Is Dialogue with a Technosubject Possible? Architectures of Artificial Intelligence and Signatures of Consciousness
Bibliographic record
Abstract
The article explores the phenomenon of consciousness through the lens of advances in artificial intelligence (AI) and of contemporary neurobiological theories, each offering a distinct account of the architecture of conscious processing. This theoretical landscape allows us to identify several specific properties – such as a global workspace, recurrent processing, metacognitive monitoring, predictive processing, and high-level information integration – as functional signatures of cognitive processes inherent to biological consciousness. While certain AI systems exhibit some of these properties, they currently manifest in a fragmented and poorly integrated manner. The transition toward hybrid, particularly neurosymbolic, architectures, coupled with the expanding use of neuroevolutionary and embodied approaches in robotics, is laying the groundwork for integrated systems that more closely approximate conscious cognitive functions. However, the necessary conditions for a genuine dialogue between humans and a potentially conscious technosubject – including elements of intersubjectivity, empathy, mutual ethical responsibility, and lived bodily and social experience, or at least functional analogues thereof – suggest that the capacity for such interaction transcends the mere simulation of functional properties. The potential emergence of artificial general intelligence (AGI) in the near future, an entity capable not only of performing all human cognitive functions but also of demonstrating autonomous behavior and engaging in genuine dialogue, necessitates a proactive discussion of the technosubject’s normative status (the bounds of agency and responsibility, rights, duties, and safeguards for humans), along with the development of appropriate ethical principles and regulatory mechanisms.
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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 | 0.003 | 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 | 0.003 | 0.023 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".