1,221 Terms.
One Reference.

Additive manufacturing technology that creates three-dimensional objects by depositing material layer by layer.

The process of capturing and creating three-dimensional models of real-world objects or environments from visual data.

3D printing with materials that can change shape or properties over time in response to stimuli.

The fifth generation of mobile network technology offering higher speeds, lower latency, and massive device connectivity.

A controlled experiment comparing two variants to determine which performs better against a defined metric.

Comparing two versions of a design to determine which performs better against a specific objective.

A controlled experiment methodology that compares two versions of a product, feature, or experience to determine which performs better against a defined metric.

A form of logical inference that seeks the simplest and most likely explanation for a set of observations.

A text summarisation approach that generates novel sentences to capture the essential meaning of a document, rather than simply extracting and rearranging existing sentences.

A document defining the permitted use of an organisation's IT resources and networks.

A set of rules defining who can access specific resources and what actions they can perform.

Designing products and services that can be used by people with a wide range of abilities and disabilities.

A blockchain architecture improvement that allows smart contracts to act as user accounts, enabling features like social recovery, gas sponsorship, and batched transactions.

Identifying and classifying human actions or activities from video sequences.

The complete set of possible actions available to an AI agent in a given environment, defining the boundaries of what the agent can do to accomplish its objectives.

A mathematical function applied to neural network outputs to introduce non-linearity, enabling the learning of complex patterns.

A machine learning approach where the algorithm interactively queries a user or oracle to label new data points.

A device that converts energy into motion to control a mechanism or system based on received commands.

An adaptive learning rate optimisation algorithm combining momentum and RMSProp for efficient deep learning training.

Small trainable modules inserted between frozen transformer layers that enable task-specific adaptation without modifying the original model weights.

A form of quantum computing based on the adiabatic theorem, gradually evolving a system from an initial to a problem-encoding Hamiltonian.

Materials engineered with novel properties for superior performance in specific applications.

Advanced red team exercises that replicate the tactics, techniques, and procedures of specific threat actors to evaluate an organisation's detection and response capabilities.

Computing that relates to, arises from, or influences emotions, recognising and responding to human affect.

A design property suggesting how an object should be used, making actions discoverable without instructions.

The degree of independence an AI agent has in making and executing decisions without human approval.

Standardised evaluation of AI agent capabilities across tasks like tool use, planning, reasoning, and task completion.

The sequential composition of multiple AI agents where each agent's output becomes the input for the next, creating automated pipelines for complex multi-stage processes.

The process of multiple AI agents working together, sharing information and coordinating actions to achieve common goals.

Standardised protocols and languages used for inter-agent communication in multi-agent systems.

A multi-agent scenario where agents pursue conflicting objectives, leading to adversarial or game-theoretic interactions.

The accumulated information, history, and environmental state that informs an AI agent's decision-making.

Methods and metrics for assessing the performance, reliability, and safety of autonomous AI agents.

Safety constraints imposed on AI agents that limit their action space, prevent dangerous operations, enforce budgets, and require approval for irreversible decisions.

Safety constraints and boundaries that limit agent behaviour to prevent harmful, unintended, or out-of-scope actions.

The transfer of a task or conversation from one specialised AI agent to another based on skill requirements, escalation rules, or domain boundaries.

An organisational structure where agents are arranged in levels, with higher-level agents delegating tasks to lower-level ones.

The processes of developing, deploying, monitoring, updating, and retiring AI agents throughout their operational life.

The iterative cycle of perception, reasoning, planning, and action execution that drives autonomous agent behaviour.

The storage mechanism enabling AI agents to retain and recall information from previous interactions and experiences.

A persistent knowledge store that enables AI agents to accumulate and recall information across sessions, supporting long-term learning and personalised interactions.

The process by which AI agents reach agreements through offers, counteroffers, and compromise strategies.

The ability to monitor, trace, and understand the internal states, decisions, and actions of AI agents in production.

The coordination and management of multiple AI agents working together to accomplish complex workflows.

The defined role, personality, and behavioural characteristics assigned to an AI agent for consistent interaction.

The ability of an AI agent to formulate a sequence of actions to achieve a goal from its current state.

The iterative cycle of observation, thought, action, and reflection that AI agents execute to break down complex goals into achievable subtasks and verify progress.

The ability of an AI agent to evaluate its own outputs and reasoning, identifying errors and improving responses.

An isolated environment where AI agents can safely execute actions and experiment without affecting production systems.

A specific capability or function that an AI agent can perform, such as web search, code execution, or data analysis.

A meta-agent that coordinates, monitors, and manages a team of sub-agents, allocating tasks and synthesising results to fulfil complex multi-domain objectives.

A large collection of AI agents operating collaboratively using emergent behaviour patterns to solve complex tasks.

The automated collection and transmission of performance data from AI agents for monitoring and analysis.

A catalogue of available tools and APIs that agents can discover and invoke, with descriptions, schemas, and authentication details enabling dynamic capability acquisition.

AI systems that can autonomously plan, reason, and take actions to achieve goals with minimal human intervention.

An organisation that has achieved autonomous scaling of operations through pervasive deployment of AI agents across all functions.

An advanced retrieval-augmented generation pattern where an agent dynamically decides what information to retrieve, from which sources, and how to refine queries iteratively.

The strategic process of redesigning business operations around autonomous AI agents to achieve hyperscale efficiency.

A business process that is partially or fully executed by autonomous AI agents rather than human workers.

An iterative approach to software development emphasising flexibility, collaboration, and rapid delivery of working software.

A robot designed for agricultural applications including planting, harvesting, monitoring, and soil analysis.

Specialised hardware designed to speed up AI computations, including GPUs, TPUs, and custom AI chips.

A framework that describes the progressive stages of organisational AI capability from experimental pilots through scaled deployment to enterprise-wide autonomous operations.

An autonomous software entity that perceives its environment, makes decisions, and takes actions to achieve specified objectives.

The coordination and management of multiple AI agents working together to accomplish complex tasks, routing subtasks between specialised agents based on capability and context.

The research field focused on ensuring AI systems act in accordance with human values, intentions, and ethical principles.

An independent assessment of an AI system's compliance with regulatory requirements, ethical standards, and organisational policies, examining data, models, outputs, and governance.

Standardised tests and datasets used to evaluate and compare the performance of AI models across specific tasks.

Systematic errors in AI outputs that arise from biased training data, flawed assumptions, or prejudicial algorithm design.

A cross-functional team within an organisation that defines AI strategy, develops best practices, builds shared capabilities, and accelerates AI adoption across business units.

A semiconductor designed specifically for AI and machine learning computations, optimised for parallel processing and matrix operations.

A persistent AI entity that forms an ongoing relationship with a user, accumulating shared history, adapting to preferences, and providing personalised assistance over time.

An AI assistant embedded in software applications that helps users complete tasks through suggestions and automation.

The movement to make AI tools, knowledge, and resources accessible to non-experts and organisations of all sizes.

The branch of ethics examining moral issues surrounding the development, deployment, and impact of artificial intelligence on society.

An advisory body within an organisation composed of diverse stakeholders who review, guide, and provide oversight on the ethical implications of AI projects and deployments.

The ability to describe AI decision-making processes in human-understandable terms, enabling trust and regulatory compliance.

The principle of ensuring AI systems make equitable decisions without discriminating against any group based on protected attributes.

A centralised platform for storing, managing, and serving machine learning features consistently across training and inference.

The frameworks, policies, and regulations that guide the responsible development and deployment of AI technologies.

Safety mechanisms and constraints implemented around AI systems to prevent harmful, biased, or policy-violating outputs while preserving useful functionality.

When an AI model generates plausible-sounding but factually incorrect or fabricated information with high confidence.

A systematic evaluation of the potential effects and risks of an AI system before and during its deployment.

