Understanding Generative AI

Generative AI is a specific form of Artificial Intelligence (AI) that is designed to generate content. This content could be text, images, video and music. 

It uses AI algorithms to analyse patterns in datasets, to mimic style, or structure, to replicate different types of content.  It is currently being used to create deepfake videos and voice messages. This potential to revolutionise content creation across various industries makes it important to understand what generative AI is, how it’s being used, and who it’s being used by.

Generative AI starts with a prompt that then returns new content in response to the prompt. Content can include essays, solutions to problems, or realistic fakes created from pictures or audio of a person. The technology, it should be noted, is not brand-new. Generative AI was introduced in the 1960s in chatbots. But it was not until 2014, with the introduction of generative adversarial networks, or GANs, a type of machine learning algorithm, that generative AI could create convincingly authentic images, videos and audio of real people.

Generative AI is AI technology geared for creating content. Generative AI combines algorithms, large language models and neural network techniques to generate content that is based on the patterns it observes in other content.

Although the output of a generative AI system is classified, loosely, as original material, in reality it uses machine learning and other AI techniques to create content based on the earlier creativity of others. It taps into massive repositories of content and uses that information to mimic human creativity; most generative AI systems have digested large portions of the Internet.

Machine Learning Algorithms

Generative AI systems use advanced machine learning techniques as part of the creative process. These techniques acquire and then process, again and again, reshaping earlier content into a malleable data source that can create “new” content based on user prompts.

Using Earlier Creativity

The content provided by generative AI is inspired by earlier human-generated content. This ranges from articles to scholarly documents to artistic images to popular music. The music of pop singer Drake and the band The Weekend was famously used by a generative AI program to create a “new” song that received considerable positive attention from listeners, but the song was soon removed from major platform in response to the musicians’ record labels.

Vast Datasets

Generative AI can accomplish tasks like analyse the entire database of an insurance company, or the entire record keeping system of a trucking company to produce an original set of data and/or business process that provides a major competitive boost.

Generative AI goes far beyond traditional machine learning. By utilising multiple forms of machine learning systems, models, algorithms and neural networks, generative AI provides a completely new form of human creativity.

The History of Generative AI

Artificial intelligence has a surprisingly long history, with the concept of thinking machines traceable back to ancient Greece. Modern AI really kicked off in the 1950s, however, with Alan Turing’s research on machine thinking and his creation of the eponymous Turing Test.

The first neural networks (a key piece of technology underlying generative AI) that were capable of being trained were invented in 1957 by Frank Rosenblatt, a psychologist at Cornell University.

Further development of neural networks led to their widespread use in AI throughout the 1980s and beyond. In 2014, a type of algorithm called a generative adversarial network (GAN) was created, enabling generative AI applications like images, video, and audio.

In 2023, the rise of large language models like ChatGPT is indicative of the explosion in popularity of generative AI as well as its range of applications.

Eweek:     Tech Target:     Github:     Altexsoft:     Investopedia    Image: JuliusH

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