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Chatbots and New Audience Opportunities for Museums and Heritage Organisations



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Proceedings of Proceedings of EVA London 2018, UK
Chatbots and New Audience Opportunities for
Museums and Heritage Organisations
Stefania Boiano
Ann Borda
Guiliano Gaia
Invisible Studio Ltd.
Health and Biomedical Informatics
The University of Melbourne
Invisible Studio Ltd.
Stefania Rossi
Museo Poldi
Milan, Italy
Pietro Cuomo
Art in the City
This paper explores how chatbots can offer opportunities for museums and galleries in engaging
their audiences through recent developments, and through a case study approach focusing on the
design and implementation of an audience development pilot in Milan involving four historic house
museums (Case Museo di Milano). The pilot aimed to find new and interesting ways to engage
teenagers in visiting these museums through visualising narrative using a convergence of chatbot
technology and gamification.
Chatbots. Artificial intelligence. Museums. Gamification. Virtual museum guides. Teenagers.
Chatbots, also known as talkbots or chatterbots or
bots, are computer programs that mimic
conversation using auditory or textual methods.
More specifically the functionality of chatbots use
natural language processing (NLP) that has a
history rooted in artificial intelligence (AI) (Corti et
al. 2015, Dale 2017).
The concept of chatbots can be traced to Alan
Turing’s seminal publication Computing Machinery
and Intelligence that addresses an overarching
question: Can machines think? (Turing 1950,
Dennett 2004, Hill et al., 2015, Copeland et al.
2017). The parameters of this question would
shape the so-called Turing test. In its most simple
form, the test is carried out as an imitation game.
The test has a human interrogator speaking to a
number of computers and humans through an
interface. If the interrogator cannot distinguish
between the computers and the humans then the
Turing Test has been passed (Dennett 2004, Hill et
al. 2015, Warwick 2017, Copeland et al. 2017).
One of the earliest such natural language
applications was a chatbot called ELIZA developed
from 1964 to 1966 at the MIT Computer Science
and Artificial Intelligence Laboratory by computer
scientist Joseph Weizenbaum (Weizenbaum 1966).
ELIZA originally was created to use simple pattern
matching and a template-based response
(prewritten scripts) to emulate the conversational
style of a Rogerian psychotherapist.
Figure 1: Image of ELIZA terminal interface accessed
online (
Chatbots and New Audience Opportunities for Museums and Heritage Organisations
Stefania Boiano, Ann Borda, Giuliano Gaia, Stefania Rossi & Pietro Cuomo
ELIZA generated global fascination in creating a
natural language bot that might pass the Turing
Test. The Loebner Prize launched in 1990 by Hugh
Loebner in conjunction with the Cambridge Center
for Behavioural Studies in Massachusetts (U.S.) is
an example of a competition expressly designed to
implement the Turing Test and builds on both the
progressive interest and advances in AI
developments (Loebner 2008).
Extending the pattern-matching techniques used in
ELIZA and advancing NLP capabilities, American
scientist Richard Wallace developed A.L.I.C.E.
(Artificial Linguistic Internet Computer Entity) in the
late 1990s. A.L.I.C.E., also known as Alicebot, is
acknowledged for its pioneering programming
using Artificial Intelligence Markup Language
(AIML), which is an XML schema for creating
natural language software agents. Wallace
released the first version of AIML in July 2001,
subsequently published the Pandora API on which
A.L.I.C.E is based (Wallace 2008). A.L.I.C.E.
became a three-time Loebner winner in 2000,
2001, and 2004.
Concurrent to A.L.I.C.E. developments,
‘Jabberwacky’ was being conceived by British
programmer, Rollo Carpenter. Jabberwacky was
intended to simulate “natural human chat in an
interesting, entertaining and humorous manner"
(Shah & Warwick 2017). The emergence of the
Internet provided Jabberwacky with a dynamic
database of thousands of online human
interactions from which to process responses.