The process of using a trained AI model to make predictions or decisions on new, unseen data.

The specialised hardware, software, and networking stack required to train and serve AI models at scale, including GPU clusters, high-bandwidth interconnects, and model serving frameworks.

The degree to which humans can understand the internal mechanics and reasoning of an AI model's predictions and decisions.

Architectures that enable AI agents to store, retrieve, and reason over information from past interactions, providing continuity and personalisation across conversations.

A documentation framework that provides standardised information about an AI model's intended use, performance characteristics, limitations, and ethical considerations.

A centralised repository for storing, versioning, and managing trained AI models across an organisation.

The coordination and management of multiple AI models, services, and workflows to achieve complex end-to-end automation.

Middleware that manages routing, fallback, load balancing, and model selection across multiple AI providers to optimise cost, latency, and output quality.

A sequence of data processing and model execution steps that automate the flow from raw data to AI-driven outputs.

A structured evaluation of an organisation's data maturity, technical infrastructure, talent, culture, and governance preparedness for successful AI adoption and deployment.

The systematic adversarial testing of AI systems to identify vulnerabilities, failure modes, harmful outputs, and safety risks before deployment.

The developing body of laws and policies governing the development, deployment, and use of artificial intelligence systems.

A structured approach to identifying, assessing, and mitigating risks associated with AI systems, as defined by standards such as NIST AI RMF and ISO/IEC 42001.

The ability of an AI system to maintain performance under varying conditions, adversarial attacks, or noisy input data.

The interdisciplinary field dedicated to making AI systems safe, robust, and beneficial while minimizing risks of unintended consequences.

The discipline of protecting AI systems from adversarial attacks, data poisoning, model theft, and prompt injection while ensuring the secure deployment of AI in production environments.

The economic model governing the pricing and allocation of computational resources for AI inference, including per-token billing, rate limiting, and credit systems.

The process of teaching an AI model to recognise patterns by exposing it to large datasets and adjusting its parameters.

The strategic reimagining of an organisation's operations, products, and business models through the systematic integration of artificial intelligence across all functions and processes.

The practice of making AI systems' operations, data usage, and decision processes openly visible to stakeholders.

Techniques for embedding imperceptible statistical patterns in AI-generated content to enable reliable detection and provenance tracking of synthetic outputs.

An organisational model where human workers are empowered by AI tools that handle routine tasks, surface insights, and enhance decision-making, amplifying human capabilities.

Text, images, audio, video, and code created by artificial intelligence systems, raising questions about authenticity, intellectual property, and the future of creative work.

Security systems that leverage machine learning and behavioural analytics to identify sophisticated cyber threats, anomalous patterns, and zero-day attacks in real time.

Automated notifications triggered when system metrics or conditions exceed predefined thresholds.

The principle that organisations should be answerable for the outcomes and impacts of their algorithmic systems.

A systematic evaluation of the potential social, economic, and civil rights impacts of an automated decision-making system before and after deployment.

Making the logic, data, and decisions of algorithms visible and understandable to affected parties.

Electronic environments that are sensitive and responsive to the presence of people, adapting to their needs.

Identifying data points, events, or observations that deviate significantly from the expected pattern in a dataset.

An open-source automation tool for configuration management, application deployment, and task automation.

Laws, regulations, and procedures designed to prevent criminals from disguising illegally obtained funds as legitimate income.

Application Programming Interface — a set of protocols and tools for building and integrating software applications.

The process of defining interfaces for software components to communicate with each other effectively.

The commercial exchange of business capabilities through application programming interfaces.

A server that acts as a single entry point for API calls, handling request routing, composition, and protocol translation.

A purpose-built blockchain designed for a single application or use case, offering customised consensus, throughput, and governance optimised for specific requirements.

Updating existing applications to newer computing approaches including cloud, containers, and microservices.

A centralised storage system for managing binary artifacts produced during the software build process.

A hypothetical form of AI that possesses the ability to understand, learn, and apply knowledge across any intellectual task a human can perform.

The simulation of human intelligence processes by computer systems, including learning, reasoning, and self-correction.

AI systems designed and trained for a specific task or narrow range of tasks, such as image recognition or language translation.

A theoretical level of AI that surpasses human cognitive abilities across all domains, including creativity and social intelligence.

A fine-grained sentiment analysis approach that identifies opinions directed at specific aspects or features of an entity, such as a product's price, quality, or design.

A method for discovering interesting relationships and patterns between variables in large datasets.

A programming paradigm where operations can proceed without waiting for other operations to complete.

A peer-to-peer exchange of cryptocurrencies across different blockchain networks using cryptographic hash time-locked contracts, eliminating the need for trusted intermediaries.

The total number of points where an unauthorised user can try to enter or extract data from a system.

The continuous discovery, inventory, classification, and monitoring of all external-facing digital assets to identify and reduce an organisation's exposure to cyber threats.

The specific path, method, or scenario used by an attacker to gain unauthorised access to a system.

An individual attention computation within a multi-head attention layer that learns to focus on different aspects of the input, with outputs concatenated for richer representations.

A neural network component that learns to focus on relevant parts of the input when producing each element of the output.

Area Under the ROC Curve, a single metric summarising a classifier's ability to distinguish between classes.

A chronological record of system activities enabling the reconstruction and examination of a sequence of events.

The use of machine learning and natural language processing to automate data preparation, insight discovery, and explanation, making analytics accessible to business users.

Technology overlaying digital information onto the real world through devices like smartphones or smart glasses.

Automatically adjusting compute resources based on current demand to maintain performance and optimise costs.

A neural network trained to encode input data into a compressed representation and then decode it back to reconstruct the original.

The end-to-end automation of the machine learning pipeline including feature engineering, model selection, hyperparameter tuning, and deployment, making ML accessible to non-experts.

A decentralised exchange protocol that uses algorithmic pricing instead of traditional order books.

Automated machine learning that automates the end-to-end process of applying machine learning to real-world problems.

An AI agent capable of operating independently, making decisions and taking actions without continuous human oversight.

A robot that can navigate and perform tasks in its environment without human guidance.

The ability of a robot or vehicle to move through an environment independently using sensors and algorithms.

The use of AI, robotics, and automation to run business processes and industrial operations with minimal human intervention, achieving continuous optimisation.

The AI subsystem in autonomous vehicles that interprets sensor data to understand the surrounding environment.

A vehicle capable of navigating and operating without human input, using sensors, AI, and advanced control systems to perceive surroundings and make driving decisions.

A multi-step business process executed entirely by AI agents with minimal human intervention, spanning planning, execution, monitoring, and error recovery phases.

An isolated location within a cloud region with independent power, cooling, and networking for high availability.

The algorithm for computing gradients of the loss function with respect to network weights, enabling neural network training.

An inference strategy that starts with a goal and works backward through rules to determine what facts must be true.

Bootstrap Aggregating — an ensemble method that trains multiple models on random subsets of data and averages their predictions.

An online learning algorithm that balances exploration of new options with exploitation of known good options to maximise reward.

The maximum rate of data transfer across a network path, typically measured in bits per second.

Training a machine learning model on the entire dataset at once before deployment, as opposed to incremental updates.

A technique that normalises layer inputs during training to stabilise and accelerate deep neural network learning.

A statistical approach to AI that uses Bayes' theorem to update probability estimates as new evidence becomes available.

A statistical approach that incorporates prior knowledge and updates probability estimates as new data is observed.

Belief-Desire-Intention — an agent architecture where agents reason about beliefs, desires, and intentions to decide actions.

A development approach where application behaviour is described in a natural language format before implementation.

Bidirectional Encoder Representations from Transformers — a language model that understands context by reading text in both directions.

Border Gateway Protocol — the routing protocol that manages how packets are routed across the internet between autonomous systems.

