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Research#AI Ethics📝 BlogAnalyzed: Jan 3, 2026 01:45

Jurgen Schmidhuber on Humans Coexisting with AIs

Published:Jan 16, 2025 21:42
1 min read
ML Street Talk Pod

Analysis

This article summarizes an interview with Jürgen Schmidhuber, a prominent figure in the field of AI. Schmidhuber challenges common narratives about AI, particularly regarding the origins of deep learning, attributing it to work originating in Ukraine and Japan. He discusses his early contributions, including linear transformers and artificial curiosity, and presents his vision of AI colonizing space. He dismisses fears of human-AI conflict, suggesting that advanced AI will be more interested in cosmic expansion and other AI than in harming humans. The article offers a unique perspective on the potential coexistence of humans and AI, focusing on the motivations and interests of advanced AI.
Reference

“Schmidhuber dismisses fears of human-AI conflict, arguing that superintelligent AI scientists will be fascinated by their own origins and motivated to protect life rather than harm it, while being more interested in other superintelligent AI and in cosmic expansion than earthly matters.”

PermalinkML Street Talk Pod
Research#reinforcement learning📝 BlogAnalyzed: Dec 29, 2025 08:04

Upside-Down Reinforcement Learning with Jürgen Schmidhuber - #357

Published:Mar 16, 2020 07:24
1 min read
Practical AI

Analysis

This article from Practical AI introduces Jürgen Schmidhuber and discusses his recent research on Upside-Down Reinforcement Learning. It highlights Schmidhuber's significant contributions to the field, including the creation of the Long Short-Term Memory (LSTM) network. The interview likely delves into the specifics of this new reinforcement learning approach, potentially exploring its advantages, applications, and how it differs from traditional methods. The article serves as an introduction to Schmidhuber's work and a specific research area within AI.
Reference

“The article doesn't contain a direct quote, but it focuses on the topic of Upside-Down Reinforcement Learning.”

PermalinkPractical AI
Research#llm📝 BlogAnalyzed: Dec 29, 2025 17:50

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs

Published:Dec 23, 2018 17:03
1 min read
Lex Fridman Podcast

Analysis

This article summarizes a podcast featuring Juergen Schmidhuber, the co-creator of LSTMs. It highlights his significant contributions to AI, particularly the development of LSTMs, which are widely used in various applications like speech recognition and translation. The article also mentions his broader research interests, including a theory of creativity. The inclusion of links to the podcast and social media platforms suggests an effort to promote the content and encourage audience engagement. The article is concise and informative, providing a brief overview of Schmidhuber's work and the podcast's focus.
Reference

“Juergen Schmidhuber is the co-creator of long short-term memory networks (LSTMs) which are used in billions of devices today for speech recognition, translation, and much more.”

PermalinkLex Fridman Podcast
Research#llm📝 BlogAnalyzed: Dec 29, 2025 08:38

LSTMs, Plus a Deep Learning History Lesson with Jürgen Schmidhuber - TWiML Talk #44

Published:Aug 28, 2017 22:43
1 min read
Practical AI

Analysis

This article highlights an interview with Jürgen Schmidhuber, a prominent figure in the AI field, discussing his work on Long Short-Term Memory (LSTM) networks and providing a historical overview of deep learning. The interview took place at IDSIA, Schmidhuber's lab in Switzerland. The article emphasizes the importance of LSTMs in recent deep learning advancements and promises an insightful discussion, likening the experience to a journey through AI history. The article also mentions Schmidhuber's role at NNaisense, a company focused on large-scale neural network solutions.
Reference

“We talked a bunch about his work on neural networks, especially LSTM’s, or Long Short-Term Memory networks, which are a key innovation behind many of the advances we’ve seen in deep learning and its application over the past few years.”

PermalinkPractical AI