Jabberwacky under the guise of ‘George’ and
‘Joan’ won the Loebner Prize in 2005 and 2006
In 2008, Jabberwacky launched a new iteration
rebranded as ‘Cleverbot’. Like Jabberwacky,
Cleverbot is designed to learn from its
conversations with humans (more than 150 million
to date according to Wikipedia). It draws on past
interactions to determine future questions and
answers (Gehl 2014).
Figure 2: Cleverbot interface from Cleverbot website
In the endeavour to extend question-answering
(QA) capabilities posed in natural language, IBM
Watson was conceived in 2006 as a QA computing
system with the goal of outperforming human
contestants on the U.S. TV game show Jeopardy!
IBM Watson was developed as part of IBM's
DeepQA project (Ferrucci et al. 2010). Watson
became the first computer to defeat contestants on
the TV game show Jeopardy!, notably in a special
match between Watson and Jeopardy! champions,
Ken Jennings and Brad Rutter in 2011 (Best n.d.).
Chatbots in general are reaching milestones in
artificial intelligence capability, as well as their
pervasiveness in consumer facing products and
services. For example in 2014, a chatbot called
Eugene Goostman, portrayed as a 13-year-old
Ukrainian boy, won an AI contest marking the 60th
anniversary of Turing's death. In the Turing Test
2014 organised by the University of Reading, 33%
of the interrogators thought that Goostman was
human (You 2015).
Created from AIML technology by programmer,
Steve Worswick, ‘Mitsuku’ is a web-based chatbot
available on the freeware instant messaging mobile
app Kik Messenger and the Pandorabots website.
It is among a growing number of sophisticated bots
that can answer questions, play games, and is
capable of basic reasoning in QA (Corti et al. 2015,
Hill et al., 2015). Mitsuku is a three-time winner of
the Loebner prize in 2013, 2016, and recently in
Figure 3: Mitsuku avatar from Pandora website
Chatbots and New Audience Opportunities for Museums and Heritage Organisations
Stefania Boiano, Ann Borda, Giuliano Gaia, Stefania Rossi & Pietro Cuomo
Generally, the term ‘chatbot’ has referred to a
software application that engages in a dialogue
with a human using natural language. Most early
advances have been associated with written
language, but with advances in speech recognition,
there is a narrowing of these associations. An early
example is Dragon a simple speech understanding
system developed in 1975 by Dr James Baker from
Carnegie Mellon University. Other advances in the
speech recognition sector have been made
possible by VoiceXML, which has been published
in a series of standards since the first release in the
year 2000. The World Wide Web Consortium
(W3C) has recently established in 2017 a W3C
Community Group on Voice Interaction
which aims to explore the future of system-initiated
directed dialogs of VoiceXML applications, such as
voice assistants.
In just a couple of years, there has been an
exponential rise of voice assistants such as Apple
Siri launched in 2010, Google Now in 2012,
Amazon’s Alexa and Microsoft’s Cortana in 2015,
and Google Assistant in 2016. Using NLP and
Internet of Things (IoT) platforms, these assistants
connect to web services to answer questions and
respond to user requests (Hoy 2018). Social media
platforms are similarly incorporating chatbot
functionality. Facebook opened up its Messenger
platform and API to developers in 2017, providing a
means to build a simple chatbot on Facebook.
Twitter opened its direct messaging channel to
chatbots in the previous year that began a hype
cycle in twitterbots (Alarifi et al. 2016), and other
messenger services are coming on board with
open APIs (Hoy 2018, Mool 2018).
Museums have been piloting related technologies
of artificial intelligence and natural language
processing as demonstrators for more than a
decade (Boiano et al. 2003, Bickmore 2013,
Swartout 2010, Borda & Bowen 2017, Bordoni et
al. 2016). Emerging free chatbot-creating platforms,
e.g., Chatfuel, Chatterbot Eliza, among others, and
the availability of open APIs, for instance, can offer
both large and smaller museums the opportunity of
experimenting with chatbots with relatively low
effort while keeping costs and staff resources at a
low level (Bordoni et al. 2016, Boiano et al. 2003)
and potentially less impact on existing
infrastructure (Dale 2016).
