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AI Glossary
Navigating this AI Glossary
We have carefully sorted the AI terms in a way that creates a progressive learning path, designed to help you navigate the exciting world of artificial intelligence.
Starting with foundational concepts and gradually progressing to more specialised topics and emerging technologies, this glossary allows you to evolve a solid understanding of AI's interconnections and explore related areas as you dive deeper.
We look forward to sharing an enlightening and empowering learning experience that will allow you to unlock the full potential of AI for personal and business growth.
“Get ready to embark on an exciting journey of discovery!”
—Justin Kabbani, Expert AI Coach & Consultant
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Foundational AI terms
Artificial Intelligence (AI)
AI refers to the development of computer systems that can perform tasks that typically require human intelligence, such as speech recognition, decision-making, and problem-solving. It enables machines to learn from experience, adjust to new inputs, and perform tasks autonomously.
Machine Learning (ML)
Machine Learning is a subset of AI that focuses on algorithms and statistical models that enable computers to learn from data and make predictions or decisions without being explicitly programmed. For example, ML algorithms can analyse customer data to predict future purchasing behaviour and personalise marketing campaigns accordingly.
Deep Learning
Deep Learning is a subfield of ML that uses artificial neural networks to model and understand complex patterns and relationships in data. It excels in areas such as image and speech recognition, natural language understanding, and recommendation systems.
Neural Network
Neural networks are computing systems inspired by the structure and functioning of the human brain. They consist of interconnected nodes (neurons) that process and transmit information, enabling complex pattern recognition and decision-making tasks.
Algorithm
An algorithm is a set of step-by-step instructions or rules followed by a computer to solve a specific problem or accomplish a particular task. For example, an algorithm can analyse customer data to identify patterns and predict customer churn.
Cognitive Computing
Cognitive Computing refers to AI systems that can mimic human cognitive abilities, such as understanding natural language, reasoning, learning, and problem-solving. It aims to enhance human capabilities and enable more intuitive interactions with machines.
AI Data & Analytics terms
Data Science
Data Science combines statistical analysis, ML techniques, and domain expertise to extract insights and knowledge from structured and unstructured data. Data scientists use various tools and methods to uncover meaningful patterns and make data-driven decisions.
Big Data
Big Data refers to large and complex datasets that are beyond the processing capacity of traditional data management tools. It involves handling massive volumes of structured and unstructured data to extract valuable insights for decision-making.
Predictive Analytics
Predictive Analytics uses historical data, statistical algorithms, and ML techniques to make predictions about future events or outcomes. For instance, it can help predict customer demand, optimise inventory levels, and forecast sales.
Data Mining
Data Mining involves extracting patterns, insights, or knowledge from large datasets using statistical and ML techniques. It helps businesses uncover hidden patterns, trends, and relationships in data that can lead to valuable insights and informed decision-making.
Feature Engineering
Feature Engineering refers to the process of selecting, transforming, and creating relevant features from raw data to improve the performance and accuracy of ML models. It involves domain expertise, statistical analysis, and data manipulation techniques to extract meaningful information for better predictions.
AI Natural Language Processing (NLP) terms
Natural Language Processing (NLP)
NLP enables computers to understand, interpret, and generate human language. It enables voice assistants like Siri or chatbots to understand and respond to user queries in a conversational manner.
Sentiment Analysis
Sentiment Analysis uses AI techniques to determine the emotional tone or sentiment expressed in text data, such as social media posts or customer reviews. It helps businesses understand customer opinions, preferences, and feedback.
Natural Language Generation (NLG)
NLG systems generate human-like language or text based on structured data or AI models. They are used to automate report generation, produce personalised content, or create narratives from data.
Neural Language Model
Neural Language Models are AI models that understand and generate human language. They are used in applications such as speech recognition, language translation, or generating conversational responses.
AI Computer Vision, Image & Speech Recognition terms
Computer Vision
Computer Vision involves teaching computers to understand and interpret visual information from images or videos. It finds applications in facial recognition, object detection, autonomous vehicles, and quality control in manufacturing.
Image Recognition
Image Recognition involves AI algorithms that can analyse and interpret visual information from images or videos. In business, it can be used for tasks such as product recognition, quality control, or facial recognition for security purposes.
Speech Recognition
Speech Recognition technology enables computers to convert spoken language into text or commands. It has applications in voice assistants, transcription services, or interactive voice response systems.
AI Learning Paradigm terms
Supervised Learning
Supervised Learning is an ML technique where the algorithm learns from labelled data, with known input-output pairs. It is used to train models that can make predictions or classify new, unseen data based on past examples.
Unsupervised Learning
Unsupervised Learning involves training ML models on unlabelled data, where the algorithm learns to discover patterns, clusters, or relationships within the data. It is useful for tasks such as customer segmentation or anomaly detection.
Semi-Supervised Learning
Semi-Supervised Learning is a combination of supervised and unsupervised learning, where the model learns from a mix of labelled and unlabelled data. It can be useful when acquiring labelled data is expensive or time-consuming.
Transfer Learning
Transfer Learning is a technique that allows pre-trained ML models to be used as a starting point for new tasks or domains with limited labelled data. It enables faster model development and better performance in situations where training data is scarce.
Reinforcement Learning
Reinforcement Learning is a type of ML where an algorithm learns by interacting with an environment and receiving feedback in the form of rewards or penalties. It is commonly used in areas such as autonomous driving, game-playing agents, and resource management.
