What does generative AI mean?
Generative AI is a broad label used to describe any type of artificial intelligence that uses unsupervised learning algorithms to create new digital images, video, audio, text, or code.
Until recently, most AI learning models were characterized as being discriminatory. The purpose of a discriminative learning algorithm is to use what is learned during training to make a decision about new input. In contrast, the goal of a generative AI model is to generate synthetic data that can pass a Turing test. Because generative AI requires more processing power than discriminative AI, it is more expensive to implement.
Generative AI models are given a limited number of parameters to use during the training period. Essentially, this approach forces the model to draw its own conclusions about the most important features of the training data. Once the generative model has identified the fundamental properties of the data, it can use a generative adversarial network (GAN) or variational autoencoder (VAE) to improve output accuracy.
While the term generative AI is often associated with deep fakes and data journalism, the technology is playing an increasingly important role in helping to automate the repetitive processes used in digital image correction and digital audio correction. Generative AI is also being used experimentally in manufacturing as a tool for rapid prototyping and in enterprises to improve data augmentation for robotic process automation (RPA).
Techopedia explains generative AI
To recap, virtually any time an AI technology generates its own content, whether text, visual, or multimedia, professionals can refer to it as “generative AI.” This includes technologies that can draw and paint images, as well as technologies that can use information gathered from the Internet to create website articles and article summaries, corporate brochures, press releases and white papers.
For example, in the case of text creation, generative AI will examine existing human-written text for everything from grammar and punctuation, to style and word choice, to narrative and thesis. With the advanced AI we have now, generative AI can create content that looks like it was written by humans and pass the Turing test set by famed mathematician and cryptographer Alan Turing in the mid-20th century. Generative AI is expected to be responsible for supporting parts of these creative processes that humans have used for centuries in publishing, broadcasting, and communications.
Here is an example of generative AI – suppose you have the task of creating an insurance brochure. You have a list of policies and costs, along with benefits and details. The traditional way this would work is that a human writer would take a look at all this raw data, then take notes and write something in a narrative form that explains to the reader what each of these things are. With generative AI, the program can examine raw data, shape a narrative around it, and create something readable for a human reader, without a human writer being directly involved.
Some people are afraid of certain generative AI technologies, especially those that simulate human creativity by writing fiction or producing works of art. This leads to a more general debate about the limits of technology and its impact on our lives. While people may think of generative AI as something that will replace human jobs, new technologies like these often have a human element in the loop (HITL). When AI is characterized as assistive technology that helps humans produce faster and more accurate results, it is called augmented artificial intelligence.
Arguably, because machine learning and deep learning are inherently focused on generative processes, they can also be considered types of generative AI.