The balance between a model's ability to minimise bias (error from assumptions) and variance (sensitivity to training data fluctuations).

Extremely large and complex datasets that require advanced computational tools and techniques to store, process, and analyse.

Using biological materials and systems to perform computational operations and information processing.

Using unique biological characteristics like fingerprints, facial features, or iris patterns to verify identity.

An organisational approach where incident reviews focus on systemic improvements rather than individual blame.

Bluetooth Low Energy — a wireless protocol designed for short-range, low-power IoT device communication.

A metric for evaluating the quality of machine-generated text by comparing it to reference translations or texts.

A geometrical representation of the state space of a single qubit as a point on the surface of a sphere.

A data storage technology that manages data as individual blocks, each acting as an independent hard drive.

A distributed, immutable digital ledger that records transactions across a network of computers without a central authority.

The analysis of blockchain transaction data to trace asset flows, identify illicit activity, and support regulatory compliance using graph analysis and clustering techniques.

The ability of different blockchain networks to communicate, share data, and transfer assets seamlessly, enabling cross-chain applications and unified digital asset ecosystems.

A service that provides smart contracts with verified external data from off-chain sources such as market prices, weather conditions, and real-world events.

A business theory that suggests companies create new market spaces rather than competing in existing ones.

A group of security professionals who defend against both real attackers and simulated attacks from red teams.

A deployment strategy using two identical production environments to achieve zero-downtime releases.

Maintaining two identical production environments to enable instant switching between versions.

An ensemble technique that sequentially trains models, each focusing on correcting the errors of previous models.

A rectangular region drawn around an object in an image to indicate its location for object detection tasks.

A direct communication pathway between the brain's electrical activity and an external computing device.

Automated security testing that continuously simulates real-world attack scenarios against production environments to validate defensive controls and identify security gaps.

An AI agent that autonomously navigates web pages, fills forms, extracts information, and completes online tasks by controlling a browser through programmatic or visual interfaces.

A programming error where data written to a buffer exceeds its capacity, potentially allowing code execution.

A programme where organisations pay individuals for discovering and reporting software vulnerabilities.

The process of automating the compilation, testing, and packaging of software applications.

A strategic decision framework for evaluating whether to develop technology capabilities internally or procure them from external vendors, weighing cost, time, differentiation, and risk.

An organisation's ability to rapidly adapt to market and environmental changes in productive and cost-effective ways.

The practice of iterative exploration of organisational data to drive business planning and decision-making.

A justification for a proposed project or undertaking based on its expected commercial benefit.

The process of creating systems of prevention and recovery to deal with potential threats to an organisation.

The application of ethical principles and moral standards to business activities, decisions, and relationships.

Technologies, practices, and strategies for collecting, integrating, and analysing business data to support decision-making.

The process of creating, refining, or transforming a company's business model to capture new value.

The discipline of modelling, automating, executing, and optimising organisational business processes.

A subword tokenisation algorithm that iteratively merges the most frequent character pairs to build a vocabulary.

The ability of a distributed system to reach consensus despite some nodes acting maliciously or failing.

Storing frequently accessed data in a fast-access storage layer to reduce latency and improve performance.

A deployment strategy where changes are gradually rolled out to a small subset of users before full deployment.

The process of determining the production capacity needed to meet changing demands for an organisation's products.

A neural network architecture that groups neurons into capsules to better capture spatial hierarchies and part-whole relationships.

Technologies for capturing carbon dioxide emissions from sources or the atmosphere and storing or utilising them.

The tendency of neural networks to completely lose previously learned knowledge when trained on new tasks, a fundamental challenge in continual and multi-task learning.

The process of determining cause-and-effect relationships from data, going beyond correlation to establish causation.

California Consumer Privacy Act — a US state law enhancing privacy rights and consumer protection for California residents.

A digital form of fiat currency issued and regulated by a country's central bank on blockchain infrastructure.

An entity that issues digital certificates, verifying the identity of organisations and encrypting communications.

A workflow pattern where multiple specialised agents are sequentially connected, with each agent's output feeding the next.

A prompting technique that encourages language models to break down reasoning into intermediate steps before providing an answer.

A decentralised oracle network that connects smart contracts with external data sources, APIs, and payment systems.

A structured approach to transitioning individuals, teams, and organisations from a current state to a desired future state.

An organisation's preparedness and capability to embrace and implement significant changes.

The discipline of experimenting on distributed systems to build confidence in their ability to withstand turbulent conditions.

A software application that simulates human conversation through text or voice interactions using NLP.

A collaboration model connecting tools, processes, and automation with team chat platforms for operations management.

A configuration management tool using Ruby-based scripts to automate infrastructure setup and maintenance.

A thought experiment by John Searle arguing that executing a program cannot give a computer genuine understanding or consciousness.

The method of dividing long documents into smaller segments for embedding and retrieval, balancing context preservation with optimal chunk sizes for vector search accuracy.

The process of analysing customer attrition to understand why customers stop using a product or service.

An automated workflow that builds, tests, and deploys software changes from development to production.

A design pattern that prevents cascading failures by stopping calls to a failing service temporarily.

Google's open-source framework for writing, manipulating, and running quantum circuits on quantum hardware and simulators.

A non-IT employee who creates business applications using low-code or no-code platforms sanctioned by IT departments.

A practice that empowers non-technical business users to create applications and automations using low-code and no-code platforms with appropriate governance guardrails.

A situation where the distribution of classes in a dataset is significantly skewed, with some classes vastly outnumbering others.

A software design philosophy separating concerns into layers with dependencies pointing inward toward business rules.

A configuration where an application runs in a private cloud and bursts into a public cloud when demand spikes.

The delivery of computing services — servers, storage, databases, networking, software — over the internet on demand.

Strategies and practices for minimising cloud computing expenses while maintaining performance and functionality.

A database service built, deployed, and accessed through a cloud platform, offering scalability and managed operations.

The policies, procedures, and tools for managing cloud resource usage, security, compliance, and costs.

The process of moving data, applications, and workloads from on-premises infrastructure to cloud environments.

The automated arrangement, coordination, and management of complex cloud computing systems and services.

The process of moving workloads back from public cloud environments to on-premises or private cloud infrastructure.

The set of policies, technologies, and controls deployed to protect cloud-based systems, data, and infrastructure.

Automated tools that continuously assess cloud infrastructure configurations against security best practices and compliance requirements, identifying and remediating misconfigurations.

Security solutions designed to protect server workloads running in cloud environments, providing vulnerability management, runtime protection, and compliance monitoring.

An approach to building applications that fully exploit cloud computing advantages like elasticity, resilience, and automation.

An integrated security platform that protects cloud-native applications across the full lifecycle, combining workload protection, configuration management, and runtime security.

A database designed from the ground up to operate in cloud environments with automatic scaling and high availability.

An approach to building applications that fully exploit cloud computing advantages including microservices, containers, dynamic orchestration, and continuous delivery.

Unsupervised learning technique that groups similar data points together based on inherent patterns without predefined labels.

Constrained Application Protocol — a specialised web transfer protocol for use with constrained devices in IoT networks.

Control Objectives for Information and Related Technologies — a framework for IT governance and management.

The automated production of source code from natural language specifications or partial code context, powered by large language models trained on programming repositories.

A systematic examination of source code by developers other than the author to identify bugs and improve quality.

An AI agent specialised in writing, debugging, refactoring, and testing software code, capable of operating across multiple files and understanding project-level context.

A theoretical framework that models the structure and processes of the human mind for building intelligent agents.

Computing systems that simulate human thought processes using self-learning algorithms, data mining, pattern recognition, and natural language processing.

The total amount of mental effort required to process and understand information in a user interface.

A behavioural analytics technique that groups users with shared characteristics to track metrics over time.

