Reliable AI: Successes, Challenges, and Limitations
Offered By: Erwin Schrödinger International Institute for Mathematics and Physics (ESI) via YouTube
Course Description
Overview
Explore the mathematical perspective on the reliability challenges in artificial intelligence through this 52-minute lecture by Gitta Kutyniok at the Erwin Schrödinger International Institute for Mathematics and Physics. Gain insights into the current wave of AI and its unprecedented impact on industry, public life, and sciences. Understand the importance of reliability in AI, highlighted by political regularization efforts like the EU AI Act and G7 Hiroshima AI Process. Delve into recent advances in generalization guarantees and explainability, with applications in imaging problems and learning physical laws. Examine fundamental limitations in AI reliability and discover surprising connections to emerging computing paradigms such as neuromorphic and quantum computing.
Syllabus
Gitta Kutyniok - Reliable AI: Successes, Challenges, and Limitations
Taught by
Erwin Schrödinger International Institute for Mathematics and Physics (ESI)
Related Courses
Stanford Seminar - Neuromorphic Chips - Addressing the Nanostransistor ChallengeStanford University via YouTube All About AI Accelerators - GPU, TPU, Dataflow, Near-Memory, Optical, Neuromorphic & More
Yannic Kilcher via YouTube TinyML Talks - The New Neuromorphic Analog Signal Processor Concept and Technology Platform
tinyML via YouTube Novel Device and Materials in Emerging Memory for Neuromorphic Computing
tinyML via YouTube Make the Signal Chain More Intelligent and Efficient with Mixed Signal Processing and In-Memory Computing
tinyML via YouTube