Generative Artificial Intelligence: Trends and Prospects

Abstract

Generative modeling is an artificial intelligence (AI) technique that generates synthetic artifacts by analyzing training examples; learning their patterns and distribution; and then creating realistic facsimiles. Generative AI (GAI) uses generative modeling and advances in deep learning (DL) to produce diverse content at scale by utilizing existing media such as text, graphics, audio, and video. 1,2 While mainly used in research settings, GAI is entering various domains and everyday scenarios. This article sheds light on the unique practical opportunities and challenges GAI brings. GAI TECHNIQUESAlthough there are many forms of GAI, we will look at four of the most common techniques being leveraged today. Generative adversarial networksGenerative adversarial networks (GANs) are the most prevalent GAI technique being used today. 3 A GAN uses a pair of neural networks. One, known as the generator, synthesizes the content (for example, an image of a human face). The second, known as the discriminator, evaluates the authenticity of the generator's content, (that is, whether the face is natural or fake). The networks repeat this generate/discriminate cycle until the generator produces content that the discriminator cannot discern between real and synthetic. Generative Pre-trained TransformerGenerative Pre-trained Transformer (GPT) models generate text in different languages and can create human-sounding words, sentences, and paragraphs on almost any topic and writing style-from convincing news articles and essays to conversations in customer-service chatbots or characters in video games. 4 These have matured over several generations, each with an increased parameter set trained on a more extensive online textual

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