ICT Revision MCQ Series (AL Grade 13) . 13  New Trends and Future 

Emotional computing uses inputs like facial expressions, processes them to detect emotions, and outputs responses tailored to the user’s mood, enhancing interaction.

Emotional computing uses algorithms to detect emotions (e.g., via facial recognition), enabling tailored responses to improve interaction.

Biology-inspired computing mimics genetic evolution (1), machine-to-machine coexistence involves device communication (2), emotional computing focuses on emotions (3), and von-Neumann computing is sequential (4).

Software agents are autonomous (a), and multi-agent systems involve collaboration (b). Software agents do not require human input (c is false), and multi-agent systems are adaptive (d is false).

Statements (a), (b), (c), and (e) are correct. Emotional computing (d) focuses on emotions, not cognitive reasoning, making it incorrect.

Neural networks mimic the human brain’s structure, making them a key example of biology-inspired computing.

Emotional computing improves healthcare interactions, multi-agent systems optimize logistics, quantum computing enhances financial analysis, and machine-to-machine coexistence manages smart city resources.

Intelligent computing mimics reasoning (1), emotional computing responds to emotions (2), quantum computing uses qubits (3), and software agents perform autonomous tasks (4).

Software agents use autonomous algorithms to execute tasks independently, such as searching for information or managing schedules.

Emotional computing focuses on detecting and responding to emotions, not on performing mathematical calculations, which is a task for intelligent or quantum computing.

Multi-agent systems coordinate digital or virtual tasks like fleet management or robotics, but physical manufacturing requires hardware processes beyond their scope.

M2M coexistence enables devices to communicate and coordinate autonomously, such as in smart city systems.

Software agents are designed to perform tasks autonomously on behalf of users, without constant human input.

The von-Neumann bottleneck occurs due to sequential data transfer between memory and CPU, limiting processing speed.

Intelligent computing focuses on replicating human cognitive functions, such as reasoning and learning, using AI techniques like machine learning.

Intelligent computing personalizes education, machine-to-machine coexistence automates manufacturing, quantum computing speeds up research simulations, and software agents offer retail recommendations.

Statements (a), (c), and (d) are correct. Software agents are autonomous (b is false), and von-Neumann computing uses fixed, not adaptive, algorithms (e is false)

Quantum computing excels in parallel processing tasks like simulation and optimization but is inefficient for routine sequential tasks like data entry, which are better suited to classical systems.

Machine-to-machine coexistence uses sensor data (input) to analyze traffic (process) and adjust signals (output) for efficient traffic management.

von-Neumann computing uses a sequential architecture where instructions and data share the same memory, typical of most traditional computers.

Nature-inspired computing includes natural systems (a), and biology-inspired computing focuses on biological processes (b). Statements (c) and (d) are incorrect, as both use adaptive, not classical or fixed, algorithms.

Man-machine coexistence involves human-machine collaboration (a), and machine-to-machine coexistence involves autonomous device communication (b). Statements (c) and (d) are incorrect, as AI is often used in man-machine coexistence, and machine-to-machine systems do not require human intervention.

AI in intelligent computing replicates cognitive tasks like learning and reasoning, enabling systems to process and analyze data intelligently.

Man-machine coexistence involves humans and machines collaborating, such as doctors using AI to enhance diagnostic accuracy.

A qubit can exist in multiple states (0, 1, or both) due to superposition, enabling parallel processing in quantum computing.

Multi-agent systems use drone GPS data (input) to optimize routes (process), ensuring efficient delivery completion (output).

Quantum computing uses superposition and entanglement to process multiple states simultaneously, offering significant speed advantages for specific tasks.

Quantum computing is used for drug discovery (1), nature-inspired computing for neural networks (2), multi-agent systems for swarm robotics (3), and von-Neumann for sequential tasks (4).

Multi-agent systems involve multiple agents working together to solve complex tasks, such as coordinating logistics or robotics.

Statements (a), (b), (d), and (e) are correct. von-Neumann computing (c) uses sequential processing, not qubits, making it incorrect.