Glossary for Advanced Users
Adversarial machine learning involves studying and defending AI models against attacks or adversarial examples designed to deceive the system. By understanding vulnerabilities and deploying robust defence mechanisms, businesses can ensure the reliability and security of AI solutions.
An algorithm is a set of rules and instructions that guide AI systems in solving problems or making decisions. Algorithms process data, analyse patterns, and deliver precise outcomes, enabling machines to perform complex tasks efficiently.
Artificial General Intelligence refers to AI systems capable of understanding, learning, and performing any intellectual task as humans do. Although AGI remains aspirational, it represents the goal of creating highly adaptable and versatile AI systems.
Artificial Intelligence refers to computer systems designed to simulate human intelligence. With AI, machines can accomplish tasks that typically require human cognition, such as problem-solving, decision-making, and natural language processing. It drives innovation, productivity, and efficiency across industries.
Artificial Neural Networks are computational models inspired by the human brain’s structure and function. They consist of interconnected nodes that process and transmit data, allowing machines to learn from experience and recognise patterns. This enables tasks such as image recognition and language processing.
Automation involves the use of technology, including AI, to perform tasks and processes with minimal human intervention. By automating repetitive or time-consuming tasks, businesses can improve efficiency, reduce errors, and free up valuable resources for more strategic and creative endeavours.
Autonomous agents are AI systems that can perform actions and make decisions independently, guided by predefined goals or learning processes. Businesses leverage autonomous agents in fields like autonomous vehicles, smart home systems, and customer service chatbots.
Backpropagation is a technique used in training Artificial Neural Networks. It involves propagating error information backward through the network, allowing adjustments to be made to the network’s parameters. Backpropagation helps improve the accuracy and effectiveness of neural networks in tasks like pattern recognition and prediction.
Bayesian networks are Probabilistic Graphical Models that represent and evaluate uncertainty and conditional dependencies between variables. Industries such as healthcare and finance use Bayesian Networks to handle complex decision-making, risk assessment, and predictive modelling.
