Intelligent behavior depends on the ecological niche: Scaling up AI to human-like intelligence in socio-cultural environments

• URL: http://arxiv.org/abs/2103.06769v1
• Date: Thu, 11 Mar 2021 16:24:00 GMT
• Title: Intelligent behavior depends on the ecological niche: Scaling up AI to human-like intelligence in socio-cultural environments
• Authors: Manfred Eppe and Pierre-Yves Oudeyer
• Abstract summary: This paper outlines a perspective on the future of AI, discussing directions for machines models of human-like intelligence. We emphasize the role of ecological niches in sculpting intelligent behavior, and in particular that human intelligence was fundamentally shaped to adapt to a constantly changing socio-cultural environment.
• Score: 17.238068736229017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper outlines a perspective on the future of AI, discussing directions for machines models of human-like intelligence. We explain how developmental and evolutionary theories of human cognition should further inform artificial intelligence. We emphasize the role of ecological niches in sculpting intelligent behavior, and in particular that human intelligence was fundamentally shaped to adapt to a constantly changing socio-cultural environment. We argue that a major limit of current work in AI is that it is missing this perspective, both theoretically and experimentally. Finally, we discuss the promising approach of developmental artificial intelligence, modeling infant development through multi-scale interaction between intrinsically motivated learning, embodiment and a fastly changing socio-cultural environment. This paper takes the form of an interview of Pierre-Yves Oudeyer by Mandred Eppe, organized within the context of a KI - K{\"{u}}nstliche Intelligenz special issue in developmental robotics.

Related papers

• Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions [67.60397632819202]
  Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal. We identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI.
  arXiv  Detail & Related papers  (2024-04-17T02:57:42Z)
• The Interplay of Learning, Analytics, and Artificial Intelligence in Education: A Vision for Hybrid Intelligence [0.45207442500313766]
  I challenge the prevalent narrow conceptualisation of AI as tools, and argue for the importance of alternative conceptualisations of AI. I highlight the differences between human intelligence and artificial information processing, and posit that AI can also serve as an instrument for understanding human learning. The paper presents three unique conceptualisations of AI: the externalization of human cognition, the internalization of AI models to influence human mental models, and the extension of human cognition via tightly coupled human-AI hybrid intelligence systems.
  arXiv  Detail & Related papers  (2024-03-24T10:07:46Z)
• The Ethics of AI in Education [0.0]
  The transition of Artificial Intelligence from a lab-based science to live human contexts brings into sharp focus many historic, socio-cultural biases, inequalities, and moral dilemmas. Questions that have been raised regarding the broader ethics of AI are also relevant for AI in Education (AIED) AIED raises further challenges related to the impact of its technologies on users, how such technologies might be used to reinforce or alter the way that we learn and teach, and what we, as a society and individuals, value as outcomes of education.
  arXiv  Detail & Related papers  (2024-03-22T11:41:37Z)
• 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)
• 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)
• World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges [51.92834011423463]
  We focus on the two concepts of world models and predictive coding. In neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment.
  arXiv  Detail & Related papers  (2023-01-14T06:38:14Z)
• 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)
• 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)
• 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.