Toward Artificial Sapience: Principles and Methods for Wise Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The current attempt to emulate human sapience (wisdom) by artificial means should be a step in the right direction beyond the Artificial/Computational Intelligence and Soft Computing disciplines, but is it warranted? Have humans achieved a level of modeling smart systems that justifies talking about sapience-wisdom? This book presents computational paradigms describing lower- and higher-level cognitive functions, including mechanisms of concepts, instincts, emotions, situated behavior, language communication and social functioning. Hierarchical organization of the mind is considered, leading to explanations of the highest human capabilities for the beautiful and sublime. A diverse international set of authors discuss Artificial / Computational Sapience and Sapient Systems in this unique and useful volume. The reader is guided through the subject in a structured and comprehensive manner, and begins with chapters discussing philosophical, historical, and semiotic ideas about what properties are expected from Sapient (Wise) systems. Following that, chapters describe mathematical and engineering views on sapience, relating these to philosophical, semiotic, cognitive, and neuro-biological perspectives. Features and topics: Begins with a solid foundation, providing a detailed description of the fundamental concepts and principles of the topic Discusses concepts and current computational tools that enable the realization, implementation and design of a Sapient System concept Presents a brief history of the evolution and development of the artificial intelligence, computational intelligence and soft computing fields Concepts are formalized and extended, as well as compared and differentiated from their counterparts in the Artificial Intelligence and Intelligence Systems disciplines Explains potential applications of key concepts Contains discussions and suggestions for future research This novel, state-of-the-art research volume is the first to focus on and explore Artificial / Computational Sapience and Sapient (Wise) Systems. It will be of real utility to all researchers, graduate students, and professionals in the field who are interested in advancing beyond the usual topics on intelligent systems and artificial intelligence. Dr Rene V. Mayorga is an Associate Professor in the Faculty of Engineering, at the University of Regina, Saskatchewan, Canada. Dr Leonid I. Perlovsky is Visiting Scholar at Harvard University and Principal Research Physicist and Technical Advisor at the U.S. Air Force Research Laboratory/SNHE, Hanscom.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it