The Human Takeover: A Call for a Venture into an Existential Opportunity
Bibliographic record
Abstract
We propose a venture into an existential opportunity for establishing a world ‘good enough’ for humans to live in. Defining an existential opportunity as the converse of an existential risk—that is, a development that promises to dramatically improve the future of humanity—we argue that one such opportunity is available and should be explored now. The opportunity resides in the moment of transition of the Internet—from mediating information to mediating distributed direct governance in the sense of self-organization. The Internet of tomorrow will mediate the execution of contracts, transactions, public interventions and all other change-establishing events more reliably and more synergistically than any other technology or institution. It will become a distributed, synthetically intelligent agent in itself. This transition must not be just observed, or exploited instrumentally: it must be ventured into and seized on behalf of entire humanity. We envision a configuration of three kinds of cognitive system—the human mind, social systems and the emerging synthetic intelligence—serving to augment the autonomy of the first from the ‘programming’ imposed by the second. Our proposition is grounded in a detailed analysis of the manner in which the socio-econo-political system has evolved into a powerful control mechanism that subsumes human minds, steers their will and automates their thinking. We see the venture into the existential opportunity described here as aiming at the global dissolution of the core reason of that programming’s effectiveness—the critical dependence of the continuity of human lives on the coherence of the socially constructed personas they ‘wear.’ Thus, we oppose the popular prediction of the upcoming, ‘dreadful AI takeover’ with a call for action: instead of worrying that Artificial Intelligence will soon come to dominate and govern the human world, let us think of how it could help the human being to finally be able to do it.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.129 |
| Scholarly communication | 0.017 | 0.031 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".