Seminar in Artificial Intelligence, November 18-19 and November 21, 2019
Professor Dr. Ralf Otte, Technische Hochschule Ulm (THU), is giving a seminar in Artificial Intelligence, November 18-19 and November 21, 2019.
Monday 18 November
Room: Teknologibygget (TEKNO): Auditorium 1.023
11:15-11:50 Introduction to AI: What does the Internet say, what you need to know, trends, applications, historical overview about AI
11:50-12:30 Overview of AI: Machine learning in the European Industry from 1990 to today, logic systems for AI and machine learning
Tuesday 19 November
Room: Medisin og helsefagbygget: Auditorium 1 (MHU6.A1AUD1)
11:15-11:50 Latest research in Europe: proposal of a mathematical modelling of consciousness, Neuromorphic computer, how can we design AI-machines with consciousness
11:50-12:30 Latest research in Europe (continue)
13:10-14:00 Outlook for AI & Discussion: medium term (up to 2050) and long-term, up to 2200
Thursday 21 November
Room: Teorifagbygget Teo H5.502 (Department of psychology)
11:15-12:00 Artificial Intelligence and psychology: neural networks, consciousness
About the presenter:
Professor Dr. Ralf Otte is professor of industrial automation at Technische Hochschule Ulm (THU), where he teaches Control Engineering and Artificial Intelligence. From 2010-2015, he was managing director and scientific director at tecData AG, Switzerland. Since 2015, he has been professor at THU, Ulm, where he has been responsible for the development of the Artificial Intelligence department.
In 2019, he published two books:
- Otte, R., Artificial Intelligence for dummies, Wiley Publishing Group.
- Otte, R., Wippermann, B., Otte, V., From Data Mining to Big Data, Hanser Publishing Group.
His research interests include:
- Research into the basics of mathematical modelling of consciousness
- Development of the physical basics for the development of machines, which are able to develop rudimentary machine consciousness
- Development of new systems to significantly improve machine vision in autonomous driving, as deep learning methods will fail
- Further development of machine vision into real physical vision, on the basis of which biological organisms can perceive their environment