Artificial Intelligence (AI) uses algorithms to analyse huge streams of data to uncover trends and patterns for improving manufacturing operation, product quality and/or reliability. Which of the following is/are NOT true?

Question

BlackTom AI · Accepted Answer

Examining the statements about AI in manufacturing, each option presents a claim that must be weighed for truthfulness.
Option A: 'In decision tree modelling-based AI algorithm, the condition for stopping partitioning a node is when all data for the node belong to the same class, there are no attributes left or there are no data left.' This reflects common stopping criteria in decision tree learning: pure nodes (single class), exhaustion of attributes, or lack of data to split further. In practice, pruning and other stopping rules may also apply, but the stated criteria are widely recognized as valid termination conditions. Therefore, this option is generally true.
Option B: 'Artificial neural network-based AI algorithm provides inherent parallelism, can explain analytics results, survives the failure of some nodes and able to handle noisy data.' Neural networks do exhibit inherent parallelism because many computations can occur simultaneously across neurons and layers. However, 'can explain analytics results' is more contentious: neural networks are often treated as black boxes, and providing clear explanations is not inherently guaranteed without additional interpretability methods. The phrase 'survives the failure of some nodes' depends on the architecture and fault tolerance; some degradation may occur if critical units fail. 'Able to handle noisy data' is true to a degree, as networks can learn from noisy inputs with proper training and regularization. Overall, this statement contains a mix of true and caveated points, so it is not entirely false but requires nuance. 
Option C: 'More data points acquired will always lead to better analysis results for decision making.' The blanket claim that more data always improves results is overly simplistic. While data augmentation can help, there are diminishing returns, potential for overfitting if data quality is poor or if labels are noisy, and computational or model capacity limits that prevent gains. Thus, this assertion is not true in all circumstances.
Option D: 'All of the listed.' Since Option C is not universally true, Option D cannot be correct as stated. If any listed claim is not true or is only partially true, 'All of the listed' is incorrect.
In summary, Option A is true, Option B is nuanced with partial truth and caveats, Option C is not universally true, and Option D asserting all are true is incorrect. The core misconception targeted by the question is that increasing data points always yields better decision-making outcomes, which is an oversimplification and often false in practice.

Artificial Intelligence (AI) uses algorithms to analyse huge streams of data to uncover trends and patterns for improving manufacturing operation, product quality and/or reliability. Which of the following is/are NOT true?

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