In 1637, when he published, The Discourse on Method, René Descartes unleashed a philosophical breakthrough, which later became a fundamental principle that much of modern philosophy now stands upon. Nearly 400 years later, if a machine says these five powerful words, "I think therefore I am," does the statement still hold true? If so, who then is this "I" that is doing the thinking? In a recent talk, Ray Kurzweil showed the complexity of measuring machine consciousness, "We can't just ask an entity, 'Are you conscious?' because we can ask entities in video games today, and they'll say, 'Yes, I'm conscious and I'm angry at you.' But we don't believe them because they don't have the subtle cues that we associate with really having that subjective state.
In this paper we argue that machine consciousness can be successfully modelled to be the base of a control system for an autonomous mobile robot. Such a bio-inspired system provides the robot with cognitive benefits the same way that consciousness does for humans and other higher mammals. The key functions of consciousness are identified and partially applied to an original computational model, which is implemented in a software simulated mobile robot. We use a simulator to prove our assumptions and gain insight about the benefits that conscious and affective functions add to the behaviour of the robot. A particular exploration problem is analyzed and experiments results are evaluated. We conclude that this cognitive approach involving consciousness and emotion functions cannot be ignored in the design of mobile robots, as it provides efficiency and robustness in autonomous tasks. Specifically, the proposed model has revealed efficient control behaviour when dealing with unexpected situations.
The term'artificial intelligence' was coined as long ago as 1956 to describe'the science and engineering of making intelligent machines'. The work that has happened in the subject since then has had enormous impact. Margaret Boden is a Research Professor of Cognitive Science at the University of Sussex, and one of the best known figures in the field of Artificial Intelligence. We put four key questions to her about this exciting area of research. It works according to (still largely unknown) scientific principles that could conceivably be simulated in computers.
Supported by CSE since 2013, Deiss first worked with the Non-Volatile Systems Laboratory of CSE Prof. Steven Swanson, but now splits his time between the Integrated Systems Neuroengineering Lab of Bioengineering professor Gert Cauwenberghs, and the new Pattern Recognition Laboratory of the Qualcomm Institute (both with CSE support). The scientist is looking particularly for neural applications to neuromorphic engineering and machine learning. The computer scientist argues that neuromorphic engineering, deep learning and other methods, as well as models based on free-energy theory and Bayesian inference, inevitably lead to the engineering of machines that can do more than we can. His first job out of graduate school involved applications of artificial intelligence, cognitive and computer science to computer-aided instruction.