interaction
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Please view Jobs at ConocoPhillips Forty years ago, Norway’s first oil production started – from the Ekofisk field. Until now, around 1,804 billion Norwegian kroner worth of value creation has been generated from this and other fields in the Greater Ekofisk Area through interaction.
Now we are investing in further development of these fields – and preparing for the next 40 years.
Looking ahead – and looking northwards.
We have ambitions of growing on the Norwegian continental shelf and continuing as a key player.
The Ekofisk Complex today
drivkraft • Photo: Kjetil Alsvik 2011
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Contrary to the myth that there exists an AI winter, the rate of research is rapidly expanding in Artificial Intelligence. One of the main drivers for future research will be the entertainment industry – the need for realistic interaction with NPCs (Non-Playing Characters) in the games industry, and the striving for greater believability in the related movie and TV industries. These industries have substantial financial clout, and have almost unlimited potential for the application of AI technology.
For example, a morphing of reality TV with online computer games could lead to fully interactive TV in the not too distant future where the audience will become immersed in, and be able to influence, the story they are watching (through voting on possible outcomes – e.g. whether to kill off one of the main actors). An alternative possibility could be the combination of computer animation, simulation and AI technologies that could lead to movies that one could watch many times, each time with different outcomes depending on what happened during the simulation.
Despite these interesting developments in the entertainment industry where AI is not seen as much of a threat, the increasing involvement of AI technologies in other aspects of our daily lives has been of growing concern to many people. Kevin Warwick in his 1997 book The March of the Machines has predicted that robots or super-intelligent machines will forcibly take over from the human race within the next 50 years. Some of the rationale behind this thinking is the projection that computers will outstrip the processing power of the human brain by as early as 2020 (Moravec, 1998; see Figure 1.1).
For example, this projection has predicted that computers already have the processing ability of spiders – and recent Artificial Life simulations of arthropods has shown how it is possible now to produce believable dynamic animation of spiders in real-time (ap Cenydd and Teahan, 2005). The same framework used for the simulations has been extended to encompass lizards. Both lizard and spider equivalent capability was projected by Moravec to already have been achieved. However, unlike Moravec’s graph, the gap between virtual spiders and virtual lizards was much smaller. If such a framework can be adapted to mimic mammals and humans, then believable human simulations may be closer than was first thought.
Misconceptions concerning machines taking over the human race which play on people’s uninformed worries and fears, can unfortunately have an effect on public policy towards research and development.
For example, a petition from the Institute of Social Inventions states the following:
“In view of the likelihood that early in the next millennium computers and robots will be developed with a capacity and complexity greater than that of the human brain, and with the potential to act malevolently towards humans, we, the undersigned, call on politicians and scientific associations to establish an international commission to monitor and control the development of artificial intelligence systems.” (Reported in Malcolm, 2008).
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Figure 1.1: Evolution of computer power/cost compared with brainpower equivalent. Courtesy of Hans Moravec (1998).
Chris Malcolm (2008) provides convincing arguments in a series of papers why robots will not rule the world. He points out that the rate of increase in intelligence is much slower than the rate of increase in processing power. For example, Moravec (2008) predicts that we will have fully intelligent robots by 2050 although we will have computers with greater processing power than the brain by 2020. Malcolm also highlights the dangers of “anthropomorphising and over-interpreting everything”. For example, it is difficult to avoid not attributing emotions and feelings when observing Hiroshi Ishiguro’s astonishingly life-like artificial clone of himself called Geminoid, or Hanson Robotics’ androgynous android Jules (Brockway, 2008). Joseph Weizenbaum, who developed Eliza, a chatbot with an ability to simulate a Rogerian psychotherapist and one of the first attempts at passing the Turing Test, was so concerned about the uninformed responses of people who insisted on treating Eliza as a real person that he concluded that “the human race was simply not intellectually mature enough to meddle with such a seductive science as artificial intelligence” (Malcolm, 2008).
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1.4 Conceptual Metaphor, Analogy and Thought Experiments
Much of language (as used in this textbook, for example) is made up of conceptual metaphor and analogy.
For example, the analogy between AI research and physical exploration in Section 1.2 uses examples of a conceptual metaphor that links the concepts ‘AI research’ and ‘exploration’. Lakoff and Johnson (1980) highlight the important role that conceptual metaphor plays in natural language and how they are linked with our physical experiences. They argue that metaphor is pervasive not just in everyday language, but in our thoughts and action, being a fundamental feature of the human conceptual system.
Recognizing the use of metaphor and analogy in language can aid understanding and facilitate learning. A conceptual metaphor framework, for example, has been devised for biology and for the teaching of mathematics. Analogy and conceptual metaphor are important linguistic devices for explaining relationships between concepts. A metaphor is understood by finding an analogy mapping between two domains – between a more abstract target conceptual domain that we are trying to understand and the source conceptual domain that is the source of the metaphorical expressions. Lakoff and Johnson closely examined commonly used conceptual metaphors such as “LIFE IS A JOURNEY”, “ARGUMENT IS WAR” and
“TIME IS MONEY” that appear in everyday phrases we use in language. Some examples are “I have my life ahead of me”, “He attacked my argument” and “I’ve invested a lot of time in that”. Understanding of these sentences requires the reader or listener to apply features from the more understood concepts such as JOURNEY, WAR and MONEY to the less understood, more abstract concepts such as LIFE, ARGUMENT and TIME. In many cases, the more understood or more ‘concrete’ concept is taken from a domain that relates to our physically embodied human experience (such as the “UP IS GOOD” metaphor used in the phrase “Things are lookup up”). Another example is the cartographic metaphor (MacroVu, 2008b) that is the basis behind the ‘maps’ of Robert Horn mentioned above in Section 1.2.