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Chatbot

From Wikipedia, the free encyclopedia
Program that simulates conversation
For the bot-creation software, seeChatBot. For bots on Internet Relay Chat, seeIRC bot.

Avirtual assistant chatbot
The 1966ELIZA chatbot
Part of a series on
Machine learning
anddata mining

Achatbot (originallychatterbot)[1] is asoftware application or web interface designed to have textual or spoken conversations.[2][3][4] Modern chatbots are typicallyonline and usegenerative artificial intelligence systems that are capable of maintaining a conversation with a user innatural language and simulating the way a human would behave as a conversational partner. Such chatbots often usedeep learning andnatural language processing, but simpler chatbots have existed for decades.

Chatbots have increased in popularity as part of theAI boom of the 2020s, and the popularity ofChatGPT, followed by competitors such asGemini,Claude and laterGrok. AI chatbots typically use afoundationallarge language model, such asGPT-4 or theGemini language model, which isfine-tuned for specific uses.

A major area where chatbots have long been used is incustomer service andsupport, with various sorts ofvirtual assistants.[5]

History

[edit]

Turing test

[edit]

In 1950,Alan Turing's article "Computing Machinery and Intelligence" proposed what is now called theTuring test as a criterion ofintelligence. This criterion depends on the ability of acomputer program to impersonate a human in areal-time written conversation with a human judge to the extent that the judge is unable to distinguish reliably—on the basis of the conversational content alone—between the program and a real human.[6]

Early chatbots

[edit]

Joseph Weizenbaum's programELIZA was first published in 1966. Weizenbaum did not claim that ELIZA was genuinely intelligent, and the introduction to his paper presented it more as a debunking exercise:

In artificial intelligence, machines are made to behave in wondrous ways, often sufficient to dazzle even the most experienced observer. But once a particular program is unmasked, once its inner workings are explained, its magic crumbles away; it stands revealed as a mere collection of procedures. The observer says to himself "I could have written that". With that thought, he moves the program in question from the shelf marked "intelligent", to that reserved for curios. The object of this paper is to cause just such a re-evaluation of the program about to be "explained". Few programs ever needed it more.[7]

ELIZA's key method of operation involves the recognition of clue words or phrases in the input, and the output of the corresponding pre-prepared or pre-programmed responses that can move the conversation forward in an apparently meaningful way (e.g. by responding to any input that contains the word 'MOTHER' with 'TELL ME MORE ABOUT YOUR FAMILY').[7] Thus an illusion of understanding is generated, even though the processing involved has been merely superficial. ELIZA showed that such an illusion is surprisingly easy to generate because human judges are ready to give the benefit of the doubt when conversational responses arecapable of being interpreted as "intelligent".

Following ELIZA, psychiatristKenneth Colby developedPARRY in 1972.[8][9][10][11]

From 1978[12] to some time after 1983,[13] the CYRUS project led byJanet Kolodner constructed a chatbot simulatingCyrus Vance (57thUnited States Secretary of State). It usedcase-based reasoning, and updated its database daily by parsing wire news fromUnited Press International. The program was unable to process the news items subsequent to the surprise resignation of Cyrus Vance in April 1980, and the team constructed another chatbot simulating his successor,Edmund Muskie.[14][13]

In 1984, an interactive version of the programRacter was released which acted as a chatbot.[15]

A.L.I.C.E. was released in 1995. This uses amarkup language called AIML,[3] which is specific to its function as aconversational agent, and has since been adopted by various other developers of, so-called, Alicebots. A.L.I.C.E. is aweak AI without any reasoning capabilities. It is based on a similarpattern matching technique as ELIZA in 1966. This is not strong AI, which would requiresapience andlogical reasoning abilities.

Jabberwacky, released in 1997, learns new responses and context based onreal-timeuser interactions, rather than being driven from a staticdatabase.

Chatbot competitions focus on the Turing test or more specific goals. Two such annual contests are theLoebner Prize and The Chatterbox Challenge (the latter has been offline since 2015, however, materials can still be found from web archives).[16]

DBpedia created a chatbot during theGSoC of 2017.[17] It can communicate throughFacebook Messenger.

