News: Dr. Devinder Pal Singh Delivers Talk on “AI and Sikhism” at Sri Guru Granth Sahib World University, Fatehgarh Sahib.
Bibliographic record
Abstract
September 12, 2025 – The Department of Physics at Sri Guru Granth Sahib World University, Fatehgarh Sahib, hosted an expert talk by Dr. Devinder Pal Singh, Director of the Center for Understanding Sikhism, Mississauga, Ontario, Canada, on the timely theme “Harnessing AI for Sikhism – Opportunities and Risks.” The event was graced by the presence of Dr. Pritpal Singh, Vice-Chancellor; Prof. S. S. Billing, Dean of Academic Affairs; Dr. Tegbir Singh, Registrar; and Dr. Preet Kaur, Head of the Department of Physics. In his remarks, Vice-Chancellor Dr. Pritpal Singh appreciated Dr. Devinder Pal Singh’s contributions to the promotion of Sikhism and emphasized the importance of exploring artificial intelligence in the context of religious studies. In her welcome address, Dr. Preet Kaur highlighted Dr. Singh’s extensive contributions to Sikh studies and his pioneering work in exploring the intersections of science, technology, and religion. During his talk, Dr. Singh discussed how artificial intelligence (AI) presents both transformative opportunities and significant ethical challenges for Sikh communities worldwide. He emphasized the potential of AI in areas such as preservation and accessibility of Sikh scriptures, educational tools for youth, and global interfaith dialogue. At the same time, he cautioned against over-reliance on technology, underlining the risks of misinformation, cultural misrepresentation, and ethical dilemmas in AI development. The event also featured the release of Dr. Singh’s newly published book, “Echoes of a Digital Dawn – AI’s Triumphs and Trials,” which explores the broader societal implications of artificial intelligence in contemporary life. A large number of students attended the event and actively engaged in a lively discussion session, raising questions about AI’s role in shaping future religious scholarship and community practices. The program concluded with a vote of thanks, expressing appreciation for Dr. Singh’s insightful perspectives and his continuing efforts to bridge the gap between emerging technologies and Sikh thought.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".