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AISB Quarterly an Anarchy of Methods: Current Trends in How Intelligence Is Abstracted in AI by John Lehman (U AISB QUARTERLY the newsletter of the society for the study of artificial intelligence and simulation of behaviour No. 141 July, 2015 Do you feel artistic? Exhibit your artwork on our front covers! Email us at [email protected]! Artwork by Alwyn Husselmann, PhD (Massey Univ., New Zealand) Visualisation is an important tool for gaining insight into how algorithms behave. There have been many techniques developed, including some to visualise 3D voxel sets [1], program space in genetic programming [2] and vector fields [3], amongst a large number of methods in other domains. A qualitative understanding of an algorithm is useful not only in diagnosing implementations, but also in improving performance. In parametric optimisation, algorithms such as the Firefly Algorithm [4] are quite sim- ple to visualise provided they are being used on a problem with less than four dimensions. Search algorithms in this class are known as metaheuristics, as they have an ability to optimise unknown functions of an arbitrary number of variables without gradient infor- mation. Observations of particle movement are particularly useful for calibrating the internal parameters of the algorithm. Pictured on the cover is a visualisation of a Firefly Algorithm optimising the three- dimensional Rosenbrock Function [5]. Each coloured sphere represents a potential min- imum candidate. The optimum is near the centre of the cube, at coordinate (1, 1, 1). Colour is used here to indicate the output of the Rosenbrock function, whereas the 3D coordinate of each particle is representative of the actual values used as input to the function. The clustering seen amongst the particles in the image is due to the local neighbourhood searches occurring. Particles tend towards the optimal solution in a small group of a certain radius, as well as moving randomly to a certain degree. The visualisa- tion assisted in improving convergence and verifying the implementation of the optimiser. [1] KA Hawick, ’3d visualisation of simulation model voxel hyperbricks and the cubes pro- gram’, Technical Report CSTN-082 102-904, Massey University, Albany, North Shore, Com- puter Science, Massey University, Albany, North Shore 102-904, (October 2010). [2] AV Husselmann and KA Hawick, ’3d vector-field data processing and visualisation on graphical processing units’, in Proc. Int. Conf. Signal and Image Processing (SIP 2012), pp. 92Ð98, Honolulu, USA, (August 2012). [3] AV Husselmann and KA Hawick, ’Parallel parametric optimisation with firefly algorithms on graphical processing units’, in Int. Conf. on Genetic and Evolutionary Methods (GEM’12), pp. 77–83, (2012). [4] AV Husselmann and KA Hawick, ’Visualisation of combinatorial program space and re- lated metrics’, in 2013 International Conference on Information and Knowledge Engineering (IKE’13), (2013). [5] XS Yang, ’Firefly algorithms for multimodal optimization. in: Stochastic algorithms: Foun- dations and applications’, in SAGA, (2009). Editorial To quote an anonymous Committee truth is, it has always been difficult to member, as spotted on one of the so- recruit people to submit material to be cial websites that dominate our lives: pushed to our members, but these days I’m back! Did you miss me? it’s becoming increasingly difficult. We What do you mean ’you are competing for time, and it’s a battle didn’t notice I was gone’? we will not win. AISB has about 400 members, Sure you did. Sure you did. mostly in the UK. In times like The truth is, the world is changing these, the Quarterly should thus at a pace faster than we are able to be seen as an outreach tool, de- cope. Gigabit ethernet is now (almost) signed to bring you followers and the internet of the past. It will soon "Likes"; something tangible, pal- be the solution that my three year old pable that we are naturally in- son will have to fall back on, when his clined to fall back on. I can only head-mounted, miniaturised computer invite you to get in touch with us, is not able to download the world news, and to make your voice heard. on his daily commute between London and New York1. ) At the time I am writing these lines, Universities are trying to adapt to this Echoing my editorial, Joel Lehman et evolving market, as it is now called. De- al. review the debates that happened partments are closing faster than new on the occasion of a recent AAAI work- ones are being open. National evalua- shop, which gathered leading figures. tion exercises and league tables dom- Their conclusion is without appeal, the inate the academic life and quite lit- anarchy of methods that makes AI is erally redefine what it means to pur- to be embraced. Significant and im- sue academic fulfilment, which is now a portant challenges remain, however, in- privilege