A Data-Driven Learning System Based on Natural Intelligence for an IoT Virtual Assistant
Bibliographic record
Abstract
The ability and functionality of today's learning systems are dependent on configuration and designed for specific use. These systems have strong dependencies on training data and remain static in capability. This translates to commercial IoT sensor management systems that are limited to a set of predefined functions. Although often labeled as or associated with artificial intelligence (AI), these systems lack the behavioral qualities associated with intelligence. Our research details a novel learning system autonomously motivated by its environment, analogously to humans. The algorithmic processes of the system produce behavioral byproducts of intelligence, resulting in an entity capable of tackling general problems of its own interest, contrary to constrained solutions. Because of the system's generic nature, the intelligence produced is limited only by its ability to sense and actuate. The research contributes a system that learns through different interfaces with the same generic algorithms-where language and image processing ability would traditionally be learned in separate modules, our system uses the same algorithms to learn ability with data from both modalities. The proposed framework is showcased through the embodiment of a next-generation intelligent IoT virtual assistant application.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".