ADAPTING TO CHANGE: AI'S INFLUENCE ON ONLINE SHOPPING DYNAMICS IN INDIA
Bibliographic record
Abstract
Artificial intelligence (AI), characterized by machines mimicking cognitive functions akin to human minds, has garnered significant attention and investment globally, including in India. This review explores the evolution and increasing prevalence of AI in India, particularly amidst the backdrop of the global pandemic. Drawing insights from authoritative sources such as "Artificial Intelligence: A Modern Approach (AIMA)" by Russell and Norvig (2020) and a study conducted by PwC India, it delves into the rapid adoption of AI technologies across various industries. Notably, India has witnessed a remarkable surge in AI usage, surpassing developed countries like the US, UK, and Japan, with a 45% increase during the pandemic. This review highlights the transformative potential of AI in India and underscores the need for continued investment and research to harness its benefits fully.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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".