An offline cryptocurrency storage solution disconnected from the internet for maximum security.

A recommendation technique that makes predictions based on the collective preferences and behaviour of many users.

A robot designed to work alongside humans in a shared workspace, equipped with safety features.

The AI capability to make inferences based on everyday knowledge that humans typically take for granted.

A condition or circumstance that puts a company in a superior business position relative to competitors.

Adherence to laws, regulations, guidelines, and specifications relevant to an organisation's business.

The practice of expressing regulatory and security compliance requirements as machine-readable policies that can be automatically validated against infrastructure and application configurations.

A structured set of guidelines and best practices for meeting regulatory requirements and industry standards.

A collection of pre-built, reusable UI components that maintain consistency across applications.

A technology design approach that assembles applications from modular, independent building blocks through APIs, enabling rapid adaptation to changing business requirements.

An organisation design principle where business capabilities are assembled from modular, interchangeable components.

A collection of interconnected computing devices that share resources and communicate using standardised protocols.

The automated control of machining tools using a computer and coded programmed instructions.

An AI agent that interacts with graphical user interfaces by perceiving screen content and executing mouse clicks, keyboard inputs, and navigation actions like a human operator.

The field of AI that enables computers to interpret and understand visual information from images and video.

Changes in the underlying patterns that a model was trained to capture, requiring model adaptation.

The ability of a system to handle multiple tasks simultaneously by interleaving their execution.

Continuously tracking equipment parameters to detect changes that indicate developing faults.

Technology that protects data during processing by performing computations in hardware-based trusted execution environments.

The practice of systematically managing and maintaining the consistency of system configurations.

Enterprise software that automates the configuration of complex products, accurate pricing calculations, and professional quote generation for sales teams.

A table used to evaluate classification model performance by comparing predicted classifications against actual classifications.

An organisation with seamlessly integrated systems, processes, and data flows across all business functions.

A vehicle equipped with internet connectivity and sensors enabling communication with other vehicles and infrastructure.

Technology solutions that equip frontline workers with digital tools, wearables, and AI assistance to improve safety, productivity, and access to information in operational environments.

An approach to AI modelling cognitive processes using artificial neural networks inspired by biological neural structures.

The method by which a distributed network agrees on the current state of the ledger and validates transactions.

An approach to AI alignment where models are trained to follow a set of principles or constitution.

A computational approach where problems are defined as a set of variables, domains, and constraints that must all be simultaneously satisfied.

A lightweight, portable software package that bundles application code with all its dependencies for consistent execution.

The automated management of containerised application deployment, scaling, networking, and availability across clusters of machines, with Kubernetes as the dominant platform.

A repository for storing, managing, and distributing container images.

A distributed network of servers that delivers web content to users based on their geographic location.

A recommendation approach that suggests items similar to those a user has previously liked, based on item attributes.

The maximum amount of text a language model can consider at once when generating a response.

Word representations that change based on surrounding context, capturing polysemy and contextual meaning.

A machine learning paradigm where models learn from a continuous stream of data, accumulating knowledge over time without forgetting previously learned information.

An automated approach to maintaining regulatory compliance through real-time monitoring, policy enforcement, and evidence collection integrated into development and operations pipelines.

A software practice where code changes can be released to production at any time through automated pipelines.

An extension of continuous integration where code changes are automatically deployed to production after passing tests.

A development practice where code changes are automatically built and tested when merged to a shared repository.

A self-supervised learning approach that trains models by comparing similar and dissimilar pairs of data representations.

A structured set of controls and processes designed to manage risk and ensure compliance with regulations.

AI systems designed to engage in natural, context-aware dialogue with humans across multiple turns.

The practice of increasing the percentage of users who complete a desired action on a website.

A neural network layer that applies learnable filters across input data to detect local patterns and features.

A deep learning architecture designed for processing structured grid data like images, using convolutional filters to detect features.

A unique capability or advantage that distinguishes a company from its competitors and creates sustainable value.

The task of identifying all expressions in text that refer to the same real-world entity.

The system of rules, practices, and processes by which a company is directed and controlled.

A dedicated unit within a corporation focused on exploring and developing new technologies and business models.

Investment of corporate funds directly into external startup companies to gain strategic benefits such as access to innovation, market intelligence, and potential acquisitions.

Statistical analysis measuring the strength and direction of the relationship between two or more variables.

Infrastructure enabling the transfer of assets and data between different blockchain networks.

The application of models trained in one language to perform tasks in another language, leveraging shared multilingual representations learned during pre-training.

A web security vulnerability allowing attackers to inject malicious scripts into web pages viewed by other users.

A resampling technique that partitions data into subsets, training on some and validating on others to assess model generalisation.

A one-way function producing a fixed-length output that is computationally infeasible to reverse or find collisions for.

A training strategy that presents examples to a model in a meaningful order, typically from easy to hard.

The practice of collecting and analysing customer data to understand behaviour, preferences, and lifetime value.

A software system that creates a unified customer database accessible to other systems for marketing and analytics.

The sum of all interactions and perceptions a customer has with a brand throughout their entire journey.

The total revenue a business can expect from a single customer account throughout their relationship.

Technology for managing a company's interactions, relationships, and data with current and potential customers.

Insurance coverage protecting organisations against financial losses from cyberattacks, data breaches, and related incidents.

A model describing the stages of a cyberattack from reconnaissance through data exfiltration.

An organisation's ability to continuously deliver intended outcomes despite adverse cyber events, encompassing prevention, detection, response, and recovery capabilities.

Evidence-based knowledge about adversary capabilities, infrastructure, motives, and tactics that informs security decisions and enables proactive defence against cyber attacks.

The practice of protecting systems, networks, and programs from digital attacks, unauthorised access, and data breaches.

A deceptive user interface design that tricks users into doing things they didn't intend to do.

A visual interface displaying key metrics and data points for monitoring performance and making informed decisions.

An AI agent that interprets datasets, generates visualisations, performs statistical analysis, and produces actionable insights through autonomous exploratory data investigation.

The process of labelling data with informative tags to make it usable for training supervised machine learning models.

Techniques that artificially increase the size and diversity of training data through transformations like rotation, flipping, and cropping.

The guarantee that all data required to verify blockchain transactions is accessible to network participants, a critical requirement for the security of rollup-based scaling solutions.

A metadata management tool that helps organisations find, understand, and manage their data assets.

A formal agreement between data producers and consumers that defines the structure, semantics, quality standards, and service levels of a shared data interface.

Making data accessible to all members of an organisation regardless of their technical expertise.

Changes in the statistical properties of data over time that can degrade machine learning model performance.

The practice of designing, building, and maintaining data infrastructure, pipelines, and architectures.

An architecture that provides a unified, intelligent layer for integrating data management across cloud and on-premises environments.

The framework of policies, processes, and standards for managing data assets to ensure quality, security, and compliance.

The process of combining data from different sources to provide users with a unified, consistent view.

The process of annotating raw data with informative tags or classifications for supervised machine learning training.

A centralised repository for storing vast amounts of raw data in its native format until needed for analysis.

A hybrid data architecture combining the flexibility of data lakes with the structured querying capabilities of data warehouses.

The documentation of data's origins, movements, and transformations throughout its lifecycle.

Technology and processes that prevent sensitive data from being lost, misused, or accessed by unauthorised users.

A subset of a data warehouse focused on a particular business area, department, or subject.

A decentralised data architecture approach where domain teams own and manage their own data products.

The process of generating measurable economic benefits from data assets, either by improving internal operations or creating external revenue streams through data products and services.

The ability to understand, diagnose, and resolve data quality issues across the data stack by monitoring freshness, distribution, volume, schema, and lineage of data assets.

A distributed training strategy that replicates the model across multiple devices and divides training data into batches processed simultaneously, synchronising gradients after each step.

