Explore our videos for deeper insights into the concepts and applications of Cognitive Computing and Natural Language Understanding
Business Case Studies highlight practical applications. These are supported by the Understanding Engine or NLU Building Blocks. Fundamentals of Understanding videos reflect conceptual Understanding and underpin our approach
This video is about “What we Do” at AUI. Why is our unique approach necessary for a language system to really UNDERSTAND? How do Understanding Systems unearth normally hidden insights from unstructured data? What unique differentiators does AUI’s proprietary NLU technology bring to the table and what are some business applications ?
This video demonstrates the cognitive analysis of Drilling Activity Reports by proprietary Natural Language Understanding (NLU). 90 nuanced drilling activities within the activity reports are understood with 90%+ accuracy. Key benefits include reduced downtime and more effective operational planning.
Our system interprets valuable information from public domain information using NLU. Our solution reads public domain pdf reports, Understands unstructured information, converts it to structured form, and populates a table for ready use by an analyst. This is cognitive automation in action, well beyond the realm of current RPA solutions.
This video demonstrates AUI Systems proprietary Conversational Access to Structured Data thus providing easy access and saving valuable analyst time.
We have provided valuable information from public domain information using NLU. Our solution reads public domain pdf reports, Understands unstructured information, converts it to structured form, and populates a table for ready use by an analyst. This is cognitive automation in action, well beyond the realm of current RPA solutions.
Typical contact centre service automation struggles with end-to-end resolution of customer issues. We demonstrate a solution that Understands and models a product's specifications and troubleshooting processes in a flexible knowledge-based system fronted by a natural language interface. The result is a satisfied customer whose issue has been resolved without human intervention. Product knowledge and processes are easily updated, no system training data is required.
Financial Analysts require insights when performing analyses and valuations. Earnings Calls contain embedded insights that reflect a mix of qualitative and quantitative information. Our Cognitive Business Assistant truly Understands qualitative unstructured information from Earnings Call transcripts and makes it available as summaries, analyses and timeline-based knowledge. Now you can explore the reasons for specific business performance drivers, across quarters – at the click of a mouse !
Banking systems typically capture a multitude of account related information. Current systems can be siloed resulting in challenges for integrated information views. We demonstrate an example NLU system that collates incoming unstructured information into meaningful structure, then available as summaries that reflect dynamic banking rules yet retain conversational memory of client interactions. Relationship managers can obtain instant insight – leading to new revenues and reduced risk !
We demonstrate how NLU helps deliver customised summaries of key features and offerings from within non- standard documents and sources such as websites. The result is useful unstructured information converted to structured form, easily merged with structured information in a Semantic knowledge system. Analysts can easily query this information to improve productivity.
Understanding the meaning and context of language is a significant challenge for current Natural Language Processing (NLP) systems. For example the meaning of the word Apple (fruit or organization) depends on the context. AUI's system can perform inferences based on common knowledge, resolve ambiguous occurrences and more.
Who, what, where, when, how are the basic questions that help humans decipher information. Our system analyses input information in the light of these basic questions and arranges data in chronological order for contextual Understanding.
Our Language processing system Understands and extracts mathematical entities stored in text. It can then perform math operations on this information. It's surprising that “Google's DeepMind recently tested its algorithms on a high school Math test but found they couldn't even translate the problems”. Well, statistical based systems have trouble with math, it could be their challenge with Understanding.
We take pronouns for granted as we Understand the context and our mind replaces the pronoun with the referred noun without any difficulty. Typical Natural Language Processing(NLP) systems face challenges replacing pronouns. Watch the video to see how we've solved this challenge.
What do we mean by “I Understand this ….”. Does it mean that we know the meaning of all the words in a sentence or is it the wider meaning and context of the sentence ? How do humans Understand context, draw upon their common knowledge and solve problems and can machines do the same ?