What is Generative AI?

Artificial Intelligence (AI) is a broad field that encompasses many different technologies and techniques. Machine learning is a subset of AI that involves training algorithms to recognize patterns in data and make predictions based on those patterns. In other words, machine learning is a way to teach computers to learn from data without being explicitly programmed and is one of the most popular and widely used techniques in AI today.

Deep learning is a subset of machine learning that involves training artificial neural networks to recognize patterns in data. Deep learning algorithms use multiple layers of artificial neurons to learn increasingly complex representations of data, it has been used to achieve state-of-the-art performance on a wide range of tasks, including image recognition, speech recognition, natural language processing, and more. The way deep learning works is by training artificial neural networks to recognize patterns in data,these algorithms use multiple layers of artificial neurons to learn increasingly complex representations of data. The process of training a deep learning model involves feeding it large amounts of labeled data and adjusting the weights of the connections between neurons to minimize the difference between the model’s predictions and the true labels. Once trained, a deep learning model can be used to make predictions on new, unlabeled data.

Generative AI is a type of artificial intelligence technology that can produce various types of content including text, imagery, audio and synthetic data, use generative models such as large language models to statistically sample new data based on the training data set that was used to create them. The goal of generative AI is to develop algorithms that can learn the underlying probability distribution of a given dataset and use this knowledge to generate new examples that are similar to the examples in the dataset.

Generative AI models can be classified into two broad categories: generative models and discriminative models. Generative models such as GPT-3, DALL-E, Variational Autoencoders etc learns the underlying probability distribution of the dataset and can generate new examples from the learned distribution. Discriminative models, such as deep neural networks, learn to differentiate between different classes or categories, and are generally used for tasks such as image or speech recognition. Generative AI sits on top of other areas of AI like unsupervised learning, supervised learning and reinforcement learning. It relies on the capability of those models to extract useful features from the data and use that for learning the underlying probability distribution of the data which is used to realize the potential to be used in a wide range of applications, such as computer vision, natural language processing, speech recognition, and many more.