The talk today was definitely one for the computer games students as the speaker was Karl Hilton, currently the MD at Crytek UK. The talk covered a brief history of Karl’s career before looking at the structure of the gaming industry. The presentation was interesting, well delivered and packed with stunning videos and images, as you would expect from a media industry professional.
I found Karl’s background interesting as he had initially studied Part 1 in Architecture, but due to his interest into the computer visualisations used in Architecture and the lack of job opportunities at the time decided to return to University and study Computer Visualisation and Animation. I have had several years’ experience working both for and with Architectural practises and am familiar with the visualisation techniques used, so was able to appreciate the similarities between the two industries. I feel it is quite fitting that one of Karl’s current ventures is to sell CryENGINE (a games development environment) to Architects as a visualisation tool. Karl pointed out that most Architects are far behind computer games when it comes to visualisations and my experience coincide with this.
After finishing his studies in Computer Visualisation and Animation Karl started his computer games career working for Rare (a company that had the biggest contribution to my childhood gaming) before reaching where he is today. Having been in the industry over fifteen years and reached such a high position, Karl was able to explain in great detail how the industry is structured, Crytek operates and where the UK stands in relation to the rest of the world. Karl also explained how that structure is changing, in particular how the pricing structure is changing from purchasing games to micro-transactions where the game itself is available for free and users purchase additional elements for use within the games: items, upgrades, etc.
Despite not studying games computing I still found the talk very interesting and relatable. The Architectural angle of the talk highlighted the fact that the tools used by the games industry are not only also suited to other industries, but are also better than many of those currently in use.
The guest speaker yesterday was Dr Shan He, a lecturer in Computational Biology at the University of Birmingham. The subject of the talk was swarm intelligence, Dr Shan He’s area of research for several years. The talk demonstrated Dr Shan He’s research into simulating the ways animals aggregate into swarms, starlings for example. The presentation made good use of videos that really added to the explanations, despite early technical problems.
One aim Dr Shan He’s research was to understand how animals maintain swarm formations. Swarms have been modelled before, but Dr Shan He took a different approach, modelling using artificial neural networks. Using such a method requires far fewer definitions and depends strongly on the system learning by trial and error. The prey have a defined field of view, travel speed and that they should always flee from predators. After running numerous cycles the system is able to develop the most successful rules for survival and the different patterns types observed in nature can be seen in the computer model. After studying the resulting rules of the neural networks, it was found that many of the networks did not demonstrate either metric or topological models, but instead only interacting with the nearest two neighbours. When modelled using only the two neighbour method the same swarm patterns will still formed, but with far lower computational overheard than either metric or topological methods.
I have touched upon artificial neural networks during my studies of Artificial Intelligence and find the subject very intriguing. As a result I found this presentation very interesting and would like to look further into artificial neural networks. Programming without programming, allowing the system to take many different approaches to identify the best fit seems a little supernatural, but very effective. If the solution to a problem is highly difficult to obtain, but you have a significant volume of data to train an artificial neural network, then this provides a very beneficial solution. Allowing a system to learn by its mistakes and carry forward the most successful results allowed for replication of evolution that has occurred in nature. Surely this could also be used to predict other developments in nature; possibly the spread of disease or future evolution?
A very interesting presentation on artificial intelligence that due to the reference to swarms in nature was relatively easy to relate to.
Today’s guest speaker was Isabella Panella, an unmanned systems technical consultant at the electronics defence firm Selex Galileo. The talk covered the company’s development of unmanned vehicles, primarily unmanned air vehicles (UAVs). The talk covered the issue of autonomous systems in great detail and was delivered enthusiastically although felt a little like a corporate presentation for the company.
The issue at the heart of the talk was developing a system that is able to adapt to a changing environment while at the same time being highly reliable. As such the talk included the topics of artificial intelligence and systems modelling. The talk effectively demonstrated the issues associated with design automated systems for UAVs including accurate sensing, quick decision making and physical limitations (size and weight). Due to the nature of unmanned vehicles the presentation also included many engineering aspects. I found myself able to relate to this as I have previously studied mechanical engineer and found it added to the overall understanding of the subject.
The presentation had relevance to my studies, in particular the ‘Artificial Intelligence’ and ‘Computer Vision and Robotics’ modules. Despite being focused on unmanned vehicles the topics covered can be applied to the wider topic of decision making systems. As such, the talk was informative, interesting and relevant to my studies.