The field focused on ensuring that artificial intelligence systems act in accordance with human intentions and values. As AI systems become more powerful, ensuring they remain beneficial to humanity becomes increasingly important.

A set of rules and protocols that allows different software applications to communicate with each other. APIs define the methods and data formats that applications can use to request and exchange information.

In 2025, API-first design has become the standard approach for building software systems, with composable architectures allowing businesses to rapidly adapt their tech stacks.

Technology that overlays digital information onto the real world, enhancing what users see, hear, and feel in their physical environment. AR applications use cameras, sensors, and sophisticated software to integrate virtual objects with the real world in real-time.

By 2025, AR has evolved beyond mobile screens to include widespread adoption of AR glasses, contact lenses, and spatial computing interfaces that seamlessly blend digital information with physical spaces.

Systems that can operate and make decisions without human intervention, often powered by AI. These include self-driving vehicles, delivery drones, industrial robots, and smart infrastructure components.

By 2025, autonomous systems have become highly sophisticated, leveraging multi-modal AI models, edge computing, and complex sensor arrays to navigate dynamic real-world environments with human-like adaptability.

A distributed database technology that maintains a continuously growing list of records (blocks) that are linked using cryptography. Each block contains a timestamp and transaction data that is resistant to modification.

By 2025, blockchain has matured beyond cryptocurrencies to power enterprise applications in supply chain, identity verification, voting systems, and interoperable data ecosystems with enhanced privacy features.

The measurement and analysis of unique physical or behavioral characteristics for authentication and identification purposes. Common biometric identifiers include fingerprints, facial patterns, voice recognition, and even gait analysis.

In 2025, biometric systems have become more sophisticated with multimodal approaches that combine several biometric factors along with liveness detection to prevent spoofing attacks.

A direct communication pathway between the brain and an external device. BCIs capture, analyze, and translate brain signals into commands that can control digital or physical systems.

By 2025, non-invasive BCIs have made significant advances, enabling new applications in accessibility, productivity enhancement, and immersive computing without requiring surgical implants.

An approach to building and running applications that takes full advantage of cloud computing delivery models. Cloud-native applications are designed as loosely coupled microservices, packaged in containers, and managed on elastic infrastructure through agile DevOps processes.

In 2025, cloud-native has evolved to support distributed cloud architectures that seamlessly span public clouds, private infrastructure, and edge computing environments with unified governance models.

A design pattern that integrates real-time analytics into business operations, processing current and historical data to prescribe immediate actions. It combines streaming data processing, advanced analytics, and automated decision-making.

By 2025, continuous intelligence has become an essential capability for responsive businesses, enabling them to detect patterns, anomalies, and opportunities in real-time across complex operational environments.

A distributed architectural approach to scalable, flexible, and reliable cybersecurity control. Rather than building a single security perimeter around all devices or nodes, cybersecurity mesh enables identity-based security perimeters around individuals or objects.

In 2025, cybersecurity mesh has become the standard security architecture for distributed organizations, supporting modern work-from-anywhere models and complex multi-cloud environments.

An architecture that facilitates flexible, reusable data integration across disparate data sources and consumers. Data fabric uses continuous analytics over existing, discoverable, and inferenced metadata to support the design, deployment, and use of integrated data across all environments.

By 2025, data fabric designs have become essential for organizations dealing with complex data ecosystems, enabling seamless access while enforcing governance and compliance requirements.

A virtual representation of a physical object, process, or system that serves as a real-time digital counterpart. Digital twins use IoT sensors, AI, and analytics to mirror the real world and simulate scenarios.

In 2025, digital twins have expanded beyond individual assets to entire systems and environments, enabling sophisticated simulations and predictive capabilities for urban planning, healthcare, and complex industrial operations.

An approach to software development that integrates security practices within the DevOps process. DevSecOps aims to build security into every stage of the development lifecycle, from design through integration, testing, deployment, and ongoing operations.

By 2025, DevSecOps has evolved to include automated security verification, compliance-as-code, and AI-driven vulnerability prediction that shifts security even further left in the development process.

A distributed computing paradigm that brings computation and data storage closer to the location where it is needed. Edge computing reduces latency, conserves bandwidth, and enables data processing in environments with limited connectivity.

In 2025, edge computing has expanded with specialized AI hardware accelerators and software platforms that enable sophisticated applications to run locally on devices ranging from industrial equipment to consumer electronics.

An umbrella term for all immersive technologies that extend reality by either blending the virtual and real worlds or creating a fully immersive experience. XR includes augmented reality (AR), virtual reality (VR), and mixed reality (MR).

