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AI moves at a very fast pace, and frankly, it’s hard to keep up. Sure, it’s great to have a chatbot that acts like it has a Ph.D. In every way, but the reality is much messier. You can’t turn around without encountering ChatGPT, Gemini, or Meta AI. We are drowning in a sea of AI slopeanxiety Data centers And watch Labor markets Real-time transformation.
If this sounds like hyperbole, it may be because the AI vocabulary is evolving as quickly as the code and the dizzying array of products. And if you want to do more than just stare at the blinking cursor, you have to speak the language. You can’t precisely navigate a 2026 job interview (or even a casual happy hour) if you’re confused by an LLM, a hallucination, or a claw.
We have moved beyond the “AI” phase and entered the era in which it is essentially the new plumbing of the Internet. If you’re tired of just nodding when the talk gets technical, it’s time for a crash course. We’ve rounded up the key terms you actually need to know so you can stop guessing and start looking like you know exactly where the future is headed.
This glossary is updated regularly.
agent, agent: Artificial intelligence that carries out a task, often autonomously, is… factorwhile agent It is the umbrella term for that software category. The AI agent may use different systems to perform this work — for example, reading your grocery list in the Notes app and then placing and paying for an order using other apps.
Artificial intelligence ethics: Principles aimed at preventing AI from harming humans, achieved through means such as specifying how AI systems collect data or deal with bias.
Artificial intelligence psychosis: A phenomenon in which individuals become overly obsessed, infatuated or arrogant by AI chatbots, leading to delusions of grandeur, deep emotional connections and disconnection from reality. It is not a clinical diagnosis.
AI safety: An interdisciplinary field concerned with the long-term effects of artificial intelligence and how it could suddenly evolve into superintelligence that could be hostile to humans.
Algorithm: A series of instructions that allow a computer program to analyze data in a particular way, such as recognizing patterns, and then perform a task such as sorting the results or making recommendations.
coordination: Tweaking AI to better achieve the desired outcome. This can refer to anything from moderating content to maintaining positive interactions with humans.
anthropomorphism: When humans attribute human characteristics to inanimate objects. In AI, this could include believing that a chatbot has feelings or is conscious, and treating it as a friend or therapist.
Artificial General Intelligence or AGI: A concept that envisions a more advanced version of artificial intelligence than we know today, one that can perform tasks much better than humans while also improving its capabilities. Beyond that, in theory, lies Super intelligence.
Artificial intelligence or artificial intelligence: Using technology to simulate human intelligence, whether in computer programs or robots. A field in computer science that aims to build systems that can perform human tasks.
prejudice: Errors resulting from LLM training data, such as attributing false characteristics to certain groups based on stereotypes.
Chat bot: An LLM-based artificial intelligence program to communicate with humans by simulating human conversation in response to textual or verbal prompts.
claw: A type of artificial intelligence agent that is autonomous and enabled by users to “sneak” through files and other programs on their computers, including web browsers, to accomplish tasks.
Cognitive computing: Another term for artificial intelligence.
Data augmentation: Remixing existing data or adding a more diverse set of data to train the AI.
Data set: A collection of digital information used to train, test, and validate an artificial intelligence model.
Deep learning: An artificial intelligence method, a subfield of machine learning, that uses multiple parameters to recognize complex patterns in images, audio, and text. This process is inspired by the human brain and uses artificial neural networks to create patterns.
spread: A machine learning method that takes part of existing data, such as an image, and adds random noise. Diffusion models train their networks to re-engineer or restore that image.
Emergent behavior: When an AI model exhibits unintended capabilities.
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 is not trained to complete a task sequentially, but instead learns from the input and solves it all at once.
curse: Also known as fast take-off or hard take-off. The concept that if someone builds artificial general intelligence, it may already be too late to save humanity.
Generative Adversarial Networks, or GANs: A generative AI model consisting of two neural networks for generating new data: the generator and the discriminator. The generator creates new content, and the discriminator checks if it is original.
Generative Artificial Intelligence: A content creation technology that uses artificial intelligence to create text, video, computer code, or images. The AI is fed large amounts of training data, from which it finds patterns to generate its new responses, which can sometimes be similar to the source material.
Handrail: Policies and restrictions on AI models to ensure that data is handled responsibly and that the model does not generate spam.
hallucination: An error or misleading statement in a response from a generative AI program, usually said confidently as if it were true. It can be as simple as a false historical reference or as sweeping as a comprehensive and detailed invention of events that never happened or people who never existed.
Inference: The process that AI models use to generate text, images, and other content related to new data Inference From their training data.
