Emerging Technologies and Current Trends in Computing
Core concepts
Be able to explain cloud computing service models (IaaS, PaaS, SaaS) and deployment models (public, private, hybrid, community), plus characteristics such as on-demand self-service and elasticity. Understand virtualization and containers. Cover artificial intelligence and machine learning basics: supervised, unsupervised, and reinforcement learning, training versus inference, and neural networks. Know big data's dimensions (volume, velocity, variety, veracity), data analytics, and distributed processing. Study the Internet of Things and edge computing, blockchain (distributed ledger, consensus, immutability), quantum computing (qubits, superposition), 5G, and AR/VR. Also consider green computing and digital transformation.
Traps
Questions often ask which cloud model gives the customer the most control - IaaS. Do not confuse machine learning (learns patterns from data) with rule-based programming. Blockchain is not automatically secure or anonymous; it is tamper-evident and pseudonymous. Quantum computing does not replace classical computing for everyday tasks. Distinguish IoT (sensors and connected devices) from edge computing (processing near the source).
Worked example
A firm wants to run its own applications without managing servers or the operating system. Compare IaaS (manages OS and above), PaaS (manages only the application), and SaaS (manages only usage). The firm needs only application control, so PaaS fits. Rule of thumb: the higher the service model, the less the customer manages - SaaS < PaaS < IaaS in customer responsibility.