AI Glossary

Explore our comprehensive AI and technology glossary. Search through 30 terms and definitions to enhance your understanding of artificial intelligence, machine learning, and modern technology.

Agent2Agent

Agent2Agent communication refers to the process by which autonomous artificial intelligence agents exchange messages, data, or signals with each other to coordinate actions, share information, or collectively solve problems within a multi-agent system. It’s what enables a group of individual AIs to function not just as isolated units, but as a cohesive team. Think […]

AI Agent

An AI agent is a system that can perceive its environment, make decisions based on its perceptions and predefined goals or rules, and take actions within that environment. Unlike AI that just answers questions or analyzes data passively, an AI agent is built to do things. Imagine a smart thermostat in your home. It […]

Artificial Intelligence

Artificial Intelligence (AI) refers to the field of computer science dedicated to creating systems capable of performing tasks that typically require human intelligence. These tasks include abilities like learning, reasoning, problem-solving, perception (seeing and hearing), understanding human language, and making decisions. Think about what makes us, humans, intelligent. We can learn from experience, adapt […]

Artificial Neural Network (ANN)

An Artificial Neural Network is a computational model designed to mimic, in a very simplified way, how biological neurons in a brain work together. Think of your own brain for a moment. It’s a vast network of interconnected cells called neurons. These neurons communicate with each other, processing information and learning from experiences. When you […]

Backpropagation

Backpropagation (short for “backward propagation of errors”) is a supervised learning algorithm used to train artificial neural networks (ANNs). It works by calculating the gradient of the loss function (the measure of error) with respect to the network’s weights and biases. This gradient information is then propagated backward through the network layers, allowing the algorithm […]

Chain-of-Thought Prompting (CoT)

Chain-of-Thought (CoT) Prompting is a technique used to improve the reasoning abilities of large language models (LLMs) by instructing them to generate intermediate reasoning steps before providing a final answer to a problem. Instead of just jumping to a conclusion, the AI is encouraged to “show its work,” mimicking the way humans often break down […]

Dataset

A dataset is an organized set of data points formatted and structured for a given use, like analysis, processing, or, most importantly for AI, training machine learning models. The data is generally related in some sense, maybe collected from a common source or meant for a particular project. Just think of it as an accumulation […]

Deep Learning

Deep Learning is a specialized subset of Machine Learning that utilizes multi-layered Artificial Neural Networks (ANNs) to automatically learn intricate patterns and representations directly from vast amounts of data. It’s particularly powerful for tasks involving unstructured information like images, sound, and text, effectively acting like the learning core or “brain” for many advanced AI applications. […]

Deepfake

Deepfakes are highly realistic, digitally manipulated, or synthetically generated videos, images, or audio recordings created using artificial intelligence (AI), specifically deep learning techniques, to convincingly mimic a real person’s appearance, voice, or actions. The term itself is a blend of “deep learning” (the AI method used) and “fake,” perfectly capturing its essence: sophisticated fakes powered […]

Exploration

Exploration in AI is about trying new things. Imagine a baby learning to walk. They don’t just stand still; they wobble, they crawl, they push, they fall, and through all these varied actions, they learn what works and what doesn’t. Similarly, an AI agent needs to experiment with different actions in its environment to discover […]

Few-Shot Prompting

Few-Shot Prompting is a method where you give a Large Language Model not only an instruction, but also a few examples (the “shots”) of the task within the prompt itself, before requesting the AI to carry out the task on some new data. These examples would better help the AI model to grasp your task’s requirements without needing any extensive retraining and, therefore, saving you time and resources. To put it simply, this kind of prompt is similar to providing a mini-tutorial on your task. It’s a means of enabling the model’s strong, general abilities towards a somewhat more particular result. This method is extremely helpful in enhancing the AI model’s precision, regulating the output structure, and addressing tasks where basic instructions would be too vague. What Does “Few-Shot” […]

Generative Adversarial Network (GAN)

Generative Adversarial Network (GAN) is a type of machine learning framework where two neural networks compete against each other to become more accurate in their predictions and generate new, synthetic data that mimics some known input data. Think of it as a high-stakes game of cat and mouse played between two AIs, constantly pushing each […]

Generative AI

Generative AI refers to a class of artificial intelligence models that learn patterns, structures, and relationships from vast amounts of existing data (like text, images, or audio) and then use that learned knowledge to generate new, original content that resembles the data it was trained on, but is not a direct copy. Think of it […]

Generative Pre-trained Transformer (GPT)

GPT stands for Generative Pre-trained Transformer. That might sound like a mouthful, but think of it like this: So, in simple terms, GPT is a very smart AI developed by OpenAI that learns from vast amounts of information and uses a clever ‘transformer’ engine to generate human-like text. These types of AI are often called […]

