Digital and information technologies

Artificial Intelligence (AI)

Definition

Artificial intelligence (AI) is a systems technology aimed at realising behaviour by machines that resembles natural intelligence. Artificial intelligence comprises several learning strategies. In supervised machine learning, the model/algorithm is able to classify or predict based on a test data set and associated labels. In unsupervised learning, the algorithm makes this categorisation without using existing labels. In reinforcement learning, the algorithm learns about the best strategy based on interaction with the environment. Deep learning allows solving problems of higher complexity and abstraction. Increasingly, hybrid forms are being developed for AI in which humans and AI work together.

Keywords (selection)

Deep learning, Supervised Machine learning, Unsupervised Machine learning, Autonomous decision making, Autonomous systems, Context awareness, Machine-Reasoning, Neural networks, Neuroevolution , Reinforcement learning, Reasoning, Swarm Intelligence, Robotic Process Automation, Turingtest, Hybrid AI, Symbolic reasoning, Natural language processing, Large scale AI models, Speech recognition, Neuromorphic computing.

Data science, data analytics and data spaces

Definition

Data science, analytics and data spaces (data ecosystems) concern all aspects of collecting, managing, accessing, sharing and analysing data to create value. The data ecosystem includes centralised and distributed data bases as well as federative solutions for data sharing. For analysis and value creation, this data must be FAIR. Also, agreement systems must exist regarding use, access, and value of the data. Data can be structured or unstructured, static or dynamic, and data can be highly heterogeneous. The extracted value can take the form of predictions, automated decisions, models learned from data, or visualisations that provide insight into the data.

Keywords (selection)

Data spaces, Data bases, Data lakes, Federated architecture, FAIR data (Findability, Accessibility, Interoperability, and Reusability), Data sharing, Autonomous analytics, Context awareness, Data as a Service (DaaS), Data accuracy, Data confidentiality, Data mining, Data science, Distributed computing, Machine learning, Pattern mining, Visual analysis, Information retrieval, Process mining, Geospatial data analytics, Text analysis, Natural language processing, Data collection, Data integration, Data cleaning, Human-Data Interaction.

Cyber security technologies

Definition

Cyber security technologies to reduce relevant digital risks to an acceptable level. This includes dealing with risks of damage or failure of digital systems and the availability, integrity and confidentiality of data. Technologies are aimed at preventing cyber incidents and - when cyber incidents have occurred - detecting them, mitigating damage and making recovery easier.

Keywords (selection)

Confidentiality, Integrity, Availability, Socio-technical systems, (post quantum) Encryption, Privacy and data protection, Secure computing, Digital identity, Identity management, Vulnerabilities, Malware, DDOS, Ransomware, Secure networks, OT/IT security, Security by design, Privacy by design, Hardware security, Platform security, Software security, Data security, Cyber espionage.

Software technologies and computing

Definition

Software technologies and computing focus on developing methods and technologies for software so that software is usable, reliable and permanently maintainable. The trend here, on the one hand, is that technologies increasingly support distributed architectures. Important examples are blockchains in view of decentralised trust systems, as well as cloud, edge, grid, high-performance and mobile computing. On the other hand, new programming languages, development methods and testing environments are becoming increasingly dominant in order to continue to cope with more stringent quality requirements and increased speed of innovation.

Keywords (selection)

Ledger technologies, Immutable ID, File sharing, Crypto currencies, Metaverse, Soft-ware Engineering, Cloud model, Data as a Service, Storage as a Service (SaaS), Data centres, Virtualization, Virtual machines, Distributed computing, Distributed Cyber Physical Systems, Fog computing, General-purpose computing, Graphics processing units (GPGPU), High performance computing cluster (HPCC), Parallel computing, Mobile cloud, Identity management, Domain- Specific Languages, Quantum compu-ting, AI-based software testing, Low-Code platforms, Autonomous systems, Control distribution, Software Verification, Software Repository Analysis, Software Verification, Legacy Renovation, Model-Driven Engineering, Programming languages, Resource modeling, discovery, and management, Open source, Holistic system engineering, Responsible and sustainable computer ecosystems, Digital continuum: IoT to Edge to Cloud, Memory and storage technologies, Hardware and software co-design, System monitoring, testing, and benchmarking, Serverless and containerization.

Digital connectivity technologies

Definition

Digital connectivity technologies will provide new generation wireless and fixed networks that can handle the increased demand for capacity, are robust and flexible, and are energy and material efficient. Many of these networks will be programmable to best meet the wide variety of requirements from applications. These include very high bandwidths for networks in high-performance computing, very low-latency networks for autonomous driving and industrial applications, and very strong security for financial and government sectors.

Keywords (selectie)

5G, 6G, Network slicing, Network Virtualization, LaserSatCom, Fiber infra, Edge Infra, Intelligent/deep connectivity, Zeekabels, IoT, logical connectivity protocols, (data link, network, transport, session), Novel multiple access (SDMA, NOMA), Cross-layer optimization, Smart networks and services, Semantic communication, Tactile internet, Digital communication networks, In-network computing, Digital and programmable infrastructure, Zero-latency networking, Optical communication, Photonics, Quantum networks, Quantum communication.

Digital Twinning and Immersive technologies

Definition

Digital Twinning and Immersive technologies are a digital representation of physical processes and systems for the purpose of digital, autonomous production, analysis, and optimisation. Digital twins are used, inter alia, in engineering and fabrication technologies for modelling machines and processes, in Life Sciences and Health and medtech for a digital counterpart of an organism (such as a human). Digital twins are increasingly evolving into more interactive and dynamic systems (which can, for example, control and adjust processes). Digital twins build on a number of other digital technologies such as computing, connectivity and communication technologies, cloud and IoT networks, data science for sharing and analysing data, AI for prediction and immersive technologies for creating realistic experiences and optimal interaction with the artificial, simulated environment. Immersive technologies transform experiences to a more realistic level by virtually bringing together users' sight (image), sound and even touch.

Keywords (selection)

Industry 4.0, Smart Industry, Virtual devices, Virtual product, Virtual worlds, Virtual human, Digital technical intelligence, Real-time and embedded systems, Physical systems, Cyber-physical systems, Predictive modeling, Optimization, Simulation, Digital interaction, Digital Engineering, performance monitoring, performance optimization, predictive maintenance, Mixed, virtual and extended reality (AR/MR/VR/XR), Social XR, Social touch, Virtual worlds, Human-machine interaction, Tele-operation, Digital data spaces, Holographic/volumetric media, Rendering engine, Haptics, Cybernetics, Metaverse, Brain-computer interaction, Human augmentation, Sensing, AI, Data science, Software technologies and computing.

Neuromorphic technologies

Definition

Neuromorphic technologies focus on bio-inspired hardware for the energy-efficient processing of information. Neuromorphic can involve direct models of biological structures such as neurons and synapses, as well as digital and/or analogue implementations of artificial neural networks as used in machine learning and robotics. Hardware implementation of neuromorphic technologies can be realised by, inter alia, memristors, spintronic devices and complex nanomaterial networks.

Keywords (selection)

Neuromorphic Computing, Unconventional Computing, In-matter Computing, AI Hardware, Memristors, Cognitive Matter, Artificial Synapses, Artificial Neurons, Spiking Neural Networks.