AI is rapidly changing the world around us. It's eliminating jobs and flooding the internet with slop. Thanks to the massive popularity of ChatGPT to Google cramming AI summaries at the top of its search results, AI is completely taking over the internet. With AI, you can get instant answers to pretty much any question. It can feel like talking to someone who has a doctoral degree in everything.
But that aspect of AI chatbots is only one part of the AI landscape. Sure, having ChatGPT help do your homework or having Midjourney create fascinating images of mechs based on the country of origin is cool, but the potential of generative AI could completely reshape economies. That could be worth $4.4 trillion to the global economy annually, according to McKinsey Global Institute, which is why you should expect to hear more and more about artificial intelligence.
It's showing up in a dizzying array of products -- a short, short list includes Google's Gemini, Microsoft's Copilot, Anthropic's Claude and the Perplexity search engine. You can read our reviews and hands-on evaluations of those and other products, along with news, explainers and how-to posts, at our AI Atlas hub.
As people become more accustomed to a world intertwined with AI, new terms are popping up everywhere. So whether you're trying to sound smart over drinks or impress in a job interview, here are some important AI terms you should know.
This glossary is regularly updated.
artificial general intelligence, or AGI: A concept that suggests a more advanced version of AI than we know today, one that can perform tasks much better than humans while also teaching and advancing its own capabilities.
agentive: Systems or models that exhibit agency with the ability to autonomously pursue actions to achieve a goal. In the context of AI, an agentive model can act without constant supervision, such as an high-level autonomous car. Unlike an "agentic" framework, which is in the background, agentive frameworks are out front, focusing on the user experience.
AI ethics: Principles aimed at preventing AI from harming humans, achieved through means like determining how AI systems should collect data or deal with bias.
AI safety: An interdisciplinary field that's concerned with the long-term impacts of AI and how it could progress suddenly to a super intelligence that could be hostile to humans.
algorithm: A series of instructions that allows a computer program to learn and analyze data in a particular way, such as recognizing patterns, to then learn from it and accomplish tasks on its own.
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