Generative Artificial Intelligence (AI) refers to a subset of AI algorithms and models that can generate new and original content, such as images, text, music, or even entire virtual worlds. Unlike other AI models that rely on pre-existing data to make predictions or classifications, generative AI models create new content based on patterns and information they have learned from training data.

One of the most well-known examples of generative AI is Generative Adversarial Networks (GANs). GANs consist of two neural networks: a generator and a discriminator. The generator network creates new content, while the discriminator network evaluates the content and provides feedback to the generator. Through an iterative process, both networks learn and improve their performance, resulting in the generation of more realistic and high-quality content.

Generative AI has made significant advancements in various domains. In the field of computer vision, generative models can create realistic images or even generate entirely new images based on certain prompts or conditions. In natural language processing, generative models can generate coherent and contextually relevant text, making them useful for tasks like text summarization, translation, or even creative writing.

However, it is important to note that generative AI models can sometimes produce biased or inappropriate content, as they learn from the data they are trained on, which may contain inherent biases. Ensuring ethical and responsible use of generative AI is an ongoing challenge in the field.

Generative AI also presents exciting opportunities for creative industries. Artists can use generative models as tools to inspire their work or create new forms of art. Musicians can leverage generative AI models to compose music or generate novel melodies.

Overall, generative AI holds great potential for innovation and creativity, but it also raises important ethical considerations that need to be addressed to ensure its responsible and beneficial use in various domains.

Some examples of text generation models include ChatGPT, Copilot, Gemini, and LLaMA which are often collectively referred to as chatbots. They generate human-like responses to queries. Image generation models include Stable Diffusion, Midjourney, and DALL-E which create images from textual descriptions. Video generation models include Sora which can produce videos based on prompts. Other domains where Generative AI finds applications are in software development, healthcare, finance, entertainment, customer service, sales, marketing, art, writing, fashion, and product design.

In Azure, a copilot can be developed with no code using Azure OpenAI studio. We just need to instantiate a studio, associate a model, add the data sources, and allow the model to train. The models differ in syntactic or semantic search. The latter uses a concept called embedding that discovers the latent meaning behind the occurrences of tokens in the given data. So, it is more inclusive than the former. A search for time will specifically search for that keyword with the GPT-3 but a search for clock will include the references to time with a model that leverages embeddings. Either way, a search service is required to create an index over the dataset because it facilitates fast retrieval. A database such as Azure Cosmos DB can be used to assist with vector search.