New for 2023, and to help our site visitors and our team understand certain articles, we’ve included an expanded and comprehensive glossary of AI terms from A to Z. These terms are linked from other pages on the site, to make comprehending articles easier.
AI and Emerging Technology Glossary of terms
The attribution of human characteristics or behaviour to a god, animal, or object. In AI, this can refer to ascribing human-like consciousness, motivations, or emotions to AI systems.
The branch of computer science that focuses on creating intelligent machines capable of performing tasks that typically require human intelligence.
The shorthand or initialism of Artificial Intelligence
A set of step-by-step instructions or rules followed to solve a specific problem or perform a specific task. Algorithms form the foundation of AI systems.
A key algorithm used in training artificial neural networks. It calculates the gradient of the error with respect to the network’s weights, allowing for the adjustment of weights to minimize the error.
Extremely large and complex datasets that cannot be easily managed, processed, or analyzed using traditional methods. AI techniques, such as machine learning, are often used to extract valuable insights from big data.
In the context of AI, bias refers to systematic errors or prejudices in the data, algorithms, or decision-making processes that result in unfair or discriminatory outcomes.
An AI-powered computer program designed to simulate human conversation. Chatbots use natural language processing (NLP) techniques to understand and respond to user queries or requests.
The field of AI that focuses on enabling computers to interpret and understand visual information from images or videos. It involves tasks such as object recognition, image classification, and scene understanding.
A machine learning technique used to group similar data points together based on their inherent similarities or patterns. Clustering is an unsupervised learning method commonly used for data exploration and analysis.
Convolutional Neural Network (CNN)
A type of neural network architecture specifically designed for processing grid-like data, such as images. CNNs are widely used in computer vision tasks and excel at capturing spatial relationships in data.
The interdisciplinary study of the structure, function, and control of complex systems, including AI systems and their interactions with the environment. Cybernetics focuses on feedback mechanisms and information flow within systems.
The interdisciplinary field that combines scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It incorporates techniques from AI, machine learning, and statistics.
The process of discovering patterns, correlations, or other meaningful insights from large datasets. AI algorithms are often used in data mining to automatically uncover hidden patterns and make predictions.
A subfield of machine learning that employs artificial neural networks with multiple layers to extract high-level representations from raw data. Deep learning has revolutionised areas such as image recognition, speech recognition, and natural language processing.
A supervised machine learning algorithm that uses a tree-like model to make decisions or predictions. It splits the data into branches based on features and assigns labels or values to the leaf nodes.
A technique in machine learning that combines predictions from multiple models to improve overall performance. It can reduce overfitting and increase generalization capabilities.
Complex patterns of behavior that emerge from simple local interactions. In AI and computational systems, this can refer to unanticipated or complex behaviors that arise from the interaction of simple AI agents or components.
An AI system designed to mimic the decision-making capabilities of a human expert in a specific domain. It uses a knowledge base of rules and heuristics to provide advice or solutions to complex problems.
A mathematical approach that deals with approximate reasoning and imprecision in decision-making. Fuzzy logic allows for the representation and manipulation of uncertainty and vagueness in AI systems.
A subfield of artificial intelligence focusing on the creation of content. AI systems can generate new, original content that can range from images, music, speech, or text.
Optimisation algorithms inspired by the process of natural selection. Genetic algorithms use techniques such as mutation, crossover, and selection to search for optimal solutions in complex problem spaces.
Generative Adversarial Network (GAN)
A class of deep learning models that consists of a generator network and a discriminator network. GANs are used to generate new data instances that resemble a given training dataset.
GPT stands for Generative Pretrained Transformer.
“Generative” refers to the model’s ability to generate creative outputs such as sentences, paragraphs, or even entire articles based on the input it is given.
“Pretrained” means the model has been trained on a large corpus of text before it is fine-tuned for specific tasks.
“Transformer” is the type of model architecture GPT is based on. Transformer models use an attention mechanism that weighs the influence of different words in the input when generating the output.
In the context of AI, hallucination refers to the generation of false or misleading patterns, often in data that aren’t actually present. This term is often used in the context of AI vision or language systems, where the AI might perceive or generate information inaccurately.
A problem-solving technique or rule of thumb that guides the search for solutions, especially in situations where an optimal solution is difficult to find. Heuristics are commonly used in AI algorithms.
The ability of AI systems to identify and classify objects, patterns, or features within digital images. It is a subfield of computer vision.
A software entity that perceives its environment, makes decisions or takes actions to achieve specific goals, and may interact with other agents or humans. Intelligent agents are a fundamental concept in AI.
Internet of Things (IoT)
The network of physical devices, vehicles, appliances, and other objects embedded with sensors, software, and connectivity. AI techniques can be applied to IoT data for analysis, automation, and decision-making.
An open-source web application that allows the creation and sharing of documents containing live code, equations, visualisations, and narrative text. Jupyter notebooks are widely used for AI experimentation and prototyping.
A structured representation of knowledge that captures relationships between entities. Knowledge graphs enable AI systems to reason and infer new knowledge from existing information.
