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All Blog Posts Tagged 'textual' (4)

Natural Language Understanding (NLU) in Fraud Risk Management – a case study

I.  Introduction

This is a continuation of my previous blog, “Natural Language Understanding – Application Notes with Context Discriminant”. 

Background:

Natural Language Understanding (NLU) is a subtopic of Natural Language Processing (NLP). Successful implementations of NLU are difficult because of limitations in prevailing technology. SiteFocus solved these limitations with a new approach to NLU. This approach has been successfully…

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Added by Sing Koo on April 10, 2018 at 1:30pm — 1 Comment

Natural Language Understanding – Application Notes with Context Discriminant

Introduction

Deep Learning can be used to automate just about every repetitive task that is currently or formerly performed by humans. Factory robots, autonomous cars, Internet of Things are example of these automations. Yet, mentally challenging tasks such as conducting research or strategic planning with natural language textual documents remain a daunting task for automation. We look into the root cause of this challenge and have implemented a solution to automate these…

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Added by Sing Koo on October 6, 2017 at 1:00pm — No Comments

Artificial Intelligence in Enterprise – Using Meta-Vision to Disrupt Business Models for Products and Service

Introduction

Enterprise business models evolve over time for many reasons. The Internet has been a key factor driving enterprise business model change in recent years. The recent popularity of smartphones has disrupted consumer habits in travel, investment, entertainment, communication, social engagement, dining, shopping and many daily activities. Consequently, enterprises have been forced to change their business models. While some changes are progressive, others are…

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Added by Sing Koo on June 1, 2017 at 4:30pm — No Comments

Introducing Context Discriminant - Artificial Intelligence for Tactical Strategic Execution with Bionic Fusion

Background
 

Prevailing AI technology for analytics prefer the use of statistical science as the foundation for machine learning (ML) on historical data to distill knowledge and experience. Whether it be supervised or unsupervised, the result is then incorporated into playback engines to analyze new data. These methods and procedures work well for predictable scenarios with known outcomes and known variables.

 

What if the variables are unknown, and…

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Added by Sing Koo on May 19, 2017 at 9:00am — No Comments

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