Chatbots are computer programs that communicate via dialogue or text. Able to simulate human conversation and pass the Turing test.
Chatbots can be used for practical purposes, such as customer service or information acquisition. Some chatbots will be equipped with natural language processing systems, but most simple systems will only retrieve the input keywords, and then find the most appropriate response sentence from the database. Chatbots are part of a virtual assistant that can connect with many organizations' apps, websites, and instant messaging platforms. Non-assistant applications include chat rooms for entertainment purposes, research and specific product promotions, social bots.
Chatbots are the support system for your customer service. Chatbots identify their needs by using natural language processing algorithms to identify the meaning of conversations or phrases provided by end users. Your chatbot can simulate conversations with users through messaging apps, websites, mobile apps, and more, providing them with accurate and relevant information.Chatbots are machine learning-based conversation engines built into Python that generate responses based on collections of known conversations. The language-independent design of the chatbot allows it to be trained to speak any language.
From the perspective of application scenarios, it can be divided into five categories: online customer service, entertainment, education, personal assistant, and intelligent question and answer. The main function of the online customer service chatbot system is to communicate with users and automatically reply to users' questions about products or services, so as to reduce the operating cost of enterprise customer service and improve the user experience. Its application scenarios are usually website homepages and mobile terminals. The main function of the chatbot system in entertainment scenarios is to conduct open-topic dialogues with users, so as to achieve the functions of spiritual companionship, emotional comfort, and psychological counseling for users. Its application scenarios are usually social media, children's toys, etc. According to the content of education, the educational chatbot system includes building an interactive language use environment to help users learn a certain language; in learning a certain professional skill, instructing the user to gradually and deeply learn and master the skill; in the user's specific age stage, to help users carry out assisted learning of certain knowledge, etc. Its application scenarios are usually learning, training software, and smart toys with human-computer interaction functions. Personal assistant applications mainly interact with the chatbot system through voice or text to realize personal affairs query and agency functions, such as weather query, air quality query, positioning, SMS sending and receiving, schedule reminder, intelligent search, etc., so as to assist more conveniently User's daily transaction processing. Its application scenarios are usually portable mobile terminal equipment. The main functions of intelligent question answering chatbots include answering factual questions raised by users in natural language and questions requiring calculation and logical reasoning, so as to directly meet users' information needs and assist users in decision-making. Its application scenarios are usually integrated into chatbot systems as a question-and-answer service.
From the perspective of expression, Chatbot can be divided into two types: single-round dialogue and multi-round dialogue. A single round of dialogue can actually be regarded as a variant of the Question Answering System, which is generally in the form of one question and one answer. The user asks a question, and the machine generates the text of the corresponding answer or comprehensively returns various information related to the answer to the user. Multi-round dialogue is closer to the dialogue mode between people that we usually understand. Usually, there are questions and answers. In addition to the user's question, the machine will also actively ask the user and will judge what to give according to the context. answer or what kind of question to ask.
From the point of view of the answering model, it is divided into a retrieval-based model and a generation-based model. Retrieval-based models, where responses are defined in advance, use a rule engine, regular matching, or deep learning-trained classifiers to pick an optimal response from the database. Based on the generated model, it does not rely on pre-defined answers, but in the process of training, a large amount of corpus is required, and the corpus includes context and response. It is popular to use LSTM and RNN to train the generated model. This method was first used to complete the task of machine translation - Sequence to Sequence Learning with Neural Networks. At present, in the production environment, chat services are generally based on retrieval models, and the emergence of Seq2Seq may make generation-based models mainstream because Seq2Seq can still perform well in the case of long conversations.
1 .Chatbot 24-hour availability: While this is obviously a huge benefit, it’s important to stress that when a robot is shut down for safety issues or maintenance, it can have a big impact。
2 .Chatbot Provide Consistent Responses: When talking to an agent, the customer cannot guarantee that other agents will provide a similar, consistent response. If the rep's answer isn't helpful, the customer may try calling again to see if the next rep has a better answer.
3 .Chatbot Recording Answers: When talking to a customer service representative, customers do not get a transcript of the conversation, and most people are reluctant to have their conversations recorded. However, customers can take screenshots at any time to preserve the conversation or can question the answers provided by the bot.
