Artificial Intelligence

Generative AI or generative artificial intelligence refers to the use of AI to create new content, like images, text, audio, music, and videos. It is powered by foundation models that can multi-task and perform out-of-the-box tasks, including summarization, classification, and more. Additionally, with minimal training required, foundation models can be adapted for targeted use cases with very little example data.

To make an AI model, you need to identify the problem you are trying to solve, collect the right data, create algorithms, train the AI model, choose the right platform, pick a programming language, and finally deploy and monitor the operation of your AI system.

Building a generative AI model requires a deep understanding of both the technology and the specific problem that it aims to solve. It involves the designing and training of the AI models to generate novel outputs based on the input data, often optimizing a specific metric.

Generative AI relies on neural network techniques such as transformers, GANs, and VAEs. Other kinds of AI, in distinction, use techniques including convolutional neural networks, recurrent neural networks, and reinforcement learning.