Even though the technology is not new, our the last few months our interaction with chatbots has come stratospheric. Recent developments made available by OpenAI now make it possible for companies and individuals to harness the power of artificial intelligence. Helping businesses with customer support, marketing, product development and more. Individuals are also learning faster and exploring new ideas and applications that are being created on a daily basis.
If you would like to learn more about chatbots and the terminology used when discussing technology, you will find this introductory Chatbot glossary of terms a useful resource. Providing a reference for those terms that you may not fully understand yet.
1. Chatbot: A chatbot is an AI software that is designed to converse with humans in their natural languages. These conversations can take place over various channels such as messaging applications, websites, mobile applications, or through telephone. Chatbots are typically used to automate tasks that would otherwise require human interaction, such as customer service queries, booking appointments, or providing information about a product or service.
2.Intent Recognition: In the context of chatbots, intent recognition refers to the ability of the bot to understand and ascertain the purpose behind the users input. Using Natural Language Processing (NLP) techniques, the bot can infer the users intent and respond accordingly. For example, if a user types Whats the weather like?, the chatbot recognizes the intent as asking about the weather and would ideally respond with a weather update.
3.Context Awareness: Context awareness refers to a chatbots ability to comprehend the surrounding context of a conversation. By keeping track of the conversation history and user preferences, the bot can provide relevant and personalized responses. This attribute is critical for maintaining meaningful interactions and providing the user with accurate information.
4.Rule-Based Chatbot: A rule-based chatbot operates based on a set of predefined rules. These bots can only respond to specific commands or queries theyre programmed for. While they are efficient at handling specific tasks, they tend to falter when faced with complex interactions or unexpected queries as they lack the ability to learn from experience.
5.AI Chatbot: An AI chatbot utilizes artificial intelligence (AI) and machine learning (ML) technologies to learn from previous interactions and refine its responses over time. This ability to learn allows these chatbots to handle more complex interactions than a rule-based chatbot. They use NLP to understand human language, making them capable of more natural and interactive conversations.
6. Conversational AI: Conversational AI refers to technologies that allow machines to engage in human-like conversations. These systems use NLP for understanding the input, natural language understanding (NLU) for processing the input, and natural language generation (NLG) for formulating responses. Conversational AI can be used in various applications, such as chatbots, voice assistants, and messaging apps.
7. Voicebot: A voicebot is a voice-enabled chatbot that can understand spoken language and respond in a conversational manner. Voicebots use voice recognition technology to understand verbal inputs, NLP to process the inputs, and text-to-speech technologies to provide spoken responses. Examples of voicebots include virtual assistants like Siri, Google Assistant, and Alexa.
8. Text-to-Speech (TTS): TTS is a technology that translates digital text into spoken voice output. This technology is crucial in the functionality of voicebots as it allows them to provide audible responses to the users queries. TTS is often used in applications that read out loud text content, like e-books or news articles.
9. Speech-to-Text (STT): STT is a technology that converts spoken language into written text. It is the reverse process of TTS and is used in voicebots to comprehend verbal inputs from users. This technology is commonly used in transcription services and voice-activated systems.
10. Bot Training: Bot training is the process of providing data to a chatbot, allowing it to learn and improve its performance. This process often involves teaching the bot to understand different user intents, derive meaningful entities from the input, and generate relevant responses.
11. Utterance: In the context of chatbots, an utterance refers to the input given by a user for the bot to interpret. This input could be in the form of written text or spoken words.
12. Entity: Entities are important pieces of information that a chatbot extracts from a users utterance. These could be specific details like dates, locations, product names, etc. For example, in the sentence I want to book a flight to Paris, the entities would be book, flight, and Paris. These details are crucial for the chatbot to carry out the required action.
13. Fallback Intent: This is the intent that a chatbot falls back on when it cant match a users input with any of its predefined intents. Its essentially a default response when the chatbot is unsure of how to respond. This could include responses like I didnt understand that, could you please rephrase? or Im sorry, I dont have the information youre looking for.
14. Dialog Flow: This refers to the sequence and structure of messages exchanged between a user and a chatbot within a conversation. A well-designed dialog flow is critical for maintaining a coherent and engaging conversation.
15. Multimodal Interaction: This involves interactions with a chatbot that go beyond text and voice and may include images, videos, and other forms of media. For example, a chatbot might show an image or a video clip in response to a user query, providing a richer and more interactive experience.
16. Omnichannel: This term refers to a sales or support approach that aims to provide a seamless user experience, irrespective of the channel of interaction. This could be online on a desktop or mobile device, or offline in a physical store. An omnichannel chatbot would be able to maintain a continuous conversation with a user across different platforms.
17. Response Time: This refers to the time taken by a chatbot to provide a response after receiving a users input. A faster response time usually leads to a better user experience.
18. Chatbot Platform: This is a software or service that provides the tools and infrastructure required to build, train, and deploy chatbots. These platforms usually offer a range of features, such as NLP, intent recognition, entity extraction, dialog flow management, etc. Examples include Googles Dialogflow, Microsofts Bot Framework, IBM Watson, and Rasa.
