Get in on the conversation with key AI terms and concepts from the Nielsen Norman Group.
Over the next decade, artificial intelligence (AI) will vastly extend human creativity and revolutionize our lives in ways we cannot yet fathom.
Caleb Sponheim at the Nielsen Norman Group has created a glossary of key AI concepts to help you understand—and intelligently discuss—the massive potential that AI represents.
The glossary outlines more than 30 concepts from generative AI to machine learning to natural language processing (NLP). Related articles that delve further into AI topics are included.
The Future of AI
The Nielsen Norman Group defines AI as “intelligence” demonstrated by machines, often implemented and exhibited differently than human intelligence. It’s also a subfield of computer science concerned with researching and developing tools that enable computers to act “intelligently.”
Recent AI advancements have been driven largely by generative AI, such as ChatGPT, and those technologies will continue to grow. However, according to a Pew Research Center report, though 90% of Americans were aware of AI, only 30% of adults correctly recognized six examples of AI in everyday life.
For others, whether it’s AI that powers smartphones or assists with work tasks, awareness may also bring concerns about AI safety. But educating yourself with resources like the Nielsen Norman Group’s AI glossary can help you navigate those concerns and make sense of the information in this rapidly changing era of technology.
From the Glossary
The Nielsen Norman Group’s AI glossary features 36 concepts, including the following. To learn more, view the full glossary.
Generative AI (genAI): AI models that learn patterns existing in training data and generate examples that fit a particular pattern requested in the prompt. The training examples can be multimodal (for example, can include an image and a piece of text that describes it).
Machine Learning: A category of algorithms that focus on identifying and incorporating trends from training data and making predictions for new data. Put another way, machine-learning algorithms effectively generalize from examples they are provided. They usually do not use explicit instructions, instead relying on probabilistic and inference solutions.
Natural Language Processing (NLP): A subfield of computer science dedicated to developing algorithms and systems that can understand and generate human language.