Palfinger

EVERY STEP OPENS UP NEW OPPORTUNITIES

Hubert Wallner, Head of Product Line “Digital Solutions” at PALFINGER, talks about the use of artificial intelligence in the company, current challenges, and perspectives for the future.

FROM CODE TO TRANSFORMATION

Walter Palmetshofer from the Center for Artificial Intelligence & Machine Learning at the Vienna University of Technology discusses the economic, social, and ecological dimensions of artificial intelligence. 

Recorded by an AI-generated voice. 


Stefan Neumann 
CAIML, VIENNA UNIVERSITY OF TECHNOLOGY
  

Developments in AI are progressing rapidly: ChatGPT is only two years old and is already an integral part of the everyday (professional) life for many people. There are big differences between areas of application when it comes to AI. While image recognition and simple text creation work well, physical systems such as robots and autonomous vehicles still face major challenges. That is why I think it unlikely that the leading AI companies will expand rapidly into all sectors of the economy. 


Stefan Woltran 
CAIML, VIENNA UNIVERSITY OF TECHNOLOGY
 

Artificial intelligence is more than ChatGPT. Its various methods offer tremendous opportunities — personalized services, better and collaborative decision-making, and advances in medicine and science. On the other hand, AI enables new ways of intruding into our privacy, it can be used to monitor workers, and its misuse can threaten our fundamental rights. That is why it is important — in the spirit of digital humanism — to educate society about the technical possibilities of AI, as well as its opportunities  and risks. Technological progress is not fate, but can be shaped by us. 


Nysret Musliu 
CAIML, VIENNA UNIVERSITY OF TECHNOLOGY 

Powerful AI systems based on deep learning and large language models are today characterized by high energy consumption. Although it is unlikely that this energy consumption can be  reduced in the near future, researchers are working intensively to reduce it without affecting the performance of AI systems. Promising strategies include neurosymbolic AI methods, innovative learning algorithms, and specialized AI chips. 

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