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Open AI launched DALL-E, an AI platform
                                                         2021          designed to generate images from textual
                                                                       descriptions
                           Two notable AI image-generating tools,
                          the open-source Stable Diffusion and the
                       proprietary Midjourney were introduced and      2022
                                         ChatGPT was introduced
                                                                       OpenAI released GPT-4, an advanced version of its
                                                                       GPT series. Also Microsoft Copilot (previously Bing
                                                         2023          Chat), Google Gemini (previously Google Bard),
                                                                       Adobe Firefly, Meta Llama were introduced




                 Generative AI vs Conventional AI
                 Generative AI and Conventional AI represent two different approaches in the field of artificial intelligence. The
                 difference between them is given in the following table:


                                               Generative AI                            Conventional AI


                    Goal          Generative AI creates new content that   Conventional AI analyses, processes, and
                                  mimics the original content. This content   classifies data. It works to improve the
                                  includes images, text, music, or other   accuracy, precision, recall, and speed within
                                  forms of media.                          the scope of the defined task.


                    Training      Generative AI models are often trained   Conventional AI models are typically
                                  using techniques such as generative      trained using supervised, unsupervised, or
                                  adversarial networks (GANs), variational   reinforcement learning techniques.
                                  autoencoders (VAEs), or autoregressive
                                  models.


                    Dataset       Generative AI models typically           Conventional AI models rely on smaller,
                                  require large amounts of diverse and     more curated datasets that are tailored to
                                  representative data to learn effectively.   the task at hand.
                                  These datasets often contain thousands or
                                  even millions of examples across various
                                  categories or classes.



                    Output        Generative AI output is fresh, innovative,   Conventional AI produces more predictable
                                  and often unexpected.                    output based on existing data.


                    Applications Generative AI is used in the fields of art,   Conventional AI is used in banking,
                                  music, literature, gaming, and design.   healthcare, image recognition, and language
                                                                           processing.







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