ENHANCING E-SHOPPING: UNVEILING THE IMPACT OF ARTIFICIAL INTELLIGENCE IN INDIA
Bibliographic record
Abstract
Artificial Intelligence (AI), characterized as machines replicating "cognitive" functions akin to human thought processes, has witnessed a surge in prominence, notably outlined in the book "Artificial Intelligence: A Modern Approach (AIMA)" by Russell and Norvig (2020). The application of AI in India has witnessed a substantial rise, prompting increased investment in AI research and development across various industries. PwC India's study (PriceWaterhouseCoopers Private Limited India, 2020) reveals that, amidst the global pandemic, India has experienced the highest surge in AI usage, with a remarkable 45% increase compared to developed nations like the US, UK, and Japan. This research delves into the confluence of AI adoption and electronic retail channels in India, a burgeoning economy offering significant opportunities for e-retailers. The technological advancements and the widespread reach of the internet have paved the way for the expansion of electronic retail channels. The study recognizes the unique circumstances during the pandemic, where Internet shopping has become a vital avenue for consumers, providing enhanced flexibility, interactivity, customization, and low-risk options. The research aims to elucidate the symbiotic relationship between AI penetration and the evolution of electronic retail channels in the Indian context. By exploring the factors influencing this dynamic interaction, the study seeks to provide insights into the strategic considerations for businesses navigating the landscape of AI-driven e-retail in India.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".