It’s a great pleasure to welcome Prof. Sepp Hochreiter as a speaker at our lab!
Title: The Next Phase of Artificial Intelligence
Date: Monday, August 17th, 2026, 12:30 PM CEST
Location: Online via Zoom — https://fau.zoom-x.de/j/68935755804?pwd=fw92lRAAA7PG1jL8hNCTqotiBE6ixB.1
Abstract: Artificial Intelligence is entering its third phase. The first phase was driven by basic research; the second by scaling where ever-larger foundation models were trained on massive datasets with massive compute. The third phase is now beginning: the industrialization of AI, bringing intelligence into machines and robots that operate in the physical world. Industrial AI demands architectures that are computationally efficient, have a small memory footprint, maintain a persistent state, and support state tracking together with advanced memory mechanisms.
xLSTM is an architecture designed for this transition. With its novel memory mechanisms and efficient recurrent computation, xLSTM combines long-range state tracking with linear-time processing and constant memory at inference. TiRex, an xLSTM-based time-series foundation model, demonstrates how this approach delivers efficient predictive intelligence for complex, evolving systems.
For robotics, however, prediction alone is not enough. An intelligent robot must maintain a working memory of its current situation, task, and environment, while drawing on an episodic memory of past experiences. Together, these capabilities allow robots to recognize familiar situations, learn from interactions, retrieve relevant experiences, and continuously adapt their behavior. This lays the foundation of truly autonomous industrial intelligence.
Bio: Sepp Hochreiter is Head of the Institute for Machine Learning, the LIT AI Lab, and Director of the ELLIS Unit at Johannes Kepler University Linz. He is also the founder of NXAI, a company driving innovation in deep learning with technologies like xLSTM and the time series foundation model TiRex.
Widely recognized as a pioneer of deep learning, Hochreiter is best known for his foundational contributions, including the development of Long Short-Term Memory (LSTM) networks and the identification of the vanishing gradient problem. LSTMs laid the groundwork for the first large language models and were instrumental in the evolution of systems like ChatGPT.