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History of chatbots

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Chatbots are gaining popularity in recent times. Digital assistants powered by Artificial Intelligence (AI) are finding its applications in various business operations. Chatbots such as Siri, Cortona, Google Now, and Alexa are the most adopted voice-enabled digital assistants. Chatbots can be used to gets weather updates, understand traffic conditions before travel, book movie tickets online, or even order food online. The users make a request and the chatbot responds with the most apt solution. Software companies are coming up with chatbots which are changing the way people seek information, making it part of regular discussion. The existing chatbots are mainly productivity oriented which are designed to complete a range of tasks within a short timeframe. Organizations are in need of solutions to find efficient and cheaper ways to retain customers by improving customer interactions- both before and after sale. A bot is a simple software which uses predefined rules to perform automated tasks whereas a chat bot is a type of bot which is powered with AI and can respond and perform personalized tasks requested by a user using a chat interface. The chat interface helps generate a communication line between the user and the bot using text or audio as the medium.

History of chatbots (new section)[edit]

ELIZA was the first chatbot developed by an MIT professor Joseph Weizenbaum in the 1960s.[1]. ELIZA was born before Natural Language Processing (NLP) which is a neuro-linguistic programming approach was developed. She acted as a psychotherapist who answered users queries with ease. ELIZA could recognize pre-recorded patterns and also transform those patterns in a way that it would make sense to the human it is conversing with. Over the years, many emerging technologies are being integrated into chatbots to develop an advanced software capable of communicating like a human. Presently, chatbots integrated with AI can find its applications in various fields The history of chatbots can best be explained using the sub-points below:

1966 ELIZA: ELIZA matched user prompts to scripted responses and could clear the Turing test (a test which tests a machine’s ability to exhibit intelligent behavior).

1972 Parry: It was more serious and advanced when compared with ELIZA and was successful in attempting to simulate a person with paranoid schizophrenia.

1988 Jabberwacky: One of the earliest attempts to create an AI with human interaction using a voice operated system.

1992 Dr Sbaitso: An AI speech synthesis program created for MS-DOS which was designed to showcase a digitized voice while interacting with users.

1996 A.L.I.C.E: Artificial Linguistic Internet Computer Entity (A.L.I.C.E) was a Natural Language Processing (NLP) bot who could apply heuristic pattern matching rules to human input.

2001 Smarterchild: It was considered as a precursor to Apple’s Siri and Samsung voice and had features such as quick data access and personalized conversations.

2006 IBM’s WATSON: WATSON uses NLP and machine learning to generate insights from vast amounts of data.

2010 Siri: It was part of Apple’s iOS interface which answered user questions and performed web service requests.

2012 Google Now: It is a mobile app developed by Google to answer questions, make recommendations, and perform actions by delegating requests to a set of web services.

2015 Alexa: Alexa uses NLP algorithms to recognize and respond to voice commands.

2015 Cortona: Cortona acts as a personal assistant. It recognizes NLP commands, uses Bing to answer user questions, and also sets reminders.

2016 Bots for Messenger: Facebook launched a platform to allow developers create bots that can interact with Facebook’s users using Facebook’s chat interface.

2016 Tay: Tay was developed to mimic the speech and habits of an American teenage girl. However, it was shut down after 16 hours of existence because of a few complications with its learning capabilities[2]

Adoption of chatbots[edit]

As technological innovations increase, chatbots are finding its applications across various industry verticals. In modern times, the concept of a conversing robot or a computer software which enables user and machine interaction has been an interesting topic among various data scientists. The reason being, chatbots finding its applications across various industry verticals. Data scientists are trying to find innovative solutions to address advanced user queries which consists of a complex sentence structure. Although chatbots will not replace humans completely, it is being integrated into applications to make the user’s life simpler. The chatbots market is estimated to grow from USD 703.3 Million in 2016 to USD 3,172.0 Million by 2021, at a Compound Annual Growth Rate (CAGR) of 35.2% during 2016–2021. The base year considered for the study is 2015 and the market size forecast is from 2016 to 2022” as per a market research report by published by MarketsandMarkets [3]. This is mainly due to its adoption across various industry verticals, especially BFSI, healthcare, communication, and retail. Many software firms are trying to bring improvements into their chatbots to cater to specific industry verticals.

