Baal Zebul You are planning a financial gain from your project? If that is the case, what prospects do you offer your potential programmers? If it is similar to English, how do you propose dealing with the ambiguities that leda noted a few posts earlier? This is a bit confusing. The back propagation algorithm is based on trial and error: it is a method of adjusting weights by comparing the output of the neural network with a sample set. How do you propose a mixture of those two terms, if they are already linked with each other? See 3D? How does that work? Even our own eyes only see in 2D, the light falls on a flat retina obviously registering a 2D version of the world. If i remember correctly, the perception of depth is introduced in our brain, where, among other factors, the different visions from left and right eye are used to create the illusion of depth. Maybe someone in the biology section can give you a more accurate or detailed description of this process. Multple levels of what? Layers in the neural network? First of all, I'm not sure if terminology is correctly applied here. As I've explained above, I can not envision a mixture between trail and error and back-propagation systems. Secondly, if such was possible i can not see how the result of it would be equivalent to genetic algorithms and expert systems. Both of these are approaches to completely different areas. Genetic algorithms is good for optimization, expert systems traditionally work on a more concretely defined rule set. What you are claiming to implement, human intelligence, is neither a pure optimilisation process nor a feasible with a concretely defined rule set. Humans learn a language by mimicking those who already mastered language. Who or what is your system mimicking? You? I'm not sure if you are using AI terminology in the manner as I've understood it. Yes, but how does your system know or discover which pieces of information are relevant and which are not? How does it make a correlation between an action and a state change? If they happen after eachother within a certain time frame? If so, how do you determine what is the most suitable time frame? But if the roof is there, and the bot can "see" the roof, why would the bot ignore it? What rule in it tells it that it should use keys, but that it should not try to use roofs? How ever silly the last piece of the question sounds, a bot without no initial knowledge what so ever, has no idea if a roof is relevant with regard to fullfilling its mission. Ok, how will it describe a wall? Attaching simply a random label is not creating a dictionary. If it is to be of any use, it should recognize what makes a wall different from other objects, like e.g. the floor. Why? There are other conclusions concievable. E.g. why not conclude that the two doors which did not open to the key appear to be not functioning? Given its lack of knowledge about the reliability of doors and its situation (a room, three doors and a key) it can not choose which hypothesis is the correct one or even assume which is more likely. How do you propose to handle this? No, the real world is significantly more complex. Simple object recognition is not a trivial matter. How to recognize the key from the texture of the floor? How to recognize the key from different angles? And I'm not even speculating about the complexity in getting the key inserted in the correct manner in the door's key hole. What safe side? You are worried that people are going to patent your ideas? I for one am not making a run on the patent office just yet. At this point, I can only see problems with your approach, rather than innovative solutions.