artificial-intelligence

I decided to do a project for fun where I want to take as input the image of a playing card and return its rank and suit. I figure that I only need look at the upper-left corner, since that has all the information. It should be robust - if I have a large image of an Ace of Diamonds, I should be able to scale it anywhere from 20 to 200% and still get the right answer. First question - is there anything already written that does this? If so I'll find something else to OCR so I don't duplicate the efforts. Second - what's the best way to go about doing this? Neural network? Something hand-coded?

How much of the concepts conveyed in natural language is RDF/OWL able to represent? I'm still learning RDF and other semantic technologies, but as I currently understand it, information is typically represented as triples of the form (subject,predicate,object). So I can imagine how the sentence "Bob has a hat" might be represented. However, how would you represent a more complicated sentence like "Bob, over on 42nd street, will have a job at the Mall after the owner approves"? Are there conventions for tags representing nouns/verbs/ownership/causality/tense/etc? Note, I'm not asking how to

There is only one question related to this in stackoverflow, and it is more about which one is better. I just dont really understand the difference. I mean they both work with vectors, which are assigned randomly to clusters, they both work with the centroids of the different clusters in order to determine the winning output node. I mean, where exactly lies the difference? In K-means the nodes (centroids) are independent from each other. The winning node gets the chance to adapt each self and only that. In SOM the nodes (centroids) are placed onto a grid and so each node is consider to have

I have clustered various articles together with the Scikit-learn framework. Below are the top 15 words in each cluster: Cluster 0: whales islands seaworld hurricane whale odile storm tropical kph mph pacific mexico orca coast cabos Cluster 1: ebola outbreak vaccine africa usaid foundation virus cdc gates disease health vaccines experimental centers obama Cluster 2: jones bobo sanford children carolina mississippi alabama lexington bodies crumpton mccarty county hyder tennessee sheriff Cluster 3: isis obama iraq syria president isil airstrikes islamic li strategy terror military war threat al

After implementing Pacman and Snake I'm implementing the next very very classic game: Pong. The implementation is really simple, but I just have one little problem remaining. When one of the paddle (I'm not sure if it is called paddle) is controlled by the computer, I have trouble to position it at the correct position. The ball has a current position, a speed (which for now is constant) and a direction angle. So I could calculate the position where it will hit the side of the computer controlled paddle. And so Icould position the paddle right there. But however in the real game, there is a

I'm working on a project at the moment where it would be really useful to be able to detect when a certain topic/idea is mentioned in a body of text. For instance, if the text contained: Maybe if you tell me a little more about who Mr Jones is, that would help. It would also be useful if I could have a description of his appearance, or even better a photograph? It'd be great to be able to detect that the person has asked for a photograph of Mr Jones. I could take a really naïve approach and just look for the word "photo" or "photograph", but this would obviously be no good if they wrote