Generative AI has revolutionized its way into many fields. It lets people create text, images, music and software code independently. That's largely down to the rapid progress in deep learning—especially with Generative Adversarial Networks (GANs) and those Transformer-based models. As a result, generative AI has become an indispensable tool across many industries. By tapping into vast datasets and clever neural network designs, generative AI can produce content that's virtually indistinguishable from human work. That drives innovation in all sorts of areas.
This review paper takes a close-up look at the journey of generative AI. It shows how it has evolved from early statistical models to the cutting-edge deep learning techniques we see today. You'll see how major architectural breakthroughs—VAEs, GANs and those Transformer models—have put generative AI at the forefront of mainstream applications. The paper also explores its many uses: natural language processing, computer vision, healthcare, finance, entertainment and software development. That's where generative AI really shines: enhancing creativity, streamlining processes and transforming research methods.
But generative AI isn't without its problems. There are biases in training data, the risk of spreading misinformation, the high computational demands, the intellectual property concerns and security threats. Tackling those issues is crucial for using AI responsibly. The paper outlines future research paths, emphasizing the need to boost model efficiency, transparency and ethics. It highlights the importance of regulatory frameworks and responsible AI principles to mitigate risks and drive the sustainable advancement of generative models.