Robotics & Artificial Intelligence
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.
Answer
Two limitations of deterministic computing are:
- Rigidness – Deterministic computing is rigid since it is based on pre-programmed instructions and predefined algorithms. It is incapable of adjusting to new or changing data and learning from it.
- Lack of reasoning capability – Deterministic computing struggles with situations that need reasoning, judgement, and pattern identification.
Related Questions
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.
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.
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
In telecommunications, AI may not be needed. Why are predefined rules and protocols sufficient for efficient data transmission and call routing?
- They ensure randomness and flexibility in data handling
- They provide a fixed, predictable framework that avoids uncertainty
- They adapt to every user's preferences and historical data
- They allow for probabilistic decision-making in uncertain environments