AI powered CHATBOTS and their Future
Introduction:
Artificial Intelligence (AI), is the ability of a digital computer or a computer-optimized robot to perform tasks similar to the ones performed by human intelligence.
Customer service is one of the largest users of Artificial Intelligence and Machine Learning where AI-powered chatbots are finding various applications for the improvement in the standard and efficiency of service. Messaging apps simply outstrip other types of applications.
Users no longer find the need of hundreds of apps on their mobile devices to support every distinct brand. Messaging applications solve this drawback — they are simple, easy, and fast. Thereafter, businesses follow their audience and the idea of AI powered chatbots for customer feedback and service emerged.
What is a Chatbot?
A chatbot is a computer program that allows humans to interact with technology using a variety of input methods such as voice, text, gesture and touch, 24/7 365.
Naturally for several years chatbots were used in customer service environments but have now found applications in a variety of other roles within enterprises to improve customer experience and business efficiencies. Identified by a number of different names such as a conversational AI bot, AI chatbot, AI assistant, intelligent virtual assistant, virtual customer assistant, digital assistant, conversational agent, virtual agent, conversational interface, and more, chatbots have become increasingly popular.
But just as chatbots have a variety of different names, they also have a fluctuating degree of intelligence. A standard chatbot might be little more than just a front-end solution for answering simple FAQs. Chatbots that are designed using some of the bot frameworks currently available may offer some more advanced features like slot filling or other simple transactional capabilities, such as taking pizza orders.
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But, it’s only advanced conversational AI chatbots that have the intelligence and capability to deliver the sophisticated chatbot experience that most entities are looking to deploy today.
Why have Chatbots become Popular?
Smartphones, wearables and the Internet of things (IoT) have transformed the technological landscape in recent years. As the digital artifacts have gotten smaller, the computing power inside has become greater. But mobile apps and data-heavy activities don’t go hand in hand. Wading through complicated menus isn’t the quick and seamless user experience businesses have promised to deliver today.
Also, users are no longer content to be limited by the communication methods chosen by an organization. They want an user-interface with technology across a varied number of channels. Chatbots offer a way to solve these issues by allowing customers to simply ask for whatever they need, across multiple channels, no matter wherever they are and whenever they want.
How do Chatbots Work?
On a humble level, a human interacts with a chatbot.
If a voice is used, the chatbot first turns the voice data input into text (using Automatic Speech Recognition (ASR) technology). Text only chatbots such as text-based messaging services will skip this step.
The chatbot then analyses the text (input) by looking at the keywords, then considers the best response, and delivers that back to the user. The chatbot’s reply (output) may be delivered in any number of different ways such as written text, voice via Text to Speech (TTS) tools, or perhaps by completing a given task.
It’s worth noting that, understanding humans is quite complex and may become difficult for a machine. The delicate and nuanced way humans communicate is a very complex task to reconstruct artificially, which is why chatbots use several natural language principles:
Natural Language Processing (NLP)
Natural language processing is the heart of AI-driven chatbots. With the help of sophisticated NLP algorithms chatbots can process the received text: interpret, infer, and determine what was meant and then define a series of appropriate actions.
It splits the user input into sentences and words. It also standardizes the text through a series of techniques, for example, converting it all to lowercase or correcting spelling mistakes before determining if the word is an adjective or verb — it’s at this stage where other factors such as sentiment are also considered.
To make the message understandable for a chatbot, NLP proceeds five main steps:
• Lexical analysis
• Syntactic analysis (parsing)
• Semantic analysis
• Discourse integration
• Pragmatic analysis
Natural Language Understanding (NLU)
Natural Language Understanding helps the chatbot understand what the user said using both general and domain-specific language objects such as lexicons, synonyms, and themes. These are then used in conjunction with algorithms or rules to construct dialogue flows that tell the chatbot how to respond.
NLU is harder than NLG, as the natural language has a significantly rich form and structure. It involves mapping the given input and analyzing various aspects of the language.
Natural Language Generation (NLG)
Delivering a meaningful, personalized experience beyond pre-scripted responses requires natural language generation. This enables the chatbot to interrogate data repositories, including integrated back-end systems and third-party databases, and to use that information in creating a response.
Conversational AI technology takes NLP and NLU to the next level. It allows enterprises to create advanced dialogue systems that utilize memory, personal preferences, and contextual understanding to deliver a realistic and engaging natural language interface.
Why Chatbots & Customer Service Go Hand-in-Hand
· Advances in AI: smart AI capabilities mean chatbots can help predict what customers will need or want to buy based on past purchase history and additional machine learning capabilities. Some of the top AI chatbot tools can then begin to take a proactive approach other than a reactive upselling approach outside of email.
· Chat apps are one of the most popular on the web: And multiple social media channels (the other most popular apps) are integrating chat functionality into their platforms — along with chatbot capabilities. This gives businesses a new way to get in front of their customers where their customers already are.
· Little to no development costs: There are tons of apps and tools out there you can use to launch a chatbot strategy and begin testing its effectiveness. There is no huge up-front cost — just conversation mapping work you need to do on your end.
· Growing conversation APIs: These APIs allow machines to speak to humans using natural language processing — which means you don’t feel like you are talking to a chatbot, though most folks know they are.
Chatbots Now and in the Future
Business applications of chatbots for consumer-facing goods are growing rapidly.
Over 59% of millennials and 60% of Gen Xers in the United States have interacted with chatbots.
And according to a Facebook survey, more than 50% of customers say they’re more likely to shop with a business that they can connect with via chat.
According to Gartner,
“By 2020, 85% of our engagement with businesses will be done without interacting with another human. Instead, we’ll be using self-service options and chatbots.”
Additionally, according to an Oracle survey,
“80% of businesses said they currently use or are planning to use chatbots by 2020.”
Customers expect to be able to find the information they’re looking for in a click of a button and the blink of an eye. When this isn’t possible, frustration brews, and this can lead to you losing a sale or even losing a potential customer forever. To understand how chatbots can mitigate some of these frustrations and improve the user experience, we need to first look at how customers are choosing to interact with businesses today.
In the 2017 Global State of Customer Service Report by Microsoft, we can see how email and telephone are still the dominant communication channels for many customers. And while live chat, self-service, social media, and chatbots are relatively lower in terms of raw volume, the growth of these channels is staggering.
Affiliation:
Sahil Bhise, Fourth Year B.Tech Integrated, Computer Engineering, NMIMS’s MPSTME.
Sources:
· https://chatbotslife.com/the-self-feeding-chatbot-a491871ea524
· https://www.ecommerce-nation.com/chatbots-advantages-and-disadvantages-of-these-tools/
