Citation
Sanders, D. (2008), "Progress in machine intelligence", Industrial Robot, Vol. 35 No. 6. https://doi.org/10.1108/ir.2008.04935faa.002
Publisher
:Emerald Group Publishing Limited
Copyright © 2008, Emerald Group Publishing Limited
Progress in machine intelligence
Article Type: Viewpoint From: Industrial Robot: An International Journal, Volume 35, Issue 6
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 MIT AI Laboratory, predicted in 1967 that “within a generation the problem of creating “artificial intelligence” will be substantially solved” (Dreyfus and 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 60 years after that first conference), we have still not managed to create a “child machine”. Programs still cannot 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 over-complicated 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 (Mazaredo et al., 1996) expert systems (Hudson et al., 1997), self-organizing maps (Burn and Home, 2008), fuzzy logic (Zoumponos and Aspragathos, 2008) and genetic algorithms (Manikas et al., 2007) and we have applied that machine intelligence technology to many areas, for example:
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Assembly (Gupta et al., 2001; Schraft and Ledermann, 2003; Guru et al., 2004).
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Building modelling (Gegov, 2004; Wong et al., 2008).
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Computer vision (Bertozzi, 2008; Bouganis and Shanahan, 2007).
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Environmental engineering (Sanders and Hudson, 2000, Patra et al., 2008).
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Human – computer interaction (Sanders et al., 2005; Zhao et al., 2008).
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Internet use (Bergasa-Suso et al., 2005; Kress, 2008).
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Medical systems (Pransky, 2001; Cardso and Cardos, 2007).
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Robotic manipulation (Tegin and Wikander, 2005; Sreekumar et al., 2007).
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Robotic programming (Tewkesbury and Sanders, 1999; Kim et al., 2008).
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Sensing (Sanders, 2007; Trivedi and Cheng, 2007).
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Walking robots (Capi et al., 2001; Urwin-Wright 2003).
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Wheelchair assistance (Stott and Sanders, 2000; Pei et al., 2007).
There appear to be some technologies that could significantly increase the practical ability of computers in these areas (Brackenbury and Ravin, 2002):
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Natural language understanding to improve communication.
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Machine reasoning to provide inference, theorem-proving, cooperation and relevant solutions.
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Knowledge representation for perception, path planning, modelling and problem solving.
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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 model-based 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-high-resolution 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).
David SandersSystems and Knowledge Engineering based in the Faculty of Technology, University of Portsmouth, Portsmouth, UK
References
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Further Reading
Bar-Cohen, Y. (2003), “Actuation of biologically inspired intelligent robotics using artificial muscles”, Industrial Robot: An International Journal, Vol. 30 No. 4, pp. 331–7
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Sanders, D.A. and Stott, I.J. (1999), “A new prototype intelligent mobility system to assist powered wheelchair users”, Industrial Robot: An International Journal, Vol. 26 No. 6, pp. 466–75
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