A Data Product View on Conversational AI

A Data Product View on Conversational AI

An overview of Conversational AI, how it shows up as products, different companies offering solutions and link sample code. Conversational AI is the technology that allows users to ask queries to a machine and get automated responses. The most notable of these machines are the virtual assistants such as Alexa, Siri, and Google Assistant. At the heart of Conversation AI, is the utilization of Natural Language Processing (NLP).

“Hey, you up?”

Unlike humans, conversational artificial intelligence (AI), most commonly deployed today via chatbots, are “up” 100% of the time.

Smallbizgenius nicely summarizes some of the amazing stats on chatbots:

  • Chatbots can cut operational costs by up to 30%.
  • 85% of customer interaction will be handled without human agents by 2021.
  • 50% of businesses plan to spend more on chatbots than on mobile apps.
  • 64% of internet users say 24-hour service is the best feature of chatbots.
  • 37% of people use a customer service bot to get a quick answer in an emergency.
  • There were over 300,000 chatbots on Facebook in 2018.

Beyond chatbots, automated voice response systems (as annoying as they may still be) and virtual voice assistants all utilize conversational AI to power human-to-machine dialog.

Conversational AI Overview

Conversational AI is the technology that allows users to ask queries to a machine and get automated responses. The most notable of these machines are the virtual assistants such as Alexa, Siri, and Google Assistant. At the heart of Conversation AI, is the utilization of Natural Language Processing (NLP).

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There are three primary NLP components needed for any conversational AI implementation:

  1. Understanding intent using Natural Language Understanding (NLU)
  2. Predicting a response using Machine Learning algorithms
  3. Human-like response through Natural Language Generation (NLG)

As humans, we understand that the question “you up?” can have multiple meanings, or intentions. At a high level, Natural Language Understanding maps the human language to intentions based on the words and phrases used, and the context upon which it was used. Once the intent is determined, different Machine Learning techniques can be used to predict the best response. That response is likely stored in a numerical representation, and again depending on the context, Natural Language Generation can be used to map the response back to the best appropriate human-understandable language.

For voice-based systems, the smart device must first utilize Automatic Speech Recognition (ASR) to convert voice to text, before leveraging NLP to process the intent and generate a response. After a response is predicted and NLG is utilized to generate a human-like response, Speech Synthesis is then utilized to convert the response from text to voice.

Artificial Intelligence, or more specifically, Deep Learning, is the core of such technologies that facilitates the human-to-machine conversation and voice-to-text conversion. Furthermore, AI allows the application to correct, learn from experience, and improve over time to deliver a better response in the future over constant usage.

As a quick aside, with the rise of Deep Learning applications, and its dark side “deep fakes”, voice cloning has become a hot topic of discussion. Based on deep learning techniques, an original voice can be used to train a model to generate new audio with a similar voice. One day, you may even use celebrities voices on your virtual assistant. How voice cloning technology gets used, for now, still depends on the humans.

Types of Data Products using Conversation AI

There are three primary ways that Conversation AI shows up in data products today.

  1. Chatbots

The Chatbot is the most common application under Conversational AI. They are the basic application that you find over the websites used for FAQ and guiding you through the various features of the website and act as customer support for service providers. The general process is to deliver a response concerning the text query by the user. However, Chatbot is the basic form and does not possess the deep learning fused capabilities such as learning and improving in future interactions.

2. Intelligent Virtual Assistants

The intelligent virtual assistants are considered to be a more advanced level of Conversational AI. The most popular devices and applications that are part of this category are Alexa by Amazon, Siri by Apple, Bixby by Samsung, and Google Assistant. These virtual assistants are targeted at different forms of services such as voice assistants for a home which include the likes of Alexa. On the other hand, Siri, Bixby falls into the category of mobile assistants that can perform various operations i.e. text to speech, navigation, quick reply, response to weather, and quick address search among other functionalities.

3. Customer service based Assistants

These are further developed to target specific service-oriented problems. The primary aim is to provide efficient customer support. Customer service assistants are gaining popularity and are used commonly among telecom providers or electronic and educational organizations for the smooth functioning of processing customer requests. Such virtual assistant pop-ups during the opening a website or browsing of a certain product to help with the purchase or in the form of a help desk.

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