B RIEF HISTORY OF CONVERSATIONAL AI

Conversational system development dates back to very beginnings of computer science when researchers started to take up serious projects aimed at having computers interact with people. These efforts have pro-duced a wide spectrum of theories, techniques, and systems, ranging from basic research to applications, from text to multimodal signals and from computational to cognitive⁶.

In 1950 the British mathematician Alan Turing published a paper enti-tled “Computing machinery and intelligence”. He wondered if a computer program could talk to a group of people without realizing that their inter-locutor was artificial⁷. This question, named Turing test, is considered by many to be the generative idea of chatbots. The first chatbot, named ELIZA, was constructed in 1966. It simulated a role of a psychotherapist by asking open questions with which she also answered, therefore she diverted atten-tion from herself to the user. Its ability to communicate was limited, but it was a source of inspiration for the subsequent development of other chat-bots. ELIZA uses pattern matching and a response selection scheme based on templates. A disadvantage of ELIZA is that it cannot keep long conversa-tions and cannot learn or discover context from the discussion. Also its

4 C. Thompson, May A.I. help You?, „The New York Times”, [https://www.nytimes.com/interac tive/2018/11/14/magazine/tech-design-ai-chatbot.html] – 27.07.2021.

5 Algorithmia, 2021 enterprise trends in machine learning, 2021, [https://info.algorithmia.com /hubfs/2020/Reports/2021-Trends-in-ML/Algorithmia_2021_enterprise_ML_trends.pdf?hsL ang=en-us] – 27.07.2021.

6 T. Nishida, A. Nakazawa, Y. Ohmoto, Y. Mohammad, Conversational Informatics – A Data-Intensive Approach with Emphasis on Nonverbal Communication, Springer, 2014, s. 43-62.

7 A. M. Turing, Computing machinery and intelligence, “Mind”, Volume LIX, Issue 236, October 1950, doi: 10.1093/mind/LIX.236.433., s. 433–460.

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knowledge is limited, and therefore, it can discuss only in a particular do-main of topics⁸.

Another well-known chatbot, considered more advanced than ELIZA, PARRY, appeared in 1972. It is supposed to have sort of own personality and a better controlling structure. It defines his responses based on a sys-tem of assumptions and emotional responses activated by the change of weights in the user’s utterances⁹. PARRY is considered a chatbot with low capabilities concerning language understanding and the ability to express emotions, it also cannot learn from the conversation and has a low speed of responding.

In 1988 Artificial Intelligence was firstly used in the domain of the chatbots with the construction of Jabberwacky. It was written in Clever-Script, a language based on spreadsheets that opened the door for the ad-vancement of chatbots, and it used contextual pattern matching to respond developed on previous discussions. However, Jabberwacky cannot cope with high speed and work with a vast number of users.

The term Chatterbot was first mentioned in 1991 and was named TI-NYMUD (multiplayer real-time virtual world) artificial player, whose prima-ry function was to chat. Human players seemed to prefer talking to Chat-terbot than a real player. The ChatChat-terbot succeeded because, in the TI-NYMUD world, players assumed that everybody was a human and might cause doubts only if it made a significant mistake¹⁰. Different chatbot, Dr.

Sbaitso (Sound Blaster Artificial Intelligent Text to Speech Operator), which was created in 1992, was designed to display the digitized voices the sound cards were able to produce. It was supposed to play the role of a psycholo-gist without any sort of complicated interaction¹¹.

In 1995, another step forward in the history of chatbots was taken. It was the creation of ALICE (Artificial Linguistic Internet Computer Entity), the first online chatbot influenced by ELIZA. ALICE was based on pattern-matching, without any actual perception of the whole conversation but with a discussion ability on the web that allowed longitude and included any topic. Most critical difference between ALICE and ELIZA is that the first one was developed with a new language created for this purpose – Artificial In-telligence Markup Language (AIML). ALICE’s Knowledge Base consisted of about 41,000 templates and related patterns, a massive number comparing to ELIZA that had only 200 rules and keywords. However, ALICE did not have intelligent features and could not generate human-like answers ex-pressing emotions or attitudes¹².

8 P. Brandtzaeg, A. Følstad, Why people use chatbots, “Internet science” Vol. 10673, Springer, 2017, doi:10.1007/978-3-319-70284-1_30., s. 2-4.

9 J.F. Heiser, K.M, Colby, W.S. Faught, R.C. Parkison, Can psychiatrists distinguish a computer simulation of paranoia from the real thing? The limitations of Turing-like test as measures of the adequacy of simulations, 1979, doi:10.1016/0022-3956(79)90008-6., s. 27-33.

