Calendar of Events
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IET: Guidelines for Managing Artificial Intelligence Projects
IET: Guidelines for Managing Artificial Intelligence Projects
Registration is via IET, here. Engineers can make important contributions to Artificial Intelligence (AI) and robotic projects. This presentation will briefly describe the three different types of AI, their limitations and how they can be applied, and especially the need for engineers to review and validate the outcomes provided by AI systems. Case studies of some recent Perth-based projects as well as examples of failures will be presented to provide additional information. Some of the challenges and options for regulating AI will also be discussed because many AI projects do not explain how outcomes are delivered. Recent developments in “Explainable AI” will be presented including creating test plans and verifying or certifying AI projects. Please see our Events Page for information regarding future events affiliated with the IEEE New Zealand Central Section.
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Neural Style Transfer
Neural Style Transfer
Prof. Paul Rosin will be giving a Research Seminar on “Neural Style Transfer” at 10:30 AM on Thursday 16th March in CO350 (VUW Kelburn Campus). No registration is necessary. Prof. Roslin will be talking about his research on style transfer based on deep neural networks. “I will start with an overview of some of my earlier non-photorealistic rendering (NPR) work, and then briefly cover my more recent work on neural style transfer. Specifically, the latter methods incorporate additional information: Depth-aware Neural Style Transfer, Neural Style Transfer with Semantic Masks and Neural Style Transfer with Local Models. If time permits I will also talk about some examples and approaches to evaluating computer vision algorithms.” A short bio: Paul L. Rosin is a Professor at the School of Computer Science & Informatics, Cardiff University. Previous posts include Brunel University, Joint Research Centre, Italy and Curtin University of Technology, Australia. His research interests include the representation, segmentation, and grouping of curves, knowledge-based vision systems, early image representations, low-level image processing, machine vision approaches to remote sensing, methods for evaluation of approximation algorithms, etc., medical and biological image analysis, mesh processing, non-photorealistic rendering, and the analysis of shape in art and architecture. Please see our Events Page for information regarding future events affiliated with the IEEE New Zealand Central Section.