The Dark Side of the Moon: The Internet of Things, Industry 4.0, and The Quantified Planet
Bibliographic record
Abstract
Industry 4.0 is an innovation framework launched initially at the 2011 Hanover Fair in Germany. It is premised on extreme digital connectivity to build smart factories and deliver extreme automation in science and society. Industry 4.0 has recently scaled up worldwide beyond Germany and Europe. Industry 4.0 employs the Internet of Things (IoT) to connect, communicate, and collect big data from embedded sensors in living and inanimate objects. When we add artificial intelligence (AI) powered real-time data analyses to the IoT, a state of worldwide extreme connectivity, or "The Quantified Planet," is created. By its focus on connectivity at a systems scale, the Industry 4.0 is of interest to health products manufacturing and service automation in medicine, biology, ecology, and society. But there are also unchecked assumptions. This article poses a question that has so far been neglected in the Industry 4.0 innovation echo chamber. Is it always good to have pervasive connectivity and extreme integration to the point that "everything is connected to everything else"? For example, extreme connectivity creates an "All Your Eggs in One Basket" problem and thus potential for complete network collapse in a domino effect when a component in a highly integrated system fails. Digital connectivity cultivates a fertile ground for new social and political power structures for authoritarian governance (i.e., a worrisome state of "pansurveillance") by one person in total control of knowledge networks in science and society, directly or through connected proxies. Filter bubbles created by extreme digital connectivity and AI can result in compressed foresight, lackluster innovation, and monocultures in laboratory life. The way forward is to harness Industry 4.0 and connectivity without such digital network toxicity. Similar to fire exits in skyscrapers and other built environments, designing innovation systems with extreme connectivity demands that we also think of "exit plans" from the omnipresent digital networks, IoT, and The Quantified Planet-both for safety and sustained creativity. Finally, the relationship between new technologies and society is far from being a simple one-directional interaction; it is not the technologies that cause social disruption. On the contrary, it is the value-loaded decisions made by individuals, organizations, and other social actors that shape sociotechnical change. Recognizing the latter narrative is important because it informs how we do science and can best respond to uncertainties and opaque assumptions embedded in emerging technologies such as AI, IoT, and Industry 4.0.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| 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".