AI’s Next Generation Natural Language Understanding API.
Meaning-as-a-Service.

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Language Based AI

Pat Inc. is teaching language to machines. We deliver meaning-as-a-service: connecting natural language with structured information that creates meaning and understanding. Pat Inc. is building the world’s most powerful natural language service for developers to build intelligent agents and applications that you can talk to or text.

Linguistics not Statistics

The difference with Natural Language Understanding (NLU) is not in how it translates words or guesses the intent of a question. It understands the true meaning of sentences and conversations. Pat Inc. takes a linguistic approach to NLU, not statistical or machine learning based. After all, humans don’t learn language reading thousands of books and memorising colocation patterns and probabilities.

Training Data Not Required

Rather than training data or annotated corpora, Pat builds knowledge on language just like a human by progressively learning the way words are combined, regarding real objects, people, processes, and events in context. Pat is not trying to learn patterns about language meaning from big data, an approach that is still struggling to beat the language capabilities of a 3-year-old.

Multi-Lingual

Pat Inc.’s approach to NLU is language independent and delivers one solution across all languages. The meaning of a sentence or conversation is mapped regardless of the language. In fact, multiple languages can even be mixed up within a single sentence or conversation and still connect to meaning.

“Pat offers next generation Natural Language Understanding technology, that is capable of being the conversational user interface of the future.”

Dr. Hossein Eslambolchi
AT&T CTO 1986-2006, 1000+ patents BS, MS, Ph.D UCSD, Top 10 Influential Leaders of the 21st Century, Chief Technology Advisor

“Statistical systems can accomplish NLP to a considerable degree, but they can never achieve NLU, which involves meaning. The answer lies in linguistics. Pat Inc. solves that.”

Professor Robert D. Van Valin, Jr
University of Düsseldorf and University at Buffalo, The State University of New York, PhD UC Berkeley

“To teach computers conversational skills and language, we need to connect language to some form of meaning representation in the computer, like Pat is doing.”

Chris Lonsdale
BSc Hons Psychology, First Class honours, Chairman Third Ear Publishing, Tedx speaker over 9m views

Natural Language for Developers

Pat Inc. offers next generation NLU for developers to build language-based applications and devices for users to engage, talk and text. Our vision is to humanize conversations with machines. Pat doesn’t need corpora to train on a new domain. We’re teaching machines language, not data.

Question Answering

Search engines put the world’s wealth of information at our fingertips, but imagine the difference if you could search with natural language? Google knows firsthand the frustration todays state-of-the-art causes users, who often need to try a number of different search results to find the answer they are looking for.

  • Human-Machine Conversation
    Language Learning
    Customer Service intelligent assistant

Information Extraction

Key decisions in the financial markets are increasingly moving away from human oversight and control. Algorithmic trading is becoming more standard, a form of financial investing that is entirely controlled by technology. But, many of these financial decisions are impacted by news and by journalism, which is still presented predominantly in English. 

  • Sentiment analysis
  • Text analysis

IoT and Robotics

Today we have started talking to our smart homes and home robots. How much better will this be with natural language that understands when we make corrections to our spoken words and its response more closely mimics a human? When ambiguity is handled or clarified. We are only at the beginning of the social robot  and home automation revolution.

  • Home Automation
  • Conversational User Interface

How does it work?

Pat maps words to meaning by storing only certainties, not probabilities. 885+ competitors in NLP using state-of-the-art statistical methodologies can only approximate meaning. This can be seen with digital assistant technology, which sometimes gets our requests right and sometimes wrong, because they don’t really understand us unless we provide recognized commands or familiar text based on the machine’s corpora training.

Counting words and tracking word order, or even parsing by syntax results in probability—or guesswork, not meaning. Pat, the meaning matcher, links straight to meanings combining the linguistic model Role and Reference Grammar (RRG) with Patom Theory. Pat matches every word to the correct meaning based on the meanings of the other words in the sentence or story, just like a 3-year-old does without guesswork. As a result, language is broken down by meaning, storing only certainties like a human does, not probabilities.

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