AI is 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 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.
alignment: Tweaking an AI to better produce the desired outcome. This can refer to anything from moderating content to maintaining positive interactions with humans.
anthropomorphism: When humans tend to give nonhuman objects humanlike characteristics. In AI, this can include believing a chatbot is more humanlike and aware than it actually is, like believing it’s happy, sad or even sentient altogether.
artificial intelligence, or AI: The use of technology to simulate human intelligence, either in computer programs or robotics. A field in computer science that aims to build systems that can perform human tasks.
autonomous agents: An AI model that have the capabilities, programming and other tools to accomplish a specific task. A self-driving car is an autonomous agent, for example, because it has sensory inputs, GPS and driving algorithms to navigate the road on its own. Stanford researchers have shown that autonomous agents can develop their own cultures, traditions and shared language.
cognitive computing: Another term for artificial intelligence.
data augmentation: Remixing existing data or adding a more diverse set of data to train an AI.
dataset: A collection of digital information used to train, test and validate an AI model.
deep learning: A method of AI, and a subfield of machine learning, that uses multiple parameters to recognize complex patterns in pictures, sound and text. The process is inspired by the human brain and uses artificial neural networks to create patterns.
diffusion: A method of machine learning that takes an existing piece of data, like a photo, and adds random noise. Diffusion models train their networks to re-engineer or recover that photo.
emergent behavior: When an AI model exhibits unintended abilities.
end-to-end learning, or E2E: A deep learning process in which a model is instructed to perform a task from start to finish. It’s not trained to accomplish a task sequentially but instead learns from the inputs and solves it all at once.
Google Gemini: An AI chatbot by Google that functions similarly to ChatGPT but also pulls information from Google’s other services, like Search and Maps.
guardrails: Policies and restrictions placed on AI models to ensure data is handled responsibly and that the model doesn’t create disturbing content.
hallucination: An incorrect response from AI. Can include generative AI producing answers that are incorrect but stated with confidence as if correct. The reasons for this aren’t entirely known. For example, when asking an AI chatbot, “When did Leonardo da Vinci paint the Mona Lisa?” it may respond with an incorrect statement saying, “Leonardo da Vinci painted the Mona Lisa in 1815,” which is 300 years after it was actually painted.
neural network: A computational model that resembles the human brain’s structure and is meant to recognize patterns in data. Consists of interconnected nodes, or neurons, that can recognize patterns and learn over time.
temperature: Parameters set to control how random a language model’s output is. A higher temperature means the model takes more risks.
text-to-image generation: Creating images based on textual descriptions.
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