An automated set of processes that moves and transforms data from source systems to target destinations.

The proper handling of personal data including collection, storage, processing, and sharing in compliance with regulations.

A reusable, well-documented, and managed dataset or analytical asset created to serve specific business needs, treated with the same rigour as software products.

The process of examining, analysing, and creating summaries of data to assess quality and structure.

A process required under GDPR for assessing the risks of personal data processing activities and identifying measures to mitigate those risks before implementation.

An individual responsible for overseeing an organisation's data protection strategy and regulatory compliance.

The measure of data's fitness for its intended purpose based on accuracy, completeness, consistency, and timeliness.

An interdisciplinary field using scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

An isolated repository of data controlled by one department, inaccessible to other parts of the organisation.

The concept that data is subject to the laws and governance structures of the country where it is collected or processed.

The practice of building narratives around data insights using visualisations and narrative techniques.

The graphical representation of data and information using visual elements like charts, graphs, and maps.

A centralised repository of integrated data from multiple sources, designed for query and analysis.

The process of cleaning, structuring, and enriching raw data into a desired format for analysis.

An organisational culture where decisions at all levels are informed by data analysis and empirical evidence rather than intuition, supported by accessible analytics and data literacy.

An enterprise where strategic decisions are made based on data analysis and interpretation rather than intuition alone.

The process of defining the structure, storage, and retrieval of data in a database system.

Density-Based Spatial Clustering of Applications with Noise — a clustering algorithm that finds arbitrarily shaped clusters based on density.

An application that runs on a decentralised peer-to-peer network rather than a single centralised server.

An organisation governed by smart contracts and token holder votes rather than centralised management.

Financial services built on blockchain technology that operate without traditional intermediaries like banks or brokerages.

A self-sovereign identity framework where individuals control their own digital identity credentials without centralised authorities.

Networks that use blockchain token incentives to coordinate the deployment and operation of physical infrastructure such as wireless networks, energy grids, and compute resources.

Distributed file storage systems where data is spread across multiple nodes rather than centralised servers.

Security solutions that deploy decoy assets such as fake servers, credentials, and data to detect, misdirect, and analyse attackers who have breached perimeter defences.

A discipline that augments human decision-making with data analytics, AI, and behavioural science to improve the speed, quality, and outcomes of business decisions.

A tree-structured model where internal nodes represent feature tests, branches represent outcomes, and leaves represent predictions.

The loss of quantum coherence when a quantum system interacts with its environment, causing errors in computation.

A subset of machine learning using neural networks with multiple layers to learn hierarchical representations of data.

Combining deep neural networks with reinforcement learning to enable agents to learn complex decision-making from raw sensory input.

AI-generated synthetic media where a person's likeness is convincingly replaced or manipulated in images or videos.

The number of independent parameters that define a robot's configuration and range of motion.

A consensus mechanism where token holders vote for delegates who validate transactions on their behalf.

An AI agent that maintains an internal model of its world and reasons about actions before executing them.

An attack designed to make a machine or network resource unavailable by overwhelming it with traffic.

A design pattern where dependencies are provided to a component rather than created within it.

The syntactic analysis of a sentence to establish relationships between head words and words that modify them.

Predicting the distance of surfaces in a scene from the camera viewpoint using visual information.

The analysis of historical data to understand what has happened in the past and identify patterns.

A reusable solution to a commonly occurring problem within a given context in software design.

A five-day process for answering critical business questions through design, prototyping, and user testing.

A collection of reusable components, guidelines, and standards for building consistent digital products.

A human-centred problem-solving methodology that emphasises empathy, ideation, prototyping, and testing.

Verifying the identity of IoT devices before allowing them to connect to a network or service.

The process of configuring and enrolling IoT devices into a management platform for secure operation.

A set of practices combining software development and IT operations to shorten the development lifecycle and deliver continuous value.

An approach integrating security practices within the DevOps process, making security a shared responsibility.

Dynamic Host Configuration Protocol — automatically assigns IP addresses and network configuration to devices.

Analysis techniques focused on understanding why something happened by examining data patterns and correlations.

The component of conversational systems that tracks conversation state, determines the next system action, and maintains coherent multi-turn interactions with users.

A computer system designed to converse with humans, encompassing task-oriented and open-domain conversation.

A generative model that learns to reverse a gradual noising process, generating high-quality samples from random noise.

Software that overlays on enterprise applications to guide users through features and processes in real time.

The convergence of biological sciences with computational methods and AI to accelerate drug discovery, protein design, genomic analysis, and synthetic biology applications.

A fixed-income security issued and managed on blockchain infrastructure, automating coupon payments, settlement, and transfer of ownership through smart contracts.

The ability and desire of individuals and organisations to leverage existing and emerging technologies for better business outcomes, encompassing both skill and mindset.

The change that occurs when new digital technologies fundamentally alter the way business is conducted.

A network of interconnected organisations, technologies, and processes that create and distribute value digitally.

The application of moral principles to the creation, use, and governance of digital technologies, addressing questions of privacy, equity, transparency, and societal impact.

A comprehensive network of digital models and simulation tools replicating the entire production process.

The process of collecting, preserving, and analysing electronic evidence for investigating security incidents.

The online representation of an individual comprising their attributes, credentials, and digital footprint.

The ability to effectively and critically navigate, evaluate, and create information using digital technologies.

An assessment of an organisation's ability to adapt and respond to digital trends and competitive threats.

A framework that assesses an organisation's current level of digital capability across dimensions such as strategy, culture, technology, data, and operations.

The blueprint for how an organisation delivers value through digital capabilities, processes, and technology.

An organisation's ability to build, assure, and review its technological integrity to ensure it can withstand all types of ICT-related disruptions and threats.

A product that exists in digital form, delivered and consumed electronically, often as software or digital content.

The discipline of guiding digital products through their lifecycle from ideation to retirement, balancing user needs, business objectives, and technical constraints.

A cryptographic mechanism that verifies the authenticity and integrity of digital messages or documents.

A plan that uses digital initiatives to create new competitive advantages and support business transformation goals.

A technology-enabled supply chain that uses AI, IoT, blockchain, and analytics to create end-to-end visibility, predict disruptions, and optimise logistics in real time.

An integrated data framework that connects information across the entire product lifecycle from design through manufacturing to service, enabling traceability and analytics.

The fundamental integration of digital technology into all areas of a business, changing how it operates and delivers value.

A virtual replica of a physical system, process, or product that simulates its real-world counterpart for analysis and optimisation.

A virtual model of a physical device or system that simulates its behaviour for monitoring and optimisation.

A dynamic virtual model of an entire organisation used to simulate, predict, and optimise business performance.

Creating new digital businesses or ventures within or alongside an established organisation.

A virtual equivalent of a physical workplace enabled by digital tools for collaboration, communication, and productivity.

Techniques that reduce the number of input variables in a dataset while preserving essential information and structure.

A simplified alternative to RLHF that directly optimises language model policies using preference data without requiring a separate reward model.

The policies, tools, and procedures for recovering technology infrastructure and systems after a natural or human-induced disaster.

A cloud computing model that enables the replication and recovery of infrastructure and data in the cloud.

Innovation that creates a new market and value network, eventually disrupting existing markets and displacing established firms.

A digital system for recording, sharing, and synchronising data across multiple sites without a central administrator.

A method of tracking requests as they flow through distributed systems to diagnose latency and failure points.

Using synthetic DNA molecules to encode and store digital data with extreme density and longevity.

Domain Name System — the hierarchical system that translates human-readable domain names into numerical IP addresses.

A platform for developing, shipping, and running applications in isolated containers with consistent environments.

AI systems that extract structured information from unstructured documents by combining optical character recognition, layout analysis, and natural language comprehension.

