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What Is Conversational AI

                                                         

Conversational Artificial Intelligence solutions can communicate with people in their natural languages. The interactions happen via speech or text – our most common forms of interaction.

The most popular example of a conversational AI solution is chatbot.

The chatbot popularity began in 2016 with Facebook’s announcement  of a developer-friendly platform to build chatbots on Facebook messenger. Soon, chatbots became the buzz of the technological community and spread across various industries. As a next step, toolkits that helped build a bot in five minutes grew popular, companies raced to the market with new bot announcements and the world woke up to a new chatbot-based reality.

A well developed conversational AI chatbot is able to interact on a near-human level. If we think about it, most companies’ customer service and sales centers deal with a core of 6-12 repeating issues. conversational AI software allows companies to develop an intelligent response channel that can cover the most common customer interactions.

Another advantage in using Conversational AI is in the marketing and branding domain. Chatbots allow the companies to stay on their message without veering off course . With AI, the scripts are all written and approved in house. Even when the AI system learns, when the appropriate training techniques are implemented, the system will adhere to the required profrssional verbiage.

 

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Beware the lure of crowdsourced data

Crowdsourced data can often be inconsistent, messy or downright wrong 

We all like something for nothing, that’s why open source software is so popular. (It’s also why the Pirate  Bay exists). But sometimes things that seem too good to be true are just that. 

Repustate is in the text analytics game which means we needs lots and lots of data to model certain  characteristics of written text. We need common words, grammar constructs, human-annotated corpora  of text etc. to make our various language models work as quickly and as well as they do. 

We recently embarked on the next phase of our text analytics adventure: semantic analysis. Semantic  analysis the process of taking arbitrary text and assigning meaning to the individual, relevant components.  For example, being able to identify “apple” as a fruit in the sentence “I went apple picking yesterday” but to  identify “Apple’ the company when saying “I can’t wait for the new Apple product announcement” (note:  even though I used title case for the latter example, casing should not matter)

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