Download Viewpoint – Progress in machine intelligence.

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:
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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):
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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).
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