The layers of an AIAn AI, in the classical sense, has different layers, and each of those layers represents an area of study with its own data structure and algorithms.Core layer The core of the AI is the data structure in which it represents its understanding of the world. Paired with this are a set of algorithms that allow basic operations to be performed on the data structure, and building on top of that are algorithms to use the data structure to evaluate whether an idea "makes sense", to answer questions via reasoning, etc. Language in layer Beyond this core is the need to interface with language as an input. This is a substantially different problem and requires new data structures and algorithms, but this layer has a strong dependency on the core layer to evaluate whether a possible interpretation of a phrase makes sense, and ultimately to store the resultant understanding. Language out layer AI doesn't just need to accept language as an input, it also needs to use language as an output medium. Auditory in layer Translating the spoken word into a textual representation. Auditory out layer Translating text into the spoken word. Social layer Any social agent in this world needs more than intelligence and language abilities: It needs an understanding of how to behave appropriately. For example, having a conversation is quite a complex interaction, with many unwritten rules. Vision layer Creating a mental model of a spacial environment via image analysis. Personality layer Perhaps least important, but still of interest, is the personality layer, perhaps mostly for its relationship with the social layer. How does an AI add color to its personality? Is this something that happens implicitly, or does it represent another layer of complexity that needs to be developed? My areas of interest I'm personally the most interested in the core and language in layers. |