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Viewpoint – Progress in machine intelligence. David Sanders is a Reader in Systems & Knowledge Engineering based in the Faculty of Technology, University of Portsmouth, UK. Keywords: machine intelligence, artificial intelligence, In the coming decades, humanity may create a powerful artificial intelligence but that said, back in 1999 I suggested in this journal that machine intelligence was just around the corner (Sanders, 1999). It has all taken longer than I thought it would… and there has been frustration along the way…. but what is the story so far? The first modern reference to a mechanical man is probably to Tik-Tok, from Ozma of Oz (1907), although artificially created beings existed before that in stories and mythology. But it was probably the idea of making a ``child machine'' that could improve itself by reading and learning from experience that began the study of machine intelligence. That was first proposed in the 1940s and after World War II, a number of people independently started to work on intelligent machines. Alan Turing was one of the first and after his 1947 lecture, Turing predicted that there would be intelligent computers by the end of the century. Later, Zadeh (1950) published a paper entitled "Thinking Machines - A New Field in Electrical Engineering”, and Turing (1950) discussed the conditions for considering a machine to be intelligent that same year. He made his now famous argument that if a machine could successfully pretend to be human to a knowledgeable observer then it should be considered intelligent. Later that decade, a group of computer scientists gathered at Dartmouth College in New Hampshire USA (in 1956) to consider a brand-new topic; artificial intelligence. It was actually McCarthy (now a professor at Stanford University) who coined the name "artificial intelligence" just ahead of that meeting when he had to write a proposal to get research support for the conference. That debate served as a springboard for further discussion about ways that machines could simulate aspects of human cognition. An underlying assumption in those early discussions was that learning (and other aspects of human intelligence) could be described precisely enough that a machine could be programmed to simulate it. By the late 1950s, there were many researchers in the area, and most of them were basing their work on programming computers. Minsky, head of the M.I.T. AI Laboratory, predicted in 1967 that "within a generation the problem of creating `artificial intelligence' will be substantially solved" (Dreyfus, 2008). Then, the field ran into unexpected difficulties around 1970 with the complete failure of any machine to understand even the most basic children's story. Machine Intelligence programs lacked the intuitive common sense of a four-year old. And Dreyfus still believes that no one knows what to do about it. Now (nearly sixty years after that first conference), we have still not managed to create a ``child machine''. Programs still can’t learn much of what a child learns naturally from physical experience. But, we do appear to be at a point in history when our human biology appears too frail, slow and overcomplicated in many industrial situations. We are turning to powerful new technologies to overcome those weaknesses, and the longer we use that technology, the more we are getting out of it. We use less energy, space, and time, but get more and more output for less cost. Our machines are exceeding human performance in more and more tasks, from guiding objects to assembling other machines. As they merge with us more intimately and we combine our brain power with computer capacity to deliberate, analyse, deduce, communicate, and invent then many scientists are predicting a period when the pace of technological change will be so fast and far-reaching that our lives will be irreversibly altered. A fundamental problem though is that nobody appears to know what intelligence (and therefore artificial or machine intelligence) really is. Varying kinds and degrees of intelligence occur in people, many animals and now some machines. A problem is that we cannot decide or agree what kinds of computation we want to call intelligent. Some people appear to think that human-level intelligence can be achieved by writing large numbers of programs of the kind that people are writing now or by assembling vast knowledge bases of facts in the languages now used for expressing knowledge. However, most AI researchers now appear to believe that new fundamental ideas are required, and therefore it cannot be predicted when human-level intelligence will be achieved (McCarthy, 2008). Machine Intelligence does combine a wide variety of advanced technologies to give machines an ability to learn, adapt, make decisions and display new behaviours. This is achieved using technologies such