From artificial to circular intelligence to support the well-being of our habitat
- URL: http://arxiv.org/abs/2512.24131v1
- Date: Tue, 30 Dec 2025 10:32:58 GMT
- Title: From artificial to circular intelligence to support the well-being of our habitat
- Authors: Francesca Larosa, Daniel Depellegrin, Andrea Conte, Marco Molinari, Silvia Santato, Adam Wickberg, Fermin Mallor, Anna Sperotto,
- Abstract summary: The proliferation of machine learning and artificial intelligence redefines the interaction between the anthropogenic and natural elements of our habitat.<n>We propose a novel conceptual and procedural framework which we call Circular Intelligence or CIntel.<n>CIntel incorporates ethical principles in its technical design to preserve the stability of the habitat, while also increasing the well-being of its inhabitants by design.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The proliferation of machine learning and artificial intelligence redefines the interaction between the anthropogenic and natural elements of our habitat.The use of monitoring tools, processing facilities and the internet of things supports the assessment of planetary health at any given time through automation. However, these data, natural resources and infrastructure intensive technologies are not neutral on the Earth. As the community of AI practitioners works on the creation of tools with minimal socio-environmental impacts, we contribute to the these efforts by proposing a novel conceptual and procedural framework which we call Circular Intelligence or CIntel. CIntel leverages a bottom-up and community-driven approach to learn from the ability of nature to regenerate and adapt. CIntel incorporates ethical principles in its technical design to preserve the stability of the habitat, while also increasing the well-being of its inhabitants by design.
Related papers
- Toward Agentic Environments: GenAI and the Convergence of AI, Sustainability, and Human-Centric Spaces [0.0]
The paper proposes a conceptual framework for agentic environments examined through three lenses: the personal sphere, professional and commercial use, and urban operations.<n>The findings highlight the potential of agentic environments to foster sustainable ecosystems through optimized resource utilization and strengthened data privacy.
arXiv Detail & Related papers (2025-12-15T20:15:02Z) - AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions [65.44445343399126]
We look at AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing.<n>Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific synthesis.<n>Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation.
arXiv Detail & Related papers (2025-11-26T02:10:28Z) - Toward Carbon-Neutral Human AI: Rethinking Data, Computation, and Learning Paradigms for Sustainable Intelligence [2.7946918847372277]
This paper critiques the prevailing reliance on large-scale, static datasets and monolithic training paradigms.<n>We introduce a novel framework, Human AI, which emphasizes incremental learning, carbon-aware optimization, and human-in-the-loop collaboration.
arXiv Detail & Related papers (2025-10-27T17:02:30Z) - A Study on the Application of Artificial Intelligence in Ecological Design [0.0]
We show how artists and designers apply AI for data analysis, image recognition, and ecological restoration.<n>We argue that AI not only expands creative methods but also reframes the theory and practice of ecological design.
arXiv Detail & Related papers (2025-07-15T17:03:33Z) - Neural Brain: A Neuroscience-inspired Framework for Embodied Agents [78.61382193420914]
Current AI systems, such as large language models, remain disembodied, unable to physically engage with the world.<n>At the core of this challenge lies the concept of Neural Brain, a central intelligence system designed to drive embodied agents with human-like adaptability.<n>This paper introduces a unified framework for the Neural Brain of embodied agents, addressing two fundamental challenges.
arXiv Detail & Related papers (2025-05-12T15:05:34Z) - On the Emergence of Symmetrical Reality [51.21203247240322]
We introduce the symmetrical reality framework, which offers a unified representation encompassing various forms of physical-virtual amalgamations.
We propose an instance of an AI-driven active assistance service that illustrates the potential applications of symmetrical reality.
arXiv Detail & Related papers (2024-01-26T16:09:39Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - Life-inspired Interoceptive Artificial Intelligence for Autonomous and Adaptive Agents [0.7852714805965528]
Building autonomous -- i.e., choosing goals based on one's needs -- and adaptive -- i.e., surviving in ever-changing environments -- has been a holy grail of artificial intelligence (AI)<n>Here, we focus on interoception, a process of monitoring one's internal environment to keep it within certain bounds.<n>To develop AI with interoception, we need to factorize the state variables representing internal environments from external environments.
arXiv Detail & Related papers (2023-09-12T06:56:46Z) - 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) - Seeing biodiversity: perspectives in machine learning for wildlife
conservation [49.15793025634011]
We argue that machine learning can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species.
In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies.
arXiv Detail & Related papers (2021-10-25T13:40:36Z) - Grounding Artificial Intelligence in the Origins of Human Behavior [0.0]
Recent advances in Artificial Intelligence (AI) have revived the quest for agents able to acquire an open-ended repertoire of skills.
Research in Human Behavioral Ecology (HBE) seeks to understand how the behaviors characterizing human nature can be conceived as adaptive responses to major changes in the structure of our ecological niche.
We propose a framework highlighting the role of environmental complexity in open-ended skill acquisition, grounded in major hypotheses from HBE and recent contributions in Reinforcement learning (RL)
arXiv Detail & Related papers (2020-12-15T19:28:45Z)
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.