Super facts about Artificial intelligence (AI)

Natural-language understanding (NLU) or natural-language interpretation (NLI) is a subtopic of natural-language processing in artificial intelligence that deals with machine reading comprehension. Natural-language understanding is considered an AI-hardproblem.

There is considerable commercial interest in the field because of its application to automated reasoning, question answering, news-gathering, text categorization, voice-activation, archiving, and large-scale content analysis.

NLU is the post-processing of text, after the use of NLP algorithms (identifying parts-of-speech, etc.), that utilizes context from recognition devices (ASR, vision recognition, last conversation, misrecognized words from ASR, personalized profiles, microphone proximity etc.), in all of its forms, to discern meaning of fragmented and run-on sentences to execute an intent from typically voice commands. A NLU has an ontology around particular product vertical that is used to figure out the probability of an intent. A NLU has a defined list of known intents that derives the message payload from designated contextual information recognition sources. The NLU will provide back multiple message outputs to separate services (SW) or resources (HW) from a single derived intent (response to voice command initiator with visual sentence (shown or spoken) and transformed voice command message too different output messages to be consumed for M2M communications and actions).

HistoryEdit

The program STUDENT, written in 1964 by Daniel Bobrow for his PhD dissertation at MITis one of the earliest known attempts at natural-language understanding by a computer.Eight years after John McCarthy coined the term artificial intelligence, Bobrow's dissertation (titled Natural Language Input for a Computer Problem Solving System) showed how a computer could understand simple natural language input to solve algebra word problems.

A year later, in 1965, Joseph Weizenbaum at MIT wrote ELIZA, an interactive program that carried on a dialogue in English on any topic, the most popular being psychotherapy. ELIZA worked by simple parsing and substitution of key words into canned phrases and Weizenbaum sidestepped the problem of giving the program a database of real-world knowledge or a rich lexicon. Yet ELIZA gained surprising popularity as a toy project and can be seen as a very early precursor to current commercial systems such as those used by Ask.com.

In 1969 Roger Schank at Stanford Universityintroduced the conceptual dependency theoryfor natural-language understanding.This model, partially influenced by the work of Sydney Lamb, was extensively used by Schank's students at Yale University, such as Robert Wilensky, Wendy Lehnert, and Janet Kolodner.

In 1970, William A. Woods introduced the augmented transition network (ATN) to represent natural language input. Instead of phrase structure rules ATNs used an equivalent set of finite state automata that were called recursively. ATNs and their more general format called "generalized ATNs" continued to be used for a number of years.

In 1971 Terry Winograd finished writing SHRDLU for his PhD thesis at MIT. SHRDLU could understand simple English sentences in a restricted world of children's blocks to direct a robotic arm to move items. The successful demonstration of SHRDLU provided significant momentum for continued research in the field. Winograd continued to be a major influence in the field with the publication of his book Language as a Cognitive Process. At Stanford, Winograd would later be the adviser for Larry Page, who co-founded Google.

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