of a few. cluding safety, ethical and societal con- On the plus side, AI is booming siderations. again, thanks to both progress made by Andy Thomason reviews the recent industry, and great outreach threads, book by van Benthem on logic in including amazing PR from companies games, and JeeHang Lee and Jeka- and a series of big bucks movies. I guess terina Novikova report on conferences this is a good thing for our field more they attended to, thanks to funding broadly, if we are in a position to lever- from AISB. age this hype. Hopefully, you have noticed some de- Etienne B. Roesch lays in receiving your Quarterly. The Editor-in-Chief 1I wonder if Fireman Sam will be viewable with 3D glasses then... p. 1 AISB Quarterly An Anarchy of Methods: Current Trends in How Intelligence is Abstracted in AI by John Lehman (U. Texas, USA), Jeff Clune (U. Wyoming, USA), & Sebastian Risi (U. Copenhagen, Denmark) Artificial intelligence (AI) is a sprawl- processes or view intelligence as symbol ing field encompassing a diversity of manipulation. Similarly, researchers fo- approaches to machine intelligence and cus on different processes for generating disparate perspectives on how intelli- intelligence, such as learning through gence should be viewed. Because re- reinforcement, natural evolution, logi- searchers often engage only within their cal inference, and statistics. The re- own specialized area of AI, there are sult is a panoply of approaches and sub- many interesting broad questions about fields. AI as a whole that often go unan- Because of independent vocabularies, swered. How should intelligence be ab- internalized assumptions, and separate stracted in AI research? Which sub- meetings, AI sub-communities can be- fields, techniques, and abstractions are come increasingly insulated from one most promising? Why do researchers another even as they pursue the same bet their careers on the particular ab- ultimate goal. Further deepening the stractions and techniques of their cho- separation, researchers may view other sen subfield of AI? Should AI research approaches only in caricature, unin- be "bio-inspired" and remain faithful to tentionally simplifying the motivations the process that produced intelligence and research of other researchers. Such (evolution) or the biological substrate isolation can frustrate timely dissem- that enables it (networks of neurons)? ination of useful insights, leading to Discussing these big-picture questions wasted effort and unnecessary rediscov- motivated us to organize an AAAI Fall ery. Symposium, which gathered partici- To address such dangers, we orga- pants across AI subfields to present and nized an AAAI Fall symposium that debate their views. This article distills gathered experts with diverse perspec- the resulting insights. tives on biological and synthetic in- telligence. The hope was that such Introduction a meeting might lead to a productive While researchers in AI all strive to examination of the value and promise create intelligent machines, separate AI of different approaches, and perhaps communities view intelligence in strik- even inspire syntheses that cross tra- ingly different ways. Some abstract in- ditional boundaries. However, organiz- telligence through the lens of connec- ing a cross-disciplinary symposium has tionist neural networks, while others risks as well. Discussion could have fo- use mathematical models of decision cused narrowly on intractable disagree- No 141, July 2015 p. 2 ments, or on which singular abstraction of images) to learn a hierarchy of in- is "the best." An unhelpful slugfest of creasingly abstract features (Figure 2; ideas could have emerged instead of col- [4]) Overall, participants agreed that laborative cross-pollination, leading to recent progress in deep networks was a veritable AI Tower of Babel. a significant step forward for process- In the end, there were world-class ing streams of high-dimensional raw keynote speakers spanning AI and bi- data into meaningful abstract represen- ology (see below), and participants tations, e.g. recognizing faces from un- were indeed collaborative. Some trav- processed pixel data. But there was elled to the United States from as far also agreement that much work yet re- as Brazil, Australia, and Singapore; mains to create algorithms that lever- but beyond geographic diversity, there age such representations to produce in- were representatives from many disci- telligent behavior and learn in real-time plines and approaches to AI (Figure 1). from feedback; in other words, scaling Drawing from the symposium’s talks deep learning to more cognitive behav- and events, we now summarize recent ior may prove problematic. progress across AI fields, as well as the Andrew Ng, affiliated with Stanford key ideas, debates, and challenges iden- University and Baidu Research,
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