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CONVERSATIONAL AI AND CHATBOTS

 





CONVERSATIONAL AI AND CHATBOT

 Conversational AI follows the same principle and builds interaction between humans and machines. It allows machines to understand, respond, and react like a real conversation with cognitive human skills. Conversational artificial intelligence is a way to simulate human communication. The main components of conversational AI are natural language processing (NLP) and machine learning (ML). Based on these two components, you can build a virtual interlocutor. It can answer and ask questions, have a conversation, joke, and anything else that was programmed into the conversational AI. The study of natural language in AI is known as Natural Language Processing (NLP). NLP at the phonetic (relationships between sounds) & phonological levels includes Speech Analysis, Speech Transcription / Speech to Text (STT), Speech Synthesis / Text to Speech (TTS), etc.


The main concept behind conversational ai is machine learning algorithms and the datasets given to it. The common conversational AI technologies are chatbots, virtual agents, virtual assistants, digital assistants, and digital employees. A chatbot or bot is a computer program that allows the user to converse with a virtual agent. Chatbots are computer programs built on the concept of artificial and data analytics and are commonly installed on websites or social media platforms. This rise in chatbots is projected to be significant; as reported by Global Market Insights, the chatbot market will be worth $1.34 billion by 2024, with 42.52% of that alone from the customer service sector. The rise of chatbots is tightly linked to new technological advancements and evolving customers’ expectations of brand interactions. With the prevalence of social media and mobile messaging applications, the average consumer now expects a company to resolve issues and respond to requests with speed.

HOW DOES CONVERSATIONAL AI WORKS?

It starts working with voice/text input from the users. The input may come from various channels, models, and languages. For Voice, Automated Speech Recognition (ASR) technology helps to translate the spoken format into a machine-readable format, text. Natural Language Understanding (NLU) helps to process the structure data and find the right contexts and languages. It then integrates with the database and external APIs to extract the required pieces of information The last stage is dialog management. It manages the responses and converts them into a human-understandable format using Natural Language Generation (NLG). After that, conversational AI applications either deliver the response in text, or text to speech.

USE CASES OF CONVERSATIONAL AI

Data Collection-Conversational AI is not just about customer interaction. It can help your business collect and analyze data that you can use to make important decisions, providing you with a competitive edge. We can even use this data to understand customers and help our staff identify cracks in your current processes.

Retail-Conversational AI is being increasingly used in retail and e-commerce. from product insights to customer recommendations AI has helped access the issues and made the work a lot easier. It also helps solve issues like the scalability of multiple inventories etc.

Healthcare-Conversational AI is proven to be revolutionary in the field of healthcare. it helps doctors and nursing staff in the process of diagnosis. Diagnosis: Conversational AI tools can diagnose health conditions online by asking questions to patients. Then it can learn from the patients’ answers to acquire insight into their health issues without having them wait for a medical assistant. It also helps in appointment scheduling and also acts as a virtual assistant for Patients and Practitioners. A conversational AI like a virtual agent (or intelligent virtual assistant) can help them understand complex medical topics. This can help reduce stress in healthcare by freeing up medical assistants to do more pressing work. Chatbots and conversational AI also contribute to various other fields like finance, banking, IoT, Real estate, etc. Conversational AI continues to evolve, making itself indispensable to various industries such as healthcare, real estate, online marketplaces, finance, customer support, retail, and more. And the conversational AI applications keep increasing with time making human agents’ lives easier.

 

Challenges of AI Technologies

Although there are a lot of use cases as well as advantages of chatbots and AI still it lacks in a few things and faces issues like Unclear user communication. Even a well-programmed conversational AI product has interoperability problems. Sometimes a chatbot cannot understand its users for various reasons. Such problems include slang, fuzzy speech, strong accent, loud background noise, strange messages, emojis, etc. Also, languages are evolving, and new words appear. In addition, simpler chatbots have a problem with unprogrammed scripts.  Another challenge that ai face is Data Security and Privacy. This is an essential point, especially when it comes to personal data. Development teams need to build conversational AI reliably. The possibility of obtaining private information should be excluded. The guarantee of the confidentiality of personal data must be respected. User apprehension is also something that challenges AI. Consumers may be wary of conversations with chatbots. They may be reluctant to share personal data with a machine rather than a person. In addition, many people prefer face-to-face communication with human workers. According to the survey, about 22% of participants say that bots cannot recreate live communication like with real people.

 

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