Machine Learning Within the Conversational Interface Technology Platform

Machine Learning Within the Conversational Interface Technology Platform

An important part of artificial intelligence is setting up a computer system for success in response and performance without providing precise, step-by-step instructions. A Conversational Interface Technology Platform enables a system to walk through a task, using facts and circumstances to come to a conclusion and react accordingly. Multi-turn conversations with the customer provide layers of information to assist AI in comprehensive tasks involving a level of judgement. These tasks may include customer service queries, quotes, or even claims inquiries. The study of the algorithms and statistical models equipping the computer AI system in these processes is called machine learning. Following are a few main components involved in machine learning.

Vertical Domains

It is imperative that a conversational interface technology platform contain industry-specific terms and acronyms within its glossary. Understanding of unique operations, issues, and trends within an industry are also a necessary piece for the models. This vocabulary and industry knowledge, the vertical domain, is foundational. Vertical domains can be custom built, though excellent pre-built vertical AI machine learning models are available for industries such as banking, healthcare, insurance, retail, and more. Deep learning requires trillions of variations for one simple sentence’s correct meaning to be identified. A pre-built model, set to a particular industry, allows for a quick setup, often ready to use within days.

Advanced NLU

With so many complex components woven into communication and troubleshooting, sometimes humans don’t even understand humans… how does one make a computer understand humans? NLU (natural language understanding) aids in AI’s processing, understanding, and responding to even the most complicated of customer needs with great accuracy. Deep neural networks focus not only on increasing accuracy in understanding, but also quality recall within different levels of difficulty. Not only does a quality NLU engine focus on intent, entity recognition, the current query tone, and the user’s past conversation history; but it can be done with multi-language support in a multi-turn conversation style.

Data Science Automation

A solid foundation of accurate data is necessary to allow data science automation to do its job. Data science automation is the process of sifting through and understanding all data in context so it can be effectively labeled and categorized. These data sets are essential to the machine learning model, resulting in smarter decisions. The data sets can be in the form of an unstructured chat, a voice transcript, or structured support queries; all depending on the chosen algorithms and machine learning techniques employed.

Knowledge Graph

In order for an AI personal assistant to correctly understand and react to a customer’s needs and their query’s natural language form, specific company information is required. A knowledge graph is a personalized library of information, coming from a company’s many knowledge sources. Company documents, web-based collaborative platform information, websites, and customer support archives are helpful pools of knowledge, ready to be put to use within machine learning. Company and industry-specific acronyms and jargon will also fill out the knowledge graph, along with inbuilt definitions. Using the knowledge graph, the AI personal assistant is able to take basic responses to common customer service needs and personalize them. In addition to personalization, convenient summaries are created from the knowledge graph, allowing the customer to glean search results from one response instead of wandering through multiple documents or pages.

An AI experience with correct, personal, and applicable responses will make for a happy customer.