Generative AI

Generative AI (also known as generative Artificial Intelligence or generative adversarial network) is a branch of Artificial Intelligence systems that are used to generate new data based on previously collected data. Generative AI is used for a wide range of tasks, including speech synthesis, image/video synthesis, and natural language processing.

Generative AI models are trained using examples of previously collected data, such as audio, text, or images. This collected data is then used to generate new data. The new data is generated with a similar overall structure, but not an exact copy of the training inputs. Generative AI is an example of unsupervised learning, a type of machine learning where the algorithm uses past data to identify patterns which can then be used to generate new data that follows those same patterns.

Generative AI has many uses in computer programming and cybersecurity. In speech synthesis, machines can be trained to generate new sound clips that sound like human voices. In image/video synthesis, generative AI can be used to create realistic-looking images and videos that don’t exist in real life. Generative AI is also being applied to natural language processing, to generate realistic-looking text conversations. These applications of generative AI can help by providing more data to train other models.

Generative AI is related to other AI technologies, such as reinforcement learning, deep learning, and supervised learning. By combining these different types of AI, more powerful and intelligent models can be created.

Generative AI is an exciting and rapidly developing field of Artificial Intelligence, paving the way for advancements in many areas of computer programming and cybersecurity.