A Review on the Impact of AI in Computer Graphics Considering IoTs
Bibliographic record
Abstract
Integrating Artificial Intelligence (AI) into computer graphics, combined with the Internet of Things (IoT), reshapes the landscape of visual content creation, processing, and application across diverse industries. AI-driven advancements in generative models, real-time rendering, and automated design tools enhance creativity, scalability, and efficiency. Simultaneously, IoT broadens the scope of computer graphics by delivering real-time, context-aware data from connected devices, enabling dynamic and interactive visualizations. This study investigates the profound impact of AI and IoT convergence on computer graphics. Emerging technologies, including augmented reality (AR) and virtual reality (VR), demonstrate the synergistic potential of AI and IoT in creating immersive, realtime environments for training simulations, urban planning, and interactive entertainment. Furthermore, IoT-enabled devices, such as smart home systems and wearables, leverage AI-enhanced graphics tools to deliver high-fidelity, adaptive visual interfaces. The findings suggest that integrating AI and IoT in computer graphics offers transformative possibilities for creating adaptive, interactive, and intelligent visual systems. However, addressing ethical concerns and promoting sustainable practices remain essential to ensuring equitable access and responsible utilization. The study addresses key challenges, including computational demands, data privacy, and ethical considerations, proposing solutions such as cloud-based resources, decentralized data systems, and AI-driven bias detection mechanisms. Finally, this paper emphasized the need for interdisciplinary collaboration among technologists, designers, and policymakers to unlock the full potential of AI and computer graphics integration with IoT while mitigating associated challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".