By 2025, XR has become more accessible through lightweight, powerful devices with natural interfaces that integrate seamlessly into everyday activities for both consumer and enterprise applications.

A machine learning approach that trains algorithms across multiple decentralized devices or servers holding local data samples, without exchanging the data itself. This preserves privacy while still benefiting from diverse training data.

By 2025, federated learning has become standard practice for privacy-preserving AI development, allowing organizations to collaborate on model training without sharing sensitive data across regulatory or competitive boundaries.

Large-scale AI models trained on vast datasets that can be adapted to a wide range of downstream tasks with minimal fine-tuning. These models serve as a "foundation" for many different applications rather than being designed for a single specific purpose.

In 2025, foundation models have evolved to be more efficient, interpretable, and trustworthy, serving as the backbone for numerous specialized AI systems across industries.

Artificial intelligence systems capable of generating new content, including text, images, audio, code, 3D models, and synthetic data. These models learn the patterns and structures in their training data and can produce original outputs that mimic those patterns.

By 2025, generative AI has matured to become an integral part of creative workflows, product development, and business operations with substantially improved capabilities in multi-step reasoning, planning, and domain-specific expertise.

The practice of designing, manufacturing, using, and disposing of computers, servers, and associated subsystems efficiently and effectively with minimal impact on the environment. Green IT includes the pursuit of energy efficiency, responsible disposal of electronic waste, and sustainable resource use.

In 2025, Green IT has evolved from a nice-to-have to a critical business imperative, with carbon-aware computing, circular IT assets, and sustainable software design becoming standard practice across the industry.

A business-driven approach to identifying, vetting, and automating as many business and IT processes as possible. Hyperautomation involves the orchestrated use of multiple technologies, tools, and platforms, including AI, machine learning, robotic process automation (RPA), and intelligent business process management software.

By 2025, hyperautomation has matured to include sophisticated AI agents capable of autonomously discovering automation opportunities and managing complex workflows with minimal human oversight.

An approach to artificial intelligence that puts human needs, values, and well-being at the center of AI system design, development, and deployment. Human-centered AI aims to create systems that augment human capabilities rather than replace them, while respecting privacy, autonomy, and dignity.

In 2025, human-centered AI has become a cornerstone of responsible technology development, with standardized frameworks for evaluating AI systems based on their impacts on individuals and society.

A work environment that leverages extended reality (XR), spatial computing, and advanced collaboration tools to create engaging, interactive spaces for remote and hybrid teams. Immersive workplaces use digital technologies to transcend physical limitations and enhance communication and collaboration.

By 2025, immersive workplaces have evolved beyond novelty to become practical, everyday environments that support distributed teams with natural interactions and shared virtual spaces.

The collection and use of data to understand, analyze, and influence human behavior through a combination of technology, data analytics, and behavioral science. IoB extends the Internet of Things (IoT) by focusing on the insights derived from data about human actions and preferences.

In 2025, IoB has matured with strong ethical frameworks governing how behavioral data can be collected and applied, balancing innovation with privacy and autonomy considerations.

A knowledge representation that uses a graph-structured data model to store and connect information. Knowledge graphs represent entities as nodes and relationships between entities as edges, creating a network of interlinked data that captures contextual relationships and semantic meaning.

By 2025, knowledge graphs have become foundational infrastructure for AI systems, enabling more sophisticated reasoning, explainability, and contextual understanding across domains.

Advanced AI systems trained on vast amounts of text data that can understand, generate, and manipulate natural language. LLMs can perform a wide range of language tasks—from translation and summarization to creative writing and complex reasoning—based on the patterns they've learned from their training data.

In 2025, LLMs have evolved to become more efficient, reliable, and contextually aware, with enhanced capabilities for complex reasoning, multilingual understanding, and specialized domain knowledge.

Development platforms that allow users to create applications through graphical user interfaces and configuration instead of traditional computer programming. These platforms enable citizen developers with minimal technical expertise to build functional applications that meet business needs.

By 2025, low-code/no-code platforms have matured to support sophisticated enterprise applications, augmented by AI assistance that can translate natural language requirements into functional components.

A set of practices that combines Machine Learning, DevOps, and Data Engineering to deploy and maintain ML models in production reliably and efficiently. MLOps aims to standardize the ML lifecycle management process with automation, monitoring, and governance.

In 2025, MLOps has matured into comprehensive platforms that manage the entire ML lifecycle with sophisticated capabilities for automated retraining, model governance, and regulatory compliance.

A collective, persistent virtual shared space created by the convergence of physical and digital reality. The metaverse encompasses interconnected immersive experiences across virtual worlds, augmented reality, and the internet, allowing for persistent social presence and shared digital economies.

