Virtual Avatars and AI chatbots are becoming increasingly popular nowadays because of their ability to influence a customer’s purchase of real-world products. If a brand is looking to incorporate a virtual avatar into its marketing efforts, it must be aware of how this digital being functions. This requires an understanding of the heart of the avatar, and that is conversational intelligence. With the increase in competition and demanding customers, brands need to rely on conversational AI to keep customer satisfaction high while keeping support costs low at the same time.
Understanding Conversational Intelligence
Conversational AI is a software that uses artificial intelligence to analyze speech or text in order to get data-driven insights from conversations that the digital bots have with customers. Conversation data from these platforms are streamed between other technology platforms like CRMs, data analytics, and digital experience platforms so that they can take action on the data in real-time. This is then used by marketing or sales teams to improve buying experiences, increase conversions, and ultimately, drive more revenue.
The Brain of the Chatbot/Digital Avatar
Conversational AI uses a combination of natural language processing, machine learning, speech recognition, natural language understanding, and other language technologies to enable automatic messaging and conversation between computers and humans for both virtual avatars and chatbots. This process is required to understand the spoken or written word as well as figure out the best way to handle a response to a customer’s input.
NLP – Natural language processing involves a mix of computer science, artificial intelligence, and data mining. NLP can be called a subset of AI, and it includes programming computers to process large volumes of data.
This process contains various tasks that break down natural language into smaller elements so that the machine understands how they work together. Common tasks include parsing, speech recognition, part-of-speech tagging, and information extraction. NLP’s main focus is to convert text to structured data.
NLU – Natural language understanding can be considered as a subset of NLP and is a vital part of achieving successful processing of data. NLU is focused primarily on machine reading comprehension. The main goal is to get the computer to understand what an entire body of text really means.
NLU works by using AI algorithms to recognize certain attributes of language like context and intent. It enables the computer to understand the subtleties and variations of the language. NLU can be particular to a chatbot.
ML – The machine learning process for NLP involves a set of techniques for identifying parts of speech and other aspects of the text. There are 2 ways of this working.
1.The ML techniques are made into a model and then applied to the other text. This is called supervised machine learning.
2.It could also be a set of algorithms that work across large sets of data to extract the meaning, and this is called unsupervised machine learning.
ML techniques use image processing and understanding computer vision to create an entity. Algorithms use data to figure out the next set of data.
Using Trained Models
Machine learning and other forms of trained models allow computers to recognize combinations of words that typically indicate an intent so that they can improve from the conversations they have with humans.
A model consists of one specific set of data and then a set of sentences are tested to see how good the model is. An accuracy test is performed and the best model is picked out from these training algorithms.
Conversational Intelligence within a Digital/Virtual Avatar
Since it is different for virtual avatars and chatbots, when it comes to digital avatars in particular, the working of conversational AI is pretty similar to chatbots, apart from a few added things. A physically-embodied human provides a more immersive experience than a chatbot because you can see the avatar visually. Since a virtual avatar talks with the help of expressions and gestures, a different set of ML needs to be present. Different ML is also required for lip-syncing (to sync with speech), which gives an emotional aspect to how the user is saying it and in the right way.
Use cases of Conversational AI
The most known example of artificial intelligence and language is the Turing Test, developed by Alan Turing in the 1950s as a way to determine whether a computer could be considered intelligent. In 1966, the program ELIZA was the first artificial intelligence chatbot to use conversational AI, which attempted to pass this test, as users believed that they were having a conversation with a real human being. ELIZA was a chatbot designed to imitate a therapist who would ask open-ended questions and even respond with follow-ups.
This is the most recent development of conversational AI in the form of a physical social robot called FURHAT. The first of its kind on the market with a back-projected face! Users are just a mouse click away from changing skin color, male or female characteristics, size of eyes or lips, etc. Animated projection allows for smoother facial movements to mimic a realistic human. Adding to this ability, the robot can participate in a conversation through natural movements such as nodding, head-shaking, and raising eyebrows to get a more human-like conversation.
DaveAI‘s virtual avatar uses a unique conversational intelligence that it is modeled around the brain of a salesperson. It uses algorithms to deliver hyper-real time recommendations based on the data collected. This experience is unique to every customer that Dave interacts with, and this gives it a personalized touch.