In this fast-paced digital era, with constantly evolving technology, Conversational Artificial Intelligence has emerged as one of the game-changing technologies that is transforming the way humans and computers interact with each other. From virtual assistants that understand and respond to our voice commands, to chatbots that engage with us in natural language conversations, Conversational AI is proving to be a revolutionizing development in technology.
Conversational AI is a type of artificial intelligence that allows computers to engage in human-like interactions with users. Leveraging Natural Language Processing (NLP), Machine Learning (ML), and other advanced AI techniques, Conversational AI systems possess the ability to understand and respond to user inputs in a conversational manner.
How Does Conversational AI Work?
From a user’s point of view, Conversational AI seems rather simple. However, there are several processes that run to make a single interaction possible. Conversational AI uses a combination of technologies including Machine Learning, Natural Language Processing, Automated Speech Recognition, and Natural Language Understanding to process each word in an interaction and figure out the best way to respond while also learning to get better from each interaction.
These Conversational AI systems are trained on huge amounts of data, including text and speech. This data is used to teach the system how to understand and process human language. This knowledge is constantly being updated and is used to interact with humans in a natural way.
- Input Processing
If the input is spoken, ASR, also known as voice recognition, is used to decipher the words and translate them into a machine readable format. The first step involves Natural Language Processing (NLP). It is responsible for correcting spellings, identifying synonyms, interpreting and correcting grammar, recognizing sentiment, and breaking down a request into words or sentences that make it easier for the computer to understand.
- Input Analysis
A number of Deep Learning and Machine Learning models, collectively known as Natural Language Understanding (NLU), take over to allow Conversational AI to identify the intent or topic of a request or input and extract other important information that is used to trigger additional actions.
In case of text-based inputs, NLU is used to decipher the meaning and intent of an input. However, in case of speech-based inputs, it will leverage a combination of ASR and NLU for the same.
- Output Generation
During this step, the application generates a response based on purpose and intent using Dialog Management. Further, natural language generation (NLG) orchestrates and converts the response into a human-understandable format. This process takes place in a matter of seconds to give the response required.
Conversational AI Vs Chatbots
A chatbot is a rule-based system that automates certain interactions and follows a specific purpose, thus following a certain flow. However, this implies that they can only give responses to predefined questions or instructions. Answers are, thus, pre-determined with very little scope of maneuvering the conversation. Moreover, chatbots are suitable for supplementing the FAQs section wherein only fixed responses are required.
Conversational AI, on the other hand, allows people to communicate with applications and websites in their own language. It goes beyond the traditional question-answer format. Yet another difference is that Conversational AI can be used via text as well as speech-based conversations.
In essence, Conversational AI is an extended development of traditional chatbots that enable authentic conversations between a human and a computer.
Why Choose Conversational AI?
Having human-like conversations with robots and machines was a concept only restricted to movies up until a few years ago. Today, with technology like Conversational AI, we can have effortless conversations with technology. This has led to it becoming a necessity, especially for businesses. It serves the following benefits:
Hiring and training a customer service team can be expensive, especially if they are expected to answer customer queries even beyond office hours. Using Conversational AI chatbot softwares can help reduce costs, responding to queries instantly and providing constant support to customers.
In case of a sudden increase in the volume of queries or inputs, Conversational AI can handle the situation. It can segregate queries based on intent, past call or text history, requirements, and so on. It can also categorize queries based on value.
Better Customer Service
Conversational AI systems comprehend the user’s intent and query’s context by using ML and NLP. As a result, the customer experience is more personalized. Based on research by MIT Technology, 4 out of 5 executives said that after implementing Conversational AI tech, customer satisfaction, service delivery, and the overall quality of agent performance improved significantly.
Most of the interactions with support are related to seeking information and may be repetitive. This can be handled by AI so that there is a sense of continuity and comprehensiveness within the customer experience. It also allows human resources to be available for more complex queries.
Use Cases of Conversational AI
Through chatbots, virtual assistants, and other conversational interfaces, all types of organizations are leveraging the power of Conversational AI to automate tasks, provide personalized interactions, and offer 24/7 support. Let’s explore some key use cases of Conversational AI, showcasing its potential to transform various industries and deliver value to businesses and end-users alike.
Conversational AI can assist customers by providing personalized recommendations, finding the right products, and facilitating purchases through virtual shopping assistants. This can enhance the customer experience, increase sales, and improve customer engagement.
Conversational AI can help health care providers offer remote care, streamline administrative tasks, and improve outcomes by providing health information, scheduling appointments, and monitoring patient symptoms.
Conversational AI can offer personalized tutoring, answer questions, and offer educational content through virtual tutors or learning assistants. This will lead to an enhanced learning experience, with individualized feedback, and self-paced learning.
Conversational AI can assist employees with HR-related inquiries, provide information about company policies, and facilitate employee self-service tasks such as leave requests and benefits enrollment. This can streamline HR processes, improve employee experience, and reduce HR workload.
It can also help with banking inquiries, provide financial advice, and facilitate transactions through virtual banking assistants. This can offer convenient and secure banking services, reduce wait times, and enhance customer engagement.
These are just a few examples of how Conversational AI can be used in various industries. The versatility and potential of Conversational AI make it a powerful tool for businesses to enhance customer experiences, streamline operations, and improve overall efficiency.
Build Your Conversational AI Model with E2E Networks
E2E Cloud offers cloud GPUs for your AI/ML models. NVIDIA A100-80GB features the world's most advanced accelerator, the NVIDIA A100 Tensor Core GPU, enabling enterprises to consolidate training, inference, and analytics into a unified, easy-to-deploy AI infrastructure that includes direct access to NVIDIA AI experts.
To launch a GPU for your workload, you can log in to the MyAccount portal. Once you have logged in, you can click on Compute. From the drop down menu, click on GPU.
You will now see the available GPU options for you to launch.
You can choose and launch the GPU as per your requirements.
For further assistance, you can get in touch with our sales team at email@example.com.