Dear Ashikur Rahman Nazil ,

There is no single "best" algorithm for all Generative AI applications; the most suitable choice depends on the specific task, with Transformer-based models being dominant for text and multimodal generation, while Generative Adversarial Networks (GANs) and Diffusion models excel at high-quality image, video, and audio synthesis. Other important architectures include Variational Autoencoders (VAEs), which are useful for various generative tasks including image recognition and anomaly detection.

https://www.techtarget.com/searchenterpriseai/tip/Generative-AI-vs-machine-learning-How-are-they-different

Regards,

Shafagat

Dear Ashikur Rahman Nazil,

Answering the question of the best machine learning (ML) algorithm for generative artificial intelligence (Gen AI) applications requires in-depth analysis and comparison of different approaches. Although early innovations such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) played a pivotal role in this field, it is the Transformer architecture that has proven to be the most optimal and versatile, becoming the de facto standard for most modern Gen AI applications, from natural language processing to image generation.

Early attempts in the field of generative AI often relied on models such as GANs and VAEs. GANs, consisting of competing generator and discriminator networks, have shown a remarkable ability to create hyper-realistic images that are, in many cases, indistinguishable from the originals. However, their key drawback is training instability, which makes them difficult to implement and often leads to a problem of result diversity known as ‘mode collapse.’ On the other hand, VAEs, although stable and easy to train, tend to generate less sharp and more blurry images, which limits their application in tasks requiring high visual fidelity. These limitations have prompted researchers to search for more efficient and scalable architectures.

The Transformer architecture, introduced in 2017, revolutionised machine learning with its attention mechanism, which allows the model to dynamically weigh and analyse the relationships between different elements of the input data, regardless of their location. Unlike earlier models, which processed data sequentially, transformers operate in parallel, drastically reducing training time on large data sets. This fundamental change in approach has made models based on this architecture, such as GPT, extremely effective at understanding context on a large scale and generating coherent, logical, and substantively rich content, which is crucial in Gen AI applications such as text, programming code, and image generation.

A practical comparative study, for example in the field of automatic content generation on an information platform, fully confirms the superiority of transformers. Instead of unstable GANs or less precise VAEs, transformer-based models can instantly process hundreds of articles, understand their main content, and generate coherent, unique headlines and summaries. Thanks to the scalability of transformers, the platform can process data in real time, personalising content for each user with unprecedented precision. It is this ability to process data efficiently and at scale, combined with flexibility and stability, that makes transformers the best algorithm for today's diverse applications of generative artificial intelligence.

Thank you very much,

I invite you to collaborate with us on research.

Best regards,

Dariusz Prokopowicz

The application/selection of machine learning algorithms for GenAI depends on the nature of the dataset; for example, text and multimodal GenAI using Transformers dominate, for image/video/audio generation using Diffusion models with Generative Adversarial Networks (GANs), and for feature compression and latent space learning using Variational Autoencoders (VAEs). Or sometimes a combination of both hybrid architectures.

There isn’t one “best” ML algorithm for GenAI—it depends on the domain. Transformer-based LLMs (with RAG and fine-tuning) lead in text and code, while diffusion models dominate image generation, with GANs still useful for speed in narrow cases. Real-world tests show hybrid setups (e.g., LLM+RAG) consistently boost accuracy and reduce hallucinations—and the future of GenAI will likely rely on such hybrid frameworks rather than any single model.