The exponential growth in the use of chatbots by
marketers and online businesses in enhancing
customer experiences, often as messaging
applications that can personalise the interaction
(e.g., recommender systems), is providing further
comparable opportunities in the cultural sector
(Swartout 2010). There are in fact a growing
number of Museums currently going down this
route, and using bots as part of their audience
engagement programming.
For instance, the Heinz Nixdorf MuseumsForum in
Paderborn Germany ( has an
early experience of using an avatar bot introduced
as MAX. Developed in 2004, MAX is a
conversational agent that directly engages with
visitors through a screen as a virtual museum guide
(Kopp et al. 2005).
Figure 4: Avatar Max, Heinz Nixdorf MuseumsForum
website, 2018 (
The Cooper-Hewitt Smithsonian Design Museum in
New York City has been a pioneer in chatbot
technologies with the creation of the Object Phone
in 2013 in which a visitor can text or call to ask for
more information about a museum object in the
collection. In 2016, Object Phone became a
subscription service so that a visitor can receive a
daily update (
In the words of Micah Walter, Director of Digital &
Emerging Media:
I think institutions like museums have a great
opportunity in the chatbot space. If anything it
represents a new way to broaden our reach and
connect with people on the platforms they are
already using. What’s more interesting to me is
that chatbots themselves represent a way to
interact with people that is by its very nature, bi-
directional (Walter 2016).
The San Francisco Museum of Modern Art
(SFMOMA) has developed Send Me SFMOMA
(, an
SMS service that provides an approachable
method of sharing the breadth of SFMOMA’s
collection with the public of which only 5% is seen
in the Galleries at any one time (Mollica 2017).
Using the service and texting the words “send me”
followed by a keyword, a colour, or an emoji, a
visitor will receive a related artwork image and
caption via text message.
Chatbots and New Audience Opportunities for Museums and Heritage Organisations
Stefania Boiano, Ann Borda, Giuliano Gaia, Stefania Rossi & Pietro Cuomo
Figure 5: Image from Send Me SFMOMA blog. SFMOM,
2017 (
In March 2017, the Anne Frank House museum
( in Amsterdam launched
a Facebook Messenger chatbot that allows users to
discover the personal history of Anne Frank and
practical visitor information. Not simply a collections
discovery bot, this application offers various
conversation paths, allowing users to follow
different paths in the Anne Frank story with concise
information and links to additional content, for
example, excerpts from her diary to the context of
World War II at the time.
In 2017 the Museum of Australian Democracy in
Canberra ( marked the
50th anniversary of a landmark 1967 referendum in
which Australians voted overwhelmingly to amend
the Constitution to include Aboriginal people in the
census and to allow the Commonwealth
government to create laws for them. The Museum
launched a referendum chatbot that allows visitors
to learn about the historic and current impacts of
this vote through chatting with it on Facebook
Messenger. Directed towards children and
accessible to adults, it uses simple gamification
and responses, including emojis.
It acts like history in your pocket and is helping
MoAD spark a conversation about the
significance of the 1967 referendum. We’re
hoping it will be an effective way for people to
get the facts, hear Indigenous perspectives on
the referendum and reflect on its continuing
relevance today. Marni Pilgrim, Digital
Engagement Manager, MOAD (O’Mallon 2017).
Not unlike the quick adoption of Facebook
Messenger among some of the Museum examples,
there is a rising trend in experimenting with Twitter
bots, such as the Museumbot (@museumbot) that
pulls open access images from a number of
archives such as the Metropolitan Museum of Art.
