We have finally arrived at a situation in our lives where people no longer crave human interaction, at least not physically. Thanks to massive growth in the data science online course curriculums, we could finally evaluate the trends in AI and machine learning that are causing a shift in machine driven conversations and interfaces. We call these chatbots, virtual assistants, or voice AIs.Every 6 out of 10 data science online course projects aspires to do something in the field of virtual chatbot communication or messenger boxes powered by AI. In the last 5 years, the demand for chatbots has exploded, and this trend is expected to continue with far more reaching effect for the next 5-10 years.
In this article, we have tried to highlight the role of chatbots in modern human civilization and how these rule the data science applications, at least as demonstrated by the thousands of projects that we review as part of the assessment of leading data science online courses.
What are chatbots?
Chatbots are communication agents, also called bots (short for robots)that convincingly simulate a human conversation using an interface such as messenger chatbox, email reply, automated IVR, voice search assistant, or automated phone dialing system. There are thousands of examples of chatbot uses and these are all powered by some or the other form of AI software designed specifically for one purpose – engage the participant in a chat and trigger a response from them to complete a communication loop. All this happens between a human recipient and an AI-based chat assistant, called a bot.
The use of chatbots is particularly popular in workforce automation, customer service and contact centers, voice search media and entertainment, and personalized services management such as virtualized medical care, online movie booking, or product search on popular e-commerce platforms such as Amazon Prime.
So, when did chatbots truly come into the picture?
Chatbots developments date back to the early 1950s when data scientist Alan Turing created the ‘TURING Test”. This triggered a tsunami of innovations and research activities to exemplify the use of machines for the simulation of human intelligence and behavior. Within 20 years, the first prototype chatbot called ELIZA came into the world. ELIZA could construct simple answers in response to coded questions typed in simple English. In 1972, Parry followed ELIZA into the world as a sibling.
But, it wasn’t until 1994 that chatbot as nomenclature was ever used to describe a machine level query responder. Michael Mauldin, the creator of Julia AI verbal robot (Verbot) coined the word chatterbox, which shortened to the more modern chatbot. Julia Verbot finds its roots (at least inspiration wise) from Rog-O-Matic, a gamer based bot system that was supposedly an epic winner in video games. Michael Mauldin was one of the authors of the Rog-o-Matic code.
Since then, practically every global company has built its own chatbot and named it to market the technology and win more customers. For example, Salesforce named its bot Einstein. AWS has a Lex. Siri, Meena, and Alexa are some of the most popular bots you would ever come across, and they certainly are a benchmark standard in their own ways.
Why do we need data science to build a chatbot?
The goal of building a chatbot is to free human agents from repetitive tasks. As per research, call center executives that use chatbots for automated answers on FAQs are found to be 63% more effective at solving critical customer problems and garner 93% more customer satisfaction ratings.
A chatbot is an AI-driven software application. You need tons and tons of data to build an algorithm for the bot interaction, not to mention a workflow automation channel for the bot to respond to questions that can answer as per input query. When a bot is unable to answer further questions, it has to be trained enough to allow human assistants to take over.
In order to build this level of bot simulation, you need a solid education in data science and cognitive intelligence that covers the entire spectrum of AI Machine learning applications such as deep learning, Natural language processing, voice analytics, text analytics. Now, we are going deeper into the realm of physical interactions thanks to advancements in areas of face detection, computer vision, image processing, artificial neural networks (ANNs/ CNNs), and gesture control.
If you are looking forward to a career in building chatbots as a service for customers, a course in data science would serve the purpose.