Related papers  (2020-05-12T03:58:52Z)

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.