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If you are only in need of implementing for a presentation/learning experience and you aren't doing this in an enterprise capacity you can essentially use any of these languages. Python would be best in my opinion for several reasons, its fast to prototype in, its easy to create visualizations of graphs in python, and if you want to continue in ai ...


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I think the first question you should answer is: "What questions should the AI be able to answer?" If the intend was that the AI should be able to answer any questions, then that is simply not doable (or at least currently it is not doable). Currently this is similar to asking for a program that can do anything. Currently the AI field is split between ...


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This is (even though it doesn't look like it at first glance) a deeply philosophical question about the nature of 'meaning'. This answer is necessarily limited in scope. There are many ways of representing knowledge, and countless formalisms have been developed since the early days of AI. Many of them are based on some kind of predicate calculus, ontologies,...


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Interpreted languages allow for a faster development cycle, as they don't require time for compilation, and fragments can often be run without having a complete program. They often also have fewer constraints for variable declaration or typing. That means they can be used to quickly scope out a problem and try different solutions. The drawback is the slower ...


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