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.