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Chatbots and Question&Answer Technology
Chatbot technology can be used as a general-purpose user interface in a variety of business domains, and due to its diversity of business opportunities, it is an area in which many companies are now entering.
The question-and-answer technology that forms the basis of chatbots is more than just user interface technology. It is the culmination of advanced technologies that combine artificial intelligence technologies such as natural language processing and inference, and machine learning technologies such as deep learning, reinforcement learning, and online learning.
In this blog, we discuss a variety of topics related to chatbots and question-and-answer technology, from their origins, to business aspects, to a technical overview including the latest approaches, to concrete, ready-to-use implementations.
Implementation
LangChain is a library that helps develop applications using language models and provides a platform on which various applications using ChatGPT and other generative models can be built. One of the goals of LangChain is to enable it to handle tasks that language models cannot, such as answering questions about information outside the scope of knowledge learned by language models, or tasks that are logically complex or computationally demanding, etc. Another is to maintain it as a framework.
This section continues the discussion of LangChain, as described in “Overview of ChatGPT and LangChain and its use”. In the previous article, we described ChatGPT and LangChain, a framework for using ChatGPT and LangChain. This time, I would like to describe Agent, which has the ability to autonomously interfere with the outside world and transcend the limits of language models.
- Overview of Question-Answering Learning and Examples of Algorithms and Implementations
Question Answering (QA) is a branch of natural language processing in which the task is to generate appropriate answers to given questions. retrieval, knowledge-based query processing, customer support, work efficiency, and many other applications. This paper provides an overview of question-answering learning, its algorithms, and various implementations.
- About Monitoring and Supporting Online Discussions Using Natural Language Processing
Monitoring and supporting online discussions using Natural Language Processing (NLP) is used in online communities, forums, and social media platforms to improve the user experience, facilitate appropriate communication, and detect problems early. It is an approach that can be used to improve the user experience, facilitate appropriate communication, and detect problems early. This paper describes various algorithms and implementations of online discussion monitoring and support using natural language processing (NLP).
Huggingface is an open source platform and library for machine learning and natural language processing (NLP). The tools and resources provided by Huggingface are supported by an open source community, where there is an active effort to share code and models. This section describes the Huggingface Transformers, documentation generation, and implementation in python.
There are open source tools such as text-generation-webui and AUTOMATIC1111 that allow codeless use of generation modules such as ChatGPT and Stable Diffusion. In this article, we describe how to use these modules for text generation and image generation.
A knowledge graph is a graph structure that represents information as a set of related nodes (vertices) and edges (connections), and is a data structure used to connect information on different subjects or domains and visualize their relationships. This section describes various applications of the knowledge graph and concrete examples of its implementation in python.
ELIZA, the world’s first chatbot system in LISP, was one of the first natural language processing programs to feature input and output in English, named after the female protagonist in the play Pygmalion.
Eliza is a system that emulates a Rogallian psychoanalyst. Rogallians are psychoanalysts who practice a “non-directive” counseling style, encouraging their patients to expose themselves by reacting passively rather than willingly providing new information.
The program is built on pattern matching, and appears to really “get it” as it responds and reacts rationally to a wide variety of inputs. However, the program only creates an illusion in the patient by carefully recognizing, transforming, and mimicking elements of the input, and does not actually understand the conversation at all.
I will describe a simple chatbot implementation using Node.js, a front-side server framework using Javascript, and React.
The basic operation is rule-based: after writing the rules of the conversation, we drop them into a json file, and the conversation proceeds along the flow of the written sequence.
Building a chatbot framework in Clojure and Javascript and integrating various AI functions (natural language processing, SVM, BERT, Transformer, knowledge graph, database, expert systems)
An open source PHP framework for cross-platform chatbot development, used as a PHP library. It is designed to simplify the task of developing bots for multiple messaging platforms, and can handle everything from “simple command bots” to “advanced conversational interfaces. There is also a BotManStudio for Laravel.
Unity is an integrated development environment (IDE) for game and application development developed by Unity Technologies and widely used in various fields such as games, VR, AR, and simulations. This paper describes the integration of Unity with artificial intelligence systems such as CMS, chatbot, ES, machine learning, and natural language processing.
PHP (Hypertext Preprocessor) is a scripting language for web development that runs mainly on the server side and is used to create dynamic web pages and develop web applications, such as embedding HTML code, accessing databases, and processing forms. It is used to create dynamic web pages and develop web applications, such as embedding HTML code, accessing databases, and processing forms. Laravel is the most popular PHP framework in the field.
This section describes specific implementations using Laravel (integration with mediawiki, cahtbot, Elasticsearch).
Technical Topics
3.5 Billion Potential Customers Await! In addition to explaining the current chatbot boom, this book provides a detailed explanation of the changes in the Web and business developments that are expected to come as chatbots become more widespread, with examples.”
