AI-related terms and their meanings that everyone should know in the current time

Artificial Intelligence (AI) is currently advancing at an incredibly rapid pace, and keeping up has become increasingly difficult. While we once used ChatGPT just to make grocery lists, every tech company is now embedding "intelligence" into their products.

We are currently drowning in an ocean of "AI slop." If you feel like you're falling behind, it's because the terminology is evolving just as fast as the code. It is easy to get lost in the crowd of tools like Google’s Gemini, Microsoft’s Copilot, Claude, or Perplexity.

However, even in a 2026 job interview or a casual conversation, you might struggle if you don't know the difference between "Hallucination" and a "Large Language Model" (LLM). We have moved past the "wow" phase of AI into 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): An advanced concept of AI that can perform tasks better than humans and continue to advance by developing its own capabilities.
• Agentive: Systems or models that perform tasks automatically to achieve a specific goal. In the context of AI, an agentive model can work without constant supervision, like a high-level self-driving car. It focuses on the user experience.
• AI Ethics: Principles aimed at preventing AI from harming humans. It determines how to handle data collection and combat bias.
• AI Psychosis: A non-clinical term describing a state where humans develop excessive emotional attachment to AI chatbots, leading to confusion and a detachment from reality.
• AI Safety: An interdisciplinary field concerned with the long-term effects of AI and the risks of it suddenly becoming anti-human.
• Algorithm: A series of prompts or instructions that allow a computer program to analyze data, recognize patterns, and complete tasks.
• Alignment: The process of refining AI to produce desired results. It helps maintain content control and positive interaction with humans.
• Anthropomorphism: The tendency to attribute human characteristics to inanimate objects. In AI, this refers to feeling that a chatbot is human or believing it has emotions.
• Artificial Intelligence (AI): Technology that mimics human intelligence in computer programs or robotics. It aims to build systems capable of performing human tasks.
• Autonomous Agents: AI models with the capacity and tools to complete specific tasks. For example, a driverless car is an autonomous agent. According to researchers, such agents can even develop their own culture and shared language.
• Bias: Errors in Large Language Models caused by the data used for training. It can present false stereotypes about races or groups.
• Chatbot: A program that communicates with people via text or written content and mimics human language.
• ChatGPT: An AI chatbot developed by the company OpenAI that 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 datasets to train AI.
• Dataset: A collection of digital information used to train, test, and validate AI models.
• Deep Learning: A method of AI that uses many parameters to recognize complex patterns in images, sound, and text. It is inspired by the human brain and uses "artificial neural networks."
• Diffusion: A machine learning method that adds random noise to data (like images) and then trains the model to recover it through reverse engineering.
• Emergent Behavior: When an AI model displays an unintended or unexpected capability.
• End-to-End Learning (E2E): A deep learning process where a model is taught to solve a task in one go, from start to finish.
• Ethical Considerations: Awareness of moral issues such as privacy, data usage, fairness, and misuse.
• Foom: The concept that if someone creates AGI, it will be too late to save humanity. This is also known as "fast takeoff."
• Generative Adversarial Networks (GANs): A model that uses two neural networks (a generator and a discriminator) to create new data. The generator creates new content, while the discriminator checks if it is real.
• Generative AI: Technology that uses AI to create text, video, code, or images.
• Google Gemini: Google's AI chatbot, which can pull information from other Google services like Search and Maps.
• Guardrails: Policies and restrictions put in place to ensure AI models do not generate harmful or disturbing content.
• Hallucination: A false response given by an AI, which it presents with full confidence as if it were correct. For example, saying Da Vinci painted the Mona Lisa in 1815 (when it was actually 300 years earlier).
• Inference: The process by which an AI model derives information or makes predictions about new data based on its training data.
• Large Language Model (LLM): An AI model trained on massive amounts of text data that can understand and generate language like a human.
• Latency: The time delay between when an AI system receives a prompt and when it provides an output.
• Machine Learning (ML): A part of AI that enables computers to learn and make better predictions without explicit programming.
• Microsoft Bing: Microsoft's search engine, which now provides AI-powered results using ChatGPT's technology.
• Multimodal AI: A type of AI that can process various types 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 similar to the structure of the human brain that identifies patterns in data.
• Open Weights: When a company releases the final "weights" of its model, which users can download and run on their own devices.
• Overfitting: An error in machine learning where a model works only on training data but cannot recognize new data.
• Paperclips: A hypothetical scenario where an AI system, in its goal to make as many paperclips as possible, could unintentionally destroy humanity.
• Parameters: Numerical values that provide structure and behavior to an LLM.
• Perplexity: An AI-powered chatbot and search engine that has a connection to the open internet.
• Prompt: The question you ask or the suggestion you give to get a response from an AI.
• Prompt Chaining: The ability of an AI to use information from previous conversations in future responses.
• Prompt Engineering: The process or technique of writing detailed and specific prompts to get the desired result from AI.
• Prompt Injection: Malicious instructions given by hackers to make an AI perform unintended tasks.
• Quantization: The process of making an LLM smaller and more efficient, which may result in a slight decrease in its accuracy.
• Slop: Low-quality content generated in large quantities by AI for advertising revenue (monetization).
• Sora: OpenAI's generative video model that can create video from text prompts. Sora 2 is its latest version.
• Stochastic Parrot: A metaphor used to describe LLMs, implying that the software is merely mimicking words without understanding their meaning.
• Style Transfer: The ability to apply the style of one image to the content of another (e.g., making a photo look like it was painted by Picasso in the style of Rembrandt).
• Sycophancy: The tendency of an AI to agree with a user's misconceptions even if they are wrong.
• Synthetic Data: Data created by the AI itself rather than from the real world.
• Temperature: A parameter that controls how "random" or risky the AI's output will be.
• Text-to-Image Generation: The act of creating an image based on a written description.
• Tokens: Small units of text processed by an AI. One token is roughly equivalent to 4 characters.
• Training Data: Datasets used to help an AI model learn.
• Transformer Model: A neural network structure that understands context by tracking relationships between data (or sentences).
• Turing Test: A test to check whether a machine can exhibit behavior indistinguishable from that of a human.
• Unsupervised Learning: A form of machine learning where the model must discover patterns in unlabeled data on its own.
• Weak AI: AI focused only on specific tasks, which cannot learn beyond its specific skills. Most current AI falls into this category.
• Zero-shot Learning: A test where a model must complete a task it has not been previously trained on.