A software design approach focusing on modelling the business domain and aligning code with business logic.

An unmanned aerial vehicle operated remotely or autonomously for applications like surveillance, delivery, and mapping.

A regularisation technique that randomly deactivates neurons during training to prevent co-adaptation and reduce overfitting.

A comprehensive appraisal of a business or investment undertaken to establish its assets, liabilities, and commercial potential.

A strategic approach that leverages partnerships and collaborative networks to create collective value.

Artificial intelligence algorithms processed locally on edge devices rather than in centralised cloud data centres.

Performing data analysis at the network edge, close to data sources, for real-time insights and reduced latency.

Processing data near the source of data generation rather than in a centralised cloud data centre.

A computing device at the boundary of a network that processes data locally before sending it to the cloud.

A regularisation technique combining L1 and L2 penalties, balancing feature selection and coefficient shrinkage.

The ability of a system to automatically scale resources up or down based on current demand.

Extract, Load, Transform — a modern data pipeline approach where raw data is loaded first and transformed within the target system.

A dedicated computer system designed for specific functions within a larger mechanical or electrical system.

A learned dense vector representation of discrete data (like words or categories) in a continuous vector space.

Artificial intelligence that operates within physical robots or virtual bodies, learning from sensory experiences and physical interaction with its environment.

Complex patterns and capabilities that arise from the interactions of simpler agent components or rules.

Abilities that appear in large language models at certain scale thresholds that were not present in smaller versions, such as in-context learning and complex reasoning.

A unified digital platform improving employee engagement, productivity, and satisfaction across the work lifecycle.

A neural network design where an encoder processes input into a fixed representation and a decoder generates output from it.

The process of converting plaintext data into ciphertext using an algorithm, making it unreadable without the decryption key.

The device at the end of a robotic arm designed to interact with the environment, such as grippers or tools.

A communication system where only the communicating users can read the messages, with encryption at both endpoints.

Testing the complete application workflow from start to finish to ensure the system meets requirements.

Security technology that monitors endpoint devices to detect, investigate, and respond to cyber threats.

Technology that captures and converts ambient energy from the environment into usable electrical power.

Combining multiple machine learning models to produce better predictive performance than any single model.

Machine learning techniques that combine multiple models to produce better predictive performance than any single model, including bagging, boosting, and stacking approaches.

An integrated software platform that provides organisations with tools for building, deploying, and managing AI applications at enterprise scale with governance, security, and compliance controls.

A strategic framework for aligning an organisation's IT infrastructure and processes with its business objectives.

A comprehensive digital replica of an entire organisation's operations, processes, and assets that enables simulation, optimisation, and predictive analysis at enterprise scale.

The practice of connecting different enterprise systems, applications, and data sources to work together seamlessly.

Integrated management software that connects core business processes including finance, HR, manufacturing, supply chain, and procurement.

Middleware architecture that enables communication between different enterprise applications through a central messaging backbone.

One complete pass through the entire training dataset during the machine learning model training process.

A technical standard for implementing fungible tokens on the Ethereum blockchain.

A technical standard for non-fungible tokens on the Ethereum blockchain, ensuring each token is unique.

The maximum amount of time a service can be unavailable within a given period based on its SLO.

Environmental, Social, and Governance — a set of standards for measuring a company's ethical impact and sustainability.

The runtime environment for executing smart contracts on the Ethereum blockchain network.

A set of principles, guidelines, and processes that an organisation adopts to ensure its AI systems are developed and deployed in a manner that is fair, transparent, and accountable.

Extract, Transform, Load — the process of extracting data from sources, transforming it to fit needs, and loading it into a target system.

An automated workflow that extracts data from sources, transforms it according to business rules, and loads it into a target system.

The European Union's comprehensive legislation establishing rules for the development and use of AI systems based on risk levels.

A programming construct that waits for and dispatches events or messages in a program.

An architecture pattern where the flow of the system is determined by events — significant changes in state.

A service management metric that measures the quality of user experience rather than just technical uptime, encompassing performance, usability, and satisfaction outcomes.

The systematic recording of machine learning experiment parameters, metrics, artifacts, and code versions to enable reproducibility and comparison across training runs.

An AI program that emulates the decision-making ability of a human expert by using a knowledge base and inference rules.

AI techniques that make model decisions transparent and understandable to humans.

A problem where gradients grow exponentially during backpropagation, causing unstable weight updates and training failure.

An approach to analysing datasets to summarise their main characteristics, often using statistical graphics and visualisation.

A unified security platform that integrates multiple security tools and data sources for comprehensive threat detection.

A unified security platform that integrates data from endpoints, networks, cloud workloads, and email to provide holistic threat detection, investigation, and automated response.

An umbrella term encompassing augmented reality, virtual reality, mixed reality, and all immersive technologies.

A summarisation technique that identifies and selects the most important sentences from a source document to compose a condensed version without generating new text.

A harmonic mean of precision and recall, providing a single metric that balances both false positives and false negatives.

Technology that identifies or verifies individuals by analysing facial features and patterns in images or video.

Quantum computing systems that can continue to operate correctly even in the presence of errors.

The process of using domain knowledge to create, select, and transform input variables to improve model performance.

The process of identifying and extracting relevant visual features from images for downstream analysis.

A software development technique allowing features to be enabled or disabled at runtime without deploying new code.

A technique for determining which input variables have the most significant impact on model predictions.

The process of identifying and selecting the most relevant input variables for a machine learning model.

A centralised repository for storing, managing, and serving machine learning features, ensuring consistency between training and inference environments across an organisation.

A machine learning approach where models are trained across decentralised devices without sharing raw data, preserving privacy.

A machine learning approach where models learn to perform tasks from only a small number of labelled examples, often achieved through in-context learning in large language models.

A technique where a language model is given a small number of examples within the prompt to guide its response pattern.

The process of taking a pretrained model and further training it on a smaller, task-specific dataset.

The process of adapting a pre-trained model to a specific task by continuing training on a smaller task-specific dataset, transferring learned representations to new domains.

A cultural practice combining technology, finance, and business to manage cloud costs through data-driven decision making.

A network security device that monitors and filters incoming and outgoing network traffic based on security rules.

Permanent software programmed into a hardware device's read-only memory that controls its basic functions.

The competitive advantage gained by being the first company to enter a new market or develop a new product.

A predictive model of human movement stating that time to reach a target depends on distance and size.

An IO-aware attention algorithm that reduces memory reads and writes by tiling the attention computation, enabling faster training of long-context transformer models.

An uncollateralised loan in DeFi that must be borrowed and repaid within a single blockchain transaction.

A concept where small consistent efforts compound over time to create self-sustaining momentum in business growth.

A distributed computing paradigm extending cloud capabilities to the edge of the network for IoT processing.

The capability of a robot to detect and measure physical forces applied to it during interaction with objects.

A divergence in a blockchain's protocol or chain, creating two separate paths — can be hard (incompatible) or soft (backward-compatible).

An inference strategy that starts with known facts and applies rules to derive new conclusions until a goal is reached.

A large AI model trained on broad data that can be adapted to a wide range of downstream tasks.

The challenge in AI of representing the effects of actions without having to explicitly state everything that remains unchanged.

A blockchain node that downloads and validates every block and transaction against the complete set of consensus rules.

A neural network layer where every neuron is connected to every neuron in the adjacent layers.

A serverless cloud computing model where individual functions are executed in response to events.

A mechanism allowing language models to invoke external functions or APIs based on natural language instructions.

A blockchain token where each unit is identical and interchangeable with any other unit of the same token.

Tracking and analysing the sequential steps users take toward a desired action to identify drop-off points.

Energy produced by fusing light atomic nuclei, potentially providing nearly limitless clean energy.

