The Internet of No Things: Making the Internet Disappear and "See the Invisible"
Bibliographic record
Abstract
Future emerging communication technologies are anticipated to fold into our surroundings, helping us get our noses off the smartphone screens and back into our environments. In doing so, they make us more (rather than less) present in the world around us. While 5G was supposed to be about the Internet of Everything, to be transformative 6G might be just about the opposite of Everything, that is, Nothing or, more technically, No Things. Building on the invisible-to-visible technology concept, this article explores how the full potential of multisensory extended reality (XR) experiences may be unleashed in Multiverse cross-reality environments. We exploit the convergence of artificial-intelligence-enhanced multi-access edge computing, intelligent mobile robots, and blockchain technologies to help realize the Internet of No Things as an important stepping stone toward ushering in the 6G post-smartphone era. In our experiments, we consider locally connected human-avatar/robot collectives and investigate our proposed extrasensory perception network, which integrates the three evolutionary mobile computing stages of ubiquitous, pervasive, and persuasive computing. As an illustrative example of advanced XR experiences, we study the delivery of sixth-sense perceptions that transverse the boundary between Multiverse realms in order to mimic the quantum realm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".