Large Language Model
A machine learning model that has been trained on a large amount of text data. It can generate human-like text by predicting the probability of a word given the previous words used in the text. GPT-3 and GPT-4 by OpenAI are examples of large language models.
The process of deriving logical conclusions from a set of premises or facts. AI systems can employ logical reasoning to make deductions or reach logical outcomes.
A subset of AI that involves developing algorithms and models that enable computers to learn from data and make predictions or decisions without explicit programming. It relies on statistical techniques to automatically identify patterns and extract insights.
Natural Language Processing (NLP)
The branch of AI that focuses on the interaction between computers and human language. NLP enables computers to understand, interpret, and generate human language, facilitating tasks such as language translation, sentiment analysis, and chatbot interactions.
An interconnected network of artificial neurons, inspired by the structure and function of biological brains. Neural networks are fundamental to deep learning and enable the modeling of complex relationships in data.
Natural Language Processing (NLP)
A field of AI that focuses on the interaction between computers and humans through natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human language in a valuable way.
A computing system inspired by the human brain’s network of neurons. These systems learn from observational data and can improve their accuracy over time.
A technique that combines neural networks and genetic algorithms to evolve artificial neural networks. Neuroevolution is used to optimize neural network structures or parameters through evolutionary processes.
Data that contains errors, inconsistencies, or irrelevant information. AI systems need to handle noisy data effectively to ensure accurate and reliable results.
A formal representation of knowledge that defines concepts, relationships, and properties within a specific domain. Ontologies enable AI systems to have a common understanding of the domain and support reasoning.
In machine learning, parameters are the part of the model that is learned from historical training data. In a neural network, the weights and biases are examples of parameters.
The practice of using historical data and statistical models to make predictions about future events or outcomes. AI techniques, such as machine learning, are commonly employed in predictive analytics.
A popular programming language widely used in AI and data science. Python provides extensive libraries and frameworks for AI development, such as TensorFlow, PyTorch, and scikit-learn.
A rapidly advancing field that leverages principles of quantum mechanics to perform computations. Quantum computing has the potential to greatly enhance AI capabilities, especially for tasks that require massive parallel processing.
An AI system that analyses user preferences and provides personalised recommendations for items or content. Recommender systems are commonly used in e-commerce, streaming platforms, and content curation.
A type of machine learning where an agent learns to interact with an environment to maximise a reward signal. The agent takes actions, receives feedback in the form of rewards or penalties, and adjusts its behaviour accordingly to achieve optimal performance.
The interdisciplinary field that combines AI, computer science, engineering, and mechanics to design, build, and operate robots. Robotics aims to create intelligent machines capable of performing physical tasks autonomously or with human collaboration.
A machine learning approach where the model is trained on labeled data, meaning each input has a corresponding target output. The model learns to map inputs to outputs based on the provided examples.
A computer vision task that involves dividing an image into coherent segments and assigning a semantic label to each segment. It is commonly used in applications like object detection and autonomous driving.
The process of determining the sentiment or emotional tone of a piece of text. Sentiment analysis can help gauge public opinion, customer feedback, or social media sentiment towards a particular topic.
A combination of supervised and unsupervised learning where the model is trained on a small labeled dataset and a larger unlabeled dataset. It leverages the unlabeled data to improve learning and generalise to new examples.
The process of creating a computer-based model or environment that imitates real-world phenomena or systems. AI systems can be trained, tested, or optimised within simulated environments.
A type of model architecture primarily used in the field of deep learning, particularly in natural language processing (NLP). Transformer models, like BERT or GPT, use attention mechanisms to weight the influence of different input parts differently in response to each input.
A machine learning technique that enables the use of pre-trained models as a starting point for solving new tasks. Transfer learning allows models to leverage knowledge gained from previous tasks and adapt it to new domains or problems.
An open-source machine learning framework developed by Google. TensorFlow provides a comprehensive ecosystem for building and deploying AI models, including support for deep learning, reinforcement learning, and distributed computing.
A machine learning approach where the model learns patterns or structures in data without explicit labels or target outputs. It aims to discover inherent relationships or groupings within the data.
Virtual Reality (VR)
A simulated experience generated by a computer that immerses users in an interactive, three-dimensional virtual environment. AI techniques can enhance VR experiences through intelligent virtual characters or object interactions.
Also known as Narrow AI or Applied AI, weak AI refers to AI systems designed to perform specific tasks or solve specific problems. Weak AI systems demonstrate intelligence in a limited domain but do not possess general human-level intelligence.
An open-source software library that provides a gradient boosting framework for machine learning. XGBoost is known for its efficiency, scalability, and accuracy, and it is widely used in various AI applications.
A human-readable data serialisation format often used for configuration files in AI systems. YAML stands for “YAML Ain’t Markup Language” and is popular for its simplicity and readability.
A machine learning approach where a model is trained to recognise and classify objects or concepts it has never seen before. Zero-shot learning relies on transferring knowledge from seen classes to unseen classes based on shared attributes or relationships.
This comprehensive glossary covers a wide range of AI terms from A to Z. If you have any more specific questions or need further clarification on any of these terms, feel free to ask!