Sepp Hochreiter, the inventor of Long Short-Term Memory (LSTM) networks and founder of NXAI, dives into the world of AI with insights from his pioneering work. He discusses the origins of LSTMs and their critical role in processing sequence data like speech and text. Sepp compares LSTMs to the newer transformer models, exploring their ongoing relevance, especially in real-time robotics. He shares his optimistic vision for AI's future, emphasizing efficiency and scalability as key to revolutionizing industries such as healthcare and autonomous vehicles.
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insights INSIGHT
Vanishing Gradient Problem
The vanishing gradient problem hindered the development of both recurrent and deep neural networks.
This problem prevented effective learning by making gradients too small during backpropagation.
insights INSIGHT
LSTM Solution
LSTMs solved the vanishing gradient problem with a memory cell.
This cell maintains constant credit assignment, enabling information storage over long sequences.
question_answer ANECDOTE
LSTM Adoption
LSTMs were widely adopted, powering technologies in cell phones and virtual assistants.
Companies like Apple, Google, Amazon, and Alibaba integrated LSTMs into their systems.
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In this special episode of the Eye on AI podcast, Sepp Hochreiter, the inventor of Long Short-Term Memory (LSTM) networks, joins Craig Smith to discuss the profound impact of LSTMs on artificial intelligence, from language models to real-time robotics. Sepp reflects on the early days of LSTM development, sharing insights into his collaboration with Jürgen Schmidhuber and the challenges they faced in gaining recognition for their groundbreaking work. He explains how LSTMs became the foundation for technologies used by giants like Amazon, Apple, and Google, and how they paved the way for modern advancements like transformers. Topics include: - The origin story of LSTMs and their unique architecture. - Why LSTMs were crucial for sequence data like speech and text. - The rise of transformers and how they compare to LSTMs. - Real-time robotics: using LSTMs to build energy-efficient, autonomous systems. The next big challenges for AI and robotics in the era of generative AI. Sepp also shares his optimistic vision for the future of AI, emphasizing the importance of efficient, scalable models and their potential to revolutionize industries from healthcare to autonomous vehicles. Don’t miss this deep dive into the history and future of AI, featuring one of its most influential pioneers. (00:00) Introduction: Meet Sepp Hochreiter (01:10) The Origins of LSTMs (02:26) Understanding the Vanishing Gradient Problem (05:12) Memory Cells and LSTM Architecture (06:35) Early Applications of LSTMs in Technology (09:38) How Transformers Differ from LSTMs (13:38) Exploring XLSTM for Industrial Applications (15:17) AI for Robotics and Real-Time Systems (18:55) Expanding LSTM Memory with Hopfield Networks (21:18) The Road to XLSTM Development (23:17) Industrial Use Cases of XLSTM (27:49) AI in Simulation: A New Frontier (32:26) The Future of LSTMs and Scalability (35:48) Inference Efficiency and Potential Applications (39:53) Continuous Learning and Adaptability in AI (42:59) Training Robots with XLSTM Technology (44:47) NXAI: Advancing AI in Industry
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