as neural networks (Sanders, 1997) expert systems (Hudson, 1997), self-organizing maps (Burn, 2008), fuzzy logic (Zoumpono, 2008) and genetic algorithms (Manikas, 2007) and we have applied that machine intelligence technology to many areas, for example: Assembly (Gupta et al, 2001, Schraft, and Ledermann, 2003, Guru et al, 2004). Building modelling (Gegov, 2004, Wong, 2008) Computer vision (Bertozzi, 2008, Bouganis, 2007). Environmental engineering (Sanders, 2000, Patra 2008). Human – computer interaction (Sanders, 2005, Zhao 2008).). Internet use (Bergasa-Suso, 2005, Kress, 2008). Medical systems (Pransky, 2001, Cardso, 2007). Robotic manipulation (Tegin, 2005, Sreekumar, 2007). Robotic programming (Tewkesbury, 1999, Kim, 2008). Sensing (Sanders, 2007, Trivedi 2007). Walking robots (Capi et al, 2001, Urwin-Wright 2003). Wheelchair assistance (Stott, 2000, Pei, 2007). There appear to be some technologies that could significantly increase the practical ability of computers in these areas (Brackenbury, 2002): Natural language understanding to improve communication. Machine reasoning to provide inference, theorem-proving, cooperation and relevant solutions. Knowledge representation for perception, path planning, modelling and problem solving. Knowledge acquisition using sensors to learn automatically for navigation and problem solving. The research papers in this issue address some of these challenges. Swarm intelligence in robotics and dialogues for human-robot interaction and learning are considered; the latter including reinforcement learning, knowledge acquisition and speech recognition. Co-operative motion planning using modelbased algorithms allows cooperating manipulators and manipulated objects to be integrated in software. Localization and path planning with obstacle avoidance for industrial mobile robots and walking mine detectors are considered along with their integration with their sensors. Finally, the robotic hand-eye coordination problem is used to assess practical advances in machine intelligence for vision-equipped robots. Where then do we appear to be going with machine intelligence? At one end of the spectrum of research there are handy robotic devices such as iRobot's Roomba vacuum cleaners and more personal robots such as the conversation character robots and Zeno robot-boy from Hanson Robotics, and Pleo, from Ugobe. These new “toy” robots could be a beginning for a new generation of ever-present, cheap robots with new capabilities. At another end of the spectrum, direct brain-computer interfaces and biological augmentation of the brain are being considered in research laboratories (along with ultra-highresolution scans of the brain followed by computer emulation). Some of these investigations are suggesting the possibility of smarter-than-human intelligence within some specific application areas. However, smarter minds are much harder to describe and discuss than faster brains or bigger brains and what does “smarter-than-human” actually mean? We may not be smart enough to know (at least not yet). REFERENCES Bar-Cohen Y (2003). Actuation of biologically inspired intelligent robotics using artificial muscles. Industrial Robot: An International Journal; Vol: 30 (4), pp: 331-337 Bergasa-Suso J, Sanders DA, Tewkesbury GE (2005). Intelligent browser-based systems to assist Internet users. IEEE Transactions 48 (4), pp 580-585. Bertozzi M, Bombini L, Broggi A, Cerri P, Grisleri P, Medici P, Zani P (2008). GOLD: A framework for developing intelligent-vehicle vision applications. IEEE Intelligent Systems. Vol: 23 (1), pp 69-71. Bouganis A, and Shanahan M (2007). A vision-based intelligent system for packing 2-D irregular shapes. IEEE Trans on Automation Science & Engineering. Vol: 4 (3), pp 382-394. Brackenbury I, Ravin Y (2002). Machine intelligence and the Turing Test. IBM Systems Journal, Volume: 41 (3), pp 524-529. Capi G, Nasu Y, Barolli L, Mitobe K, Yamano M (2001). Real time generation of humanoid robot optimal gait for going upstairs using intelligent algorithms. Industrial Robot: An International Journal; Vol: 28 Issue: 6; pp 489 – 497. Cardso JS and Cardos MJ (2007). Towards an intelligent medical system for the aesthetic evaluation of breast cancer conservative treatment . AI in medicine, Vol 40 (2), pp 115-126. Dreyfus HL and Dreyfus SE (2008). From Socrates to Expert Systems: The Limits and Dangers of Calculative Rationality. WWW Pages of the Graduate School at Berkeley (accessed June 2008). http://garnet.berkeley.edu/~hdreyfus/html/paper_socrates.html Feigenbaum E (1990). Knowledge Processing--From File Servers to Knowledge Servers. The Age of Intelligent Machines, MIT Press. Gegov, A (2004). Application of computational intelligence methods for intelligent modelling of buildings. Applications and science in soft computing ISSN: 1615-3871 (Advances in soft computing ISBN: 3-54040856-8 Editors: Loffi A, Garobaldi J), Springer-Verlag, Berlin, pp 263-270. Gupta SK, Paredis CJJ, Sinha R, Brown PF (2001). Intelligent assembly modelling and simulation, Assembly Automation, ISSN: 0144-5154, Vol: 21 Issue: 3, pp 215 - 235 Guru SM, Fernando S, Halgamuge S and Chan K (2004). Intelligent fastening with A-BOLT technology and sensor networks. Assembly Automation ISSN: 0144-5154. Vol: 24 (4), pp 386 – 393. Hudson AD, Sanders DA, Golding H, et al. (1997). Aspects of an expert design system for the wastewater treatment industry. Jnl of Systems Architecture 43 (1-5): 59-65. Kim J, Choi, M, Kim M, Kim S, Kim M, Park S, Lee J, Kim B (2008). Intelligent robot software architecture. 