Artificial Theory of Mind and Self-Guided Social Organisation
- URL: http://arxiv.org/abs/2411.09169v1
- Date: Thu, 14 Nov 2024 04:06:26 GMT
- Title: Artificial Theory of Mind and Self-Guided Social Organisation
- Authors: Michael S. Harré, Jaime Ruiz-Serra, Catherine Drysdale,
- Abstract summary: One of the challenges artificial intelligence (AI) faces is how a collection of agents coordinate their behaviour to achieve goals that are not reachable by any single agent.
We make the case for collective intelligence in a general setting, drawing on recent work from single neuron complexity in neural networks.
We show how our social structures are influenced by our neuro-physiology, our psychology, and our language.
- Score: 1.8434042562191815
- License:
- Abstract: One of the challenges artificial intelligence (AI) faces is how a collection of agents coordinate their behaviour to achieve goals that are not reachable by any single agent. In a recent article by Ozmen et al this was framed as one of six grand challenges: That AI needs to respect human cognitive processes at the human-AI interaction frontier. We suggest that this extends to the AI-AI frontier and that it should also reflect human psychology, as it is the only successful framework we have from which to build out. In this extended abstract we first make the case for collective intelligence in a general setting, drawing on recent work from single neuron complexity in neural networks and ant network adaptability in ant colonies. From there we introduce how species relate to one another in an ecological network via niche selection, niche choice, and niche conformity with the aim of forming an analogy with human social network development as new agents join together and coordinate. From there we show how our social structures are influenced by our neuro-physiology, our psychology, and our language. This emphasises how individual people within a social network influence the structure and performance of that network in complex tasks, and that cognitive faculties such as Theory of Mind play a central role. We finish by discussing the current state of the art in AI and where there is potential for further development of a socially embodied collective artificial intelligence that is capable of guiding its own social structures.
Related papers
- 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) - Shifting the Human-AI Relationship: Toward a Dynamic Relational Learning-Partner Model [0.0]
We advocate for a shift toward viewing AI as a learning partner, akin to a student who learns from interactions with humans.
We suggest that a "third mind" emerges through collaborative human-AI relationships.
arXiv Detail & Related papers (2024-10-07T19:19:39Z) - Explainable Human-AI Interaction: A Planning Perspective [32.477369282996385]
AI systems need to be explainable to the humans in the loop.
We will discuss how the AI agent can use mental models to either conform to human expectations, or change those expectations through explanatory communication.
While the main focus of the book is on cooperative scenarios, we will point out how the same mental models can be used for obfuscation and deception.
arXiv Detail & Related papers (2024-05-19T22:22:21Z) - 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) - AI-enhanced Collective Intelligence [2.5063318977668465]
Humans and AI possess complementary capabilities that can surpass the collective intelligence of either humans or AI in isolation.
This review incorporates perspectives from complex network science to conceptualize a multilayer representation of human-AI collective intelligence.
We explore how agents' diversity and interactions influence the system's collective intelligence and analyze real-world instances of AI-enhanced collective intelligence.
arXiv Detail & Related papers (2024-03-15T16:11:15Z) - 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) - 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) - SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents [107.4138224020773]
We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and humans.
In our environment, agents role-play and interact under a wide variety of scenarios; they coordinate, collaborate, exchange, and compete with each other to achieve complex social goals.
We find that GPT-4 achieves a significantly lower goal completion rate than humans and struggles to exhibit social commonsense reasoning and strategic communication skills.
arXiv Detail & Related papers (2023-10-18T02:27:01Z) - Human-AI Coevolution [48.74579595505374]
Coevolution AI is a process in which humans and AI algorithms continuously influence each other.
This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science.
arXiv Detail & Related papers (2023-06-23T18:10:54Z) - Social Neuro AI: Social Interaction as the "dark matter" of AI [0.0]
We argue that empirical results from social psychology and social neuroscience along with the framework of dynamics can be of inspiration to the development of more intelligent artificial agents.
arXiv Detail & Related papers (2021-12-31T13:41:53Z) - Crossing the Tepper Line: An Emerging Ontology for Describing the
Dynamic Sociality of Embodied AI [0.9176056742068814]
We show how embodied AI can manifest as "socially embodied AI"
We define this as the state that embodied AI "circumstantially" take on within interactive contexts when perceived as both social and agentic by people.
arXiv Detail & Related papers (2021-03-15T00:45:44Z)
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.