Sepp Hochreiter, the inventor of LSTM (Long Short-Term Memory) networks – a foundational technology in AI. Sepp discusses his journey, the origins of LSTM, and why he believes his latest work, XLSTM, could be the next big thing in AI, particularly for applications like robotics and industrial simulation. He also shares his controversial perspective on Large Language Models (LLMs) and why reasoning is a critical missing piece in current AI systems.
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TRANSCRIPT AND BACKGROUND READING:
https://www.dropbox.com/scl/fi/n1vzm79t3uuss8xyinxzo/SEPPH.pdf?rlkey=fp7gwaopjk17uyvgjxekxrh5v&dl=0
Prof. Sepp Hochreiter
https://www.nx-ai.com/
https://x.com/hochreitersepp
https://scholar.google.at/citations?user=tvUH3WMAAAAJ&hl=en
TOC:
1. LLM Evolution and Reasoning Capabilities
[00:00:00] 1.1 LLM Capabilities and Limitations Debate
[00:03:16] 1.2 Program Generation and Reasoning in AI Systems
[00:06:30] 1.3 Human vs AI Reasoning Comparison
[00:09:59] 1.4 New Research Initiatives and Hybrid Approaches
2. LSTM Technical Architecture
[00:13:18] 2.1 LSTM Development History and Technical Background
[00:20:38] 2.2 LSTM vs RNN Architecture and Computational Complexity
[00:25:10] 2.3 xLSTM Architecture and Flash Attention Comparison
[00:30:51] 2.4 Evolution of Gating Mechanisms from Sigmoid to Exponential
3. Industrial Applications and Neuro-Symbolic AI
[00:40:35] 3.1 Industrial Applications and Fixed Memory Advantages
[00:42:31] 3.2 Neuro-Symbolic Integration and Pi AI Project
[00:46:00] 3.3 Integration of Symbolic and Neural AI Approaches
[00:51:29] 3.4 Evolution of AI Paradigms and System Thinking
[00:54:55] 3.5 AI Reasoning and Human Intelligence Comparison
[00:58:12] 3.6 NXAI Company and Industrial AI Applications
REFS:
[00:00:15] Seminal LSTM paper establishing Hochreiter's expertise (Hochreiter & Schmidhuber)
https://direct.mit.edu/neco/article-abstract/9/8/1735/6109/Long-Short-Term-Memory
[00:04:20] Kolmogorov complexity and program composition limitations (Kolmogorov)
https://link.springer.com/article/10.1007/BF02478259
[00:07:10] Limitations of LLM mathematical reasoning and symbolic integration (Various Authors)
https://www.arxiv.org/pdf/2502.03671
[00:09:05] AlphaGo’s Move 37 demonstrating creative AI (Google DeepMind)
https://deepmind.google/research/breakthroughs/alphago/
[00:10:15] New AI research lab in Zurich for fundamental LLM research (Benjamin Crouzier)
https://tufalabs.ai
[00:19:40] Introduction of xLSTM with exponential gating (Beck, Hochreiter, et al.)
https://arxiv.org/abs/2405.04517
[00:22:55] FlashAttention: fast & memory-efficient attention (Tri Dao et al.)
https://arxiv.org/abs/2205.14135
[00:31:00] Historical use of sigmoid/tanh activation in 1990s (James A. McCaffrey)
https://visualstudiomagazine.com/articles/2015/06/01/alternative-activation-functions.aspx
[00:36:10] Mamba 2 state space model architecture (Albert Gu et al.)
https://arxiv.org/abs/2312.00752
[00:46:00] Austria’s Pi AI project integrating symbolic & neural AI (Hochreiter et al.)
https://www.jku.at/en/institute-of-machine-learning/research/projects/
[00:48:10] Neuro-symbolic integration challenges in language models (Diego Calanzone et al.)
https://openreview.net/forum?id=7PGluppo4k
[00:49:30] JKU Linz’s historical and neuro-symbolic research (Sepp Hochreiter)
https://www.jku.at/en/news-events/news/detail/news/bilaterale-ki-projekt-unter-leitung-der-jku-erhaelt-fwf-cluster-of-excellence/
YT: https://www.youtube.com/watch?v=8u2pW2zZLCs
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