By 2025, the metaverse concept has evolved from early hype to practical implementations, with specialized industrial metaverses, educational environments, and entertainment spaces built on interoperable standards.

AI systems that can process and understand multiple types of information or "modalities," such as text, images, audio, video, and sensor data. Multimodal AI integrates information across these different formats to develop a more comprehensive understanding of content and context.

In 2025, multimodal AI has become standard for advanced applications, with systems capable of seamlessly understanding and generating content across visual, auditory, and textual domains with contextual awareness.

A computing architecture inspired by the structure and function of the human brain. Neuromorphic systems use artificial neural networks implemented in hardware to process information in ways that mimic biological neural networks, offering potential advantages in energy efficiency and processing certain types of data.

By 2025, neuromorphic computing has moved beyond research labs to commercial applications, particularly for edge devices that require sophisticated AI capabilities with minimal power consumption.

Technologies that interact directly with the brain or nervous system to monitor or influence neural activity. Neurotech includes tools for recording brain activity, stimulating neural tissues, and interfacing between neural systems and computers or other devices.

In 2025, neurotech has advanced significantly with non-invasive devices becoming more capable of precise neural monitoring and subtle influence, opening applications in healthcare, accessibility, and cognitive enhancement.

A type of computing that uses quantum-mechanical phenomena, such as superposition and entanglement, to perform operations on data. Quantum computers use quantum bits or "qubits" instead of classical bits, potentially allowing them to solve certain complex problems much faster than classical computers.

By 2025, quantum computing has progressed from primarily experimental systems to early practical applications in specific domains, with hybrid quantum-classical approaches becoming increasingly important for real-world problem-solving.

Cryptographic algorithms designed to be secure against attacks from both quantum and classical computers. Also known as post-quantum cryptography, these approaches aim to replace current cryptographic standards that could be vulnerable to quantum computing attacks.

In 2025, the transition to quantum-resistant cryptography has become a top priority for organizations, with standardized algorithms being widely implemented to protect sensitive data from future quantum threats.

An approach to developing and deploying artificial intelligence that emphasizes ethical considerations, transparency, fairness, privacy, and accountability. Responsible AI aims to ensure that AI systems benefit humanity, respect human autonomy, and avoid causing harm.

By 2025, responsible AI has evolved from high-level principles to concrete governance frameworks with standardized assessment methodologies and regulatory requirements across major jurisdictions.

Technology that allows organizations to automate routine, rule-based tasks by configuring software "robots" to mimic human interactions with digital systems. RPA can capture data, navigate applications, perform calculations, and complete transactions across multiple systems.

In 2025, RPA has evolved from simple task automation to intelligent automation that combines RPA with AI capabilities like natural language processing, computer vision, and machine learning, enabling automation of more complex and judgment-based processes.

Artificially generated information that mimics the statistical properties and patterns of real-world data without containing any actual original data points. Synthetic data is created using algorithms like GANs (Generative Adversarial Networks) and other AI approaches.

By 2025, synthetic data has become an essential resource for training AI models, enabling development in domains where real data is scarce, sensitive, or expensive to collect, while addressing privacy concerns and regulatory constraints.

A collective behavior of decentralized, self-organized systems, natural or artificial. Swarm intelligence is inspired by the collective behavior of social insect colonies and other animal societies, where simple interactions between agents lead to the emergence of intelligent behavior.

In 2025, swarm intelligence principles are being applied to coordinate fleets of autonomous robots, drones, and vehicles, enabling them to collaborate on complex tasks with resilience to individual failures.

A process that replaces sensitive data with unique identification symbols (tokens) that retain essential information about the data without compromising its security. Unlike encryption, tokenization doesn't use a mathematical process to transform the original data, making it more secure against brute force attacks.

By 2025, tokenization has expanded beyond payment security to become a fundamental approach for protecting various types of sensitive data while maintaining its utility for analytics and processing.

A machine learning technique where a model developed for one task is reused as the starting point for a model on a second task. Transfer learning leverages knowledge gained from solving one problem to improve performance on a related but different problem.

In 2025, transfer learning has evolved with sophisticated adaptation techniques that allow models to efficiently leverage knowledge across diverse domains, significantly reducing the amount of task-specific data and computational resources needed for new applications.

A security concept centered on the belief that organizations should not automatically trust anything inside or outside their perimeters and must verify everything trying to connect to their systems. Zero Trust assumes breach and verifies each request as though it originates from an open network.

By 2025, Zero Trust has evolved from a buzzword to a comprehensive architectural approach implemented across organizations of all sizes, with continuous verification, least privilege access, and micro-segmentation as core principles.