Talking about artificial intelligence (AI) may sound a bit mysterious at first. After all, the term encompasses technologies which ensure that machines can solve human-like tasks. At least, that’s one simplified definition. However, a closer look reveals that current AI systems don’t have any intellectual capabilities. Such so-called strong AI is still a thing of the future.
Weak AI, on the other hand, is increasingly taking over our everyday lives. These are primarily self-learning processes that are geared to precisely one task. The systems aren’t programmed like classic software but trained with a large amount of data. When in use, they’re constantly learning.
Chatbots take over communication with customers
Such systems perform amazing things in many different applications. Intelligent chatbots, for example, take over communication with customers for companies. When people complain or want to exchange a product, they don’t speak to a flesh-and-blood person but to a pre-trained computer program.
In manufacturing companies, AI systems analyze data gathered from manufacturing machines to detect problems at an early stage. As an example, BMW collects sensor data from 600 welding guns used by robots in the body shop at its Munich plant. Software continuously evaluates the data with the help of AI and reports when failure is imminent.
Machine Learning in Quality Control
Quality control also relies on machine learning. At Fiat Chrysler China, an AI system uses cameras to identify defective assemblies or missing components, such as screws so small that they’re difficult for the human eye to detect. The parts in question are then weeded out. AI software works similarly in medicine, for example by being fed images of skin cancer variants. If the number of training images is large enough, the system can then make appropriate diagnoses on its own. In individual studies and under certain conditions, the AI was able to deliver better results than a human doctor.
Another area of application for artificial intelligence is automated driving. The more tasks the machine is supposed to take over from humans, the higher the degree of AI application. In this case, however, the application’s limits also become apparent. In automated driving, the demands on AI systems are significantly higher than in quality assurance of a manufacturing company. A camera aimed at a product for inspection generally operates under non-varying conditions. By contrast, city center traffic is very complex. Vehicles can run into unpredictable situations.