What is generative AI?

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1. 1

   Generative AI (Wikipedia)

   WikipediaPublished Oct 10, 2026Checked Oct 10, 2026

   “Generative artificial intelligence (GenAI) is a subfield of artificial intelligence (AI) that uses generative models to generate text, images, videos, audio, computer code or other forms of digital data. These models learn the underlying patterns and structures of their training data, and use them to generate new data in response to input, which often takes the form of natural language prompts. The prevalence of generative AI tools has increased significantly since the AI boom of the early 2020s. This boom was made possible by improvements in deep neural networks, particularly large language models (LLMs), which are based on the transformer architecture. Generative AI applications include chatbots such as ChatGPT, Claude, Microsoft Copilot, DeepSeek, Doubao, Google Gemini, Grok, Kimi and Qwen; text-to-image models such as DALL-E, Firefly, Stable Diffusion, and Midjourney; and text-to-video models such as Veo, LTX and Sora. Companies in a variety of sectors have used or proposed the use of generative AI, including those in software development, healthcare, finance, entertainment, sales and marketing, screenwriting, and product design.”

2. 2

   Generative Artificial Intelligence Models: A Survey

   Artificial Intelligence Review (Almuammar et al.)Published Apr 9, 2026Checked Oct 10, 2026

   “Generative Artificial Intelligence (GenAI) has seen a remarkable growth in recent years, expanding beyond traditional discriminative tasks to the generation of new content, such as text, images, and videos, based on patterns discovered in the existing data. Advances in deep learning, particularly the development of novel architectures such as Transformers, Generative Adversarial Networks, Diffusion Models, and Variational Autoencoders have significantly contributed to this progress. GenAI has rapidly evolved, demonstrating advanced capabilities across a wide range of real-world applications such as healthcare, and creative industries. In this paper, we explore the current foundations of GenAI to provide a comprehensive overview of its principal models and their variants, highlighting their use cases, strengths, and limitations. This survey introduces an architecture-based structured taxonomy of GenAI models, laying the groundwork for the development of more efficient generative applications and encouraging further advancements within the field.”

3. 3

   A Survey of Generative Artificial Intelligence Techniques

   Babylonian Journal of Artificial Intelligence (Sakirin & Kusuma)Published Mar 10, 2023Checked Oct 10, 2026

   “Architectural innovations and illustrations of generated outputs are highlighted for major models under each category. We give special attention to generative techniques for constructing realistic images, tracing rapid progress from early GAN samples to modern diffusion models like Stable Diffusion. The paper further reviews generative modeling to create convincing audio, video, and 3D renderings, which introduce critical challenges around fake media detection and data bias. Additionally, we discuss common datasets that have enabled advances in generative modeling. Finally, open questions around evaluation, technique blending, controlling model behaviors, commercial deployment, and ethical considerations are outlined as active areas for future work. This survey presents both long-standing and emerging techniques molding the state and trajectory of generative AI. The key goals are to overview major algorithm families, highlight innovations through example models, synthesize capabilities for multimedia generation, and discuss open problems around data, evaluation, control, and ethics. Please let me know if you would like any clarification or modification of this proposed abstract.”

4. 4

   Generative AI

   Business & Information Systems Engineering (Feuerriegel et al.)Published Sep 12, 2023Checked Oct 10, 2026

   “Tom Freston is credited with saying “Innovation is taking two things that exist and putting them together in a new way”. For a long time in history, it has been the prevailing assumption that artistic, creative tasks such as writing poems, creating software, designing fashion, and composing songs could only be performed by humans. This assumption has changed drastically with recent advances in artificial intelligence (AI) that can generate new content in ways that cannot be distinguished anymore from human craftsmanship.”

5. 5

   A Comprehensive Overview of Large Language Models

   ACM Transactions on Intelligent Systems and Technology (Naveed et al.)Published Jun 18, 2025Checked Oct 10, 2026

   “These works encompass diverse topics such as architectural innovations, better training strategies, context length improvements, fine-tuning, multimodal LLMs, robotics, datasets, benchmarking, efficiency, and more. With the rapid development of techniques and regular breakthroughs in LLM research, it has become considerably challenging to perceive the bigger picture of the advances in this direction. Considering the rapidly emerging plethora of literature on LLMs, it is imperative that the research community is able to benefit from a concise yet comprehensive overview of the recent developments in this field. This article provides an overview of the literature on a broad range of LLM-related concepts. Our self-contained comprehensive overview of LLMs discusses relevant background concepts along with covering the advanced topics at the frontier of research in LLMs. This review article is intended to provide not only a systematic survey but also a quick, comprehensive reference for the researchers and practitioners to draw insights from extensive, informative summaries of the existing works to advance the LLM research.”

6. 6

   Generative AI in Saudi Arabia: A National Survey of Adoption, Risks, and Public Perceptions

   arXiv (Cornell University) (AlDakheel et al.)Published Jan 26, 2026Checked Oct 10, 2026

   “Findings show that 93% of respondents actively use GenAI primarily for text-based tasks, while more advanced uses such as programming or multimodal generation are less common. Despite the prevalence of use, overall awareness and conceptual understanding remain uneven, with many reporting limited technical knowledge. Participants recognize GenAI's benefits for productivity, work quality, and understanding complex information, yet caution that sustained reliance may undermine critical thinking and key professional skills. Trust in AI-generated outputs remains cautious, with widespread concerns about privacy, misinformation, and ethical misuse, including potential job displacement. Respondents show strong interest in structured GenAI training that combines foundational skills, domain-specific applications, and clear guidance on privacy, ethics, and responsible use. These results establish a baseline for GenAI engagement in Saudi Arabia and highlight priorities for policymakers and developers: expanding AI literacy, ensuring culturally and linguistically aligned GenAI solutions, and strengthening frameworks for privacy and responsible deployment.”