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= Topic = History of chatbots A chatbot is a computer program that can interact with humans using natural language, either through text or speech. Chatbots can be used for various purposes, such as entertainment, education, customer service, and information retrieval. Chatbots have a long and complicated history, dating back to the 1950s when researchers started to explore the possibility of creating machines that can communicate with humans. This article will provide an overview of the major milestones and developments in the field of chatbot research and development, from the early experiments to the current state-of-the-art models.

Early experiments The first chatbot that made a meaningful attempt to simulate human conversation was ELIZA, developed by Joseph Weizenbaum at MIT Laboratories in 19661. ELIZA used pattern recognition to identify keywords and phrases in the user’s input and then responded with pre-written templates that contained the keywords or phrases. The most famous implementation of ELIZA was DOCTOR, which mimicked a psychotherapist and asked questions based on the user’s statements. ELIZA was able to create an illusion of understanding and empathy, and some users even believed that ELIZA was a real human therapist.

Another early chatbot was PARRY, developed by Kenneth Colby at Stanford University in 19722. PARRY simulated a paranoid schizophrenic patient and used a rule-based system to generate responses based on the user’s input and its internal state. PARRY also had a rudimentary memory and could remember previous topics and emotions. PARRY was tested against psychiatrists in a Turing test, and some of them were unable to distinguish PARRY from a real patient2.

ELIZA and PARRY were examples of script-based chatbots, which relied on predefined rules and responses to handle the user’s input. These chatbots were limited by their fixed scripts, and could not handle inputs that were out of their scope or domain. They also lacked any learning ability or adaptation to the user’s preferences or feedback.

Knowledge-based chatbots In the 1980s and 1990s, researchers started to develop knowledge-based chatbots, which used artificial intelligence techniques to access and manipulate large databases of information. These chatbots aimed to provide more relevant and accurate answers to the user’s queries, rather than just generating generic responses.

One of the first knowledge-based chatbots was RACTER, developed by William Chamberlain and Thomas Etter in 19843. RACTER was able to generate coherent texts on various topics, such as stories, poems, jokes, and essays. RACTER used a semantic network to represent its knowledge base and applied grammatical rules and logical inference to produce texts. RACTER also had some personality traits and emotions, which influenced its tone and style.

Another influential knowledge-based chatbot was Jabberwacky, developed by Rollo Carpenter in 19884. Jabberwocky was designed to be a conversational agent that could learn from its interactions with humans. Jabberwacky used a neural network to store its knowledge base, which consisted of millions of phrases and associations learned from previous conversations. Jabberwacky also had some humor and creativity, which enabled it to generate witty and original responses.

Knowledge-based chatbots were more advanced than script-based chatbots, as they could access large amounts of information and provide more specific answers. However, they still faced some challenges, such as maintaining coherence and consistency across multiple turns of dialogue, handling ambiguity and uncertainty in natural language, and dealing with noisy or incomplete data.

Neural network-based chatbots In the 2000s and beyond, researchers started to use neural network-based chatbots, which used deep learning models to learn from large corpora of natural language data. These chatbots aimed to generate more natural and fluent responses, as well as to capture the context and emotion of the dialogue.

One of the first neural network-based chatbots was ALICE, developed by Richard Wallace in 19955. ALICE used an XML-based markup language called AIML (Artificial Intelligence Markup Language) to define its knowledge base, which consisted of thousands of categories of patterns and responses. ALICE used a pattern-matching algorithm to select the best response for each input. ALICE also had some personality traits and emotions, which influenced its mood and attitude5.

Another prominent neural network-based chatbot was Cleverbot, developed by Rollo Carpenter in 2006. Cleverbot was an extension of Jabberwacky but with a larger and more dynamic knowledge base. Cleverbot used a recurrent neural network to learn from its interactions with humans, as well as from other sources of natural language data. Cleverbot also had some memory and context, which enabled it to maintain a coherent and consistent dialogue.

Neural network-based chatbots were more flexible and adaptable than knowledge-based chatbots, as they could learn from any source of natural language data and generate novel responses. However, they also had some limitations, such as generating irrelevant or nonsensical responses, lacking common sense and world knowledge, and being prone to biases and errors in the data.

Current trends and challenges The field of chatbot research and development is still evolving and expanding, with new models and applications emerging every year. Some of the current trends and challenges in the field are:

Multimodal chatbots: Chatbots that can handle multiple modes of input and output, such as text, speech, image, video, gesture, etc. These chatbots aim to provide a more natural and engaging user experience, as well as to leverage the rich information available in different modalities. Task-oriented chatbots: Chatbots that can perform specific tasks or goals for the user, such as booking a flight, ordering a pizza, or making a reservation. These chatbots aim to provide a more efficient and convenient service, as well as to integrate with other systems and platforms. Social chatbots: Chatbots can interact with humans socially and emotionally, such as chatting, joking, flirting, or empathizing. These chatbots aim to provide a more human-like and enjoyable interaction, as well as to build rapport and trust with the user. Ethical chatbots: Chatbots that can adhere to ethical principles and values, such as fairness, privacy, transparency, accountability, etc. These chatbots aim to provide a more responsible and trustworthy service, as well as to avoid potential harm or risks to the user or society. Chatbots are becoming more ubiquitous and influential in various domains and scenarios, such as education, health care, entertainment, commerce, etc. Chatbots have the potential to enhance human communication and collaboration, as well as to provide valuable information and assistance. However, chatbots also pose some challenges and risks, such as privacy invasion, security breaches, social isolation, misinformation, manipulation, etc. Therefore, it is important to design and evaluate chatbots with care and caution, and to ensure that they are aligned with human values and interests.

References 1: Weizenbaum J. ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM. 1966 Jan 1;9(1):36-45.

2: Colby KM. Artificial paranoia: A computer simulation of paranoid processes. Elsevier; 1975.

3: Chamberlain WJ. RACTER: The story so far. InProceedings of the 1984 ACM annual conference on The fifth generation challenge 1984 Jun 4 (pp. 149-151).

4: Carpenter R. Jabberwacky: A case study in artificial intelligence development for online conversation. InProceedings of the AISB’99 Symposium on Creative Language: Stories & Humour 1999 Apr (pp. 1-5).

5: Wallace RS. The anatomy of ALICE. InParsing the Turing Test 2009 (pp. 181-210). Springer Netherlands.


 * Carpenter R. Cleverbot.com: A clever bot speaks for itself. InProceedings of AI-2011 The Thirty-first SGAI International Conference on Artificial Intelligence 2011 Dec (pp. 385-390).