tap@ai.toronto.edu (Tony Plate) (07/22/89)
Does anyone have any good ideas for a science center exhibit on neural networks which will give the average visitor some idea of what neural networks are about? I'm helping to design a neural networks exhibit for the Ontario Science Center, a science "museum" in Toronto. The exhibit will probably be interactive - with a few buttons or knobs the visitor can play with. The exhibit should be something which a person with high school education understand. The concepts involved in it should be simple, concrete and interesting - thus things like truth tables are out because they are both abstract and incredibly boring. One problem is that many simple neural networks don't do very surprising or difficult things, e.g. Xor is not really a very difficult problem, it is only interesting to researchers because we know that a perceptron cannot do it. The network has to be simple because the computers available are not very powerful, and we would like it to learn while the visitor is with the exhibit, i.e. in 2 to 5 minutes. They are using Amigas, which seem to be able to do about 1200 link per second for back-propagation training (but maybe that could be improved by doing integer arithmetic and table lookup). We already have a few ideas, but none of them are truly inspirational, so I shan't bore you with them. So if you have or have heard of any good ideas for an interesting and understandable exhibit networks, please mail them to me. If there is enough interest I will post a summary to comp.ai.neural-nets. More context: The exhibit is to be part of a rather large show on Psychology, which will stay on show in Toronto for approximately 8 months, and then move to a museum in the U.S. The organizers want a section on AI, but have decided that this section will consist of one exhibit on neural networks... I'm not getting paid for this, so neither will you if you contribute a great idea. However, I will do my best to make sure you get credited. My connection with the ontario science center is entirely informal, so if you don't want to give away all rights to your ideas, then don't mail them to me. -- ---------------- Tony Plate ---------------------- tap@ai.utoronto.ca ----- Department of Computer Science, University of Toronto, 10 Kings College Road, Toronto, Ontario, CANADA M5S 1A4 ----------------------------------------------------------------------------
M.Nigri@ucl-cs.UUCP (07/27/89)
From: "Meyer E. Nigri" <M.Nigri@uk.ac.ucl.cs> Tony, One simple demonstration is to show how a nn can recognise patterns. For example, one can present a serie of characters to the nn to be learnt. After the learning phase, one can recover a correct character presenting a corrupted version. With a good graphical interface, I think this example can be interesting. If the graphical interface is a little bit better, a true image could be presented to the nn (like a picture of a car, house, horse, tree etc). Meyer. +--------------------------+-----------------------------------------------+ |Meyer Elias Nigri | JANET:mnigri@uk.ac.ucl.cs | |Dept. of Computer Science | BITNET:mnigri%uk.ac.ucl.cs@UKACRL | |University College London |Internet:mnigri%cs.ucl.ac.uk@nsfnet-relay.ac.uk| |Gower Street | ARPANet:mnigri@cs.ucl.ac.uk | |London WC1E 6BT | UUCP:...!mcvax!ukc!ucl-cs!mnigri | +--------------------------+-----------------------------------------------+