AI Emerging Technologies & Application terms
Robotics
Robotics involves the design, development, and use of robots that can perform tasks autonomously or with minimal human intervention. In business settings, robots can be deployed for tasks such as assembly line automation, warehouse management, or healthcare assistance.
Chatbot
A chatbot is an AI-powered virtual assistant that can interact with users in natural language, providing automated responses and assistance. It can be used in customer service, answering frequently asked questions, or guiding users through processes, enhancing customer experience and reducing support costs.
Internet of Things (IoT)
IoT refers to a network of interconnected physical devices embedded with sensors, software, and connectivity, allowing them to collect and exchange data. AI can be integrated with IoT to enable smart devices and systems that can make intelligent decisions based on real-time data.
Virtual Reality (VR)
Virtual Reality creates a simulated, immersive environment that users can interact with using special devices, such as VR headsets. In business, VR can be utilised for virtual training simulations, remote collaboration, or creating virtual showrooms.
Augmented Reality (AR)
Augmented Reality overlays digital information or virtual objects onto the real-world environment, enhancing the user's perception and interaction with their surroundings. AR has applications in areas such as product visualisation, remote assistance, or training simulations.
Robotic Process Automation (RPA)
RPA involves using software bots or AI-powered robots to automate repetitive and rule-based tasks within business processes. It can streamline workflows, reduce errors, and free up human workers to focus on more complex and strategic activities.
Digital Twin
A digital twin is a virtual representation of a physical object, system, or process that provides real-time insights and simulations. It allows businesses to optimise operations, predict maintenance needs, or simulate scenarios for improved decision-making.
Edge Computing
Edge Computing refers to processing and analysing data at or near the edge devices or sensors, rather than relying on centralised cloud infrastructure. It enables faster response times, reduced data transfer, and increased privacy for AI applications in remote or real-time environments.
Blockchain
Blockchain is a distributed and decentralised ledger technology that securely records and verifies transactions. It can be used to enhance the transparency, security, and traceability of AI systems, particularly in areas like supply chain management or data sharing.
Quantum Computing
Quantum Computing leverages quantum phenomena to perform computations that traditional computers cannot handle. It has the potential to accelerate AI advancements, solve complex problems, and optimise algorithms in areas such as optimisation, cryptography, or drug discovery.
Edge AI
Edge AI involves running AI algorithms and ML models directly on edge devices or sensors, without relying on cloud-based processing. It enables real-time, privacy-preserving AI applications in resource-constrained environments or situations requiring low-latency responses.
Generative Adversarial Networks (GANs)
GANs are a class of ML models that involve two neural networks, a generator and a discriminator, competing against each other. They are used to generate realistic synthetic data, enhance data augmentation, or create deepfake content.
AutoML (Automated Machine Learning)
AutoML refers to the automation of various stages in the ML pipeline, including data preprocessing, feature selection, algorithm selection, and model optimisation. It aims to simplify the ML process, democratise AI, and enable non-experts to build ML models.
AI Ethics & Governance terms
Ethics in AI
Ethics in AI focuses on the responsible development and use of AI systems, addressing issues such as fairness, transparency, privacy, accountability, and bias. It ensures that AI technologies align with societal values and adhere to ethical guidelines.
Bias in AI
Bias in AI refers to the potential for AI systems to discriminate or exhibit unfairness towards certain individuals or groups. It can arise from biased training data, flawed algorithms, or lack of diversity in the development process.
Explainable AI (XAI)
Explainable AI (XAI) focuses on developing AI models and techniques that can provide understandable and transparent explanations for their decisions, predictions, or recommendations. XAI helps build trust, improve user acceptance, and ensure accountability in AI systems.
AI Ethics Framework
An AI Ethics Framework is a set of principles, guidelines, and best practices that organisations follow to ensure ethical and responsible use of AI technologies. It helps guide decision-making, risk mitigation, and promotes ethical behaviour throughout the AI lifecycle.
AI Governance
AI Governance refers to the policies, frameworks, and processes in place to ensure the responsible and ethical development, deployment, and management of AI systems within an organisation. It encompasses aspects such as accountability, transparency, fairness, and compliance.
Algorithmic Bias
Algorithmic Bias refers to biases that can be present in AI algorithms due to the data used for training or the design of the algorithms themselves. It can result in unfair or discriminatory outcomes, particularly in areas such as hiring, lending, or criminal justice. Addressing algorithmic bias is essential to ensure equitable and unbiased decision-making.
Data Privacy
Data Privacy refers to the protection of personal and sensitive data, ensuring that it is collected, stored, processed, and shared in a way that respects individuals' rights and complies with privacy regulations. AI systems must adhere to robust data privacy practices to safeguard confidentiality and maintain trust.
Specific AI Application terms
Computer-aided Diagnosis (CAD)
CAD systems use AI algorithms to assist medical professionals in diagnosing diseases or conditions. By analyzing medical images or patient data, CAD systems can provide insights and support clinical decision-making.
Recommender System
Recommender Systems use ML algorithms to analyze user preferences, behaviours, and historical data to provide personalized recommendations. They are commonly used in e-commerce platforms, streaming services, or content recommendation engines.
Anomaly Detection
Anomaly Detection involves identifying unusual patterns, outliers, or deviations from the norm in data. In business, it can be used to detect fraudulent transactions, network intrusions, or equipment failures.
AI-powered Chat Support
AI-powered Chat Support uses AI algorithms and Natural Language Processing to provide automated and intelligent customer support through chat interfaces. It can handle common queries, provide instant responses, and escalate complex issues to human agents, improving customer satisfaction and reducing response times.
