Machine learning methods are used in a wide range of applications:
Image detection: Machine vision algorithms can be used to detect and categorize images, allowing enormous amounts of data to be processed very rapidly. Machine vision is found in areas such as medical diagnostics and face recognition, but is also used for translating handwritten characters in printed publications. Image detection is also critical for autonomous driving applications.
Voice recognition: Recognizing and interpreting verbal language can also be learned with machine learning methods. These algorithms are used in applications such as voice assistant systems.
Semantic voice recognition: Written text can be semantically interpreted through machine learning, allowing context-based translation applications or chatbots that generate reasonable solutions without human assistance.
Pattern recognition: Machine learning can also be used to recognize a pattern of events that would be impossible for humans to identify because of the large number of data points, variables and dependencies that must be analyzed. AI can use data to recognize an error pattern in a vehicle electronics system and compare this anomaly with the operational behavior. This results in faster recognition of anomalies and thus creates an opportunity to counteract the problem earlier, such as swapping out a component before it causes a malfunction.
Process optimization: The recognized patterns can also be used as an information basis for optimization processes. These contain process models created by machine learning methods that enable optimized process controls.