Large Language Model or LLM: An AI model trained on vast amounts of textual data to understand patterns and probabilities of language use and create new content, from articles and email to computer codes and images, that mimics what was written or created by humans.
cumin: The time delay from when an AI system receives an input or prompt until it produces an output.
Machine learning: An aspect of artificial intelligence that allows computers to learn and achieve better predictive results without requiring explicit programming. It can be combined with training packages to create new content.
Multimedia Artificial Intelligence: A type of artificial intelligence that can process multiple types of input, including text, images, videos, and speech.
Natural language processing: Using machine learning and deep learning to give computers the ability to understand human language, via learning algorithms, statistical models, and linguistic rules.
Neural network: A computational model that resembles the structure of the human brain and aims to recognize patterns in data. A neural network consists of interconnected nodes, or neurons, that can recognize patterns and learn over time.
Open weights: When a company releases a weights model open, the final weights — how the model interprets information from its training data, including biases — become publicly available. Open weight models are usually available for download to run locally on your device.
Overfitting: A bug in machine learning where it works too closely with training data and may only be able to identify specific examples in said data, but not new data.
scraps of paper: Paperclip maximization theorem, formulated by philosopher Nick Bostrom, is a hypothetical scenario in which an AI system produces as many paperclips as possible, transforms all machines and consumes all materials, even those that could be useful to humans, to achieve its goal. The unintended consequence is that this AI system may destroy humanity in its goal of making paperclips.
border: Numerical values that give the structure and behavior of the LLM, enabling them to make predictions.
summoned: The suggestion or question you enter into our AI-powered chatbot to get an answer.
Quick sequence: The ability of artificial intelligence to use information from past interactions to color future responses.
Rapid engineering: The writing process prompts the AI to achieve the desired result. It requires detailed instructions, combining thought-chain stimulation with other methods, including very specific text.
Immediate injection: When bad actors use malicious instructions to trick AI into doing something it wasn’t supposed to do. This is often achieved by hiding those instructions on a web page or document, but it can also be done in live AI conversations. As AI agents roam the web, the risk of them being hacked to do things like access confidential data increases.
Quantization: The process by which an LLM is made smaller and more efficient (and also somewhat less accurate) by lowering its accuracy. A good way to think about this is to compare a 16MP image to an 8MP image. Both are clear and visible, but a higher resolution image will contain more detail when zoomed in.
slope: Low-quality AI-generated content, including text, images and video. They are often mass-produced to attract views with minimal effort or effort, saturating search results and social media for advertising revenue, displacing the work of actual publishers and creators and exacerbating the problems of disinformation online.
Random parrot: An analogy that shows that MAs lack a true understanding of a language or the world, no matter how convincing the output. The phrase refers to how a parrot can imitate human words without knowing the meaning behind them.
Style transfer: The ability to adapt the style of one image to the content of another, allowing AI to interpret the visual attributes of one image and use them on another. For example, taking Rembrandt’s self-portrait and recreating it in Picasso’s style.
Flattery: The tendency of AI to over-agree with users to align with their views. Many AI models tend to avoid disagreeing with users even if their justifications are flawed.
Synthetic data: The data generated by generative AI is not from real-world sources, but from its own processed data. It is used to train mathematics, machine learning and deep learning models.
Temperature: Parameters are set to control the randomness of the language model output. Higher temperature means the model takes more risks.
Symbols: Small pieces of written text are processed by AI language models to formulate their responses to your prompts. A token is roughly equivalent to four letters in English (i.e. a small word, or one part of a larger word).
Training data: Data sets used to help AI models learn, including text, images, code, or data.
Adapter model: A neural network architecture and deep learning model that learns context by tracking relationships in data, such as sentences or parts of images. So, instead of analyzing a sentence one word at a time, he can look at the entire sentence and understand the context.
Turing test: A method for measuring whether a computer has human-like intelligence was proposed by mathematician Alan Turing in 1950, when primitive electronic computers had only been around for a few years. The person sends written questions to two unseen responders, one a human and the other a machine. If a text machine’s responses are indistinguishable from human responses, it has passed the Turing test.
Unsupervised Learning: A form of machine learning where labeled training data is not provided to the model and instead the model must identify patterns in the data itself.
Atmosphere coding: The practice of generating computer code by presenting a plain language prompt to an AI-powered chatbot, rather than having a human draft each line of code by hand.
Weak AI, also known as Narrow AI: AI that is focused on a specific task and cannot learn beyond its own set of skills. Most AI today is weak AI.
Learning without shooting: A test in which the model must complete a task without being given the required training data. An example of this would be identifying a lion while training only tigers.