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Using Machine Learning to Build Conversations

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Using Machine Learning to Build Conversations

Learn how one company uses machine learning to improve their conversational app and why machine learning is needed for this type of platform.

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Machine Learning "TheBrain"

We have an omnichannel platform for building conversational solutions that leverages machine learning (ML) to constantly improve conversational dictionaries and build an enriched, fluent, and natural correspondence using any device whilst maximizing the user’s communication. But why is machine learning needed for conversational apps, and how we use it?

Why Do We Need Machine Learning?

The success (and failure) of conversational communications lies in the level of pre-configured conversation. The more fluent, natural, broad, enriched, and flexible the conversation between the users and the device is, the better chances they will continue to communicate using this and similar channels. The Conversation.one machine learning algorithm, TheBrain, is constantly building, enhancing, and improving conversations between the end users and different conversational devices and services by building sustainable and evolving “dictionaries” that offer a vertical-focused specialty and produce global and wide conversations and interactions. TheBrain creates a continuous feedback and learning cycle between businesses and end-users that grows independently based on market needs. The larger this feedback cycle is, the better conversations we make and the higher value we generate from conversational solutions.

How Does It Work?

TheBrain feeds off endless conversations that are running through the Conversation.one platforms’ cross-channels and devices, cross-applications, cross-verticals, and cross-customers. The enormous amount of data is being analyzed in real-time and categorized into “successful” and “unsuccessful” interactions. A successful interaction is when a user asks for a specific action (intent) and the conversational device has provided a relevant response in return. An unsuccessful interaction is a case when the conversational service or device fails to understand the user’s request and is unable to provide an answer. As part of its ongoing ML process, TheBrain collects and analyzes all “unsuccessful” conversations and categorizes them.

Example: Improve an Existing Intent

The user asks a question to which an answer is available as part of the conversation; however, a different phrasing that was unknown to the device was used. The system identifies the user’s intent and offers a new wording as a permanent sample for future users. Example:

“Alexa, what’s my account status?”

Checking an account balance is available on the skill; however, this set of words wasn’t pre-configured when building it. TheBrain automatically identifies the most similar intent (Check Balance) and adds the new sample to the list. The sample is automatically added to all channels, devices, verticals, and clients.

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Topics:
machine learning ,conversational interfaces ,ai

Published at DZone with permission of Rachel Batish, DZone MVB. See the original article here.

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