This glossary provides a concise overview of the most commonly used AI terms, most of which are used on the X0PA website, aiding your understanding of the technology and its applications.
A step-by-step set of rules or instructions that a machine follows to perform tasks or solve problems, often central to machine learning models.
A theoretical form of AI that possesses the ability to perform any intellectual task a human can do. AGI is still a long-term goal in AI research.
AI refers to the simulation of human intelligence in machines that are programmed to think, learn, and make decisions. It encompasses various subfields like machine learning, natural language processing, and robotics.
Refers to systematic errors in an AI system’s output caused by biased training data or flawed algorithms, leading to unfair or inaccurate predictions.
Extremely large and complex datasets that are difficult to process using traditional data-processing techniques. AI models use big data to find patterns and insights.
A field of AI focused on enabling machines to interpret and understand visual information from the world, such as images and videos, often used in facial recognition and object detection.
A subfield of machine learning, deep learning involves artificial neural networks with many layers (deep networks) that can automatically learn and extract features from large datasets.
The concept of making AI decisions understandable to humans. Explainability helps ensure that AI systems are transparent and can be trusted by users.
The process of selecting, modifying, and creating features (input variables) from raw data to improve the performance of machine learning models.
Generative AI refers to models that create new content—such as text, images, audio, or video—based on the data they are trained on. Unlike traditional AI, which focuses on analyzing or recognizing patterns, generative AI can produce original outputs by learning the underlying structure of data.
Horizontal AI refers to artificial intelligence systems designed to perform a wide range of tasks across different industries and domains, without being specialized in a particular field. These systems, such as virtual assistants or chatbots, can handle diverse applications like customer service, data processing, and automation in various contexts.
The process of applying a trained AI model to new data to make predictions or decisions. Inference is a key step after the model has been trained.
A subset of AI, ML focuses on building systems that can learn from data and improve their performance over time without being explicitly programmed.
A model is a mathematical representation of a system that has been trained to recognize patterns, make decisions, or predict outcomes based on input data. Once trained, models are used for inference, applying their learned knowledge to new, unseen data. Common types include decision trees, neural networks, and support vector machines.
NLP enables machines to understand, interpret, and generate human language. It powers applications like chatbots, translation services, and sentiment analysis.
A computational model inspired by the human brain, neural networks consist of layers of interconnected nodes (neurons) that process data to recognize patterns and solve complex problems.
A problem in machine learning where a model performs well on the training data but fails to generalize to unseen data, often due to excessive complexity.
A branch of data analytics that uses machine learning models to analyze current and historical data to make predictions about future outcomes.
A type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize cumulative rewards over time.
Sentiment analysis is a natural language processing (NLP) technique used to determine the emotional tone or attitude expressed in a piece of text or in our video interviews. By analyzing words, phrases, and context, it identifies whether the sentiment is positive, negative, or neutral.
A machine learning approach where the model is trained on labeled data, meaning the input comes with the correct output, allowing the system to learn the mapping between inputs and outputs.
A dataset used to teach a machine learning model, enabling it to recognize patterns and make predictions. High-quality training data is critical for model accuracy.
A machine learning technique where a model trained on one task is repurposed for a different, but related, task. It is used to speed up learning and improve performance on new tasks.
Vertical AI refers to artificial intelligence systems specifically designed and tailored for specialized tasks within a particular industry or domain. Vertical AI focuses on deep, domain-specific expertise.
Have we missed any terms or do you have a question about the impact that AI could have on your business? Get in touch for a quick chat with one of our team using the form below, or book a demo with us using the button:
