With the evolution of AI, researchers and technologists early on realised it held the power to understand the complex nuances human speech comprises of. Human conversations have always been a challenging area for machines as it is not limited to words, there’s context, tone, sentiments, cultural and social depth amongst other nuances that can lead to multiple interpretations.xt, it’ll be an achievement of sorts. A goal completion of Artificial Intelligence.
The Conversation AI And Human Interaction Pattern
The fundamental goal of the conversational pattern is to enable the machine to communicate with us humans in a natural way. Instead of requiring us to communicate in machine-modes that we usually do like typing, tapping, swiping or clicking, the power of the conversational pattern is such that we can interact with machines the way we interact with each other by speaking or writing; communicating in a way that comes naturally to us.
Machine-to-machine, human-to-machine and machine-to-human, all interactions are examples of how AI communicates. Voice assistants, chatbots, sentiment analysis, intent analysis all are real-time examples of AI-enabled conversation systems. The applications of the conversational pattern are so broad that industries across are focused on the use of AI-enabled conversational systems, from finance to healthcare to travel and beyond. Reason being, the power of conversational AI goes beyond understanding what we write or say, it understands the context, sentiment, mood and intent and translates them into machine understandable forms.
Why Is Conversation AI The Talk Of The Town?
The pattern of human and computer communication is receiving much more focus these days because of social distancing so human-to-human interactions will now be limited and human-to-machine interactions will increase. But interacting with a system can be difficult at times. Typing or swiping is time-consuming and static content like an FAQ rarely prove to be helpful for most customers. People want to interact with machines efficiently and effectively.
Voice assistants such as Apple Siri, Amazon Alexa, Google Assistant, Microsoft Cortana, and web-based chatbots have made people familiar with how convenient human-to-machine conversations can get. However, if you have interacted with them, you know they still lack understanding in many ways. But with the advancement in AI, the ways that machines can understand and generate human language is rapidly developing.
Exploring new applications for neural networks and natural-language processing to help voice assistants and chatbots conduct smarter conversations we are using deep reinforcement learning to make it easier for algorithms to understand grammar and meaning in human language.