The enormous energy requirements of artificial intelligence (AI) are increasingly becoming the focus of discussion. No wonder, as early as 2019, a US study warned that training a single neural network alone causes as much CO2 emissions as five (conventional) cars.

Late detected faults during EOL (End of Line) testing can be expensive. A car engine whose defect is only detected after it has been installed in the vehicle can easily cause costs of five to ten thousand euros for reassembly, repair, and delivery delays.

Overcrowded airports and increasing air traffic following the COVID-19 pandemic call for new innovative solutions in safety, efficiency, and sustainability, both on the ground and in the air.

The perspectives on artificial intelligence (AI) in the medical and pharmaceutical industries range from excitement to scepticism, particularly concerning this megatrend’s yet-to-be-fully understood implications. While some embrace the technological advances, others fear being supplanted by machines.