Artificial Intelligence
Building a Conversational AI Strategy

Building a Conversational AI Strategy

Conversational Artificial Intelligence (AI) emerged as a buzzword a decade ago, and it has led to a significant shift in the way businesses and people operate. Humans have used language as a medium to communicate for a much longer time than they have used graphical user interfaces (GUIs). Systems that use language as their medium of interaction are growing rapidly. These systems mimic conversations with real people through digital and telecom technologies to deliver conversational engagements through chatbots, voice experiences, and digital assistants.

The rise of conversational AI is attributed to its success in the customer service area, where it was implemented in its early stages. Thereafter, it has expanded its boundaries and has been improved by combining powerful technologies that empower computers to comprehend processes and respond to human utterances and texts efficiently. Conversational AI has helped to address the challenge to interpret behavioral and emotional interactions in voice conversations to deliver impactful experiences.   

The widespread adoption of conversational AI has enabled developers and researchers to build a robust strategy that can be implemented for streamlining the process of building conversational applications. A well-defined strategy requires a combination of advanced technology enablement and solid design judgment to ensure accuracy and utility. A step-by-step design approach, followed by technology implementation, must be followed for developing an application from conception to production.

The conversational application design building process includes the following:

Selecting an Apt Use Case

It is crucial to select an appropriate use case that resembles real-world human interactions to ensure the application is practical and delivers real value to users. The best use cases mimic an existing real-world interaction with the statement of probable questions and relevant answers. The use case should also ensure a well-defined graphical user interface that saves time and accomplishes specific tasks.

Scripting Dialogues

Scripting ideal dialogues for interactions forms a crucial step in the building exercise. This step requires multiple iterations to work through usability issues and reach a conclusion.

Dialogue flows should be scripted to capture application responses that are not only as per user flows but also out of scope. Diagrammatic representation of scripted dialogue flows should support use cases to enable convenience.

Defining Domain, Intent, Entity, and Role Hierarchy

Applications rely on machine learning classifiers to model and understand natural language. NLP is a widely used foundation of all conversational assistants for production. Conversational applications hugely rely on machine learning models to identify, understand and fulfill requests.

Creating a Knowledge Base

A knowledge base is a comprehensive repository that contains the universe of helpful information needed to understand requests and answer questions. A broad knowledge base should be constructed in the conversational application to leverage facts and owes intelligence and utility to an underlying global knowledge base.

Generating and Deploying Training Data

Data sets are the fuel that power all supervised learning algorithms. Generating comprehensive and representative training data is critical to ensure the success of conversational applications. A supervised machine learning model is an effective way to understand human language by observing chunks of representative trained data.

The Conversational Applications Technology enablement includes the following:

Cloud-based NLP Services

Cloud-based Natural Language Processing (NLP) services have evolved to reduce the complexity of building basic language understanding capabilities. These services offer a clear path for developers to build conversational applications without requiring machine learning knowledge to create NLP Capabilities. It provides developers with browser-based consoles to streamline tasks such as launching a web service to handle and parse natural language requests. These services improve conversational AI offerings, as they leverage the pre-trained models for popular consumer tasks such as sending a text message, setting an alarm, or updating a to-do list.

Rule-based System

In rule-based frameworks, the developer must implement the core logic and use it to understand, interpret, and respond to incoming messages helpfully. The system typically has a set of rules that state the scripted response that must be returned for a specific message matching a specified pattern. These systems are relatively straightforward to create; they are trained on a pre-defined hierarchy of simple or complex rules that govern how to transform user input into actions or dialogs. However, these systems cannot respond to input patterns or keywords that don’t match existing rules. A rule-based approach is one of the fastest ways to create and launch a voice assistant or chat assistant demo.

Retrieval-based System 

These systems are primarily used today. In the retrieval method, the system uses heuristics to locate the best response from its predefined response database. Heuristics are employed to identify the most appropriate response template and involve simple algorithms such as matching keywords. It may also require complex processing with machine learning or deep learning. These systems require a lot of data pre-processing for their data and custom applications. They only use predefined responses and do not generate new output.

Generative Method 

The generative method is an intelligent, creative way to create new content. This method uses a large amount of conversational training data to generate new dialogs. It does not adhere to predefined responses. Instead, it prepares data and defines functionality.

Ensemble Method

The ensemble method is the most recent conversational AI building method that uses a combination of rule-based, retrieval-based, and generative method approaches. This method may use a rule-based method for a specific task and a generative method for an advanced task.

Conclusion 

Conversational AI systems are not built themselves, they have to be built over time by training machine learning and natural language processing models and designing experiences that are effective, memorable, and engaging. If you are interested in building a conversational AI strategy for your business, then you must go to SmartBots and get a chatbot for your business instantly.

Author: Stallin Sanamandra – Reach out to me to explore more on Conversational AI


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