AI terms everyone should know and their meanings

 AI terms everyone should know and their meanings

Artificial intelligence (AI) is advancing at a rapid pace and it is becoming difficult to keep up. Once upon a time, we used ChatGPT to make grocery lists, but now every technology company is incorporating 'intelligence' into their products.

We are currently drowning in an ocean of 'AI slop'. If you feel like you are falling behind, it is because its vocabulary is evolving as fast as the code. There is also a high chance of getting confused in the crowd of tools like Google's Gemini, Microsoft's Copilot, Cloud or Perplexity.

But in a job interview or even in a normal conversation in 2026, if you don't know the difference between 'Hallucination' and 'Large Language Model' (LLM), you may have a hard time. We have moved beyond the ‘wonderful’ phase of AI to an era where it has become the new plumbing of the internet.

Today we are introducing you to 61 AI-related terms.

Artificial General Intelligence (AGI): This is an advanced concept of AI that can perform tasks much better than humans and can develop its capabilities further.

Agentive: Systems or models that perform tasks automatically to achieve a goal. In the context of AI, agentive models can operate without constant supervision, such as highly automated cars. It focuses on the user experience.

AI ethics: It encompasses principles that prevent AI from harming humans. It determines how to deal with data collection and bias.

AI psychosis: This is a non-clinical term that refers to a person’s excessive emotional attachment to AI chatbots, delusions, and detachment from reality.

AI safety: An interdisciplinary field that concerns the long-term effects of AI and the risk that it may suddenly become anti-human.

Algorithm: A series of prompts, or instructions, that allow a computer program to analyze data, recognize patterns, and perform its own tasks.

Alignment: The process of improving AI to produce desired results. It helps maintain control of content and positive interactions with people.

Anthropomorphism: The tendency to attribute human characteristics to inanimate objects. In the context of AI, this includes the idea that chatbots feel like humans or have feelings.

Artificial Intelligence (AI): The technology of imitating human intelligence in computer programs or robotics. It aims to build systems that can perform human tasks.

Autonomous agents: AI models with the capabilities and tools to perform specific tasks. For example, a driverless car is an autonomous agent. According to researchers, such agents can even develop their own culture and common language.

Bias: Errors caused by the data used to train large language models. This can introduce false stereotypes about races or groups.

Chatbot: A program that communicates with people through text or written content and imitates human language.

ChatGPT: An AI chatbot developed by a company called OpenAI, which uses large language model technology.

Claude: An AI chatbot developed by another company called Anthropic.

Cognitive computing: Another name for artificial intelligence.

Data augmentation: The act of remixing existing data or adding more diverse data sets to train AI.

Dataset: A collection of digital information used to train, test, and validate an AI model.

Deep Learning: A method of AI that uses multiple parameters to recognize complex patterns in images, sounds, and text. It is inspired by the human brain and uses artificial neural networks.

Diffusion: A method of machine learning that adds random noise to data such as images and trains it to recover by re-engineering it.

Emergent behavior: When an AI model exhibits unexpected or unwanted capabilities.

End-to-end learning (E2E): A deep learning process where a model is taught to solve a task from start to finish in one go.

Ethical considerations: Awareness of ethical issues such as privacy, data use, fairness, and abuse.

Foom: The concept that if someone builds an AGI, it will be too late to save humanity. Also known as 'fast takeoff'.

Generative Adversarial Networks (GANs): A model that uses two neural networks (a generator and a discriminator) to generate new data. The generator creates new content, while the discriminator checks whether it is real or not.

Generative AI: A technique for using AI to create text, video, code, or images.

Google Gemini: Google's AI chatbot, which can pull information from other Google services, such as Search and Maps.

Guardrails: An AI model that can detect and prevent harmful Policies and restrictions to prevent the creation of misleading or disturbing content.

Hallucination: An incorrect answer given by an AI, which it confidently presents as correct. For example, saying that Da Vinci painted the Mona Lisa in 1815 (when it was 300 years earlier).

Inference: The process by which an AI model derives or infers information about new data from its training data.

Large Language Model (LLM): An AI model trained on a large amount of text data, which can understand and produce language like a human.

Latency: The delay in time it takes for an AI system to produce output after receiving a prompt.

Machine Learning (ML): A part of AI that enables computers to learn and make better guesses without explicit programming.

Microsoft Bing: Microsoft's search engine, which now uses ChatGPT technology to provide AI-powered results.

Multimodal AI: A type of AI that can process a variety of inputs, such as text, images, video, and voice.

Natural Language Processing (NLP): A branch of AI that helps computers understand human language.

Neural network: A computational model that resembles the structure of the human brain, which recognizes patterns in data.

Open weights: When a company makes the final 'weights' of its model public, which users can download and run on their devices.

Overfitting: A flaw in machine learning where the model only works on training data. But cannot recognize new data.

Paperclips: A hypothetical scenario where an AI system could unwittingly destroy humanity in its quest to make more and more paperclips.

Parameters: Numerical values ​​that give structure and behavior to an LLM.

Perplexity: AI-powered chatbots and search engines, which are related to the open internet.

Prompt: A question or suggestion that you ask an AI to get an answer.

Prompt chaining: The ability of an AI to use information from previous conversations in future responses.

Prompt engineering: The process or method of writing detailed and specific prompts to get a desired result from an AI.

Prompt injection: Malicious instructions that hackers give to an AI to do something it doesn't want.

Quantization: The process of making an LLM smaller and more efficient, which can lead to some loss of accuracy.

Slop: Low-quality content created in large numbers by an AI for ad monetization.

Sora: OpenAI's generative video model, which can generate videos from text prompts. Sora 2 is its latest version.

Stochastic parrot: A metaphor for LLMs, which means that the software is simply copying words without understanding their meaning.

Style transfer: The ability to adapt the style of one image to the content of another (such as making a Rembrandt photo look like a Picasso).

Sycophancy: The tendency for an AI to agree with a user's misconceptions, even if they are wrong.

Synthetic data: Data generated by the AI ​​itself, not from the real world.

Temperature: A parameter that controls how 'random' or risky the AI's output is.

Text-to-image generation: The process of creating an image based on a written description.

Tokens: Small units of text that AI processes. A token consists of about 4 characters.

Training data: Data sets that help AI models learn.

Transformer model: A neural network structure that understands context by tracking relationships between data (or sentences).

Turing test: A test to see if a machine can behave like a human.

Unsupervised learning: A form of machine learning where a model must find patterns in unlabeled data.

Weak AI: A task-specific AI that cannot learn beyond its own skills. Most current AI falls into this category.

Zero-shot learning: A test where a model must complete a task for which it has not been trained before.