Table of Contents
Advances in Artificial Intelligence
Volume 2010, Article ID 629869, 12 pages
Research Article

Quo Vadis, Artificial Intelligence?

1Systems Biology Research Group, Centre for Molecular Biosciences, School of Biomedical Sciences, University of Ulster, Cromore Road, BT52 1SA Coleraine, UK
2Systems Biology Department, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo 1358550, Japan
3Department of Complex Systems, Future University Hakodate, 116-2 Kamedanakano-cho, Hakodate, Hokkaido 041-8655, Japan
4School of Computing and Mathematics, Faculty of Computing and Engineering, University of Ulster, Shore Road, Newtownabbey, County Antrim BT37 0QB, UK
5Laboratory for Dynamics of Emergent Intelligence, RIKEN Brain Science Institute, Wako-shi, Saitama 351-0198, Japan

Received 9 October 2009; Accepted 4 January 2010

Academic Editor: David Glass

Copyright © 2010 Daniel Berrar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Since its conception in the mid 1950s, artificial intelligence with its great ambition to understand and emulate intelligence in natural and artificial environments alike is now a truly multidisciplinary field that reaches out and is inspired by a great diversity of other fields. Rapid advances in research and technology in various fields have created environments into which artificial intelligence could embed itself naturally and comfortably. Neuroscience with its desire to understand nervous systems of biological organisms and systems biology with its longing to comprehend, holistically, the multitude of complex interactions in biological systems are two such fields. They target ideals artificial intelligence has dreamt about for a long time including the computer simulation of an entire biological brain or the creation of new life forms from manipulations of cellular and genetic information in the laboratory. The scope for artificial intelligence in neuroscience and systems biology is extremely wide. This article investigates the standing of artificial intelligence in relation to neuroscience and systems biology and provides an outlook at new and exciting challenges for artificial intelligence in these fields. These challenges include, but are not necessarily limited to, the ability to learn from other projects and to be inventive, to understand the potential and exploit novel computing paradigms and environments, to specify and adhere to stringent standards and robust statistical frameworks, to be integrative, and to embrace openness principles.