Why are algorithms important for machine learning?
Artificial intelligence (AI) and machine learning
Machine learning processes are used in very different areas:
Image recognition: Images can be recognized and categorized using machine vision algorithms. So a lot of data can be processed at lightning speed. Machine vision is used, among other things, in medical diagnostics or face recognition, but it can also be used to translate handwritten characters into print. Image recognition is also crucial for autonomous driving.
voice recognition: Recognizing and interpreting verbal language can also be learned using machine learning processes. These algorithms are used, for example, in voice assistance systems.
Semantic speech recognition: Written text can be interpreted semantically using machine learning. This allows context-related translation applications or chatbots that independently generate meaningful solutions.
Pattern recognition: Machine learning processes can also be used to recognize patterns in sequences of events that humans cannot recognize due to the large number of data points, variables and dependencies. For example, an AI can learn error patterns in vehicle electronics from data and compare these anomalies with the behavior in operation. Anomalies are recognized more quickly, which means that countermeasures can be taken early on. For example, by replacing a component before it actually causes a fault.
Process optimization: The recognized patterns can also be used as an information base for optimization processes. In this, machine learning methods are machine-generated process models that enable optimized process control.
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