10 E. Adamopoulou, L. Moussiades, Chatbots: History, technology, and applications, “Machine Learning with Applications”, Volume 2, 2020, 100006, ISSN 2666-8270., s. 2-3.

11 E. Adamopoulou, L. Moussiades, Chatbots: History, technology, and applications, “Machine Learning with Applications”, Volume 2, 2020, 100006, ISSN 2666-8270., s. 2-3.

12 B. Abushawar, E. Atwell, ALICE chatbot: Trials and outputs. “Computación y Sistemas”, 2015, 19. doi:10.13053/cys-19-4-2326., s. 1-3.

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Chatbot technology revolution reached a milestone in 2001, with the development of SmarterChild by ActiveBuddy, which was available on mes-sengers like America Online and globally on Microsoft. It was the chatbot which could help people with practical daily tasks as it could retrieve in-formation from databases about movie times, stock prices, news and weather. This ability marked a significant development in both the machine intelligence and human– computer relations as information systems could be accessed through discussion with a chatbot¹³.

The evolution of intelligent conversational systems went another step further with the creation of smart personal voice assistants, built into smartphones and dedicated home speakers. They understood voice com-mands, talked by digital voices, and handled tasks like monitoring home automated devices, calendars, email. Apple Siri, IBM Watson, Google Assis-tant, Amazon Alexa and Microsoft Cortana are the most popular voice as-sistants. There also exist many other less famous voice assistants owing unique characteristics, but the same core functions. They connect to the Internet and create quickly meaningful responses. in the contrary to their predecessors.

In 2010, Apple Siri was the pioneer of personal assistants. It includes integration with audio, video, and image files, and conversations between it and its users are based on voice commands. Siri makes recommendations and responds to user requests using various internet services, while it adapts, during use, to device owners’ searches, preferences and language usages. Although Siri is sophisticated, it is not without weaknesses. To run, it requires an internet connection. Despite being multilingual, there are many languages it does not support and it has difficulties hearing the interlocutor, with heavy accent or in the presence of noise¹⁴.

A chatbot called Watson was created by IBM in 2011. It could under-stand the natural human language well enough to win two previous cham-pions on the quiz competition ‘‘Jeopardy’’, in which participants received answers and should guess the corresponding with them questions¹⁵. Years later, Watson enabled businesses to create better virtual assistants. Moreo-ver, Watson Health was designed to help doctors in healthcare diagnose diseases.

In 2014, Microsoft developed a personal assistant named Cortana. It recognizes voice commands and performs tasks such as identification of time and position, support people-based reminders, send emails and texts, create and manage lists and find information which user requests. The same year, Amazon introduced Alexa, which is built into devices for home automation and entertainment. Although personal voice assistants enable

13 E. Adamopoulou, L. Moussiades, Chatbots: History, technology, and applications, “Machine Learning with Applications”, Volume 2, 2020, 100006, ISSN 2666-8270., s. 3.

14 V. Këpuska, G. Bohouta, Next-generation of virtual personal assistants (Microsoft Cortana, Apple Siri, Amazon Alexa and Google Home), IEEE 8th Annual Computing and Communica-tion Workshop and Conference (CCWC), 2018, s. 99-103, doi: 10.1109/CCWC.2018.

8301638., s. 1-4.

15 R. Chandrasekar, Elementary? Question Answering, IBM’s Watson, and the Jeopardy! Chal-lenge, “Resonance March 2014”, 2014., s. 226-231.

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voice communication with their users, misunderstandings often occur, as they cannot understand the particular language people use in oral speech or fail to understand the whole context of the conversation¹⁶. Google Assis-tant, which was developed in 2016, constitutes the next generation of Google Now, presented in 2012. It was initially used to give information to the user taking into account the time of day, location, and preferences.

Nowadays it has a more complexed artificial intelligence with a more com-panionable, conversational interface and delivers information to users prognosticating their requirements. Although, its lack of personality and its questions may violate privacy of the users as it is linked directly to their Google Account.

Evolution of AI Technology changed dramatically the way people com-municate with manufacturers. Social media platforms allowed developers to create chatbots for their brand or service to enable customers to perform specific daily actions within their messaging applications. In 2016, over 34.000 chatbots covered a wide range of uses in fields like marketing, sup-porting systems, education, health care and management. Variety of text-based chatbots with specific features were developed for popular messaging platforms, research problems and industrial solutions. These days, chat-bots communication is completely different from their predecessor Eliza.

They can share personal thoughts, be relevant and also deceive human beings¹⁷. After 2016, growing increase in the use and research of chatbots was observed, which allows to assume new developments creation in near future.

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