Other museum archive bots are steadily growing on
Twitter, including the Los Angeles County Museum
of Art LACMA bot (@LACMAbot), New York Public
Library NYPL postcards bot (@NYPLpostcards),
and the Museum of Modern Art MoMaR bot
Figure 6: Image from NYPL Postcard Bot on Twitter
(@NYPLpostcards) (retrieved 13 March 2018).
The House Museums of Milan (Case Museo di
Milano) is a group of 4 historical homes in Milan:
Poldi Pezzoli Museum, Bagatti Valsecchi Museum,
Necchi Campiglio Villa and Boschi Di Stefano
House Museum ( The House
Museums launched a strategic initiative in 2016
Chatbots and New Audience Opportunities for Museums and Heritage Organisations
Stefania Boiano, Ann Borda, Giuliano Gaia, Stefania Rossi & Pietro Cuomo
that aimed to motivate people to visit the four
A London-based cultural innovation company,
InvisibleStudio (, was
approached to introduce gamification into the
engagement process, specifically to attract teenage
audiences. The development of a chatbot-based
game aimed at teenagers was a direction chosen
by the House Museums curators and the
InvisibleStudio team due to several factors.
Teenagers are a notoriously difficult public to
engage in museums and often far more interested
in social interactions between themselves than in
cultural content and this was proven to be even
more challenging if that content is associated with a
19th century historic house. In general, teenagers
visiting museums are identified with high levels of
distraction and highly adapted to the use of social
media (Kelly & Russo 2008, Endo 2016, Fors
2016). Recent research in the U.S., for example,
shows that online chatting is one of the highest
forms of social interaction of teenagers (Statista
The InvisibleStudio team had already experimented
with earlier chatbot technology in 2002, while
working at the Museum of Science and Technology
“Leonardo da Vinci” in Milan (Boiano et al. 2016,
Boiano & Gaiia 2017a). The process provided
important lessons in the development of a chatbot
application. This early chatbot was developed to
mimic a Leonardo da Vinci character with whom
the user would interact. This set high expectations
for the user experience, and led to frustrations
when the chatbot was not able to understand the
user beyond simple introductory chat (Hill et al.
2015). Consequently, user issues occurred quite
soon in the conversation (Boiano & Gaia 2017a).
With these lessons in hand, InvisibleStudio
changed their approach when creating a chatbot for
the House Museums project. The chatbot would
only be used as a tool or “virtual companion” to
help younger audiences solve a game set in the
real physical environment of the museum. By
engaging teenagers through gamification, it could
shift the user’s focus from the conversation with the
chatbot to the actual exploration of the Museum
collections (Boiano 2016, Cawston 2017).
The chatbot game for the House Museums was
developed using Facebook Messenger, and
intended mainly for young users and teenagers to
engage them in exploring the four homes. Aspects
of visualisation and storytelling were utilised with
the chatbot platform to simulate a teenaged girl
asking visitors to help her in defeating a mysterious
Renaissance magician (based on a real historical
figure) by solving mysteries in the galleries.
Exploration is encouraged by users in looking for
hidden clues that lead to a final discovery, which
provides a further incentive to engage with the
Figure 7: Teenagers using the chatbot game in Milan.
Source: InvisibleStudio, 2017.
Before operational release of the chatbot, it was
piloted with teenagers aged 16-18. The pilot was
conducted by InvisibleStudio with 80 teenaged
students from local high schools in Milan. The pilot
resulted in the following findings:
90% of students managed to complete the
30% had connection problems
34% were worried for their data traffic
88% found the length of the game was right
72% evaluated the game as highly
66% found it a useful learning tool,
especially if it was used with another
student or in a small group.
These results offered some clear directions for the
final development stages. Especially interesting for
the developers was the fact that students liked
using the chatbot in small groups, rather than on
their own, because the game triggered
collaboration within the team and created a friendly
competitive environment with other teams. (Boiano
& Gaia 2017b)
At the same time, the pace and quality of the
conversation emerged as a critical aspect for future
consideration, requiring a larger effort from the
development team to create engaging and realistic
non-linear narratives (Hill et al. 2015). For instance,
key features which required considerable tweaking
before publishing the application included: making
conversations more realistic by studying real chats
between users and guides, referencing objects
which the user can actually see “here and now” in
the galleries, and finding the perfect length for the
game (Boiano & Gaia 2017a, 2017b).
