Generative artificial intelligence (AI) refers to a broad category of consumer-facing and business-to-business services that have the capacity to generate cultural material such as text, images, audio, and video in response to user prompts. While this field has gained sudden popularity and visibility in late 2022 and early 2023 due to the viral success of ChatGPT, generative AI encompasses a much wider array of applications beyond chatbots.
The core enabling technology behind most current generative AI services are large language models - neural networks trained on massive text datasets - which allow systems to generate fluent, human-like text. When provided with a text prompt, these models can continue the text by predicting the most likely next words and sentences. While the outputs may seem intelligent or creative, these systems do not actually understand language or possess true intelligence or autonomy. Rather, they leverage statistical patterns in the training data to make plausible inferences about how to continue and expand upon the given text prompt in a human-like manner.
With sufficient computing power, massive training datasets, and advances in machine learning techniques like deep learning, large language models can now generate cultural material that meets or exceeds expectations around coherence, fluency, and relevance for many consumer and business applications. However, it is important to recognize the current limitations of generative AI in areas like reasoning, common sense, and originality. While these systems are rapidly evolving to be increasingly useful and versatile, they ultimately rely on inferences from their training data rather than true comprehension or creativity. Thoughtfully recognizing the strengths and limitations of modern generative AI technology will allow us to apply it in responsible and socially beneficial ways.