Recent developments in chatbots[edit]

Nowadays, various software companies are trying to customize chatbots in order to connect with the local communities. For instance, Microsoft created a chatbot called Xiaoice which communicates in Chinese and in available on messaging apps such as line and WeChat [4]. The chatbot became an instant success in China with more than 20 million registered users. Xiaoice was able to remember past conversations it had with the users, and mimic natural patterns by using data from past conversations, other databases, and the internet. Chinese users were using the chatbot to find emotional comfort when heart-broken, lonely, or in need of answers to complex situations. Microsoft also launched another chatbot called Zo which was integrated in the kik mobile app [5]. Microsoft claimed that Zo not only had Intellectual Quotient (IQ), but, was also posessed an Emotional Quotient (EQ) which makes her learn through user interactions and respond intellectually just like a human i.e with feelings. Rinna, a chatbot launched by Microsoft in Japan was powered with AI to understand user choices, likes, and dislikes in order to provide customized solutions [6]. Since the learning phase in this chatbot required user engagement, many manufacturing firms and retail outlets could use it to understand customer preferences and then provide them with apt products. In India, Hike-a messaging app, integrated a chatbot called Natasha into their application . Natasha was able to provide movie reviews, product recommendations, and other personalized information by using online search engines to get answers. As the adoption of chatbots increase, software companies are carrying out various research programs to understand local user preferences so that they could deliver a chatbot that helps connect with the customers easily which can give them an edge over their competitors (Thies, Menon, Magapu2, Subramony, & O’Neill). The Microsoft research team conducted a study called Wizard-of-Oz to explore the idea of creating a chatbot specifically for the Indian youth. They developed three personalities which were integrated into the chatbot. The first two personalities were sympathetic and the third was emphatic. An intellectual individual very fluent in English was called to input the case scenarios into the chatbot, after which, the chatbot was tested with data scientists as its users. The data scientists were very happy with the performance of the chatbot and claimed that 90% of the information delivered by the chatbot was in line with their query. Additionally, in 2018, the bot is still being tested on the local youth of India to help the bot learn Indian linguistics and deliver apt results.

See also[edit]

An internet bot, also known as web robot

A virtual assistant or intelligent personal assistant is a software agent that can perform tasks or services for an individual.

Outline of natural language processing


  1. Shum, H., He, X. & Li, D. (2018). "Frontiers Inf Technol Electronic Eng". Frontiers of Information Technology & Electronic Engineering. 19: 10–26. doi:10.1631/FITEE.1700826.CS1 maint: Multiple names: authors list (link)
  2. Hunt, Elle (24 March 2016). "Tay, Microsoft's AI chatbot, gets a crash course in racism from Twitter". Guardian News and Media Limited. Retrieved 30 November 2018.
  3. MarketsandMarkets. "Chatbots Market Worth 3,172.0 Million USD by 2021". Retrieved 2018-12-02.
  4. "Microsoft created a chatbot in China that has millions of loyal followers who talk to it like in the movie 'Her'". Business Insider. Retrieved 2018-12-02.
  5. "Microsoft officially outs another AI chatbot, called Zo". TechCrunch. Retrieved 2018-12-02.
  6. McKirdy, Andrew (2015-08-19). "Microsoft says Line's popular Rinna character is new way to engage customers". The Japan Times Online. ISSN 0447-5763. Retrieved 2018-12-02.

I have added content and also references. As chatbot is an evolving technology, there will be many aspects which will be added by other users including me in times to come[edit]

History about chatbots is very essential as people are still confused as to what chatbots really are. Although the page "chatbots" exists it speaks about chatbots as a whole. The page I created shows that chatbots were simple and later on, with companies like Google and apple realized its potential, it is being integrated into various applications. I agree, the content might be less, however, people will continue to add more in time.[edit]

This article "History of chatbots" is from Wikipedia. The list of its authors can be seen in its historical and/or the page Edithistory:History of chatbots. Articles copied from Draft Namespace on Wikipedia could be seen on the Draft Namespace of Wikipedia and not main one.