Guest Editorial Special Issue on Hybrid Brain–Computer Collaborative Intelligent System
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
Brain–machine fusion, also known as hybrid intelligence or brain–computer interface (BCI), is considered one of the most promising technologies of the 21st century. Its potential impact spans a wide range of disciplines, including cognitive science, information science, artificial intelligence, biology, neuroscience, and engineering. The research in this field aims to seamlessly integrate biological intelligence (i.e., the human brain) with machine intelligence (computers or robots) to create a new, powerful form of hybrid intelligence that far surpasses the limitations of current biological and machine intelligence systems. Brain–machine fusion not only signifies the convergence of cutting-edge science and technology but also heralds a new era in which the way humans interact with machines undergoes a profound transformation. The research in this field delves deep into the understanding of human thought processes and cognition, as well as the creation of novel sensory and motor channels to facilitate more natural and intuitive interactions. The scope of brain–machine fusion research extends beyond mere information exchange, encompassing the integration of emotions and motivations. Understanding and interpreting a user’s emotional state and motivations are crucial for optimizing the performance of fusion systems, aiding in better meeting user needs and providing more personalized experiences. A key objective is enhancing a user’s operational capacity in handling complex tasks. This can encompass highly intricate decision making, problem solving, and task execution, with broad applications in fields, such as healthcare, military, industry, and entertainment. Furthermore, brain–machine fusion necessitates the development of cognitive interaction models that can adapt actively to a user’s cognitive characteristics and integrate with machine learning algorithms to achieve personalized adaptability in intelligent systems, thereby enhancing the level of interaction between the user and the system.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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