I'm seriously considering a temporary ban on NN threads. Seriously, folks. What is up with all the NN threads in the past week or so? Are you guys all students who are reaching what is likely going to be the least relevant part of your education at the same time? Just because the Terminator says that he has one doesn't mean they are cool. The truth is, for most decision-making processes, they simply suck. There... I feel better now. How cathartic.

OMG maybe you should write a NN to automatically close all NN-related threads!!!</bad-joke>

NN? Non-Nude?? Ohhh Neural Network!

I love them, I made one that controlled a car autonomously that nailed me a 1st.... sweet.

Seriously, folks. What is up with all the grumpy moderators?

Quote: Original post by Hodgman
Seriously, folks. What is up with all the grumpy moderators?

C'mon... this NN stuff is the equivalent of wandering into the graphics forum and suggesting that we experiment more with the Apple 2 low-res, 16-color graphics and trying to make it more like the Apple 2 hi-res, 8 color, 2-palette graphics. That wouldn't go over too well, would it? Why? Because it is old-school and sucks at doing anything productive compared to other techniques.

*sigh*

Seriously, enough with the NNs. GAs are the only tool you ever need. They work like nature so they must be smart!!

On the other hand NNs are simple to understand and provide a smooth introduction to soft computing. In particular, many of the better alternatives people suggest are more complex to understand and are less well covered by the literature. Perhaps more needs to be done to explain the alternatives.

Edit following Sneftel's post: I actually make a lot of use of GAs. Again, for a specific task it's unlikely that they are anything like the optimal solution but they're a pretty flexible tool that is fairly easy to tweak to your needs once you understand the concepts. There's a lot to be said for a tool that is easy to understand and adapt, even if better ones exist.

I actually think both NNs and GAs can be very useful. The problem is when people assume that "AI" means "AI algorithm", and that that means "brainy algorithm", and that that means "NN". So some guy who wants his agents to seek out food spends a week figuring out NNs and backpropagation and what his inputs and outputs should look like and deciding on a reasonable layout, and then after a week, he's done, and it TOTALLY GOES TOWARDS THE FOOD!

Hey, guy, spoiler alert:

if(food.x < agent.x) {    go_left();} else {    go_right();}

Yes, I know these toy problems are ways to learn about neural networks while keeping complexity under control. But the whole POINT of NNs is that you use them when the model is too complex to understand. The considerations are totally different. The approaches are totally different. The results are totally different. The domains are TOTALLY DIFFERENT. If you want game AI, pick an approach that fits the problem. And if you're going to cut your teeth on machine learning, pick a problem that fits the approach.

The same thing happens with GAs, though GAs have the disadvantage of not having "neural" in the name, so people jump for them less.

Actually, I've just had a brainstorm. Guess what, guys? I've just come up with a new AI technique. It's called Brain Intellect Vectors, and it's the same as k-nearest neighbors, only it has a name that'll make people think it's so smart. After all, if you do have just one tool in your ML toolbox, kNN isn't a bad choice for that tool, and it's easy to understand and apply. (Or maybe SVMs... I'm open to suggestions.) We just need to convince everyone that BIVs are based on how the human brain works, so that they can make decisions in the same way that human brains make decisions! What's a neuron? I forget! BRAIN INTELLECT VECTORS!

Quote: Original post by Kylotan
On the other hand NNs are simple to understand and provide a smooth introduction to soft computing. In particular, many of the better alternatives people suggest are more complex to understand and are less well covered by the literature. Perhaps more needs to be done to explain the alternatives.

Yeah... someone should write a book describing how to construct behavior algorithms using stuff like utility theory, response curves, weighted randoms, and stuff.