The term \u201everification of neural networks\u201c is primarily found in the fields of artificial intelligence, automation, and cybersecurity. It describes methods and processes used to check whether artificial intelligences \u2013 in other words, neural networks \u2013 are actually doing what they are supposed to do. <\/p>\n
Neural networks are used, for example, in autonomous driving to recognise traffic signs or pedestrians. Verification checks whether the system operates safely and also reacts correctly in unusual situations. This prevents the AI from making mistakes that could lead to dangerous situations.<\/p>\n
A simple example: A neural network is to recognise cats in photos consistently. Verification checks whether the AI truly recognises all possible cats, even if they look different or the background changes. Furthermore, one checks that the system does not accidentally mistake dogs for cats.<\/p>\n
The verification of neural networks is particularly important in safety-critical applications to build trust. Companies and users want to be sure that the AI functions reliably and that unintended errors are excluded. Therefore, the verification of neural networks is becoming increasingly important as intelligent systems are used more widely in our everyday lives.<\/p>","protected":false},"excerpt":{"rendered":"