The Way of the DAO: Toward Decentralizing the Tactile Internet
Bibliographic record
Abstract
There has been a growing interest in adapting blockchain technologies to the specific needs of the Internet of Things (IoT) in order to develop a variety of blockchain-based IoT (BIoT) applications such as smart cities and Industry 4.0, where smart contracts play an important role. After briefly reviewing recent progress on BIoT, we explore the symbiosis of blockchain with other key technologies such as artificial intelligence (AI) and robots, while putting our focus on the emerging Tactile Internet for advanced human-to-machine interaction. Our interest is in exploiting the concept of the decentralized autonomous organization (DAO), which executes smart contracts and requires the involvement from humans to perform certain tasks that autonomous AI based software agents and robots themselves cannot do. In our search for synergies between human-agent-robot teamwork (HART) and the complementary strengths of the DAO, AI, and robots, we decentralize the Tactile Internet by leveraging mobile end-user equipment via partially or fully decentralized multi-access edge computing, and crowdsourcing of human expertise to decrease the completion time of physical tasks in the event of unreliable feedback forecasting of teleoperated robots. Finally, we aim at enhancing the human capabilities of unskilled crowd members by using our proposed nudge contract.
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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 | 0.002 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| 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".