The xLSTM paper and a 2D Grasping solution

Hochreiter's xLSTM paper was eagerly awaited. Now it is here.
Quote: "We have enhanced LSTM to xLSTM by exponential gating with
memory mixing and a new memory structure. xLSTM models perform
favorably on language modeling when compared to state-of-the-art
methods like Transformers and State Space Models. The scaling laws
indicate that larger xLSTM models will be serious competitors to
current Large Language Models that are built with the Transformer
technology. xLSTM has the potential to considerably impact other
deep learning fields like Reinforcement Learning, Time Series
Prediction, or the modeling of physical systems." Industrial
applications in particular are eyeing xLSTM. The next step is the
commercial distribution of the architecture. We met Timo Gessmann,
CTO of Schunk, at the Hannover Messe. He uses a GenAI approach to
accelerate labeling. The Industrial AI Podcast reports weekly on
the latest developments in AI and machine learning for the
engineering, robotics, automotive, process and automation
industries. The podcast features industrial users, scientists,
vendors and startups in the field of Industrial AI and machine
learning. The podcast is hosted by Peter Seeberg, Industrial AI
consultant and Robert Weber, tech journalist. Their mission:
Demystify Industrial AI and machine learning, inspire industrial
users. The hosts: Peter Seeberg is an Industrial AI and machine
learning expert for the manufacturing industry. He worked over 25
years in IT (Intel) and 10 years in Automation. He co-initiated the
Industrial Data Intelligence Startup (Softing) where he was
responsible for managing machine learning projects in industrial
environments. Today he advises companies when it comes to
Industrial AI and machine learning. Together with Robert Weber,
journalist for industrial topics, he discusses AI and ML
applications, standards, and education topics, make or buy
decisions as well as regulation for AI in manufacturing.