南开大学机器人与信息自动化研究所 天津市智能机器人技术重点实验室

Institute of Robotics and Automatic Information System Tianjin Key Laboratory of Intelligent Robotics

2018年秋季先进机器人与人工智能系列学术讲座（第123期）

Seminar Series：Advanced Robotics & Artificial Intelligence

 

报告题目：Foundations of AI: Machine Spaces for Universal Induction

报告人：Armin B. Cremers, B-IT, University of Bonn, Germany

时间：11月18日（周日）上午10:00-11:00

地点：人工智能学院102会议室

 

Abstract: In the sixties Solomonoff introduced a universal induction scheme which eventually would learn every computable pattern in a data sequence. 
Unfortunately, this generality comes at a price: the universal induction scheme is not computable, and within the asynchronous learning framework used by Solomonoff and followers the trade-off between universality and effectivity is, in fact, unavoidable. But if one changes the asynchronous learning framework into a synchronous one, i.e., one within which the time scales of the learning system and the data generating process are coupled, this trade-off will vanish and effective universal induction becomes possible. Axioms and metrics for realistic reference machine spaces are derived and related to advances in deep learning applications. (From joint work with J. Zimmermann)

Biography: Armin B. Cremers received his doctoral degree in mathematics and his lectureship qualification in computer science from the University of Karlsruhe (now KIT). He has served on the Computer Science Faculties of the University of Southern California, the University of Dortmund, and, since 1990, the University of Bonn as Head of the Artificial Intelligence / Robotics/ Intelligent Vision Systems Research Groups. In 2002 he became Founding Director of the Bonn-Aachen International Center for Information Technology (B-IT), Emeritus since 2014.