The Evolving Visage of Customer Service with AI and ML
Introduction
Today, I can hardly find a good online store that does not put forward at least one form of high-tech #customer support. Customer service #virtual assistants can answer simple queries like informing me about the status of my order, helping me look for a particular product based on the filters I put in, and so on. This greatly enhances our online shopping experience due to instant responses, personalized product displays and also through reminders and notifications about great deals.
But do you know how this whole superior customer service experience is brought forward? It is with the help of AI-powered ML.
Difference between AI and ML
The terms #Artificial Intelligence and #Machine Learning are often used interchangeably by tech companies, but there are differences between the two. I am trying to give you a clearer perspective of the two terms so that you can better understand the #difference between AI and ML. Don't use plagiarised sources.Get your custom essay just from $11/page
I found the aptest description of Machine Learning to have been given by Tom M. Mitchell, the Interim Dean at the School of Computer Science at CMU, Professor and Former Chair of the Machine Learning Department at Carnegie Mellon University. He says the scientific field of Machine Learning (#ML) tries to find to answer the question: “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?” Mitchell considers machine learning to be the study of computer algorithms that enables computers to improve through experience in an unsupervised manner, without human intervention.
I feel the best way to explain #Artificial Intelligence (AI) is specified by Andrew Moore, Former-Dean of the School of Computer Science at Carnegie Mellon University, “Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence.”
The objective here is to learn from pre-existing data and get the full potential out of the machine on a certain task related to that data. AI involves decision making, whereas ML allows the system to learn new concepts from the data and helps the system develop into a simulation of the human brain in terms of responding to particular circumstances.
Real-Time #Examples of AI and ML
Today we see so many amazing uses of AI and ML that impact our everyday lives and help businesses make informed decisions and maximize output from operations.
Human-AI interaction devices like Google Home, Siri, and Alexa all represent AI in the real world. Netflix, Amazon Prime, and YouTube use ML-powered video prediction systems to bring our preferred video content to us. These are all intelligent virtual assistants that increase our skills as humans and make us more productive as professionals.
AI Cycle and Knowledge Reasoning
We, humans, are considered to be intelligent because we can understand, analyze and interpret knowledge. We perform a number of actions in the real world based on our knowledge. But how can AI help machines perform all these things? That comes under the role of #AI in knowledge reasoning and representation’.
#Knowledge representation (KR) involves AI agents thinking and behaving intelligently as a direct result of such thinking. KR allows the representation of information about the real world in such a way that computers can understand and employ this knowledge to solve complex problems in real-time, like communicating with humans in their own natural language. Thus KR helps an intelligent machine to learn from the knowledge and act intelligently as a human.
Say for example if you meet a person whose native language you do not understand, then you will not be able to communicate with them. Similarly, if the AI agent cannot understand the human language, they cannot act on it to display intelligent behavior. I have tried to explain how an AI system can relate to the real world and what components help it to show intelligence with the help of this line-diagram:
The different #components of an AI system include:
- Perception
- Learning
- Knowledge Representation and Reasoning
- Planning
- Execution
The Perception component gathers information from its environment, be it audio, visual or any other sensory input. The Learning component then learns from the data captured by the Perception component. The main components are Knowledge representation and Reasoning, which are involved in displaying intelligence by forming a meaningful and useful representation of the incoming information. The Knowledge component may be coupled with the adaptive Learning component to draw trends from the data that has been perceived. Planning and Execution by the AI agent depend on the analysis by the KR and R components.
What I find most interesting is that the chatbots which are able to extract information from their respective websites and present to us on request need to be adept at understanding our natural language. This complicated task is made possible by rapid advancements in natural language processing.
A number of platforms are available in the market these days that help in the design, development, management, and operation of solutions that leverage technologies making use of NLP. Not only chatbots that can stimulate meaningful conversations with us in our own language but also voice assistants that are speaking interfaces that enable spoken interactions have come of age.
Gartner, the global market research and advisory firm, predicts that within 2020, more than 25% of customer service and support operations with integrating intelligent virtual assistants (#IVAs) across customer engagement channels, which is a huge jump from less than 2% in 2015. This will be made possible by advancements in speech recognition powered by AI and improvements in NLP technology. In the coming years, voice-based virtual agents will be our first line of interaction with companies.
Conclusion
Synergy with other digital solutions like IoT, analytics and image recognition will facilitate the voice assistants in bringing together information from these devices to fetch meaningful insights for users. Coupled with advanced analytics, the IVAs will also be able to foretell customer needs and behavior. This will cut the costs of implementing traditional support channels for businesses and valuable human resources can be directed toward more complicated and creative tasks.
Tags
#Difference between AI/ML, #Examples of AI/ML, #AI in Knowledge reasoning, #speech recognition, #Voice assistants, #chatbots, #customer support, #virtual agents
References
- https://www.forbes.com/sites/bernardmarr/2018/04/30/27-incredible-examples-of-ai-and-machine-learning-in-practice/#4d7bc1b67502
- https://medium.com/datadriveninvestor/differences-between-ai-and-machine-learning-and-why-it-matters-1255b182fc6
- https://www.javatpoint.com/knowledge-representation-in-ai
- https://www.skedsoft.com/books/artificial-intelligence/the-ai-cycle
- https://www.geeksforgeeks.org/difference-between-machine-learning-and-artificial-intelligence/
- https://interestingengineering.com/17-everyday-applications-of-artificial-intelligence-in-2017
- https://www.docsity.com/en/knowledge-representation-and-reasoning-artificial-intelligence-lecture-notes/198857/
- https://chatbotsmagazine.com/why-artificial-intelligence-plays-an-important-role-in-chatbot-development-7a6da9fd1817
- https://towardsdatascience.com/understanding-ai-chatbots-challenges-opportunities-beyond-fb657fa3e0da
- https://clearbridgemobile.com/7-key-predictions-for-the-future-of-voice-assistants-and-ai/
Author Details
Sebanti Ghosh is a tech enthusiast who is always scanning technical write-ups for the latest news in the innovations in technology and loves sharing her insights online.