Artificial Intelligence (AI) is a field of computer science that aims to create intelligent machines that can mimic human behavior and decision-making. It encompasses a range of techniques, including machine learning, natural language processing, robotics, and computer vision. As the field of AI continues to evolve, new terms are being introduced at a rapid pace. In this glossary, we will define and explain some of the most common AI terms.
Artificial Intelligence (AI): The field of computer science that seeks to create intelligent machines capable of performing tasks that typically require human intelligence.
Machine Learning (ML): A subfield of AI that involves training algorithms to automatically recognize patterns in data, and make predictions or decisions based on those patterns.
Deep Learning (DL): A subfield of machine learning that involves training algorithms to learn multiple layers of representations of data, which enables them to perform more complex tasks, such as image and speech recognition.
Natural Language Processing (NLP): A subfield of AI that involves the interaction between computers and human language, including speech recognition, language translation, and sentiment analysis.
Robotics: The design and construction of robots capable of performing tasks that typically require human intelligence, such as movement and manipulation.
Computer Vision: The ability of machines to interpret and understand visual data from the world around them, such as images and videos.
Artificial General Intelligence (AGI): The theoretical concept of an AI system that can perform any intellectual task that a human can do.
Artificial Narrow Intelligence (ANI): An AI system that is designed to perform a specific task or set of tasks, such as image recognition or speech synthesis.
Supervised Learning: A type of machine learning in which an algorithm is trained on labeled data to make predictions or decisions based on similar but unlabeled data.
Unsupervised Learning: A type of machine learning in which an algorithm is trained on unlabeled data to identify patterns and make predictions or decisions.
Reinforcement Learning: A type of machine learning in which an algorithm learns through trial and error by receiving feedback in the form of rewards or penalties.
Neural Networks: A type of algorithm inspired by the structure and function of the human brain, consisting of layers of interconnected nodes that process and transmit information.
Convolutional Neural Networks (CNNs): A type of neural network commonly used in computer vision tasks, such as image recognition, that involves the use of convolutional layers to identify patterns and features in images.
Recurrent Neural Networks (RNNs): A type of neural network commonly used in natural language processing tasks, such as speech recognition and language translation, that involves the use of recurrent connections to process sequences of data.
Generative Adversarial Networks (GANs): A type of neural network that involves two networks, a generator and a discriminator, that are trained to compete against each other to create realistic outputs, such as images or text.
Overfitting: A common problem in machine learning in which an algorithm is trained too well on a specific set of data and is unable to generalize to new data.
Underfitting: A common problem in machine learning in which an algorithm is too simple and is unable to capture the complexity of the data, resulting in poor performance.
Bias: A common problem in AI in which an algorithm produces results that are skewed towards certain groups or individuals, often due to the quality or quantity of the data used to train it.
Explainable AI (XAI): An approach to AI that aims to create models and algorithms that are transparent and interpretable, making it easier for humans to understand how the AI arrived at its decisions.
Artificial Consciousness: The theoretical concept of an AI system that possesses subjective experiences