Modern chatbots based on large language models

[edit]
ACharacter.ai conversation with aWittgenstein chatbot

Modern chatbots likeChatGPT are often based onlarge language models calledgenerative pre-trained transformers (GPT). They are based on adeep learning architecture called thetransformer, which containsartificial neural networks. They generate text after being trained on a largetext corpus.

Application

[edit]
This section needs to beupdated. Please help update this article to reflect recent events or newly available information.(December 2024)
See also:Virtual assistant

Messaging apps

[edit]

Many companies' chatbots run onmessaging apps or simply viaSMS. They are used forB2C customer service, sales and marketing.[18]

In 2016, Facebook Messenger allowed developers to place chatbots on their platform. There were 30,000 bots created for Messenger in the first six months, rising to 100,000 by September 2017.[19]

Since September 2017, this has also been as part of a pilot program on WhatsApp. AirlinesKLM andAeroméxico both announced their participation in the testing;[20][21][22][23] both airlines had previously launched customer services on the Facebook Messenger platform. The Nigerian event platformDemfati, for example, uses its Deeva chatbot on WhatsApp for dedicated B2C functions like ticket purchasing and event voting.[24]

The bots usually appear as one of the user's contacts, but can sometimes act as participants in agroup chat.

Many banks, insurers, media companies, e-commerce companies, airlines, hotel chains, retailers, health care providers, government entities, and restaurant chains have used chatbots toanswer simple questions, increasecustomer engagement,[25] for promotion, and to offer additional ways to order from them.[26] Chatbots are also used inmarket research to collect short survey responses.[27]

A 2017 study showed 4% of companies used chatbots.[28] In a 2016 study, 80% of businesses said they intended to have one by 2020.[29]

As part of company apps and websites

[edit]

Previous generations of chatbots were present on company websites, e.g. Ask Jenn fromAlaska Airlines which debuted in 2008[30] orExpedia's virtual customer service agent which launched in 2011.[30][31] The newer generation of chatbots includesIBM Watson-powered "Rocky", introduced in February 2017 by the New York City-basede-commerce company Rare Carat to provide information to prospective diamond buyers.[32][33]

Chatbot sequences

[edit]

Used by marketers to script sequences of messages, very similar to anautoresponder sequence. Such sequences can be triggered by user opt-in or the use of keywords within user interactions. After a trigger occurs a sequence of messages is delivered until the next anticipated user response. Each user response is used in the decision tree to help the chatbot navigate the response sequences to deliver the correct response message.

Company internal platforms

[edit]

Companies have used chatbots for customer support, human resources, or inInternet-of-Things (IoT) projects.Overstock.com, for one, has reportedly launched a chatbot named Mila to attempt to automate certain processes when customer service employees request sick leave.[34] Other large companies such asLloyds Banking Group,Royal Bank of Scotland,Renault andCitroën are now using chatbots instead ofcall centres with humans to provide a first point of contact.[citation needed] In large companies, like in hospitals and aviation organizations, chatbots are also used to share information within organizations, and to assist and replace service desks.[citation needed]

Customer service

[edit]

Chatbots have been proposed as a replacement forcustomer service departments.[35]

In 2016, Russia-based Tochka Bank launched a chatbot onFacebook for a range of financial services, including a possibility of making payments.[36] In July 2016,Barclays Africa also launched a Facebook chatbot.[37]

Healthcare

[edit]
See also:Artificial intelligence in healthcare

Chatbots are also appearing in the healthcare industry.[38][39] A study suggested that physicians in the United States believed that chatbots would be most beneficial for scheduling doctor appointments, locating health clinics, or providing medication information.[40] A 2025 review found that participants often rated chatbot responses as more empathic than those from clinicians.[41]

In 2020,WhatsApp worked with theWorld Health Organization and theGovernment of India to make chatbots to answers users' questions onCOVID-19.[42][43][44][45]

In 2023, US-basedNational Eating Disorders Association replaced its humanhelpline staff with a chatbot but had to take it offline after users reported receiving harmful advice from it.[46][47][48]

Politics

[edit]
See also:Government by algorithm § AI politicians

In New Zealand, the chatbot SAM – short forSemantic Analysis Machine[49] – has been developed by Nick Gerritsen of Touchtech.[50] It is designed to share its political thoughts, for example on topics such as climate change, healthcare and education, etc. It talks to people through Facebook Messenger.[51][52][53][54]

