Rule -based artificial intelligence VS machine learning
The machine learning system learns and adapts to the new situation from the past, while the rules -based system depends on human intervention for any modification.
What is rules -based artificial intelligence?
Rules -based artificial intelligence is an AI system based on a set of predetermined rules. These rules are created by humans and define the actions that the system will take under different circumstances.
For example, if X occurs, Y should be executed. Rule -based artificial intelligence is essentially certain, which means that it uses causality.
Rules -based artificial intelligence models require basic data and information to run successfully, and they are limited to tasks and functions of performing programming. They are a more advanced form of robotic process automation, which can be used for tasks such as data input, document classification, and fraud detection.
What is machine learning?
Rule -based artificial intelligence VS machine learning
Machine learning is a branch of artificial intelligence, focusing on using data and algorithms to imitate human learning methods. After training, machine learning algorithms can be predicted and classified according to past data, and the accuracy is gradually improved over time.
Machine learning models are divided into three main categories: supervision learning, unsupervised learning, and semi -supervised learning. Supervision learning includes the use of marking data training models to predict. Unsupervised learning includes looking for models in unblocked data, and semi -supervised learning is a combination of the two.
Machine learning algorithms usually create frameworks developed by accelerated solutions, such as TensorFlow and Pytorch. Machine learning has extensive use cases, including natural language processing, image recognition and fraud testing.
What are the main differences between rules -based artificial intelligence and machine learning?
The main difference between rules -based artificial intelligence and machine learning is that the rules -based system depends on human encoding rules to make decisions, while machine learning systems learn from past data and adapt to the new situation by themselves. Rules -based artificial intelligence models are certain, limited to tasks of performing programming, and machine learning models can be used for extensive tasks and functions.
Rule -based artificial intelligence VS machine learning
When to use rules -based models?
Model -based models are most suitable for the definition of clear problems, input data structure, clear and easy to understand. For problems that can be decomposed as a series of logical steps, they are very effective. In these steps, the results can be predicted according to a set of IF-THEN rules. Rules -based system examples include expert systems in medical and legal fields, fraud detection systems in the financial field, and chat robots in the customer service field.
In these cases, the rules are usually fixed and will not be changed frequently. The data operation data is relatively simple and structured. However, rules -based models may not apply to more complicated issues that are not structured or constantly changing, because they may not be able to handle the necessary flexibility and adaptability.
When do you use machine learning?
Machine learning is very suitable for the problem that the problem is complex and the input data is non -structured, noise or variable. It is also very suitable for the rules or patterns used to manage data, but it can be found through analysis. Machine learning models can process a large amount of data and can identify complex modes and relationships. These modes and relationships may not appear immediately for human analysts.
They can be used for extensive applications, including image and voice recognition, natural language processing, recommendation system and prediction analysis. When the problem is dynamic and rules or patterns changes over time, machine learning models are particularly useful. However, machine learning models require a large number of high -quality training data, and may require a large number of computing resources to train and infer, which may become obstacles to use in some cases.
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in conclusion
Rule -based artificial intelligence VS machine learning
Although the rules -based artificial intelligence and machine learning have their advantages and disadvantages, the choice between the two depends on specific use cases. Rule -based artificial intelligence is most suitable for tasks that are certain and do not need to adapt to new situations, and machine learning is most suitable for tasks that need to adapt and learn from past data. With the continuous development of artificial intelligence, rules -based systems and machine learning will play an important role in shaping their future.
