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Glossary

What is Conversational AI?

Conversational AI is essentially a branch of artificial intelligence that enables computers to converse or talk to humans in a virtually human-like language, whether it is a text or speech. As a conversational AI application, it involves the use of NLP, machine learning, and deep learning technologies that would enable computers to process and interpret human input much like humans would in responding.

Key Terms and Concepts

  • NLP: It stands for Natural Language Process. It refers to the area that concerns the interaction between computers and human (or natural) languages. Some of these tasks include speech recognition, natural language understanding, and generation.
  • Machine Learning: A subset of AI where an algorithm is trained to learn from data; it applies its learning to make predictions or decisions, as opposed to explicit hand programming.
  • Deep Learning: A form of extreme machine learning using an artificial neural network, with two or more layers, to learn complex patterns from the data.
  • Chatbot: A computer program trying to converse with a human through text or voice.
  • Voice Assistant: a voice-activated assistant - can set the alarm, play radio, and answer questions, just to name a few.
  • Conversational Interface: It is a conversational interface that uses natural language for communication between users and the computer system.
  • Intent Recognition: It refers to the recognition of the actual intent of a user's question or statement.
  • Dialog Management: It involves managing the flow of conversation in order to ensure that the chatbot provides the proper kind of response.
  • Sentiment Analysis: Identifying and categorizing the emotional tone of a text.
  • Conversational AI Platform : Software platform that provides tools and infrastructure to develop and deploy conversational AI applications.

Types of Conversational AI

  • Rule-Based Chatbots: These chatbots have predefined rules and scripts to answer user inputs.
  • Machine learning-based chatbot- The chatbot uses the algorithms of machine learning that learn data and provide better responses with time.
  • Hybrid Chatbots: These use the combination of rule-based approaches with machine learning approaches, therefore achieving a balance between efficiency and flexibility.

Applications of Conversational AI

  • Customer Service: Organizations through their chatbots and voice assistants can provide customers with 24x7 service. They can answer frequent questions the customers have and can solve some simple problems.
  • Virtual assistants: Personal assistants, such as Siri and Alex, Google Assistant, can schedule an appointment, give you reminders, and run your smart home appliances.
  • Education: It can provide personalized tutoring and learning experiences.
  • Healthcare: It can help the patients in setting up appointments, finding doctors, and answering their medical questions.

Challenges and Future Directions

  • Technical Limitations: Conversational AI is in its developmental stages and has the technical limitation of not being able to deal with high-order language complexity or nuanced questions in every situation.
  • Ethical Concerns: There are also ethical concerns related to the use of conversational AI, issues regarding privacy, bias and accountability.
  • Integration with Other Systems: It integrates systems but is more complex and time-consuming.
  • Training and Maintenance: Conversational AI models need to be constantly trained and maintained for performance efficiency.

Despite the current under-development of conversational AI technology and the growing awareness of its applications, innovation and sophisticated uses would emerge over the coming years.

 

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