Robotics & Artificial Intelligence
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.
Answer
The performance and accuracy of the AI model would be negatively impacted.
Reason — AI algorithms require a huge amount of relevant, good-quality data to learn patterns and make accurate predictions. The performance and accuracy of AI models are considerably impacted by the quantity, quality, and relevance of the data used. Therefore, if an AI model is trained with insufficient or poor-quality data, it will produce incorrect predictions and unreliable results.
Related Questions
Data visualisation techniques, such as bar graphs, scatter plots, and heat maps, are used to explore and understand large datasets. These visual representations help in identifying patterns and making decisions based on the data's characteristics.
Why is data visualisation important in AI projects? How does it help in understanding complex data patterns and relationships?
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.
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.
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.