Does artificial intelligence exhibit basic fundamental subjectivity? A neurophilosophical argument
Bibliographic record
Abstract
Abstract Does artificial intelligence (AI) exhibit consciousness or self? While this question is hotly debated, here we take a slightly different stance by focusing on those features that make possible both, namely a basic or fundamental subjectivity. Learning from humans and their brain, we first ask what we mean by subjectivity. Subjectivity is manifest in the perspectiveness and mineness of our experience which, ontologically, can be traced to a point of view. Adopting a non-reductive neurophilosophical strategy, we assume that the point of view exhibits two layers, a most basic neuroecological and higher order mental layer. The neuroecological layer of the point of view is mediated by the timescales of world and brain, as further evidenced by empirical data on our sense of self. Are there corresponding timescales shared with the world in AI and is there a point of view with perspectiveness and mineness? Discussing current neuroscientific evidence, we deny that current AI exhibits a point of view, let alone perspectiveness and mineness. We therefore conclude that, as per current state, AI does not exhibit a basic or fundamental subjectivity and henceforth no consciousness or self is possible in models such as ChatGPT and similar technologies.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.026 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".