In 2022, the chatbot "Leader Lars" or "Leder Lars" was nominated forThe Synthetic Party to run in theDanish parliamentary election,[55] and was built by the artist collective Computer Lars.[56] Leader Lars differed from earlier virtual politicians by leading apolitical party and by not pretending to be an objective candidate.[57] This chatbot engaged in critical discussions on politics with users from around the world.[58]

InIndia, the state government has launched a chatbot for its Aaple Sarkar platform,[59] which provides conversational access to information regarding public services managed.[60][61]

Toys

[edit]

Chatbots have also been incorporated into devices not primarily meant for computing, such as toys.[62]

HelloBarbie is an Internet-connected version of the doll that uses a chatbot provided by the company ToyTalk,[63] which previously used the chatbot for a range of smartphone-based characters for children.[64] These characters' behaviors are constrained by a set of rules that in effect emulate a particular character and produce a storyline.[65]

TheMy Friend Cayla doll was marketed as a line of 18-inch (46 cm) dolls which usesspeech recognition technology in conjunction with anAndroid oriOS mobile app to recognize the child's speech and have a conversation. Like the Hello Barbie doll, it attracted controversy due to vulnerabilities with the doll'sBluetooth stack and its use of data collected from the child's speech.

IBM'sWatson computer has been used as the basis for chatbot-based educational toys for companies such asCogniToys,[62] intended to interact with children for educational purposes.[66]

Malicious use

[edit]

Malicious chatbots are frequently used to fillchat rooms with spam and advertisements by mimicking human behavior and conversations or to entice people into revealing personal information, such as bank account numbers. They were commonly found onYahoo! Messenger,Windows Live Messenger,AOL Instant Messenger and otherinstant messaging protocols. There has also been a published report of a chatbot used in a fake personal ad on a dating service's website.[67]

Tay, an AI chatbot designed to learn from previous interactions, caused major controversy after being targeted by internet trolls on Twitter. Soon after its launch, the bot was exploited, and with its "repeat after me" capability, it started releasing racist, sexist, and controversial responses to Twitter users.[68] This suggests that although the bot learned effectively from experience, adequate protection was not put in place to prevent misuse.[69]

If a text-sendingalgorithm can pass itself off as a human instead of a chatbot, its message would be more credible. Therefore, human-seeming chatbots with well-crafted online identities could start scattering fake news that seems plausible, for instance making false claims during an election. With enough chatbots, it might be even possible to achieve artificialsocial proof.[70][71]

Data security

[edit]

Data security is one of the major concerns of chatbot technologies. Security threats and system vulnerabilities are weaknesses that are often exploited by malicious users. Storage of user data and past communication, that is highly valuable for training and development of chatbots, can also give rise to security threats.[72] Chatbots operating on third-party networks may be subject to various security issues if owners of the third-party applications have policies regarding user data that differ from those of the chatbot.[72] Security threats can be reduced or prevented by incorporating protective mechanisms. Userauthentication, chatEnd-to-end encryption, and self-destructing messages are some effective solutions to resist potential security threats.[72]

Mental health

[edit]

Chatbots have shown to be an emerging technology used in the field of mental health. Its usage may encourage users to seek advice on matters of mental health as a means to avoid the stigmatization that may come from sharing such matters with other people.[73] This is because chatbots can give a sense of privacy and anonymity when sharing sensitive information, as well as providing a space that allows for the user to be free of judgment.[73] An example of this can be seen in a study which found that with social media and AI chatbots both being possible outlets to express mental health online, users were more willing to share their darker and more depressive emotions to the chatbot.[73] Users may also turn to chatbots because their replies can be perceived as empathic and emotionally supportive.[74]

Findings prove that chatbots have great potential in scenarios in which it is difficult for users to reach out to family or friends for support.[73] It has been noted that it demonstrates the ability to give young people "various types of social support such as appraisal, informational, emotional, and instrumental support".[73] Studies have found that chatbots are able to assist users in managing things such as depression and anxiety.[73] Some examples of chatbots that serve this function are "Woebot, Wysa, Vivibot, and Tess".[73]

Evidence indicates that when mental health chatbots interact with users, they tend to follow certain conversation flows.[75] These being guided conversation, semi guided conversation, and open ended conversation.[75] The most popular, guided conversation, "only allows the users to communicate with the chatbot with predefined responses from the chatbot. It does not allow any form of open input from the users".[75] It has also been noted in a study looking at the methods employed by various mental health chatbots, that most of them employed a form of cognitive behavior therapy with the user.[75]