The book describes various case studies from the perspective of the chatbot business. In terms of technology, the book focuses on simple rule-based (some AI chatbots include natural language processing technology) configurations.
- User-Customized Learning Assistance with Natural Language Processing
User-customized learning aids utilizing natural language processing (NLP) are being offered in a variety of areas, including the education field and online learning platforms. This section describes the various algorithms used and their specific implementations.
In this paper, I will discuss the Turing Test (an imitation game designed to determine whether or not a machine can have intelligence, and if so, how it should be judged), which was proposed by Turing in 1950 and has been passed down from generation to generation, and discuss the relationship between dialogue and intelligence.
An introduction to early dialogue engines (artificial incompetence) that do not understand the meaning of Eliza’s lineage, and an analysis of the relationship between the meaning of words and dialogue through an introduction to Wittgenstein’s philosophy of logic, James Joyce’s meta-literature, and the Ten Ox Diagrams leading to Zen enlightenment and Zen questions and answers, as well as an introduction to the recently developed BERT-based BuddhaBot.
This paper describes the technical classification of AI dialogue engines at the heart of chatbot technology, as well as technical overviews, benchmarks, and implementations using Clojure for the use of natural language processing, deep learning technology, and the combination of knowledge graphs that have attracted attention in recent years.
Chatbot implementation using SWI-Prolog (external link)
This paper describes an expert system, which is one of the evolutionary systems of chatbot. The expert system is a system that builds a flexible if/then rule system by combining data called rule knowledge. It was basically built based on Prolog and Lisp, which are one of the AI languages, but here I introduce CLIPS, which is built in Java, from downloading the tool to actually using it.
It describes a rule-based system that uses data called a knowledge base.
For example, a database system called UniProtKB is one of the knowledge bases used in the life sciences. European institutions collaborate to collect protein information, and through annotation and curation (collecting data, examining it, integrating it, and organizing it), UniProt (Th UniversalProteinResource, URL http://www.uniprot.org/) and analysis tools.
Dendral, a project developed at Stanford University in 1965, is a system for inferring the chemical structure of a measured substance from the numerical value (molecular weight) of the location of a peak obtained by mass spectrometry. The language used is LISP.
MYCIN, a system derived from Dendral and developed in the 1970s, is also an expert system. MYCIN is an expert system that diagnoses patients and contagious blood diseases, and presents the antibiotics to be administered along with the dosage.
A knowledge graph can be defined as “a graph created by describing entities and the relationships among them. Entities” here are things that “exist” physically or non-physically and are not necessarily material entities, but are abstracted to represent things (events in mathematics, law, academic fields, etc.).Examples of knowledge graphs include simple and concrete things such as “there is a pencil on the table” and “Mt. Fuji is located on the border between Shizuoka and Yamanashi prefectures,” as well as more abstract things such as “if a=b, then a+c = b+c,” “the consumption tax is an indirect tax that focuses on “consumption” of goods and services,” “in electronically controlled fuel injection systems In the case of electronically controlled fuel injection systems, the throttle chamber is an intake throttling device that is attached to the collector of the intake manifold and contains a throttle valve to control the amount of intake air.The advantage of using these knowledge graphs, from AI’s perspective, is that machines can access the rules, knowledge, and common sense of the human world through the data in the knowledge graphs. In contrast to the recent black-box approaches, such as deep learning, which require a large amount of teacher data in order to achieve learning accuracy, AI can produce results that are easy for humans to interpret, and AI can generate data based on knowledge data to enable machine learning with small data. Machine learning with small data is possible by generating data based on knowledge data.By applying this knowledge graph to question-answer systems, it is possible to create a hierarchical structure of key terms, rather than simple FAQ question-answer pairs, and further associate them with context-specific questions and their alternatives, synonyms, and machine-learned response classes to provide an intelligent FAQ experience. It is possible to provide an intelligent FAQ experience.
Natural Language Processing (NLP) is a discipline that began around 1950 with the introduction of the famous Turing Test Turing (1950). Virtual assistants are programs that communicate with users in natural language. These NLP programs, called chatbots, have the advantage of being close to natural and intuitive interactions. Typically, these programs understand information from a specific domain. Thus, chatbots often provide specific information in an entertaining and anonymous way. Several studies predict the rise of the chatbot market in the future, so it will be essential to address the functionality of these systems Følstad and Brandtzæg (2017), Grudin (2019). Until now, only a few testing approaches exist to check the correctness of chatbots (e.g., Vasconcelos et al. (2017); Bozic (2019)) . However, users can talk to chatbots in a variety of ways, which may make it difficult to predict their input range. In addition to that, testing chatbots in a generalized way proves to be problematic due to the lack of expectations.
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