Creating 'Full-Stack' Hybrid Reasoning Systems that Prioritize and Enhance Human Intelligence
- URL: http://arxiv.org/abs/2504.13477v1
- Date: Fri, 18 Apr 2025 05:38:21 GMT
- Title: Creating 'Full-Stack' Hybrid Reasoning Systems that Prioritize and Enhance Human Intelligence
- Authors: Sean Koon,
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The idea of augmented or hybrid intelligence offers a compelling vision for combining human and AI capabilities, especially in tasks where human wisdom, expertise, or common sense are essential. Unfortunately, human reasoning can be flawed and shortsighted, resulting in adverse individual impacts or even long-term societal consequences. While strong efforts are being made to develop and optimize the AI aspect of hybrid reasoning, the real urgency lies in fostering wiser and more intelligent human participation. Tools that enhance critical thinking, ingenuity, expertise, and even wisdom could be essential in addressing the challenges of our emerging future. This paper proposes the development of generative AI-based tools that enhance both the human ability to reflect upon a problem as well as the ability to explore the technical aspects of it. A high-level model is also described for integrating AI and human capabilities in a way that centralizes human participation and control.
Related papers
- A Beautiful Mind: Principles and Strategies for AI-Augmented Human Reasoning [0.0]
This paper outlines a human-centered augmented reasoning paradigm.<n>It offers examples of interaction modes that can serve as bridges between human reasoning and AI algorithms.
arXiv Detail & Related papers (2025-02-05T20:57:29Z) - 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) - Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
We argue that shortcomings stem from one overarching failure: AI systems lack wisdom.
While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems.
We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
arXiv Detail & Related papers (2024-11-04T18:10:10Z) - Untangling Critical Interaction with AI in Students Written Assessment [2.8078480738404]
Key challenge exists in ensuring that humans are equipped with the required critical thinking and AI literacy skills.
This paper provides a first step toward conceptualizing the notion of critical learner interaction with AI.
Using both theoretical models and empirical data, our preliminary findings suggest a general lack of Deep interaction with AI during the writing process.
arXiv Detail & Related papers (2024-04-10T12:12:50Z) - 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) - 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) - 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) - 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)
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.