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What You Should Know About Generative AI Models

The world of artificial intelligence (AI) is expanding rapidly. More and more organizations are investing in the developing technology and what it has to offer across countless industries. One form of AI that is becoming particularly sought after is generative AI, specifically the various types of generative AI models. Below, we’ll briefly go over what this cutting-edge tech is and what it is capable of delivering.

First, what is a generative AI model? Put simply, it is a type of AI solution capable of generating new and original data or content. It utilizes neural networks and complex algorithms to identify patterns from given datasets. Depending on the data it was trained on, the model can generate a wide range of new material including text, code, images, music and even videos.

Generative AI models work to understand the patterns and structures detected in the data and use machine learning techniques to apply the information when producing new material. It is this implementation and the power of machine learning that sets generative AI models apart from conventional rule-based systems.

Although generative AI goes back to the 1960s, it was not until 2014 when the introduction of generative adversarial networks, or GANs, pushed it to new, greater heights. GANs are a type of generative AI model that uses two separate neural networks: a generator to produce new data instances and a discriminator to evaluate them for authenticity and quality. Deep learning architectures such as this continuously learn through a cyclical process of material creation and evaluation as the generator attempts to deceive the discriminator.

There are many applications of GANs and other such generative AI models. A major one that has been making waves is content creation. Whether for artistic, entertainment or marketing purposes, this expansion within the realms of artificial content devolvement has shown promising but not-yet-reliable results across various fields. Along the same lines is creative assistance. By supplementing the creation process, this application can support designers, artists and even authors through concept development, first drafts, mockups and so on.

Another notable application is in digital simulation. Generative AI models are being utilized to handle all manner of tasks in gaming, scenario generation and beyond. They work by creating fictitious yet believable characters, scenarios and settings. Once established, they can go on to build more immersive storytelling and unique experiences as well as assist in decision-making or strategic planning for practically any type of enterprise.

These examples barely scratch the surface of what generative AI can do. If you would you like to learn more about generative AI models and the future of generative AI, please see the accompanying resource from Web Age Solutions.

Generative AI Models & Their Applications this infographic was contributed by Web Age Solutions, an organization providing machine learning training courses

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