Bibliographic record
Abstract
This article explores the relevance of classical Theravāda Buddhist doctrine to the present-day development of artificial general intelligence. Specifically, it addresses the interconnected possibilities of machine consciousness and machine agency. The first section consists of a philosophical exploration of the notion of artificial consciousness in light of ordinary language considerations. This is followed by a Buddhist theoretical account of the conditions necessary for the arising of consciousness, relying in good part upon the medieval Abhidhamma commentary, the Abhidhammattha Saṅgaha. Serious doubts are raised as to whether consciousness could ever be created in a machine environment. The final section examines the possibility of machine agency in relation to Buddhist understandings of action (kamma). Here, it is argued that if conscious machines ever were developed, whatever agency they might demonstrate would be amoral in nature and reflective of the values of their corporate and state programmers. Their development would pose considerable dangers to living beings. While the main argument of the article is made in Buddhist terms, it is supported throughout by more general philosophical considerations and with reference to some of the relevant scientific literature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.051 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".