Roger Wattenhofer
ETH Zurich
Distributed Algorithms as a Gateway to Deductive Learning
Abstract
With the book Thinking Fast and Slow, Daniel Kahneman popularized the idea that the human brain can think in two different modes. The fast mode is instinctive and automatic, while the slow mode is deliberative and logical. As of 2023, one can argue that machine learning understands how to think fast. Deep neural networks are remarkably successful in rapidly classifying and regressing data. Thinking slow on the other hand is still a mystery. Large language models may provide an illusion of being able to think slow. However, prompts that need multiple deductive steps are generally beyond the capabilities of large language models. Distributed algorithms have the potential to help understanding deductive reasoning. Distributed algorithms usually consist of several little steps, iteratively applied, each step being easily learnable. As such distributed computing may provide an interesting bridge towards understanding deduction, extrapolation, reasoning, and everything else needed to think slow. In the talk, we will discuss some exciting case studies from graph generation to origami folding.
About the speaker
Roger Wattenhofer is a full professor at the Information Technology and Electrical Engineering Department, ETH Zurich, Switzerland. He received his doctorate in Computer Science from ETH Zurich. He also worked multiple years at Microsoft Research in Redmond, Washington, at Brown University in Providence, Rhode Island, and at Macquarie University in Sydney, Australia. Roger Wattenhofer's research interests include a variety of algorithmic and systems aspects in computer science and information technology, e.g., distributed systems, positioning systems, wireless networks, mobile systems, social networks, financial networks, deep neural networks. He publishes in different communities: distributed computing (e.g., PODC, SPAA, DISC), networking and systems (e.g., SIGCOMM, SenSys, IPSN, OSDI, MobiCom), algorithmic theory (e.g., STOC, FOCS, SODA, ICALP), and more recently also machine learning (e.g., ICML, NeurIPS, ICLR, ACL, AAAI). His work received multiple awards, e.g. the Prize for Innovation in Distributed Computing for his work in Distributed Approximation. He published the book "Blockchain Science: Distributed Ledger Technology", which has been translated to Chinese, Korean and Vietnamese.