13th International Conference on Advanced Robotics. Published in lecture notes in control and information sciences, Vol: 370, pp 385-397. Kress M (2008). Intelligent Internet knowledge networks: Processing of concepts and wisdom. Information processing & management. Vol: 44 (2), pp 983-984. Legg, S; Hutter, M (2007). Universal intelligence: A definition of machine intelligence. Minds and Machines. Vol 17, pp 391-444. McCarthy J (2008). What is artificial intelligence? Computer Science Department WWW Pages at Stanford University (retrieved 19 June 2008). http://www-formal.stanford.edu/jmc/whatisai Patra JC, Juhola M, Meher P K (2008). Intelligent sensors using computationally efficient Chebyshev neural networks. IET Science measurement & technology. Vol: 2 (2), pp 68-75. Pei J, Huosheng H, Tao L and Kui Y (2007). Head gesture recognition for hands-free control of an intelligent wheelchair. Industrial Robot: An International Journal; Vol: 34 (1), pp 60–68. Pransky J (2001). An intelligent operating room of the future – an interview with the University of California Los Angeles Medical Center. Industrial Robot: An International Journal; Vol: 28 Issue: 5. pp. 376-382. Sanders DA, Haynes BP, Tewkesbury GE, et al. (1996). The addition of neural networks to the inner feedback path in order to improve on the use of pre-trained feed forward estimators. Mathematics and computers in simulation 41 (5-6), pp 461-472. Sanders D (1999). Perception in robotics. Industrial Robot: An International Journal; Vol: 26 (2), pp 9091. Sanders DA and Stott IJ (1999). A new prototype intelligent mobility system to assist powered wheelchair users. Industrial Robot: An International Journal; Vol: 26 (6), pp. 466-475 Sanders DA and Hudson AD (2000). A specific blackboard expert system to simulate and automate the design of high recirculation airlift reactors. Mathematics & Computers in simulation 53 (1-2), pp 41-65 Sanders DA, Urwin-Wright SD, Tewkesbury GE, et al (2005). Pointer device for thin-film transistor and cathode ray tube computer screens. Electronics Letters 41 (16), pp 894-896. Sanders D (2007). Viewpoint - Force sensing. Industrial Robot: An International Journal; Vol: 34 (4), pp 268-268. Schraft RD and Ledermann T (2003). Intelligent picking of chaotically stored objects. Assembly Automation, ISSN: 0144-5154. Vol: 23 (1), pp 38-42 Searle JR (1990). Consciousness, Explanatory Inversion, and Cognitive Science. The Behavioral and Brain Sciences 13 (4), (December), pp 585-696. Sreekumar M, Nagarajan T, Singaperumal M, Zoppi M and Molfino R (2007). Critical review of current trends in shape memory alloy actuators for intelligent robots. Industrial Robot: An International Journal; Vol: 34 (4), pp 285 – 294. Stott I, Sanders D (2000). New powered wheelchair systems for the rehabilitation of some severely disabled users. Int Jnl of rehabilitation research, 23 (3), pp 149-153, Tegin J and Wikander J (2005). Tactile sensing in intelligent robotic manipulation – a review. Industrial Robot: An International Journal; Vol: 32 (1), pp. 64-70. Tewkesbury G, Sanders D (1999). A new robot command library which includes simulation. Industrial Robot: An International Journal; Vol: 26 (1), pp 39-48. Turing A (1950). Computing machinery and intelligence. Mind, 1950 Trivedi MM, Cheng Y (2007). Holistic sensing and active displays for intelligent driver support systems. Computer. Vol: 40 (5), pp 60-62. Urwin-Wright S, Sanders D, Chen S (2003). Predicting terrain contours using a feed-forward neural network. Engineering Applications of Artificial Intelligence 16 (5-6), pp 465-472. Wang, Y (2007). Keynote address. Proc’ 6th IEEE International Conferene on Cognitive Informatics, CA, pp 3-12. Wong J, Li H, Lai J (2008). Evaluating the system intelligence of the intelligent building systems Automation in construction. Vol: 17 (3), pp 303-321. Zadeh LA. (1950). Thinking machines. A new field in electrical engineering. Columbia Engineering Quarterly 3: pp 12-13 and pp 30-31. Zhao M, Nowatzyk AG, Lu T, Farkas DL (2008). Intelligent non-contact surgeon-computer interface using hand gesture recognition. Three dimensional image capture and applications 2008, Proc of SPIE-IS&T Electronic Imaging , Vol: 6805, pp U8050-U8050.