artificial-intelligence

For my graduate research I am creating a neural network that trains to recognize images. I am going much more complex than just taking a grid of RGB values, downsampling, and and sending them to the input of the network, like many examples do. I actually use over 100 independently trained neural networks that detect features, such as lines, shading patterns, etc. Much more like the human eye, and it works really well so far! The problem is I have quite a bit of training data. I show it over 100 examples of what a car looks like. Then 100 examples of what a person looks like. Then over 100 of

I'm working on a 2D game that has a huge amount of dynamic entities. For fun's sake, let's call them soldiers, and let's say there are 50000 of them (which I just randomly thought up, it might be much more or much less :)). All these soldiers are moving every frame according to rules - think boids / flocking / steering behaviour. For each soldier, to update it's movement I need the X soldiers that are closest to the one I'm processing. What would be the best spatial hierarchy to store them to facilitate calculations like this without too much overhead ? (All entities are updated/moved every

I realize that this is probably a very niche question, but has anyone had experience with working with continuous neural networks? I'm specifically interested in what a continuous neural network may be useful for vs what you normally use discrete neural networks for. For clarity I will clear up what I mean by continuous neural network as I suppose it can be interpreted to mean different things. I do not mean that the activation function is continuous. Rather I allude to the idea of a increasing the number of neurons in the hidden layer to an infinite amount. So for clarity, here is the

We need to decide between Support Vector Machines and Fast Artificial Neural Network for some text processing project. It includes Contextual Spelling Correction and then tagging the text to certain phrases and their synonyms. Which will be the right approach? Or is there an alternate to both of these... Something more appropriate than FANN as well as SVM? I think you'll get a competitive results from both of the algorithms, so you should aggregate the results... think about ensemble learning. Update: I don't know if this is specific enough: use Bayes Optimal Classifier to combine the

I tried to understand about rasa from official documentation of Rasa core and Rasa nlu but not able to deduce much. What I am able to understand is Rasa core is used to guide the flow of conversation while Rasa nlu is to understand and process the text to extract information (entities) Second thing, there are examples to build chatbot in Rasa core as well as Rasa nlu both can be used to build chatbot but couldn't understand what's the difference in two approaches and when to follow which one. Could you please help me to understand in a better way. Trinca You got it right. Both work together

I want to have an artificial neural network: 42 input neurons 168 hidden neurons 7 output neurons This network is to play the game of "Connect Four". At the end of each game, the network gets feedback (game result / win?). Learning should be done with Temporal Difference Learning. My questions: What values should be in my reward array? And finally: How can I apply it to my game now? Thank you so much in advance! First hit is: you're assigning '0' to t in 'main', but your arrays' low-bound is '1', so you're accessing a non-existing element in the loops, hence the AV. If you had enabled range

I am currently working on a neural network based approach to short document classification, and since the corpuses I am working with are usually around ten words, the standard statistical document classification methods are of limited use. Due to this fact I am attempting to implement some form of automated synonym detection for the matches provided in the training. My question more specifically is about resolving a situation as follows: Say I have classifications of "Involving Food", and one of "Involving Spheres" and a data set as follows: "Eating Apples"(Food);"Eating Marbles"(Spheres);