Building Jarvis, part 2

An artificial intelligence created from Neural Networks must be multi layered; This kind of structure is considered a “Hierarchical Neural Network” and is required as the information from lower level functions are necessary to feed into the higher level abstract functions, exactly how the human brain operates.

Any AI that plans on interfacing with humans will need to have one fundamental module, a language interpreter.

Query front end  netStarting from the beginning we start to see multiple layers of neural networks that feed into each other required to complete open ended query/solution tasks that Jarvis will need to be adept at solving. Word parsing is by far the easiest of all steps, as it is a simple English (or equivalent) word parsing module that is trained via dictionary/thesaurus and by reading the responses to strings on webpages (forums are a great resource for natural language datasets) and by training the network using data mined from these resources we can program a network that fully understand not only words and grammar but entire sentence structures and query/answers in the natural language of conversation.

Once the language is interpreted and the program understands what you’re asking right now, it may go back and look into previous queries to uncover patterns in your questions. An example would be if you were looking for a waterproofing device to fit around a cable, and your previous question was about silicone sealants, Jarvis may ask in response “Are you looking for other waterproofing techniques as well?” and by going back and forth multiple times you will end up with a solution you didn’t even know you were asking.

 

Tomorrow I will talk about how such an open-ended solver could not only be used for data retrieval, but also used to actually act upon something, eventually I will be able to say “Jarvis, please install yourself onto my USB drive” and in response Jarvis will say “Sure James, do you need me to call the movers? They seem to be running an hour late.”

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