A Survey of Next-Generation AI and Its Evolving Landscape
Bibliographic record
Abstract
AI began as a theory only, but now it has evolved into a strong & revolutionary force. This force is changing industries workflow, global economies operate, governments run, and how we live our daily life via various emerging AI technologies. Considering the current evolution and rapid advancements of AI, we present this survey to deliver an in-depth review of 15 emerging AI technologies. In this survey, we will discuss their technical foundations, impact, real-world applications with statistical evidence followed by challenges & future directions. Key emerging technologies we’ve covered in this survey include Edge AI, Generative AI, Self-Supervised Learning, Explainable AI, Multi-Modal models, Causal AI, Synthetic Data Generation, Transfer Learning, Group Policy Optimization, Mixture of Experts, Neuromorphic Computing, AI for Sustainability, Federated Learning, as well as AI Ethics and Fairness. Our survey is also extended to Quantum ML. What makes our survey stand out is the multidimensional approach we have taken. In addition to the technical progress, we address critical concerns such as algorithmic bias, data privacy, and environmental sustainability, while emphasizing solutions for secure collaboration and efficient large-scale modeling. Moreover, we have enlisted the latest challenges, limitations in existing technologies following future directions. Also, we’ve excluded outdated literature to ensure that our survey addresses the latest concerns and developments in AI. Hence, this survey is a roadmap for policymakers, researchers, and industry leaders navigating through the future of AI, pointing out the need for interdisciplinary collaboration and responsible innovation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".