Invited Speakers

Keynote Speakers

    • Professor Wolfram Burgard, Albert-Ludwigs-Universität Freiburg – Profile

      Title: Probabilistic Techniques for Mobile Robot Navigation.

      Abstract: Probabilistic approaches have been discovered as one of the most powerful approaches to highly relevant problems in mobile robotics including perception and robot state estimation. Major challenges in the context of probabilistic algorithms for mobile robot navigation lie in the questions of how to deal with highly complex state estimation problems and how to control the robot so that it efficiently carries out its task. In this talk, I will present recently developed techniques for efficiently learning a map of an unknown environment with a mobile robot. I will also describe how this state estimation problem can be solved more effectively by actively controlling the robot. For all algorithms I will present experimental results that have been obtained with mobile robots in real-world environments.

       

    • Professor Kate Smith Miles, Monash University – Profile

      Title: Visualising the diversity of benchmark instances and generating new test instances to elicit insights into algorithm performance 

      Abstract: Objective assessment of algorithm performance is notoriously difficult, with conclusions often inadvertently biased towards the chosen test instances. Rather than reporting average performance of algorithms across a set of chosen instances, we discuss a new methodology to enable the strengths and weaknesses of different algorithms to be compared across a broader instance space. Results will be presented on various combinatorial and continuous optimization problems as well as machine learning algorithms to demonstrate: (i) how pockets of the instance space can be found where algorithm performance varies significantly from the average performance of an algorithm; (ii) how the properties of the instances can be used to predict algorithm performance on previously unseen instances with high accuracy; (iii) how the relative strengths and weaknesses of each algorithm can be visualized and measured objectively; and (iv) how new test instances can be generated to fill the instance space and provide desired insights into algorithmic power.

    • Professor Toby Walsh, University of New South Wales – Profile

      Title: Will AI end jobs, wars or humanity?

      Abstract: AI is definitely in the zeitgeist. The Chief Economist of the Bank of England just predicted AI will destroy 50% of jobs in the UK. Thousands of AI researchers signed an Open Letter predicting that AI could transform warfare and lead to an arms race of "killer robots". And Stephen Hawking and others have predicted that AI could end humanity itself. What should we make of all these predictions, and what should a careful AI researcher do?