Thoughts About Machine Learning
B9, Lecture Hall 1, R-2322
A weekly seminar series exploring advanced AI concepts that go beyond the scope of standard deep learning courses.
Overview
Overview
Thoughts About Machine Learning is a weekly in-person seminar series led by Jürgen Schmidhuber, Co Chair of the KAUST Center of Excellence for Generative AI. Going beyond the scope of standard deep learning curricula, this series draws on decades of foundational research to offer a deeper exploration of Artificial Intelligence. The series will kick off on Monday, January 26, 2025, and will run weekly in the spring semester. Participants will explore ideas that challenge conventional machine learning approaches and examine the emerging perspectives shaping the field's future.
Who should attend
The seminar is open to all members of the KAUST community. Basic knowledge of machine learning is recommended for participants to maximize the learning experience.
Schedule and Location
Date: Mondays during the Spring semester, starting Jan. 26, 2025
Time: 3:30–5:30 p.m.
Location: Building 9, Lecture Hall 1 (R-2322)
Schedule update
On Monday, February 9, 2026, the Thoughts About Machine Learning seminar will take the form of a special talk by Prof. Jürgen Schmidhuber at 2 p.m. in B19, Hall 1, as part of the Rising Stars in AI Symposium.
During Ramadan, the seminar will take place every Monday from 3 to 4 p.m.
Presenters
Brief Biography
Jürgen Schmidhuber is the co-chair of the Center of Excellence for Generative AI (GenAI) at KAUST and a professor in the Computer Science Program.
His pioneering work in deep learning neural networks has shaped modern AI. The New York Times captured this influence with the headline: "When A.I. Matures, It May Call Jürgen Schmidhuber' Dad.'"
Between 1990 and 1991, he laid foundations of Generative AI by introducing the principles of Generative Adversarial Networks, the basis for deepfakes; unnormalised linear Transformers, embodying principles behind the "T" in ChatGPT; self-supervised Pre-Training for deep learning, e.g., for the "P" in ChatGPT; and neural network distillation, the “teacher–student” method that underpins efficient training and deployment of many modern AI models, including systems such as DeepSeek.
His lab developed Long Short-Term Memory (LSTM), the most cited AI of the 20th century, and the Highway Net, a variant of which has become the most cited AI of the 21st century.
He has pioneered meta-learning, machines that learn to learn, since 1987, and neural AIs that set their own goals since 1990. His formal theory of creativity, curiosity & fun (2006-2010) mathematically explains art, science, music, and humor.
His contributions became embedded in everyday technologies, underpinning large-scale deployment of AI systems in smartphones, speech recognition, and machine translation during the rapid expansion of consumer AI.
Before joining KAUST, he served as the Director of the Swiss AI Lab, IDSIA, and was a professor of Artificial Intelligence at the University of Lugano (USI) from 2009 to 2021.
Schmidhuber earned his doctorate in Computer Science from the Technical University of Munich (TUM), Germany, in 1991. He has co-founded various Swiss AI companies and authored over 400 peer-reviewed papers. He is a frequent keynote speaker and adviser on AI strategies to multiple governments.
At KAUST, Schmidhuber leads various AI research projects, contributes to the development of AI-related educational programs, and engages with public and private sector organizations in Saudi Arabia and globally.