What is generative AI?
Generative AI is type of AI that can be used to create new text, images, video, audio, code, or synthetic data.
Techopedia editor Margaret Rouse offers a comprehensive explanation of generative artificial intelligence (AI), describing it as “a broad label that’s used to describe any type of AI that can be used to create new text, images, video, audio, code or synthetic data. While the term [is] often associated with ChatGPT and deep fakes, the technology was initially used to automate the repetitive processes used in digital image correction and digital audio correction”.1
Generative AI includes learning algorithms that make predictions and algorithms that can leverage prompts to autonomously compose articles and generate images. “Therefore, because machine learning and deep learning are inherently focused on generative processes, they can be considered types of generative AI, too”.1
George Lawton notes that generative AI first begins with “a prompt in the form of a text, image, video design, [some] musical notes, or any input that the AI system can process, [followed by] various AI algorithms [that] return new content [such as essays, solutions to problems, or realistic fakes created from pictures or audio of a person] in response to the prompt”.2
Rouse states that early generative AI “required submitting data via an API or an otherwise complicated process, [requiring] developers [to] familiarize themselves with special tools and write applications using programming languages such as Python”.1
Modern generative AI has a much more flexible user experience where ender users can input their requests using natural language instead of code. “Generative AI was introduced in the 1960s in chatbots. But it was not until 2014, with the introduction of GANs [that] generative AI could create convincingly authentic images, videos and audio of real people”.2 GANs and variational autoencoders (VAE) are two common generative models for image and text creation.
Random noise can be leveraged by some generative AI models as an input to generate new outputs. To do this, the generative AI model “takes a random noise vector as input, passes it through the network and generates output that is similar to the training data. The new data can then be used as additional, synthetic training data for creative applications in art, music and text generation”.1
Generative AI that is leveraged as a means of enhancing human creativity “can be categorized as a type of augmented artificial intelligence”.1
With the immense capabilities that generative AI offers, it’s no surprise that there’s a myriad of different applications for end users looking to create text, images, videos, audio, code, and synthetic data. Here are some examples of the most popular generative AI applications.
Bernard Marr writes that traditional AI, (aka narrow AI or weak AI) “focuses on performing a specific task intelligently [and] refers to systems designed to respond to a particular set of inputs”.3 These traditional AI systems can process data and make learned choices or predictions from that data. Some of these systems function similarly to something like the IBM supercomputer Deep Blue. They’re fed a considerable amount of data, in Deep Blue’s case chess specific data, and use it to either develop a game winning strategy or to respond to an opponent’s strategy. Other traditional AI systems operate similarly to Siri or Alexa, responding to and predicting the needs of a household, while others function more like recommendation engines for Google, Netflix, or Amazon. “AIs [that] have been trained to follow specific rules, do a particular job, and do it well, but they don’t create anything new”.3
Inversely, generative AI can create new things (text, art, music, videos, and more) from the plain language prompts that it receives. “Generative AI models are trained on a set of data and learn the underlying patterns to generate new data that mirrors the training set”.3
Generative AI is perhaps the most recognizable type of AI today. It has immense potential to help enterprises produce high quality content quickly, help users to innovate, creating new products, and offers avenues for improving customer service and communication. Generative AI models are commonly leveraged for creating visual or audio art, writing web content or essays, running web searches, and much more.
For enterprises looking to leverage generative AI tools, here are some of the benefits that your organization can hope to leverage: