A Survey of Next-Generation AI and Its Evolving Landscape
Bibliographic record
Abstract
Artificial Intelligence (AI) has matured from a speculative discipline into a central pillar of technological progress, shaping economies, industries, and the fabric of modern life. In light of its rapid evolution, this survey provides an in-depth review of 16 next-generation AI technologies. We examine their technical foundations, real-world applications supported by statistical evidence, and their broader impact, followed by a discussion of prevailing challenges, limitations, and future directions. The technologies covered include Edge AI, Generative AI, Self-Supervised Learning, Explainable AI, Causal AI, Synthetic Data Generation, Transfer Learning, Group Policy Optimization, Mixture of Experts, Neuromorphic Computing, AI for Sustainability, Federated Learning, Agentic AI, Quantum Machine Learning, and AI Ethics and Fairness. What distinguishes this survey is its multidimensional approach: beyond charting technical progress, we address critical issues such as algorithmic bias, data privacy, and environmental sustainability, and emphasize strategies for secure collaboration and efficient large-scale modeling. To ensure relevance, only the most recent advancements are reviewed, while outdated literature is deliberately excluded. Ultimately, this survey aims to serve as a roadmap for researchers, policymakers, and industry leaders, highlighting the importance of interdisciplinary collaboration and responsible innovation in shaping the future of AI.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".