In the beginning, there was ELIZA.
The year was 1966 and an MIT professor by the name of Joseph Weizenbaum had a vision of creating a computer program that would mimic human conversation. Using pattern matching and substitution methodology, the German-American computer scientist designed a program that would pair the words that users entered in a computer with a list of possible scripted responses so they would feel like they were interacting with an empathic psychologist. He named his creation ELIZA and the world’s first chatbot was born.
Fast forward to 2021 and Prof Weizenbaum’s vision has resulted in a world where seemingly every company is rushing to jump aboard the chatbot bandwagon and reap the rewards that come with utilising the ground-breaking technology. Siri, Alexa, Einstein and Replika are just a few of the world’s most popular chatbots that are driving a global market expected to be worth an incredible $9.4 billion by 2024. Companies that are yet to develop a chatbot strategy risk being left behind as the technology driving the revolution continues to gather momentum.
This brings us to AI chatbot technology and the fact that the late Prof Weizenbaum would not believe just how far his chatbot dream has come.
While the word ‘chatbot’ gets thrown around a lot these days, it is crucial to remember there are essentially two types – scripted chatbots and AI chatbots. The former is the early version of the technology, with ELIZA leading the charge in the 1960s as a text-based mechanism that could reply to a limited set of simple queries with answers that had been pre-written by its developers. Operating like an interactive FAQ, scripted chatbots are programmed to respond in pre-defined ways to specific questions but fail when presented with a complex query or one not envisaged by the developers.
While this style of automated chatbot still has a significant role to play, the growing buzz in the chatbot world stems from how artificial intelligence has taken the concept to another level. AI chatbots are developed using Natural Language Processing (NLP) and Machine Learning (ML) and allow end-users to experience a more conversational tone. More importantly, the latest versions are contextually aware and can learn as they are exposed to more human language.
Evolution of Conversational AI (source: Deloitte)
A conversational computer program that can learn from the experience? That’s right and that is why the possibilities associated with AI chatbots are seemingly endless.
The mechanism that determines how an AI-enabled chatbot is going to work is its architecture, which primarily depends on three components:
Every AI chatbot is unique, with its development based on a company’s individual requirements and the usability and context of business operations.
The key is that today’s AI chatbots use NLU to discern the user’s need and then use advanced AI tools to determine what the user is trying to accomplish. The combination of machine learning and deep learning help the program develop an increasing knowledge base of questions and responses based on previous interactions, thus improving its ability to predict future needs and responses.
For example, a traditional chatbot can only respond in basic terms if a user asks for a weather forecast (eg: ‘It will be sunny tomorrow’). Conversely, an AI chatbot can use its knowledge base of previous interactions to not only tell the user it will be sunny but suggest sharing surf conditions for a potential day at the beach.
The number of uses for AI chatbots are not only extensive – they are increasing as quickly as the technology evolves. From ordering Ubers to engaging with in-home devices such as smart kitchen appliances, daily life is filled with countless moments when everyday people are benefiting from conversational chatbots. Then there is the business world, with marketers using AI chatbots to make customer experiences more personal, IT teams embracing their ability to enable self-service and customer service departments benefiting from streamlined communications.
Specific industries that have been won over by AI chatbots include:
For many businesses, the conversation has long passed the debate about whether they will launch an AI chatbot and entered the discussion of when it will happen. Add into the mix the all-important issue of ‘how to do it’, with IBM suggesting these guidelines as a great starting point when considering which platform will work best for you.
It has never been easier to launch an AI chatbot but that does not mean it is a project that should be rushed. While this blog is an excellent introduction to the concept, the next step should involve more research, garnering more knowledge and partnering with a team that can deliver the best solution for your individual needs.
Interested in further expanding your knowledge? Discover why mistaking an Intelligent Virtual Agent for a regular chatbot is akin to saying a Mini Minor and Formula 1 race car are one and the same.
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