Chatbots and New Audience Opportunities for Museums and Heritage Organisations
Stefania Boiano, Ann Borda, Giuliano Gaia, Stefania Rossi & Pietro Cuomo
Another key challenge was the necessity of
keeping open a continuous online connection
between users and the chatbot. This can prove
difficult in historical house museums, where the
older infrastructure is comprised of complex layouts
and thick walls, which can prevent wireless
connectivity or an even distribution of Wi-Fi
Other challenges still need to be addressed. These
are mainly related to the Facebook Messenger
platform itself, and changing preferences by
teenagers and audiences for different messaging
services and platforms which may not have open
APIs for chatbot development at this stage, such as
WhatsApp (Mool 2018). With the possibility of more
open APIs, museum chatbots could be developed
for a potentially larger audience (e.g., teenagers),
and potentially a larger uptake, which does not
depend on a subscription.
Despite the current limitations, the advanced
chatbot application in the House Museums has
become successfully operational through design
thinking, design iteration, and importantly,
teenagers’ participation in the development
There is clear evidence of the potential of cultural
heritage organisations to play a significant role in
the development of chatbots. The sector already
has a strong provenance with decades long
experience in piloting emerging and embedded
technologies relevant to smart city services, and in
its understanding of user-centric interaction
characterised by context-awareness,
personalisation, and adaptation.
What emerged from this project is that the
convergence of chatbot and gamification has
proved to be a powerful tool in involving younger,
digital savvy generations in novel and interesting
ways to them (Cawston et al. 2017, Kelly & Russo
2008, Endo 2016, Fors 2016).
Our findings particularly suggest that users enjoy
interacting with a chatbot in a game context, and
that this engagement can provide a smarter way of
leading younger audiences to interact with objects
and historic environments with greater attention.
However with all the successes of this chatbot
launch, there also remain challenges that need
further consideration beyond the scope of this
paper. As mentioned above, the availability of a
wider range of chatbot platforms is one such
challenge. A more involved issue is the pace and
quality of the bot conversation which emerged as a
critical aspect of this project. The present chatbot
application required a bigger effort from the
developing team to create engaging and realistic
non-linear narrative lines, and this will be part of a
continuing iteration in future developments.
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... Visitors' engagement and attraction, size and location of the museum, content presentation and manipulation, and financial capacities are some crucial factors that determine the type and the capabilities of the proposed AI chatbot solution. The designers must also design on a long-term basis and consider factors such as sustainability, interoperability, scalability, usability, and others [4][5][6][7][8][9]. ...
... Museums have been working with chatbot technology for more than a decade. The first chatbot-related applications were conversational bots interacting with the audience as avatars through screens or through phone or text services [5]. Gradually, with the rise of advanced AI technologies and the wide use of social media networking, more advanced chatbots were developed and used in several domains. ...
... The chatbot is developed with the platform and uses gamification techniques in order to engage sole users or groups of visitors to play a "treasure hunt" tour game by finding clues and learning new things. The chatbot also tries to engage visitors by creating realistic non-linear narrative dialogue tours, though the chatbot has no free conversational skills [5,6,9]. The chatbot was active on 30 August 2021 from, and is accessible only in the Italian language. ...
