Tuesday, May 18, 2010
Conference paper extension: ICNC 2010
The deadline for the International Conference on Neural Computation (ICNC) 2010 has been extended to 31 May 2010.
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.
Friday, January 22, 2010
Posting hiatus
Myself and my family will be moving to Adelaide next week, where I will be taking up a new position as a research fellow in ecological modelling. Since this will involve a fair amount of chaos and interrupted net access, posting to this blog will be quite sporadic over the next few weeks.
Conference paper deadline: VSST 2010
The paper submission deadline for the symposium VSST 2010 (website in French) is May 15, 2010. This symposium includes themes on web intelligence and mining temporal data, topics which are strongly related to computational intelligence. The symposium will be held in Toulouse, France, in October 2010.
Tuesday, January 12, 2010
AI in Second Life
The IEEE Computer Society is building an AI learning centre on its island in Second Life. It's intended to be a place where AI technologies can be shown off to the public, including the use of intelligent virtual guides (the first of which is based on the famous strategist Sun Tzu, author of the Art of War).
It strikes me as a good idea, and a fairly safe way of testing out technologies in a fairly real-world setting (for various values of "safe" and "real world"). I wonder how much cross-over there will be between this project and the AI in games research community?
Perhaps I will be taking a closer look at Second Life in the future.
It strikes me as a good idea, and a fairly safe way of testing out technologies in a fairly real-world setting (for various values of "safe" and "real world"). I wonder how much cross-over there will be between this project and the AI in games research community?
Perhaps I will be taking a closer look at Second Life in the future.
Saturday, January 9, 2010
Conference paper deadline: IPMU 2010
The deadline for paper submissions to the International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU) 2010 is January 17th, 2010. This conference will be held in Dortmund, Germany, 28th June - 2nd July, 2010.
Friday, January 8, 2010
Conference paper deadline: EIS 2010
The deadline for paper submission to the International Symposium on Evolving Intelligent Systems (EIS) 2010 is 15 January 2010. This symposium will be held in Leicester, UK 29th March to 1st April, 2010.
Thursday, January 7, 2010
Conference paper deadline: KES AMSTA 2010
The deadline for paper submissions to the 4th International KES Symposium on Agents and Multi-agent Systems – Technologies and Applications is January 15, 2010. This conference will be held in Gdynia, Poland on the 23rd-25th June, 2010.
Saturday, January 2, 2010
Turing Test for Game Bots
The Turing Test is very well known not only within the CI community but within the general public. Made really simple, a machine is intelligent if a human carrying out a conversation with that machine can't tell if it is a machine. In other words, we think it is intelligent, therefore it is intelligent.
A similar test has been proposed for game bots. It is described as follows:
"Suppose you are playing an interactive video game with some entity. Could you tell, solely from the conduct of the game, whether the other entity was a human player or a bot? If not, then the bot is deemed to have passed the test."
Playing games requires, in my opinion, more intelligence than having a conversation. It requires comprehension of the gaming environment, at least on some level, as well as anticipation of the actions of the player and the formulation and application of strategy. I have a suspicion that true general purpose AI will come from the gaming world. There's even a dedicated journal for it, the IEEE Transactions on Computational Intelligence and AI in Games.
The best part of this is that playing games can be part of your job.
A similar test has been proposed for game bots. It is described as follows:
"Suppose you are playing an interactive video game with some entity. Could you tell, solely from the conduct of the game, whether the other entity was a human player or a bot? If not, then the bot is deemed to have passed the test."
Playing games requires, in my opinion, more intelligence than having a conversation. It requires comprehension of the gaming environment, at least on some level, as well as anticipation of the actions of the player and the formulation and application of strategy. I have a suspicion that true general purpose AI will come from the gaming world. There's even a dedicated journal for it, the IEEE Transactions on Computational Intelligence and AI in Games.
The best part of this is that playing games can be part of your job.
Wednesday, December 30, 2009
Conference paper deadline: ICNC'10-FSKD'10
The deadline for the sixth International Conference on Natural Computation and seventh International Conference on Fuzzy Systems and Knowledge Discovery (ICNC'10-FSKD'10) is 15 January 2010. These conferences will be held in Yantai, China, 10-12 August 2010.
Tuesday, December 29, 2009
Surprise in ANN
A new paper in Neural Networks describes integrating the concept of surprise from information theory with ANN learning. It's an interesting idea that I've only seen once or twice before (a colleague at Otago University has investigated something similar for a different kind of neural network). It also makes sense - things that are surprising to people are more strongly remembered (they stick in your mind, which is the same as learning them well). I'll be looking at integrating this concept into some of my own work.
Wednesday, December 23, 2009
Conference paper deadline: IITSI 2010
The deadline for paper submissions to the third International Symposium on Intelligent Information Technology and Security Informatics (IITSI) 2010 is December 30, 2009. It will be held in Jingganshan, China, 2-4 April 2010.
Conference presentations
I have sat through a large number of conference presentations, and a significant proportion of those were pretty bad. Another proportion of these were mediocre at best, and only a few were pretty good. From what I have observed, and from talking with other conference attendees, I have formulated the following general rules for giving presentations. While none of these rules are inviolable, please do at least give them some thought the next time you give a conference presentation.