Graphics Processing Unit (GPU)

Simply put, a GPU (Graphics Processing Unit) is a special kind of computer chip originally designed to make graphics and videos look smooth and fast on your screen. But have you heard the term “GPU” thrown around more often lately, especially when people talk about powerful gaming computers or the magic behind Artificial Intelligence (AI)? […]

Machine Learning

At its heart, Machine Learning (ML) is a way of teaching computers to learn from information (data) and make decisions or predictions without being explicitly programmed for every single step. It’s about creating algorithms that let computers figure things out from examples, much like humans learn from experience. Think of traditional programming like giving a […]

Multimodal AI

Multimodal AI refers to artificial intelligence systems that are designed to process, understand, and often combine information from more than one type of data source or “modality.” Instead of just reading text or just looking at images, a multimodal AI can potentially do both, and perhaps also listen to audio, watch videos, or even interpret […]

Neural Network

Neural Network is a computational model designed to simulate the way a biological brain processes information. It’s made up of many interconnected nodes, or “neurons,” organized in layers. These networks are exceptionally good at recognizing patterns and relationships within data that are too complex for traditional computer programs to spot. Inspired by Biology: The […]

Prompt

When using tools powered by artificial intelligence (AI)—like ChatGPT—you interact with them by typing something in. That input you give is called a prompt. It could be a question, a command, or even a few lines of context. The AI reads that prompt and tries to respond in a way that makes sense based on […]

Prompt Engineer

A prompt engineer is a professional who designs and refines the inputs—known as prompts—that guide generative AI models like ChatGPT to produce accurate, relevant, and creative outputs. Think of Prompt Engineers as AI translators. They specialize in talking to AI in a language it understands best. What Exactly Does a Prompt Engineer Do? At […]

Prompt Engineering

Prompt engineering is the process where you guide generative artificial intelligence (generative AI) solutions to generate desired outputs.” Even though AI tries to mimic humans, it needs clear directions to produce high-quality results. How Does it Work and Why is it Important? Generative AI models, often built on complex architectures, learn from vast amounts […]

Reinforcement Learning

Reinforcement Learning is a type of machine learning where a computer program, called an agent, learns to make decisions by interacting with an environment. Instead of being fed correct answers, the agent tries out different actions and receives feedback in the form of rewards (positive points) or penalties (negative points). The goal is simple: learn […]

Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is an AI framework that improves the quality of LLM responses by grounding the model on external sources of knowledge. Think of RAG as a clever technique that gives AI like ChatGPT a “cheat sheet” or access to a specific library of information while it’s working on your request. Rather than having to simply work with what it […]

Supervised Learning

Supervised Learning is a type of machine learning where we teach a computer by showing it lots of examples with the correct answers included. Think of it like studying with flashcards – each card has a question (the input) and the correct answer (the output or “label”). As defined by Google Cloud, it “uses […]

Temperature

Temperature is a hyperparameter used during the text generation process that influences the probability distribution of the next potential tokens, thereby controlling the level of randomness and creativity in the output. A higher temperature results in more random and potentially creative output, while a lower temperature makes the output more deterministic, focused, and less surprising. […]

Tensor Processing Unit (TPU)

Have you ever talked to a voice assistant, used your phone to identify a plant, or seen incredibly realistic images generated by a computer? All these amazing feats are powered by something called Artificial Intelligence, or AI. AI is rapidly changing our world, making computers smarter and capable of tasks that once seemed impossible. But […]

Token

A token is essentially a snippet of text that serves as a basic unit for the model to work with. Think of them as the AI’s vocabulary building blocks. These aren’t always whole words; they can be parts of words, punctuation marks, or even just spaces. The process of converting raw text into these tokens […]

Transformer

A Transformer is a more powerful type of neural network that can comprehend and produce sequences of information, like text, audio, or even images. Imagine a Transformer as an extremely intelligent AI that can look at an entire sentence (or even a whole paragraph!) in one go and know how each of the words is […]

Unsupervised Learning

Unsupervised Learning is a type of machine learning that works with unlabeled data. Unlike supervised learning, there’s no “teacher” providing correct answers during training. Instead, the goal is for the algorithm to explore the data and find meaningful patterns, groupings, or relationships all by itself. Think of it like being given a huge box of […]

Zero-Shot Prompting

Zero-Shot Prompting refers to the method of training a Large Language Model to do a task it has not been specifically trained or demonstrated examples for within the prompt alone. You provide the AI with a command for a task, and it applies its vast pre-existing knowledge acquired through its initial training to determine how […]