19. Human-in-the-Loop (HITL): This is a model where a human intervenes in the decision-making process of a chatbot. Typically, the human steps in when a chatbot is unable to handle a query. This not only helps in addressing user queries more effectively but also provides additional data for training the chatbot.
20. Predictive Suggestions: These are AI-powered suggestions provided by a chatbot based on its understanding of user intent and context. For instance, if a user asks a restaurant chatbot about vegetarian options, the bot could predictively suggest the most popular vegetarian dishes.
21. Widget: A widget is a small software application that can be embedded into another application. In the case of chatbots, a chatbot widget can be added to a website or mobile application, allowing users to interact with the chatbot without leaving the webpage or app.
22. On-Premises Chatbot: This type of chatbot is hosted on the users own servers instead of the cloud. This type of deployment allows for greater control over data and can potentially offer better data security. However, scalability and access can be more challenging compared to cloud-based solutions.
23. Cloud-Based Chatbot: A cloud-based chatbot is hosted on cloud servers and can be accessed from anywhere with an internet connection. While this offers ease of access and scalability, data security and privacy rely on the protocols of the cloud service provider.
24. Application Programming Interface (API): An API is a set of rules and protocols that allow different software applications to communicate with each other. In the context of chatbots, APIs are often used to integrate the chatbot with other software systems, such as CRM software or databases.
25. Active Learning: This refers to a type of machine learning where the model can ask for clarification or more data when it encounters a situation or input its unsure of. By querying the user or another intelligent system, the model can learn more effectively and continuously improve its performance.
26. Sentiment Analysis: This is the process of using natural language processing, text analysis, and computational linguistics to identify and extract subjective information from source materials. By understanding the sentiment behind a users input (e.g., positive, negative, neutral), chatbots can better tailor their responses and handle interactions more effectively.
27. Chatbot Efficacy: This refers to the ability of a chatbot to fulfil a users intent or answer a query accurately and effectively. Its essentially a measure of how well the chatbot is performing its intended function. High chatbot efficacy can lead to improved user satisfaction and efficiency in tasks like customer support or data gathering.
28. Context Switching: This refers to the ability of a chatbot to handle changes in the topic of a conversation, without losing the context from earlier in the conversation. This is important for maintaining a coherent and natural conversation, especially in longer interactions or when users bring up new topics.
29. Training Data: This is the initial set of data used to help a machine learning model (like a chatbot) learn and respond to specific situations. This data is used to train the chatbot to recognize patterns, understand different intents, extract meaningful entities, and generate appropriate responses.
30. Chatbot Analytics: This involves the analysis of data from chatbot interactions to understand its performance, identify areas for improvement, and make informed decisions for future developments. Metrics could include user satisfaction scores, response times, success rates, fallback rates, and more.
31. Conversational Interface: This is a user interface that mimics human conversation. Instead of interacting through traditional UI elements (like buttons, menus, and forms), users interact using natural language. Examples of conversational interfaces include chatbots and voice assistants.
32. Supervised Learning: This is a type of machine learning where the AI model is trained on a labeled dataset. In other words, the correct answers (or outputs) are provided alongside the inputs. This allows the model to learn the relationship between the inputs and outputs and make accurate predictions.
33. Unsupervised Learning: This is a type of machine learning where the AI model is trained on an unlabeled dataset. The model is tasked with finding patterns and relationships in the data without any guidance or predetermined labels.
34. Natural Language Processing (NLP): NLP is a field of AI that focuses on enabling machines to understand, interpret, and generate human language. NLP is the backbone of chatbot technology as it allows bots to understand and respond to user inputs in a conversational manner.
35. Natural Language Understanding (NLU): NLU is a subset of NLP that focuses on understanding the meaning and intent behind human language. This is crucial for chatbots to accurately interpret user inputs and generate relevant responses.
36. Natural Language Generation (NLG): NLG is another subset of NLP that deals with generating human language. In the context of chatbots, NLG is used to formulate human-like responses to user inputs.
37. Artificial Intelligence (AI): AI refers to the capability of a machine or software to mimic human cognitive functions such as learning and problem-solving. In the context of chatbots, AI is used to understand user inputs, learn from interactions, and generate relevant responses.
38. Machine Learning (ML): ML is a subset of AI that involves the development of algorithms that allow computers to learn and improve from experience. In the context of chatbots, ML is used to improve the accuracy and effectiveness of the bot over time by learning from past interactions.
39. Deep Learning: This is a subset of machine learning that is inspired by the structure and function of the human brain. It uses artificial neural networks with many layers (hence deep) to model complex patterns in large amounts of data. In the context of chatbots, deep learning can be used to improve the understanding of user inputs and generate more accurate responses.
40. Transfer Learning: This is a machine learning method where a pre-trained model is used as a starting point for a related task. For example, a chatbot could be pre-trained on a large corpus of general conversation data, and then fine-tuned with specific data relevant to its final task (like customer service for a particular product). This allows the chatbot to benefit from the general language understanding learned from the larger dataset, while also becoming proficient at its specific task.
For more information on the new ChatGPT chatbot created by OpenAI jump over to the official website.
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Chatbot glossary of terms - Geeky Gadgets