Published On 7/6/2026
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Last update: 7/8/2026 14:27 (Mecca time)
Artificial intelligence is no longer just a specialized technology limited to laboratories or large companies, but has become part of the daily life of hundreds of millions of users around the world.
Since the launch of tools such as GPT Chat, Gemini, Cloud, and Copaylet, new terms have begun to appear frequently in news and technical reports, until they have become something like a “new language” that the user needs to understand in order to understand how these systems work, their limits, and their capabilities.
Understanding these terms is not limited to developers and researchers, but has also become important for ordinary users, because these concepts explain the working mechanism of the applications that they rely on daily, and also help in evaluating their capabilities and limitations.
artificial intelligence
Artificial intelligence refers to a group of computer systems designed to mimic human mental abilities, such as learning, reasoning, decision-making, language understanding, and image recognition. This field is the umbrella area under which many sub-technologies fall, such as machine learning, deep learning, computer vision, and natural language processing.
Algorithms
Algorithms are the brains and mathematical and logical instructions that are dictated to machines to enable them to emulate human capabilities. These algorithms rely on processing huge amounts of data and analyzing them accurately, which allows the system to extract patterns, predict outcomes, and self-learn from its mistakes and previous experiences without the need to manually reprogram it for each task. There are many types of algorithms to cover complex fields, the most prominent of which are “machine learning algorithms” and “artificial neural networks.”
Machine learning
Machine learning is one of the most important branches of artificial intelligence, and is based on the idea that a computer can learn from data and discover patterns without having to write code for every possible case.
Today, machine learning is used in recommendation systems on streaming platforms, such as recommending movies or TV shows on Netflix and YouTube videos, detecting banking fraud, analyzing financial markets, and predicting industrial malfunctions.
Deep learning
Deep learning represents a more advanced stage of machine learning, and relies on multi-layered artificial neural networks that can extract complex patterns from huge amounts of data.
Most modern artificial intelligence applications, such as image recognition, instant translation, and smart assistants, rely on deep learning techniques.
Neural networks
Neural Networks are the mathematical structure on which deep learning is based, and their design was inspired by the way neurons communicate in the human brain.
The network consists of interconnected layers that receive data and process it gradually until it reaches the final result, and with each training session the network adjusts its mathematical weights to improve its accuracy.
Big language models
Large language models (LLMs) are the backbone of most current generative AI applications, such as ChatGPT, Cloud, and Gemini. These models are trained on billions of words taken from books, articles, websites, scientific papers, etc., with the goal of learning statistical relationships between words and sentences.
Token
Token is one of the most commonly used terms in the world of artificial intelligence, and it is the smallest text unit that the model deals with while reading or writing. A single word may represent a single token, or it may be divided into several tokens if it is long or uncommon.
Training
Training is the stage during which the model learns from big data, where billions of mathematical weights are gradually modified until its outputs become closer to the correct answers.
This stage is considered the most expensive in the model’s life cycle, as it may take weeks or months, and requires thousands of graphics processing units (GPU), in addition to consuming large amounts of electricity.
Inference
After training (Inference) ends, the inference phase begins, which is the process of using the model to answer users’ questions or perform required tasks.
The speed of inference is one of the most important factors in competition between artificial intelligence companies, because it directly affects the speed of response of applications and the user experience, and it also represents the largest part of the costs of running models after their launch.
Generatively enhanced retrieval
Retrieval-Augmented Generation (RAG) is one of the most important recent developments in generative artificial intelligence, as it combines large language models with databases or external information sources. Instead of relying solely on the knowledge the model has acquired during training, the system first searches for relevant information in recent documents or databases, and then uses it to generate the answer.
Smart agents
AI Agents represent the next generation of AI applications. Instead of simply answering questions, an agent can perform a series of actions to achieve a specific goal.
Reinforcement learning from human feedback
After training the model on the data, comes the Reinforcement Learning from Human Feedback (RLHF) phase.
In this stage, people evaluate and rank the model’s answers, and these evaluations are then used to train the model to provide more accurate, useful, and safe responses.
Fine tuning
Not all companies need to build an AI model from scratch, so they resort to fine-tuning, that is, retraining a ready-made model using specialized data.
distillation
As models became larger, there was a need to run them on less powerful hardware. Here comes the Distillation technology, which transfers knowledge from a large model to a smaller, more efficient model. The new model maintains a large part of the capabilities of the original model, but it requires less memory and operates at a higher speed, which makes it suitable for smartphones and peripheral devices.
Hallucinations
Despite the great development of artificial intelligence models, they may sometimes produce incorrect information with high confidence, a phenomenon known as hallucination. Hallucinations occur when the model tries to complete the answer based on statistical patterns, generating information that seems logical but does not exist in reality or is inaccurate.
Expert mix
The Mixture of Experts (MoE) architecture has become one of the most widely used techniques in modern models. Instead of all parts of the model running for each question, only a small group of “experts” who specialize in the required task are activated, while the rest of the components remain inactive.
Model context protocol
Model Context Protocol (MCP) is an open standard that aims to standardize the way artificial intelligence models communicate with different tools, files, databases, and applications. It can be likened to a USB-C port for computers, as it allows the model to interact with multiple services without the need to develop a special integration for each service separately.
General artificial intelligence
Artificial General Intelligence (AGI) is one of the most debated concepts, and refers to an artificial intelligence system capable of performing most intellectual tasks that a human can, with the ability to learn and adapt in different domains without separate training for each task.
