The Laureate’s Lecture 2025

The Exner Lectures 2025 will begin with the Laureate’s Lecture, delivered by Sepp Hochreiter, this year’s recipient of the Wilhelm Exner Medal. In his talk, Hochreiter will examine the current trajectory of artificial intelligence, offering a thoughtful perspective on its development and the shifts shaping its next chapter. 

Hear directly from a pioneer in the field as he explores the vast potential of AI and its profound implications for society. Get curious, get informed, and get inspired! 

Those interested in exploring the next steps in AI’s development: Register now!

Industrial Artificial Intelligence 

Abstract:

Technological revolutions often come in three distinct phases: basic research, scale-up, and industrial application—each characterized by differing degrees of methodological diversity: high in research, low during scaling, and moderate in industrial deployment. Historic breakthroughs such as the steam engine and the Haber-Bosch process exemplify this pattern and their transformative societal impact. A similar trajectory is now evident in the development of modern artificial intelligence (AI).  

In the scale-up phase, large language models (LLMs) emerged as the most visible and widely deployed form of AI. While LLMs represent powerful methods for knowledge representation, they have not fundamentally redefined the core of AI itself. This phase was dominated by the transformer architecture. Recently, however, alternative architectures—such as state-space models and recurrent neural networks—have also been successfully scaled. A notable example is the Long Short-Term Memory (LSTM) network, which has been significantly enhanced to xLSTM. In many language tasks, xLSTM now surpasses transformers in performance. The xLSTM-based model TiRex has set new standards in time series forecasting, outperforming U.S. industry leaders like Amazon, Salesforce, and Google, as well as Chinese competitors such as Alibaba.

We are now entering the third phase: industrial AI, where the focus shifts to adapting and deploying AI systems in real-world, high-impact applications. Time series analysis plays a central role in this phase, enabling intelligent solutions across key domains: predictive maintenance (equipment failure detection), demand forecasting, route and logistics optimization, smart grid energy forecasting, traffic prediction, predictive diagnostics in healthcare, dynamic pricing in retail, algorithmic trading in finance, and automated quality control in manufacturing.