Welcome to this page, where we aim to provide you with a clear and simple explanation of common terms you might come across when reading about Artificial Intelligence. Our goal is to help you better understand the concepts and jargon that are commonly used in the AI field, so that you can make informed decisions and stay up-to-date on the latest developments.

Whether you’re new to the field of AI or just looking to expand your knowledge, you’ll find explanations of key terms such as “Machine Learning,” “Neural Networks,” “Deep Learning,” “Natural Language Processing,” “Big Data,” “Algorithm,” and more.

Our explanations are written in plain language, avoiding technical jargon and complex terminology as much as possible, to ensure that you can easily grasp the meaning of each term. By the end of your visit, we hope that you’ll have a better understanding of AI and be able to use the terms and concepts with confidence.

  1. AI – Artificial Intelligence: A branch of computer science that deals with the creation of intelligent machines that work and think like humans.
  2. Machine Learning: A subfield of AI that involves training machines to learn from data, recognize patterns, and make decisions without being explicitly programmed.
  3. Neural Networks: A type of machine learning algorithm modeled after the structure and function of the human brain, consisting of interconnected nodes that process and transmit information.
  4. Deep Learning: A type of machine learning that uses neural networks with many layers to process complex data and perform tasks such as image and speech recognition.
  5. Natural Language Processing (NLP): A subfield of AI that focuses on the interaction between computers and human language, including tasks such as speech recognition and language translation.
  6. Big Data: Refers to large and complex data sets that are too difficult to process with traditional data processing applications.
  7. Algorithm: A set of instructions that a computer follows to perform a specific task, such as sorting data or making predictions.
  8. Supervised Learning: A type of machine learning in which the algorithm is trained on labeled data, meaning the desired output is already known.
  9. Unsupervised Learning: A type of machine learning in which the algorithm is trained on unlabeled data, meaning the desired output is not known.
  10. Reinforcement Learning: A type of machine learning that involves training a machine to make decisions by rewarding or punishing it based on its actions.
  11. Chatbot: A computer program that uses AI and natural language processing to simulate human conversation and provide information or assistance.
  12. Computer Vision: A subfield of AI that focuses on enabling computers to interpret and understand visual information from the world, such as images and videos.
  13. Autonomous Vehicles: Vehicles that are capable of sensing their environment and navigating without human input.
  14. Internet of Things (IoT): Refers to the interconnected network of physical objects, devices, and sensors that are embedded with technology to enable them to collect and exchange data.
  15. Ethics of AI: The study of the moral and social implications of AI, including issues such as bias, privacy, and accountability.
  16. Large Language Models (LLMs): Advanced machine learning models that are trained on large amounts of text data to understand natural language and generate human-like responses.
  17. Generative AI: A type of AI that uses machine learning to generate new data, such as images, music, or text, based on patterns learned from existing data.
  18. ChatGPT: A specific LLM developed by OpenAI for generating human-like text responses to user input.
  19. OpenAI: A research organization that focuses on advancing AI in a safe and ethical manner.
  20. Diffusion Models: A type of generative AI model that generates data by iteratively refining a set of probabilities until a final output is produced.
  21. Synthetic Data: Artificially generated data that can be used to train machine learning models without relying on real-world data.
  22. Edge Computing: A computing infrastructure that processes data on devices at the “edge” of a network, rather than relying on centralized cloud computing.
  23. Transfer Learning: A technique in machine learning where a model trained on one task is re-purposed for a different, but related, task.
  24. Neural Architecture Search: A process for automatically discovering the optimal architecture, or structure, for a neural network to perform a specific task.
  25. Explainable AI (XAI): A subfield of AI that focuses on creating models that can explain how they arrive at their decisions, making them more transparent and interpretable to humans.
  26. Adversarial Attacks: Techniques used to manipulate or deceive machine learning models by adding or altering data in a way that is imperceptible to humans.