Robotics & Artificial Intelligence
Artificial Intelligence (AI) has played a crucial role in expanding the capabilities of computing systems. AI applications like speech recognition, autonomous driving, and natural language understanding rely heavily on algorithms, big data, and computational power.
Mention two specific AI applications and their impact on real-world systems.
Answer
Two specific AI applications and their impact on real-world systems are:
- Speech Recognition – Speech recognition is an AI application that enables machines to understand and process human speech. It allows computing systems to interact with users through voice commands. Its impact can be seen in virtual assistants, voice-controlled devices, and accessibility tools, making human–computer interaction more natural and efficient.
- Autonomous Driving – Autonomous driving uses AI algorithms, big data, and high computational power to enable vehicles to drive without human intervention. This application helps vehicles analyse surroundings, make decisions, and navigate safely. Its real-world impact includes improved road safety, reduced human error, and increased efficiency in transportation systems.
Related Questions
How is data different from information in the context of decision-making?
- Data and information are the same in decision-making.
- Data becomes information only when it is processed and holds meaning or context.
- Data is always more useful than information.
- Information refers only to numerical data while data includes all forms of content.
What would happen if AI models were trained with insufficient or poor-quality data?
- The AI model would likely provide highly accurate predictions.
- The AI model would not work at all.
- The performance and accuracy of the AI model would be negatively impacted.
- The AI model would become faster but less accurate.
In deterministic computing, the system's output is determined solely by the input and a predefined set of rules or algorithms. This rigid system is highly structured but lacks adaptability to new data or real world complexities.
Mention any two limitations of deterministic computing.
Probabilistic computing is used to manage uncertainty in complex systems. In which scenario would probabilistic computing be more beneficial than deterministic computing?
- When precise and predictable results are required for simple tasks
- When the system needs to adapt to uncertain or changing conditions
- When the program requires the execution of a fixed set of instructions
- When the input data is always complete and accurate