Monday, May 17, 2010
International Neural Network Society - Australian Chapter
I'm a member of the INNS, the International Neural Networks Society, which is a good organisation to belong to if you're interested in neural networks. I'm in the process of setting up a regional chapter for Australia. If you are a member of the INNS and live in Australia, please contact me so I can add you to my mailing list for this chapter.
Wednesday, April 21, 2010
Conference paper deadline: KDIR 2010
The paper submission deadline for the International Conference on Knowledge Discovery and Information Retrieval (KDIR) 2010 is May 20, 2010. This conference will be held in Valencia, Spain, 25-28 October, 2010.
Friday, April 16, 2010
Conference paper deadline: ISICA 2010
The paper submission deadline for the International Symposium on Intelligence Computing and Applications (ISICA) 2010 has been extended to May 20, 2010. This symposium will be held in Wuhan, China, 22-24 October, 2010.
Thursday, April 8, 2010
Conference announcement: IJCNN 2011
Tutorial, special session, competition and paper deadlines have just been released for the International Joint Conference on Neural Networks (IJCNN) 2011. This will be held in San Jose, California, July 31 - August 5, 2011.
Competition proposals are due August 31, 2010.
Special session, tutorial and workshop proposals are due December 1, 2010.
Papers are due February 1, 2011.
Competition proposals are due August 31, 2010.
Special session, tutorial and workshop proposals are due December 1, 2010.
Papers are due February 1, 2011.
Friday, March 26, 2010
What is computational intelligence?
While I was giving a presentation last week to my new research group (the Global Ecology Group at the University of Adelaide), I was asked by an ecologist, "What is computational intelligence?"
This is one of those questions that sound really simple, but is actually really hard to answer. At WCCI 2008 in Hong Kong, I attended a panel session on the future of computational intelligence. The panelists spent almost the entire time arguing over what computational intelligence is.
One answer is that computational intelligence is a sub-group of artificial intelligence. But classical AI tends to be more of a top-down approach, that is, the developer tells the machine what it needs to know to solve the problem. To me, computational intelligence is a bottom-up approach, where the algorithm learns what it needs to know to solve the problem.
There are, of course, many algorithms that learn from data, like the C4.5 algorithm for building decision trees, that most people would not consider to be computational intelligence, so I will extend the definition above to include bio-inspired algorithms. That is, algorithms that are inspired by biological processes such as living brains (artificial neural networks), evolution (evolutionary computation), flocking (particle swarm optimisation, which is often included in evolutionary computation) and path-following in ants (ant colony optimisation, which is also often included in evolutionary computation).
However, fuzzy rule-based systems are usually included in the definition of computational intelligence, despite their top-down approach and lack of biological inspiration (although there are ways of constructing fuzzy rules directly from data, like using backpropagation to train the rules and fuzzy membership functions).
The website of the IEEE Computational Intelligence Society (of which I am a member) defines the scope of the society as:
"The Field of Interest of the Society shall be the theory, design, application, and development of biologically and linguistically motivated computational paradigms emphasizing neural networks, connectionist systems, genetic algorithms, evolutionary programming, fuzzy systems, and hybrid intelligent systems in which these paradigms are contained."
Which is good enough for me. I don't think there will ever be a universally-accepted definition of what computational intelligence is, but that's probably a good thing, because it allows plenty of scope for the field to grow.
This is one of those questions that sound really simple, but is actually really hard to answer. At WCCI 2008 in Hong Kong, I attended a panel session on the future of computational intelligence. The panelists spent almost the entire time arguing over what computational intelligence is.
One answer is that computational intelligence is a sub-group of artificial intelligence. But classical AI tends to be more of a top-down approach, that is, the developer tells the machine what it needs to know to solve the problem. To me, computational intelligence is a bottom-up approach, where the algorithm learns what it needs to know to solve the problem.
There are, of course, many algorithms that learn from data, like the C4.5 algorithm for building decision trees, that most people would not consider to be computational intelligence, so I will extend the definition above to include bio-inspired algorithms. That is, algorithms that are inspired by biological processes such as living brains (artificial neural networks), evolution (evolutionary computation), flocking (particle swarm optimisation, which is often included in evolutionary computation) and path-following in ants (ant colony optimisation, which is also often included in evolutionary computation).
However, fuzzy rule-based systems are usually included in the definition of computational intelligence, despite their top-down approach and lack of biological inspiration (although there are ways of constructing fuzzy rules directly from data, like using backpropagation to train the rules and fuzzy membership functions).
The website of the IEEE Computational Intelligence Society (of which I am a member) defines the scope of the society as:
"The Field of Interest of the Society shall be the theory, design, application, and development of biologically and linguistically motivated computational paradigms emphasizing neural networks, connectionist systems, genetic algorithms, evolutionary programming, fuzzy systems, and hybrid intelligent systems in which these paradigms are contained."
Which is good enough for me. I don't think there will ever be a universally-accepted definition of what computational intelligence is, but that's probably a good thing, because it allows plenty of scope for the field to grow.
Tuesday, February 16, 2010
Conference paper deadline: ICONIP 2010
The deadline for paper submission to the International Conference on Neural Information Processing (ICONIP) 2010 is 14 July 2010. This conference will be held in Sydney, Australia, November 29 - December 3, 2010.
I used to be a regular contributor to ICONIP, but I have had to scale back my involvement in recent years due to budgetary constraints of the positions I've been working in (the last one I attended was ICONIP'08 in Auckland). ICONIP tends to be fairly good quality and good value. Also, I used to live in Sydney, and I can strongly recommend it as a beautiful city in which to hold a conference.
I used to be a regular contributor to ICONIP, but I have had to scale back my involvement in recent years due to budgetary constraints of the positions I've been working in (the last one I attended was ICONIP'08 in Auckland). ICONIP tends to be fairly good quality and good value. Also, I used to live in Sydney, and I can strongly recommend it as a beautiful city in which to hold a conference.
Thursday, February 11, 2010
Conference paper deadline: ICNC / ICFC / ICEC 2010
The deadlines for paper submission to the International Conference on Neural Computation (ICNC), International Conference on Fuzzy Computation (ICFC) and International Conference on Evolutionary Computation (ICNC) are all 18 May 2010. These three conference will be held jointly in Valencia, Spain, 24-26 October, 2010. One registration grants access to all three conferences.
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