Generative AI seems to have popped up everywhere in the mainstream lately—primarily via the popularity of ChatGPT, but also through the proliferation of text-to-image tools and AI avatars in our social media feeds. But beyond fun smartphone apps and ways for students to shirk essay-writing assignments, what can generative AI do? How does it work? How will it AI technology affect the way that businesses operate? And possibly most heavy on your mind: will generative AI models take your job?
As Ainsley Harris has written in Fast Company, in a profile of Mira Murati, the CTO of ChatGPT-developer OpenAI, “Nearly every aspect of economic activity could be affected by OpenAI’s tools. According to the McKinsey Global Institute, relatively narrow applications of AI, such as customer service automation, will be more valuable to the global economy this decade than the steam engine was in the late 1800s. [Artificial general intelligence] will be worth many, many trillions of dollars more, or so say its most ardent believers.”
What is generative AI?
Generative AI is artificial intelligence that can generate novel content, rather than simply analyzing or acting on existing data.
AI (short for artificial intelligence) broadly refers to the idea of computers that can learn and make decisions in a human-like way. Machine learning is a subfield of artificial intelligence focused on teaching computers to recognize patterns through data and algorithms. It differs from traditional programming in that the computer doesn’t need to be explicitly coded to address every potential scenario. Natural language processing (or NLP) refers to the specific ability of certain machine learning models to understand written or spoken language (as opposed to numerical data or specific, previously coded language).
Generative AI models take this pattern recognition a step further and use it to create text, images, computer code, and other novel content.
How does generative AI work?
If you’re using a generative AI model, you enter a prompt describing the output you’d like, and the program gives it to you, whether it’s in the form of text, code, images, or—increasingly—sound and video. But behind the scenes, things are a bit more complicated.
When programming a generative AI model, developers will feed it massive amounts of training data. For example, consider a large language model (or LLM). LLMs are the kind of generative AI used in ChatGPT and the basis of many generative AI tools in use today. These machine learning models utilize deep learning algorithms to process and understand language. LLMs are trained with immense amounts of previously written text to learn language patterns so they can perform tasks. Those tasks can range from translating content to responding in chatbot conversations—basically anything that requires language analysis of some sort.