The Tactile Internet: Automation or Augmentation of the Human?
Bibliographic record
Abstract
The idea of remotely controlling machines via the Internet for the purpose of automated tele-manufacturing and shared use of manufacturing facilities by users worldwide has been studied since the late 1990s. This idea is now part of the vision of the emerging Tactile Internet, which lies at the nexus of computerization, automation, and robotization. Similar to future 5G cellular networks, the Tactile Internet is anticipated to rely on the full yet flexible convergence of different fixed and mobile access technologies, given that some use cases do not require mobility all the time. While necessary, though, the design of ultra-reliable and low-latency converged communication network infrastructures is not sufficient to unleash the full potential of the Tactile Internet. In this paper, we put forward the idea that the Tactile Internet may be the harbinger of human augmentation and human–machine symbiosis envisioned by contemporary and early-day Internet pioneers. In search for synergies between humans and machines/robots, we explore the idea of treating the human as a “member” of a team of intelligent machines rather than keep viewing him as a conventional “user” while putting a particular focus on developing systems that are human-aware and help advance the human condition, e.g., economic inequality.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".