4 .Chatbot Programmable: Because bots are located on digital platforms where people typically spend most of their time at work, bots can be used to automate common tasks such as scheduling meetings and providing advanced search capabilities.
1 .The use of chatbots is steadily growing
In the next 5 years, will chatbots be used in a more effective way and more frequently in real-life situations? During this epidemic, the rigid demand under special conditions made digital medical care a bright spot in the biotechnology industry. The 5G era and AI-ML computing modules are becoming more and more personalized, so tailor-made chat AI bots will be the beginning of success.
2 .Next-generation AI robots become part of the medical and health process
The development trend of chat AI robots is also the process of AI technology research and development and the extensive application of intelligent robot systems in healthcare service processes. The main applications of chat AI bots are (still) relatively simple: mostly used to get accurate answers or relevant information.However, there will be great progress and development in the accuracy of questions answered by robots, acquisition time and communication methods. For example, "The zombie app can be used 24/7, suitable for communication and consultation in any situation, and integrates more and more other intelligent systems and information through AI.
3 .Chatbots will reveal personal privacy secrets
Conversational AI chatbots can meet many private needs of individuals, and give full play to the precise recognition ability and memory function of chatbots (exceeding human beings). In terms of handling problems and protecting private information, as long as pre-made procedures are well-prepared, they will also exceed human loyalty. Degree (people will leak or spread secrets). Intelligent robots can keep secrets and obey the rules very well.
At present, the challenges of chatbot research include: dialogue context modeling, knowledge representation during dialogue, dialogue strategy learning, and evaluation of chatbot intelligence.
Chat is a context-specific continuous interaction process in which contextual omission and referencing often occur. The meaning of a sentence is sometimes determined by combining the dialogue context or related context, and existing natural language understanding is mainly based on context-free assumptions, so the modeling of dialogue context has become one of the main challenges of chatbot systems.
Knowledge representation has always been an important topic in the field of artificial intelligence, and it is also the basis for chatbots to provide information services. Domain tasks related to chatbots may have complex compositions and involve many factors. Only by understanding the relationship and meaning of these factors can we communicate with users in a real sense.
Dialogue strategy involves many aspects, chief among which is the way in which the dialogue is dominant. Dialogue-dominant methods can be divided into three types: user-dominant, system-dominant, and hybrid-dominant. In the current dialogue management research, the goal of the system response is to be natural, friendly, and positive, and to allow users to be as autonomous as possible without problems, and to achieve mixed leadership of the dialogue.
The current evaluation of the intelligence of chatbots is also a challenge. Although some general objective evaluation criteria can be used, such as correct answer rate, task completion rate, number of dialogue rounds, dialogue time, system average response time, error message rate, etc., to evaluate chatbots, the basic unit of evaluation is a single round of dialogue . However, since the process of human-machine dialogue is a continuous process, the evaluation of continuous dialogues of different chatbot systems can only ensure the consistency of the input of the first sentence. Divide the continuous dialogue into single rounds of dialogue for evaluation, so a reasonably designed artificial subjective evaluation may become an important indicator for evaluating the intelligence of the chatbot system in addition to the objective evaluation standard.
In this article, we take a quick look at the history of chatbots, and introduce the features of Alibaba Cloud's Intelligent Service Robot.
This article discusses chatbots: what they are, how they work, and future possibilities. It also outlines how organizations can leverage the Alibaba Cloud Intelligent Service Robot.
This solution provides you with Artificial Intelligence services and allows you to build AI-powered, human-like, conversational, multilingual chatbots over omnichannel to quickly respond to your customers 24/7.
Alibaba Clouder - May 20, 2020
Alibaba Clouder - October 9, 2019
Alibaba Clouder - July 10, 2018
Alibaba Clouder - March 19, 2020
Alibaba Clouder - May 20, 2020
Alibaba Clouder - March 11, 2021
This solution provides you with Artificial Intelligence services and allows you to build AI-powered, human-like, conversational, multilingual chatbots over omnichannel to quickly respond to your customers 24/7.Learn More
Accelerate AI-driven business and AI model training and inference with Alibaba Cloud GPU technologyLearn More
Apply the latest Reinforcement Learning AI technology to your Field Service Management (FSM) to obtain real-time AI-informed decision support.Learn More
A high-quality personalized recommendation service for your applications.Learn More
More Posts by Clouders