Artificial intelligence (AI) models continue to support many applications across various industries. While traditional AI systems are designed to perform specific tasks like image recognition or natural language processing, more creative applications like generating new images or videos require the use of generative AI.

Generative AI is a subset that focuses on creating new data, such as images, videos, audio, or text, that resemble human-made content. Traditional AI systems rely on pre-defined rules and patterns based on logical and mathematical formulas and are used for tasks such as decision-making and classification.

Generative AI models use a data-driven approach using neural networks and other machine learning algorithms to learn from human-created datasets, allowing them to create new content similar to human-made content. This has recently gained significant attention with its use in various applications, including image and video synthesis, text generation, music composition, and game development.

Apart from applications in the creative industry, generative AI is used across many other industries. Fintech companies use it for tasks like fraud detection, credit scoring, and portfolio management. In the healthcare industry, it helps analyze images such as X-rays, CT scans, and MRIs. It is also used to make customized treatment plans that can be recommended based on a patient’s medical history, genetics, and lifestyle. The manufacturing industry uses it for applications like predictive maintenance, quality control, and supply chain optimization.

In this article, we present a roundup of recent announcements involving utilization of generative AI and building supporting architecture to deploy generative AI at the edge.

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