Glossary for Advanced Users
Cognitive Robotics involves the integration of AI and robotics to create intelligent machines that can interact and collaborate with humans in a human-like manner. These robots can understand and respond to natural language, make decisions, and learn from their experiences. Cognitive robotics finds applications in various industries, including healthcare, manufacturing, and customer service.
Computer vision is a field of AI that focuses on training machines to understand and interpret images and videos. By analysing visual data, machines can identify objects, recognise faces, and perceive depth and motion. Computer Vision has diverse applications, from autonomous vehicles and surveillance systems to medical imaging and augmented reality.
Constraint Satisfaction Problems are mathematical problems where a set of variables must satisfy a given set of constraints. CSPs are used in AI for tasks such as scheduling, resource allocation, and puzzle-solving. Techniques like Backtracking and Constraint Propagation are used to find feasible solutions.
A convolutional neural network is a powerful deep learning model designed for processing and analysing visual data. It excels in tasks such as image recognition and object detection, providing businesses with valuable insights from visual information.
Cybersecurity in Artificial Intelligence addresses the protection of AI systems from security threats and vulnerabilities. It involves implementing strategies and technologies to safeguard AI models, data, and algorithms from unauthorised access, data breaches, and malicious attacks. Ensuring cybersecurity in AI is crucial for maintaining the integrity and reliability of AI-driven solutions.
Data Preprocessing involves preparing and cleaning raw data before analysis. By removing noise, selecting relevant features, and addressing missing values, businesses can ensure data quality and improve the accuracy and effectiveness of AI models.
Data Science encompasses the collection, analysis, interpretation, and visualisation of data to extract valuable insights and make informed decisions. It combines statistical techniques, Machine Learning algorithms, and domain expertise to uncover patterns, trends, and correlations within data. By leveraging data science, businesses can gain a competitive advantage, optimise operations, and drive growth.
Decision networks, also known as Probabilistic Graphical Models, are a type of AI model that represents uncertain knowledge using a graph structure. Decision Networks enable reasoning under uncertainty and can be used for tasks such as decision-making, risk assessment, and planning.
Decision Trees are Machine Learning models that use a branching structure to make decisions or predictions. By determining the most important features and creating logical rules, Decision Trees can aid businesses in making informed decisions based on available data.