A form of logic that handles approximate reasoning, allowing variables to have degrees of truth rather than strict binary true/false values.

Automatic memory management that reclaims memory occupied by objects no longer referenced by the program.

The transaction fee paid to blockchain network validators for processing and confirming transactions.

A blockchain transaction where the end user does not pay network fees directly, with costs covered by a third party or subsidised through meta-transaction relay services.

A simplified variant of LSTM that combines the forget and input gates into a single update gate.

General Data Protection Regulation — EU legislation governing the collection and processing of personal data of EU residents.

A framework where two neural networks compete — a generator creates synthetic data while a discriminator evaluates its authenticity.

AI systems that can create new content including text, images, music, code, and video from learned patterns.

The analysis of geographic and spatial data to discover patterns, relationships, and trends tied to location.

A distributed version control system for tracking changes in source code during software development.

An operational framework using Git repositories as the single source of truth for declarative infrastructure and applications.

Global Vectors for Word Representation — an unsupervised learning algorithm for obtaining word vector representations from aggregated word co-occurrence statistics.

A plan that specifies how a company will reach target customers and achieve competitive advantage with a new product.

An AI agent that formulates and pursues explicit goals, planning actions to achieve desired outcomes.

The system of policies, rules, and processes by which activities are directed, controlled, and managed.

A cryptocurrency token that grants holders voting rights on protocol decisions and changes.

Generative Pre-trained Transformer — a family of autoregressive language models that generate text by predicting the next token.

Cloud infrastructure providing on-demand access to graphics processing units optimised for AI training and inference, enabling organisations to scale compute without capital investment.

A design approach where a system continues to operate with reduced functionality when components fail.

An ensemble technique that builds models sequentially, with each new model correcting residual errors of the combined ensemble.

A memory optimisation that trades computation for memory by recomputing intermediate activations during the backward pass instead of storing them all during the forward pass.

A technique that caps gradient values during training to prevent the exploding gradient problem.

An optimisation algorithm that iteratively adjusts parameters in the direction of steepest descent of the loss function.

An open-source analytics and visualisation platform for monitoring metrics from multiple data sources.

Analysing relationships and connections between entities represented as nodes and edges in a graph structure.

A neural network designed to operate on graph-structured data, learning representations of nodes, edges, and entire graphs.

A single layer of carbon atoms arranged in a hexagonal lattice with extraordinary electrical, thermal, and mechanical properties.

A query language for APIs that lets clients request exactly the data they need in a single request.

Cloud computing practices that minimise environmental impact through renewable energy usage, efficient cooling, workload consolidation, and carbon-aware scheduling of compute tasks.

Connecting language model outputs to real-world knowledge, facts, or data sources to improve factual accuracy.

A quantum search algorithm that provides quadratic speedup for searching unsorted databases.

A high-performance remote procedure call framework developed by Google using Protocol Buffers for serialisation.

Techniques for identifying when AI language models generate plausible but factually incorrect or unsupported content.

Technology that creates touch-based sensations, allowing users to feel virtual objects or robot interactions.

A radical change to a blockchain's protocol that makes previously invalid blocks or transactions valid, requiring all nodes to upgrade.

A mathematical function that converts input data of any size into a fixed-size output, used extensively in blockchain security.

An e-commerce architecture that decouples the front-end presentation layer from back-end commerce logic through APIs, enabling flexible omnichannel customer experiences.

An automated test that verifies a service or system component is functioning correctly.

A package manager for Kubernetes that simplifies the deployment and management of applications using charts.

A usability inspection method where evaluators examine an interface against established design principles.

Problem-solving techniques that use practical rules of thumb to find satisfactory solutions when exhaustive search is impractical.

A clustering method that builds a tree-like hierarchy of clusters through successive merging or splitting of groups.

A system design approach that ensures a certain degree of operational continuity during a given measurement period.

An encryption method allowing computations on encrypted data without decrypting it first.

A decoy system designed to attract attackers and study their methods while protecting real systems.

Adding more machines or nodes to a system to handle increased load.

A cryptocurrency wallet connected to the internet for convenient but less secure asset management.

Hypertext Transfer Protocol — the application-layer protocol for transmitting hypermedia documents on the World Wide Web.

HTTP Secure — an encrypted version of HTTP using TLS for secure communication over the internet.

Software and strategies for recruiting, managing, developing, and optimising an organisation's workforce.

The study of how people interact with computers and designing technologies for effective human use.

A system design where human oversight and approval are required at critical decision points in automated processes.

A system where humans monitor AI operations and can intervene when necessary, but don't approve every action.

A robot designed to resemble the human body in shape and movement capabilities.

An IT architecture combining on-premises infrastructure with public and private cloud services.

Computing architectures that combine quantum processors with classical computers to leverage the strengths of both.

An economic system where hydrogen serves as the primary energy carrier for power generation and transportation.

An approach combining multiple automation technologies (RPA, AI, ML, process mining) to automate as many processes as possible.

An open-source collaborative project hosting enterprise-grade blockchain frameworks and tools.

The process of optimising the external configuration settings of a machine learning model that are not learned during training.

Software that creates and manages virtual machines, allowing multiple operating systems to share a single hardware host.

A statistical method for making decisions about population parameters based on sample data evidence.

The property where an operation produces the same result regardless of how many times it is executed.

A framework for managing digital identities and controlling user access to resources and systems.

Security solutions focused on detecting and responding to identity-based attacks such as credential theft, privilege escalation, and compromised service accounts.

Applying transformations like rotation, flipping, and colour adjustment to training images to improve model robustness.

Automatically generating natural language descriptions of the content depicted in images.

The task of assigning a label or category to an entire image based on its visual content.

Creating new images from scratch using generative AI models like GANs, diffusion models, or VAEs.

The process of aligning two or more images of the same scene taken at different times, viewpoints, or by different sensors.

Partitioning an image into multiple segments or regions, assigning each pixel to a specific class or object.

The property of blockchain data that prevents alteration or deletion once recorded on the ledger.

An approach where infrastructure components are never modified after deployment but replaced entirely with updated versions.

The ability of large language models to learn new tasks from examples provided within the input prompt without parameter updates.

The processes and tools for detecting, responding to, resolving, and learning from service disruptions.

The formal process of documenting and communicating security incidents, breaches, or compliance violations.

A documented set of procedures for detecting, responding to, and recovering from cybersecurity incidents.

The application of IoT technology in industrial settings for monitoring, automation, and optimisation of operations.

A programmable mechanical device used in manufacturing for tasks like welding, painting, assembly, and material handling.

The fourth industrial revolution characterised by smart automation, IoT, cloud computing, and AI in manufacturing.

The next evolution of industrial production that emphasises human-machine collaboration, sustainability, and resilience, moving beyond the automation focus of Industry 4.0.

The component of an AI system that applies logical rules to a knowledge base to derive new information or make decisions.

The structural design of shared information environments, organising content for findability and usability.

The process of categorising data based on its sensitivity level and the impact of unauthorised disclosure.

The process of automatically extracting structured information from unstructured or semi-structured text sources.

The overarching strategy for managing an organisation's information assets, balancing the need for data availability with security, privacy, compliance, and lifecycle management.

The practice of protecting information by mitigating information risks including unauthorised access, use, and disruption.

Cloud computing model providing virtualised computing resources like servers, storage, and networking over the internet.

Managing and provisioning computing infrastructure through machine-readable configuration files rather than manual processes.

The systematic process of managing an organisation's innovation procedure from ideation to implementation.

Detecting and delineating each distinct object instance in an image at the pixel level.

The capability of language models to accurately interpret and execute natural language instructions, a core skill developed through instruction tuning and alignment training.

Training a language model to follow natural language instructions by fine-tuning on instruction-response pairs.

Testing the interaction between different software modules or components to verify they work together correctly.