Adverse effects

[edit]
Further information:Chatbot psychosis

Research has identified potential barriers to entry that come with the usage of chatbots for mental health.[76] There are ongoing privacy concerns with sharing user's personal data in chat logs with chatbots.[76] There is a lack of willingness from those in lower socioeconomic statuses to adopt interactions with chatbots as a meaningful way to improve upon mental health.[76] Though chatbots may be capable of detecting simple human emotions in interactions with users, they are incapable of replicating the level of empathy that human therapists do.[76]

Due to the nature of chatbots being language-learning models trained on numerous datasets, the issue ofalgorithmic bias exists.[76] Chatbots with built in biases from their training can have them brought out against individuals of certain backgrounds and may result in incorrect information being conveyed.[76]

There is a lack of research about how exactly these interactions help with a user's real life.[75] There are concerns regarding the safety of users when interacting with such chatbots.[75] When improvements and advancements are made to such technologies, how that may affect humans is not a priority.[75] It is possible that this can lead to "unintended negative consequences, such as biases, inadequate and failed responses, and privacy issues".[75]

A risk in the usage of chatbots to deal with mental health is increased isolation, as well as a lack of support in times of crisis.[75] A 2025 study by Sentio University evaluated how six major chatbots responded to disclosures of suicide risk and other acute mental health crises, finding that none consistently met clinician determined safety standards.[77] Another notable risk is a general lack of a strong understanding of mental health.[75] Studies have indicated that mental-health-oriented chatbots have been prone to recommending users medical solutions and to rely upon themselves heavily.[75]

Obsessive use of chatbots has been linked tochatbot psychosis[78] in people already prone to delusional and conspiratorial thinking. This is caused in part by chatbots "hallucinating" information,[79] as they are designed for engagement, and to keep people talking.[80]

Limitations

[edit]

Traditional chatbots particularly lacked understanding of user requests, leading to clunky, repetitive conversations. Their pre-programmed responses would often fail to satisfy unexpected user queries, causing frustration. These chatbots were particularly unhelpful for users who lacked a clear understanding of their problem or the service they needed.[81]

Chatbots based onlarge language models are much more versatile, but require a large amount of conversational data to train. These models generate new responses word by word based on user input, and are usually trained on a large dataset of natural-language phrases.[3] They sometimes provide plausible-sounding but incorrect or nonsensical answers, referred to as "hallucinations". They can for example make up names, dates, or historical events.[82] When humans use and apply chatbot content contaminated with hallucinations, this results in "botshit".[83] Given the increasing adoption and use of chatbots for generating content, there are concerns that this technology will significantly reduce the cost it takes humans to generatemisinformation.[84]

Impact on jobs

[edit]

Chatbots and technology in general used to automate repetitive tasks. But advanced chatbots likeChatGPT are also targeting high-paying, creative, and knowledge-based jobs, raising concerns about workforce disruption and quality trade-offs in favor of cost-cutting.[85]

Chatbots are increasingly used bysmall and medium enterprises, to handle customer interactions efficiently, reducing reliance on largecall centers and lowering operational costs.[86]

Prompt engineering, the task of designing and refining prompts (inputs) leading to desired AI-generated responses has quickly gained significant demand with the advent of large language models,[87] although the viability of this job is questioned due to new techniques for automating prompt engineering.[88]

Impact on the environment

[edit]
See also:Environmental impacts of artificial intelligence andData_center § Energy_use

Generative AI uses a high amount ofelectric power. Due to reliance onfossil fuels in itsgeneration, this increasesair pollution,water pollution, andgreenhouse gas emissions. In 2023, a question toChatGPT consumed on average 10 times as much energy as a Google search.[89] Data centres in general, and those used for AI tasks specifically, use significant amounts of water for cooling.[90][91]

See also

[edit]

References

[edit]
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Further reading

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External links

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  • Media related toChatbots at Wikimedia Commons
General terms
Text analysis
Text segmentation
Automatic summarization
Machine translation
Distributional semantics models
Language resources,
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standards
Data
Automatic identification
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Topic model
Computer-assisted
reviewing
Natural language
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