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Jürgen Schmidhuber (born 17 January 1963) is a German computer scientist noted for his work in the field of artificial intelligence, specifically artificial neural networks. He is a scientific director of the Dalle Molle Institute for Artificial Intelligence Research in Switzerland . [2]
J. Schmidhuber. Learning to control fast-weight memories: An alternative to recurrent nets. Neural Computation, 4(1):131-139, 1992 The LSTM forget gates are related to this: F. Gers, N. Schraudolph, J. Schmidhuber. Learning precise timing with LSTM recurrent networks. JMLR 3:115-143, 2002.
Schmidhuber did a lot of ground breaking stuff, no doubt about it. But I think it's unfair for him to be critical of the community for not picking up on the utility of his old work when he isn't able to make those connections himself until after someone else publishes a related application.
Schmidhuber interview expressing his views on the future of AI and AGI. Original source. I think the interview is of interest to r/MachineLearning, and presents an alternate view, compared to other influential leaders in AI. Juergen Schmidhuber, Renowned 'Father Of Modern AI,' Says His Life’s Work Won't Lead To Dystopia. May 23, 2023.
Jan 24, 2024 · Jürgen Schmidhuber. In addition to LSTM, Jurgen Schmidhuber is proud to have introduced a PM Minimization model in 1992. Why is “proud” in quotation marks? Jurgen Schmidhuber and Ian Goodfellow, the promoter of GAN, had a fierce battle online and offline, which is still fresh in the mind of the industry.
Since age 15 or so, the main goal of professor Jürgen Schmidhuber has been to build a self-improving Artificial Intelligence (AI) smarter than himself, then retire. His lab's Deep Learning Neural Networks (NNs) based on ideas published in the "Annus Mirabilis" 1990-1991 have revolutionised machine learning and AI.
May 2, 2024 · Schmidhuber’s research focuses on developing new AI algorithms that can learn complex patterns in data. His work has led to several breakthroughs, including: 1. Recurrent Neural Networks (RNNs): Schmidhuber developed the first RNN architectures capable of learning long-term dependencies. 2.