The combination of RPA with AI capabilities like machine learning and NLP to automate complex cognitive tasks.

The measurement framework for evaluating the financial returns of AI and automation investments, considering cost savings, productivity gains, error reduction, and revenue impact.

An organisation that applies advanced technologies like AI and analytics to automate processes and enhance decision-making.

The combination of robotic process automation with artificial intelligence capabilities such as natural language processing and machine learning to automate complex business processes.

The classification of user utterances into predefined categories representing the user's goal or purpose, a fundamental component of conversational AI and chatbot systems.

A blockchain interaction model where users specify desired outcomes rather than exact execution steps, with solvers competing to fulfil the intent optimally.

An independent assurance function that evaluates the effectiveness of an organisation's internal controls and governance.

A centralised web interface that provides developers with self-service access to infrastructure, services, documentation, and templates within their organisation.

The network of physical devices embedded with sensors, software, and connectivity that exchange data over the internet.

The ability of different blockchain networks to communicate, share data, and transfer value between each other.

The practice of entrepreneurial behaviour within an established organisation to drive innovation.

A system that monitors network traffic or system activities for malicious activity or policy violations.

A network security technology that examines network traffic to detect and prevent vulnerability exploits.

Computing the joint parameters needed to place a robot's end effector at a desired position and orientation.

A device that connects IoT sensors and devices to cloud platforms, handling protocol translation and data filtering.

A middleware solution connecting IoT devices with applications, providing device management, data processing, and integration.

The practices and technologies for protecting IoT devices, networks, and data from unauthorised access and attacks.

A unique numerical label assigned to each device connected to a computer network for identification and routing.

InterPlanetary File System — a peer-to-peer distributed file system for storing and sharing data in a decentralised manner.

The most recent version of the Internet Protocol, providing a vastly larger address space than IPv4.

An international standard for information security management systems specifying requirements for establishing and maintaining security.

The international standard for AI management systems that specifies requirements for establishing, implementing, maintaining, and improving AI governance within organisations.

The process of updating legacy IT systems to modern technologies and architectures for improved performance.

A partitioning algorithm that divides data into k clusters by minimising the distance between points and their cluster centroids.

A simple algorithm that classifies data points based on the majority class of their k closest neighbours in feature space.

A visual workflow management method that limits work in progress and optimises the flow of tasks through a system.

A measurable value that demonstrates how effectively an organisation is achieving key business objectives.

An optimisation in autoregressive transformer inference that stores previously computed key and value tensors to avoid redundant computation during sequential token generation.

The study of motion without considering forces, used in robotics for calculating joint positions and movements.

The process of verifying the identity, suitability, and risks of customers in financial transactions.

A model compression technique where a smaller student model learns to mimic the behaviour of a larger teacher model.

A structured representation of real-world entities and the relationships between them, used by AI for reasoning and inference.

The field of AI dedicated to representing information about the world in a form that computer systems can use for reasoning.

An open-source container orchestration platform for automating deployment, scaling, and management of containerised applications.

Errors or inconsistencies in the annotations of training data that can degrade model performance and lead to unreliable predictions if not properly addressed.

A sales strategy where a vendor establishes an initial foothold with a small deployment and systematically grows the account through demonstrated value and expanded use cases.

A probabilistic model that assigns probabilities to sequences of words, enabling prediction of the next word in a sequence.

A neural network trained on massive text corpora that can generate, understand, and reason about natural language.

A regularised regression technique that adds an L1 penalty, enabling feature selection by driving some coefficients to zero.

A generative probabilistic model for discovering topics in a collection of documents.

The base blockchain protocol layer that handles consensus, data availability, and transaction settlement.

Scaling solutions built on top of a base blockchain that process transactions off-chain while inheriting security from the main chain.

Solutions built on top of a base blockchain that process transactions off the main chain to improve throughput and reduce fees while inheriting the security of the underlying network.

A normalisation technique that normalises across the features of each individual sample rather than across the batch.

A systematic approach to minimising waste within a system while maximising value delivery to customers.

A hyperparameter that controls how much model parameters are adjusted with respect to the loss gradient during training.

Outdated computing software or hardware that remains in use despite the availability of more modern alternatives.

Light Detection and Ranging — a remote sensing method using laser light to measure distances and map environments.

A blockchain node that only downloads block headers and relies on full nodes for complete transaction verification.

A high-visibility pilot initiative designed to demonstrate the value of a new technology or approach, building organisational confidence and momentum for broader adoption.

A fully automated manufacturing process that requires no human presence on the factory floor.

A statistical method modelling the relationship between a dependent variable and one or more independent variables using a linear equation.

A mechanism that allows users to stake cryptocurrency while receiving a liquid derivative token representing their staked position, maintaining capital flexibility during the lock-up period.

A collection of funds locked in a smart contract that provides liquidity for decentralised trading and lending.

A device or software that distributes network traffic across multiple servers to ensure no single server is overwhelmed.

Testing a system's behaviour under expected and peak load conditions to ensure adequate performance.

A situation where a customer is dependent on a vendor for products and services and cannot easily switch.

The practice of recording events, errors, and system activities for debugging, auditing, and analysis.

A classification algorithm that models the probability of a binary outcome using a logistic function.

A recurrent neural network architecture designed to learn long-term dependencies by using gating mechanisms to control information flow.

Techniques and architectures that enable language models to process and reason over extremely long input sequences, from tens of thousands to millions of tokens.

Low-Rank Adaptation — a parameter-efficient fine-tuning technique that adds trainable low-rank matrices to frozen pretrained weights.

Long Range Wide Area Network — a low-power protocol designed for wirelessly connecting IoT devices over long distances.

A mathematical function that measures the difference between predicted outputs and actual target values during model training.

Development platforms that require minimal hand-coding, using visual interfaces and pre-built components to build applications.

A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed.

The use of AI to automatically translate text or speech from one natural language to another.

Malicious software designed to disrupt, damage, or gain unauthorised access to computer systems.

A selective state space model that achieves transformer-level performance with linear-time complexity by incorporating input-dependent selection mechanisms into the recurrence.

An attack where the attacker secretly relays and potentially alters communication between two parties.

A cloud service where the provider handles infrastructure management, maintenance, updates, and monitoring.

An outsourcing arrangement where a service provider assumes responsibility for the ongoing management, operation, and optimisation of defined business or technology functions.

A data mining technique discovering associations between items frequently purchased together.

Dividing a market into distinct groups of consumers with similar needs, characteristics, or behaviours.

A mathematical framework for modelling sequential decision-making where outcomes are partly random and partly controlled.

The processes, governance, policies, and technologies for ensuring the uniformity, accuracy, and accountability of master data.

A technique that decomposes a matrix into constituent matrices, widely used in recommendation systems and dimensionality reduction.

The maximum profit that can be extracted from block production by reordering, inserting, or censoring transactions, a fundamental economic phenomenon in blockchain networks.

The average time between system failures, measuring reliability and availability.

The average time it takes to restore a system to normal operation after a failure or incident.

Application of computer vision and deep learning to analyse medical images for diagnosis, screening, and treatment planning.

A type of resource leak where a program fails to release memory that is no longer needed.

The consolidation of companies through various types of financial transactions including mergers, acquisitions, and takeovers.

A hash-based data structure that efficiently verifies the integrity and consistency of large sets of data.

A network topology where each node relays data for the network, providing self-healing and redundant paths.

A communication method where messages are stored in a queue until the receiving application can process them.

Learning to learn — algorithms that improve their learning process by leveraging experience from multiple learning episodes.

A persistent, shared virtual world where users interact through avatars using VR, AR, and other immersive technologies.

Quantitative measurements collected over time to track system performance, health, and business outcomes.

Small, contained product moments that revolve around a single task, enhancing the user experience.