Full-text available
Nowadays, museums are developing chatbots to assist their visitors and to provide an enhanced visiting experience. Most of these chatbots do not provide a human-like conversation and fail to deliver the complete requested knowledge by the visitors. There are plenty of stand-alone museum chatbots, developed using a chatbot platform, that provide predefined dialog routes. However, as chatbot platforms are evolving and AI technologies mature, new architectural approaches arise. Museums are already designing chatbots that are trained using machine learning techniques or chatbots connected to knowledge graphs, delivering more intelligent chatbots. This paper is surveying a representative set of developed museum chatbots and platforms for implementing them. More importantly, this paper presents the result of a systematic evaluation approach for evaluating both chatbots and platforms. Furthermore, the paper is introducing a novel approach in developing intelligent chatbots for museums. This approach emphasizes graph-based, distributed, and collaborative multi-chatbot conversational AI systems for museums. The paper accentuates the use of knowledge graphs as the key technology for potentially providing unlimited knowledge to chatbot users, satisfying conversational AI’s need for rich machine-understandable content. In addition, the proposed architecture is designed to deliver an efficient deployment solution where knowledge can be distributed (distributed knowledge graphs) and shared among different chatbots that collaborate when is needed.
... Museums and cultural heritage places are using those technologies to engage visitors' in their spaces [7,1,4]. In addition to the technological challenges and constraints, using AI in museums also introduce new difficulties due to the use of open and public spaces in the experience: time length, environmental noises, crowds, and visitors' engagement. ...
... The roleplaying activity seems to have contributed to designers, researchers, and museum curators as a possibility to materialize the experience and deal with the particularities of developing conversational systems in informal settings. The Datathon helped to start the conversational AI experience with an initial training data set tailored to the audience and Basic description of the exhibition experience: 1 The guide explains that the robots learn by matching the question asked to previously stored examples. ...
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In this paper, we describe and reflect on the process of co-designing an Artificial Intelligence (AI) exhibition aimed to teach children basic AI concepts in the Catavento Science Museum in São Paulo, Brazil. We focus on two of the co-design process with the museum staff: one which sought to design the flow of the experience and get the sense of the target audience; and another which intended to provide content for the AI exhibition. We describe the activities and show how they assisted in the design process and opened possibilities for the design team to develop an exciting experience , tailored to children, promoting informal learning of Artificial Intelligence concepts.
... Several cultural and scientific institutions have adopted technologies to connect beyond the labels displayed next to the artworks. These include: chatbots [5], robots [24], QR codes [22], RFID tags [13], and augmented reality [4,28]. This paper focuses on the use of conversational chatbots and, more specifically, on how to design their content. ...
... At the same time, some cultural heritage places and museums are starting to adopt interactive conversational systems (e.g., such as chatbots, virtual agents, and conversational robots) to engage with the public and promote learning. Some cultural heritage locals have used devices with text-based interaction capabilities [5,21,26], others have used voice-based systems [3,14,20,25], as well as hybrid interactions with text and audio features [7] were made available to the public. Conversational systems have sometimes been employed to trigger more complex questions and to promote conversations among users. ...
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If an artwork could talk, what would visitors ask? This paper explores what types of content voice-based AI conversational systems should have to attend visitors' expectations in a museum. The study analyses 142,463 conversation logs from 5,242 unique sessions of a nine-month long deployment of a voice-based interactive guide in a modern art museum in Brazil. In this experiment, visitors freely asked questions about seven different artworks of different styles. By grouping the visitor utterances into eight types of content, we determined that more than half of the visitors asked about the meanings and intentions behind the artwork, followed by facts about the artwork and author-related questions. We also determined that the types of questions were not affected by each artwork, the artwork style, or its physical location. We also saw some relationships between the visitor's overall evaluation of the experience with the types of questions she asked. Based on those results, we identified implications for designing content for voice-based conversational systems in museums.
... Several cultural and scientific institutions have adopted technologies to connect beyond labels displayed next to the artworks. These include: chatbots (Boiano, 2018), robots (Shiomi, 2006), QR codes (Schultz 2013), RFID tags (His, 2005 ), and augmented reality (Wojciechowski, 2004)This paper presents a conversational voice-based system and, supporting user studies for understanding how visitors engage with the experience and content by acting in society. ...
