Information about the career pathway for the profession of AI Mind Maintainer. Privacy policy: Third parties advertising here may place and read cookies on your browser; and may use web beacons to collect information as a result of ads displayed here.

Tuesday, September 26, 2017


Using Natural Language Understanding (NLU) to answer questions.

Although in the proof-of-concept searchable AI Mind we are dealing with an initially small knowledge base (KB), our coding of the ability to search for knowledge would apply equally well to an entire datacenter full of information. Today we are using the spreading-activation SpreadAct() mind-module to activate the conceptual elements of knowledge which will supply answers based upon input queries in the format of "Who + verb + noun", as in "Who makes robots?" The query-word "who" is subject to de-activation upon input, while the verb-concept and the noun-concept in the query are passed through SpreadAct() not as random parameters for an associative search, but in their specific roles as Main Verb and as Direct Object of the verb. Thus the AI Mind should respond with answers tailored to the structure of the query, in such a way as truly to demonstrate Natural Language Understanding (NLU).

We start by declaring the new flag-variable of "query-condition for who+verb+direct-object" $qvdocon to segregate the pertinent code in SpreadAct(), and also the "query-condition for who+verb+indirect-object" $qviocon to hold in reserve for when we code the AI response to input queries in the format of "To whom does God give help?" The creation of the one flag suggests the creation of the similar flag, so we declare both of them.

In the InStantiate() module we insert code to detect a who-query with a verb other than "be", and we set $qv2psi with the concept number of the verb. We set $qv4psi with the concept number of any input noun assumed to be the direct object of the incoming verb. Then in the pertinent area of SpreadAct() we need to start searching backwards through memory for instances of the verb in the who-query.

Eventually we obtain a rough but correct response to our queries of "Who does such-and-such?" but we need to debug and fine-tune the parameters. We ask, "Who makes robots" and we get "KIDS MAKES THE ROBOTS." We ask, "Who has a child" and we get "WOMEN HAS THE CHILD". We need to upload and release the code which achieves the objective, albeit primitively, and we must not code the same version further lest we wreck or corrupt the new functionality of answering who-queries in the form of "who" plus verb plus direct object. As we debug future releases of our code, the version remains safe and intact.