The RWTH AI Center has launched a new series of events: the "Artificial Intelligence Colloquium", or in short: AIC. Renowned scientists from RWTH and other universities will present cutting-edge research on methods and applications of artificial intelligence.
On 15 December, Prof. Christian Bauckhage will be presenting on "Quantum Computing for AI – Hype or Hope?" (in English).
The event will be held in a hybrid mode. The talk will take place in SuperC, Ford Hall and will be livestreamed. After the talk, we’d like to invite you to join us for some drinks at the networking event in front of the lecture room. You will receive the link for the livestream after the registration.
Over the past decade there has been encouraging progress on building quantum computers so that hopes regarding the practical applications and disruptive potential of quantum computing are rising. It is particularly noticeable that the quantum computing community has jumped on the machine learning (ML) bandwagon and is promising quantum advantages for artificial intelligence (AI). Our goals with this presentation are threefold: First, we provide an ever so brief introduction to quantum computing and its expected benefits. Second, we point out the sobering fact that most promises as to quantum supremacy for ML and AI are and will likely remain severely exaggerated. Third, we emphasize that there is still hope for quantum AI and show how quantum algorithms can accelerate Bayesian network inference.
Christian Bauckhage is a professor for intelligent learning systems at the University of Bonn, lead scientist for machine learning at Fraunhofer IAIS, and one of the directors of the Lamarr Institute for ML and AI.
After obtaining his PhD in computer science from Bielefeld University, he worked as a PostDoc at the Center for Vision Research in Toronto and as a senior research scientist at Deutsche Telekom Laboratories in Berlin before being appointed in Bonn in 2008. He has (co)authored more than 200 papers on data mining, pattern recognition and machine learning several of which received awards. His current research focuses on hybrid systems which integrate data- and knowledge driven models and techniques and on quantum computing for optimization problems in AI.