Recognising fakes

It is already known that people can train themselves to develop a feel for real and fake images. As part of the »Detect Fakes« project at Nothwestern University Kellogg School of Management, anyone can try this out and test how well they can distinguish between real and fake (https://detectfakes.kellogg.northwestern.edu/). The entries are collected anonymously and used for research purposes.

This small experiment makes it clear where the weak points of artificially generated texts lie: they do not have to be error-free. ChatGPT itself points this out after each response. In addition, the user is not informed about the sources on which ChatGPT bases its answers. This is because the application is not a search engine and does not currently access one. ChatGPT is trained to generate texts that sound as human as possible, based on millions of training texts that can come from anywhere.

Nevertheless, it is hard to imagine schools without ChatGPT. According to a survey conducted by the digital association BITKOM last spring, only 8 per cent of pupils have never heard or read about ChatGPT. This figure is likely to have fallen even further in the meantime. Generative artificial intelligence such as ChatGPT is impressive, but the areas of application for artificial intelligence are far more diverse. For example, robots can learn how to grasp an object. Artificial intelligence is used in image recognition and has applications in medical diagnostics, facial recognition and autonomous driving. They can optimise processes and recognise patterns and language. Many systems can already be found in our everyday lives and are used quite naturally, such as voice control systems or smart home components. Training data is always important for safe use. If it contains errors, this can lead to system malfunctions.