Humanoide Roboter: vom Maschinenwesen über Dialogpartner zum Markenbotschafter

Humanoiden Robotern (HRO) wird für die Zukunft eine gewichtige Rolle in unterschiedlichen Lebensbereichen zugeschrieben. In Wissenschaft und Praxis herrscht weitgehende Übereinstimmung, dass sich ihre Einsatzpotenziale mit dem technischen Fortschritt weiter ausdehnen werden. In diesem Zusammenhang stellen HRO ein innovatives Instrument für die Markenführung dar. Insgesamt ist zu konstatieren, dass bis dato kein Roboter in Gänze die kognitiven, sensorischen und motorischen Fähigkeiten besitzt, um vollumfänglich auf Umwelteinflüsse abgestimmt zu reagieren. Dennoch ergeben sich bereits heute interessante Anwendungen mit unmittelbarer Relevanz für die Markenbeeinflussung. Diese reichen von der Generierung von Kundenwissen über die Präsentation von Markeninhalten bis zur empathischen Dialogführung.Humanoid robots are expected to play an important role in different areas of life. There is agreement in science and practice that their potential applications will expand widely with the ongoing technological progress. In this context, humanoid robots represent an innovative tool for brand management. Currently, no robot possesses all cognitive, sensory and motoric skills that are required to fully congruently respond to outside stimuli. Nevertheless, there are already interesting applications with direct relevance for brand perception. These range from the generation of customer knowledge to the presentation of brand content to empathetic dialogues

A Light in the Dark: Deep Learning Practices for Industrial Computer Vision

In recent years, large pre-trained deep neural networks (DNNs) have revolutionized the field of computer vision (CV). Although these DNNs have been shown to be very well suited for general image recognition tasks, application in industry is often precluded for three reasons: 1) large pre-trained DNNs are built on hundreds of millions of parameters, making deployment on many devices impossible, 2) the underlying dataset for pre-training consists of general objects, while industrial cases often consist of very specific objects, such as structures on solar wafers, 3) potentially biased pre-trained DNNs raise legal issues for companies. As a remedy, we study neural networks for CV that we train from scratch. For this purpose, we use a real-world case from a solar wafer manufacturer. We find that our neural networks achieve similar performances as pre-trained DNNs, even though they consist of far fewer parameters and do not rely on third-party datasets