General Rules
There are two general rules – most of the specific rules come from these two.
1. Don’t waste time, either yours or the audience’s.
2. Don’t insult the intelligence of your audience.
Specific Rules
1. If you have just been introduced with your name and the title of your presentation, don’t repeat this information.
2. If you are presenting to a specialised audience, leave out the background material. For example, if you are presenting to a conference on evolutionary computation, spending even one or two slides explaining what evolutionary computation is violates both general rules.
3. If you have long sentences on your slides, don’t read them aloud. This violates both general rules.
4. Outline slides are not necessary. They waste time and assume that the audience isn’t smart enough to notice what you are currently talking about.
5. Don’t place equations on your slides unless they are absolutely, positively and irrefutably necessary. If the math is complex enough that it needs to be explained, then it is unlikely that the audience will be able to parse it fast enough to be useful to the presentation. If it is simple, then it can be left out.
6. Know the length of your presentation. A good rule of thumb is an absolute maximum of one slide per minute of presentation, including title, summary and conclusions. Thus, for a fifteen minute presentation, fifteen slides is a good count, ten is better, less than ten is best.
7. Keep to the point of the presentation. If your talk is on bioinformatics, I don’t want to hear about your university’s teaching computer lab.
8. Proof-read your presentation. Use a spell checker. Have someone else check your presentation. If English is not your first language, have it proof-read by someone who is a native speaker.
9. Know your presentation material. If you have to stop talking to work out what something on a slide actually means, you are wasting everyone’s time.
10. If you are presenting a group of numbers, use a plot of the values, rather than a table, especially if the intention is to compare and contrast the groups.
11. Move about. Moving energetically is even better. A presenter with physical vigour commands more attention from, and inspires more energy in, an audience than one who stands still, or worse, sits while speaking. That said, moving around like your feet are on fire is distracting. Use your best judgement.
12. Make eye contact with your audience. You should try to make eye-contact with each member of the audience at least once during your presentation. They are here to listen to you speak, so you should acknowledge their existence by actually looking at them.
General Rules
There are two general rules – most of the specific rules come from these two.
1. Don’t waste time, either yours or the audience’s.
2. Don’t insult the intelligence of your audience.
Specific Rules
1. If you have just been introduced with your name and the title of your presentation, don’t repeat this information.
2. If you are presenting to a specialised audience, leave out the background material. For example, if you are presenting to a conference on evolutionary computation, spending even one or two slides explaining what evolutionary computation is violates both general rules.
3. If you have long sentences on your slides, don’t read them aloud. This violates both general rules.
4. Outline slides are not necessary. They waste time and assume that the audience isn’t smart enough to notice what you are currently talking about.
5. Don’t place equations on your slides unless they are absolutely, positively and irrefutably necessary. If the math is complex enough that it needs to be explained, then it is unlikely that the audience will be able to parse it fast enough to be useful to the presentation. If it is simple, then it can be left out.
6. Know the length of your presentation. A good rule of thumb is an absolute maximum of one slide per minute of presentation, including title, summary and conclusions. Thus, for a fifteen minute presentation, fifteen slides is a good count, ten is better, less than ten is best.
7. Keep to the point of the presentation. If your talk is on bioinformatics, I don’t want to hear about your university’s teaching computer lab.
8. Proof-read your presentation. Use a spell checker. Have someone else check your presentation. If English is not your first language, have it proof-read by someone who is a native speaker.
9. Know your presentation material. If you have to stop talking to work out what something on a slide actually means, you are wasting everyone’s time.
10. If you are presenting a group of numbers, use a plot of the values, rather than a table, especially if the intention is to compare and contrast the groups.
11. Move about. Moving energetically is even better. A presenter with physical vigour commands more attention from, and inspires more energy in, an audience than one who stands still, or worse, sits while speaking. That said, moving around like your feet are on fire is distracting. Use your best judgement.
12. Make eye contact with your audience. You should try to make eye-contact with each member of the audience at least once during your presentation. They are here to listen to you speak, so you should acknowledge their existence by actually looking at them.
Tuesday, December 22, 2009
ANN on GPU
There are quite a few publications now on implementing ANN on Graphics Processing Units (GPU) (see for example here, here and a brief review here). There are even a couple of programming libraries available that do this. The great advantage of using GPU are, of course, that GPU are massively parallel while being relatively cheap and ANN are inherently parallel models (this cheapness lends them to being used for other high-performance projects and GPU-based supercomputers are becoming more widely used, for example here and here).
I have yet to see, however, any publications describing constructive neural networks implemented on GPU. I suspect this may be because many constructive algorithms require some steps that are difficult to paralleise, such as a finding the maximum activation in a layer of neurons (which can be done in log2(n) iterations if you compare the values in pairs).
That said, I do see a very bright future for ANN research in using GPU. Definitely something I will be following more closely in the future.
I have yet to see, however, any publications describing constructive neural networks implemented on GPU. I suspect this may be because many constructive algorithms require some steps that are difficult to paralleise, such as a finding the maximum activation in a layer of neurons (which can be done in log2(n) iterations if you compare the values in pairs).
That said, I do see a very bright future for ANN research in using GPU. Definitely something I will be following more closely in the future.
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