Unconscious Mind-Inspired Algorithm: A Novel Approach to Machine Learning
Bibliographic record
Abstract
This work aims to present a novel algorithm referred by unconscious mind-inspired algorithm (UMIA), that targets to incorporate principles derived from the unconscious mind and put it into computational processes.This work will seek to replicate fundamental aspects of the unconscious mind, such as efficient information processing, instinctive decision-making, and flexible learning and by sketching upon theories derived from psychoanalysis and cognitive psychology.The design algorithm encompasses a series of steps aimed at the development of a conceptual framework the utilization of data processing models influenced by subliminal perception and the implementation of intuitive decisionmaking algorithms.The phase of testing and validation involves the utilization of simulations and practical applications, with a specific emphasis on factors such as accuracy, efficiency, adaptability and user feedback.UMIA holds the potential to bring about a paradigm shift in algorithmic methodologies by integrating cognitive processes that resemble human intelligence.This integration has the potential to yield enhanced performance across a range of applications exceeding the capabilities of current machine learning algorithms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".