Final answer:
The biggest difference between symbolic systems and machine learning is the way they process information. Symbolic systems rely on predefined rules and symbols to represent knowledge and make decisions, while machine learning algorithms learn from data and make predictions or decisions based on patterns and statistical analysis.
Step-by-step explanation:
The biggest difference between symbolic systems and machine learning is the way they process information. Symbolic systems rely on predefined rules and symbols to represent knowledge and make decisions. On the other hand, machine learning algorithms learn from data and make predictions or decisions based on patterns and statistical analysis.
For example, in symbolic systems, a rule might state that if a person is younger than 18, they are considered a minor. This rule is explicitly defined and does not change. In machine learning, the algorithm would learn from a dataset of individuals' ages and their legal status, and then it can predict whether a new person is a minor or not.
Symbolic systems approach is more focused on representing explicit knowledge and reasoning, while machine learning is more data-driven and able to learn complex patterns. Both approaches have their own strengths and weaknesses, and their applications can vary depending on the problem at hand.
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