March 21, 2023
Peter Dueben from European Centre for Medium Range Weather Forecasts (ECMWF)
This talk will provide an overview on the state-of-the-art in machine learning in Earth system science. It will outline how conventional weather and climate models and machine learned models will co-exist in the future, and the challenges that need to be addressed when building the best machine learning forecast systems.t behavior in air combat.
March 14, 2023
Nazım Kemal Üre, ITU
Reinforcement learning (RL) has attracted significant interest in both academia and industry in recent years. The main premise of RL is the ability to control a system efficiently, without requiring any prior knowledge of the dynamics of the system. That being said, using RL as an out of the box approach only works for relatively simple problems with well-defined episodic structures, small number of actions and dense reward signals. On the other hand, many real-world problems possess extremely delayed reward signals, gigantic action spaces and non-episodic dynamics. In this talk, we will show that such high complexity decision making problems can be solved by wrapping RL algorithms with other powerful machine learning techniques, such as curriculum learning, hierarchical decompositions and imitation learning. We will demonstrate the potential of these methods across three different use cases; i) autonomous driving in urban environments, ii) playing real-time strategy games and iii) cloning fighter pilot behavior in air combat.
Feb 28, 2023
Iryna Gurevych, TU Darmstadt
Digital texts are cheap to produce, fast to update, easy to interlink, and there are a lot of them. The ability to aggregate and critically assess information from connected, evolving texts is at the core of most intellectual work – from education to business and policy-making. Yet, humans are not very good at handling large amounts of text. And while modern language models do a good job at finding documents, extracting information from them and generating natural-sounding language, the progress in helping humans read, connect, and make sense of interrelated texts has been very much limited.Funded by the European Research Council, the InterText project brings natural language processing (NLP) forward by developing a general framework for modelling and analysing fine-grained relationships between texts – intertextual relationships. This crucial milestone for AI would allow tracing the origin and evolution of texts and ideas and enable a new generation of AI applications for text work and critical reading. Using scientific peer review as a prototypical model of collaborative knowledge construction anchored in text, this talk will present the foundations of our intertextual approach to NLP, from data modelling and representation learning to task design, practical applications and intricacies of data collection. We will discuss the limitations of the state of the art, report on our latest findings and outline the open challenges on the path towards general-purpose AI for fine-grained cross-document analysis of texts.