What are interesting ideas for experimenting with Artificial Neural Networks? [closed]

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I'm after a list of possible neural network implementations that can be experimented with. Possibly something that could take an hour to a week to write.

What other possibilities are there?

Here's the list so far:

• Games
  • tic-tac-toe
  • Connect 4
  • Chess
  • Go
  • Sudoku
  • paper/scissors/rock
  • horse racing predictor
• Visual recognition
  • Character recognition (typefaces, letters, numbers, etc)
  • Facial recognition
• Audio recognition
  • Language detection
  • Male vs female
• Word recognition
  • Language detection (natural, programming)
• Pathfinding
  • "Artificial neural network driven mobile robots learn how to drive on roads in simulation." http://cig.felk.cvut.cz/projects/robo/, http://www.youtube.com/watch?v=lmPJeKRs8gE

Some links to more:

• http://www.cs.colostate.edu/~anderson/res/project-ideas.html

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You can combine Genetic Algorithms and Neural Networks to evolve simple neural configurations, such as Neural Networks that perform logic operations (including the phantomatic XOR!).

This is a topic I very much like because - if you think about it - it's a bare bones model of how our brains evolved (I am not saying we have logic gates in our head).

It is simple enough - and should be good fun!

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In a wider way, all that cover pattern recognition and signal processing could take great advantage of neural networks.

Also, you could use neural networks to develop "pseudo-AI" for games (strategy, soccer games).

Anyway, as neural network is a tool more than a "solution", it can be used in economics, physics, navigation, signal processing, etc.

Also, many types of neural networks exist (perceptron, hopfield), the thing is to use them wisely according to the problem.

Neural networks are not panacea, just a (very interesting and powerful) tool.

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what about face recognition?

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Here are some problems that I think feed forward neural nets (with multiple hidden layers) might be able to solve.

1. Given the number of packets sent/recieved on the network interface, the volume of ambient noise, and the level of ambient light, attempt to predict the time of day.
2. Given a latitude and longitude, attempt to predict the elevation, or crime rate.
3. Given some simple metrics about the keywords in the title of an article, predict how many upvotes it has.
4. Given the digits of a random phone number, predict where the line terminus is located.

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This is more challenging: visualize (ie, plot) the decision boundary surface of a 2-layer neural network. (With 1 layer the boundary is linear, so it's easy).