Final answer:
Option 'd' is not an attribute of Machine Learning because ML models derive rules from the data, rather than starting with pre-defined rules. Machine Learning algorithms are meant to develop their understanding of the data to make decisions or predictions.
Step-by-step explanation:
Which Attribute is NOT Associated with Machine Learning?
The option that does NOT describe an attribute of Machine Learning is 'd. Takes data and rules as input and uses these inputs to develop an algorithm that will give us an answer.' Machine Learning typically involves the model deriving the rules from the data during the learning process, rather than taking pre-defined rules as input. Unlike traditional software that operates on clearly defined rules and algorithms set by programmers, Machine Learning algorithms identify patterns in data and develop their own rules to make predictions or decisions.
Machine Learning models are indeed created by taking data and outcomes (answers) as input (option 'a'), and they do find patterns in large data sets (option 'b'). They are also capable of continuous training to improve their performance over time (option 'c'). However, they do not start with a set of predefined rules as that would imply a more conventional algorithmic approach.
Scientists often use models, including Machine Learning models, for predictions. Models provide answers more quickly compared to analyzing real-world systems. However, the trade-off is that, sometimes, models might produce errors, especially if they are not provided with sufficient or relevant training data. Nevertheless, when designed and trained appropriately, models can deliver both swift and reliable predictions.