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Museums and Art exhibition spaces are adopting Artificial Intelligence (AI) systems to engage and attract visitors in several contexts. The use of AI can boost visitors’ attention, promote informal learning through conversations in front of the exhibits, and motivate visitors to act socially. In this paper, we describe a voice-based conversational system Iris+, in which visitors are inquired to answer questions to an agent. It is a proactive agent that invites visitors to reflect and take action to improve future world. First, we will describe how this system works. Second, we will show the outcomes of evaluation studies with visitors in situ and, a survey shows how visitors engaged in social action after interacting with IRIS+ and public demographics. Based on those visitors’ studies, we propose a set of challenges to design conversational systems in public spaces and the improvements were incorporated into the redesign of IRIS++.
... Several cultural and scientific institutions have adopted technologies to connect beyond labels displayed next to the artworks. These include: chatbots (Boiano, 2018), robots (Shiomi, 2006), QR codes (Schultz 2013), RFID tags (His, 2005 ), and augmented reality (Wojciechowski, 2004)This paper presents a conversational voice-based system and, supporting user studies for understanding how visitors engage with the experience and content by acting in society. ...
Conference Paper
Full-text available
Museums and Art exhibition spaces are adopting Artificial Intelligence (AI) systems to engage and attract visitors in several contexts. The use of AI can boost visitors' attention, promote informal learning through conversations in front of the exhibits, and motivate visitors to act socially. In this paper, we describe a voice-based conversational system Iris+, in which visitors are inquired to answer questions to an agent. It is a proactive agent that invites visitors to reflect and take action to improve future world. First, we will describe how this system works. Second, we will show the outcomes of evaluation studies with visitors in situ and, a survey shows how visitors engaged in social action after interacting with IRIS+ and public demographics. Based on those visitors' studies, we propose a set of challenges to design conversational systems in public spaces and the improvements were incorporated into the redesign of IRIS++.
Museums are the place of wonders. Museum designers or curators work continuously to improve the visitor experience. The best way to create this wow effect is to use the most advanced technologies for our exhibits and displays. Nowadays Artificial Intelligence is one of the most discussed technological words used in the scientific community. Though it is yet to reach the masses, to give the flavour of AI to the general public and also to use the goodness of this technology in communicating science, a customized chatbot is developed to work as a museum guide. In order to achieve this, we have taken help from different AI and Internet of Things (IoT) based technologies. Finally, a virtual humanoid bot is developed with capabilities to operate and explain physical exhibits through interaction with visitors. This bot can replace museum guides for explaining different exhibits to the visitors.
This study examines the relationship between house museums and ICTs by looking at well-known examples which demonstrate that the use of ICTs in this context is not generalised. The benefits derived from the implementation of ICTs prove that this implementation is necessary to allow house museums to take a step forward and integrate data analysis into their communication plans with the objective of placing museum visitors at the centre of museum services. House museums will thereby be able to optimise their decision-making and adopt strategies that will improve the quality of visitors’ museum experiences. Obtaining and analysing data about museum visitors and their experiences is made easier with Data technology, which can forecast patterns that illustrate types of visitor behaviour, helping museums to better understand and more fully profile visitors. In an increasingly digitalised market, house museums must generate interaction and interest to improve visitors’ museum experiences.
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Recently, understanding their unique role in storytelling and aiming to attract more visitors, several museums have integrated modern ICT technologies. The problem with these technologies however is that gradually tend to be of no real interest to visitors, lack of significant interaction, cannot be continuously updated, and eventually distract visitors from experiencing the exhibits. Museum visitors do not need to be impressed by a technological application but need to learn about the stories of the exhibits in a creative, human-centered and interactive manner. This paper presents an ongoing work towards implementing a new interactive technological trend for museums, i.e., a museum chatbot platform, namely MuBot. The MuBot platform aims to provide museums the opportunity to create simple, interactive and human-friendly apps for their visitors. Such apps will integrate an intelligent chatbot that uses some of the most advanced AI technologies of Machine Learning, Natural Language Processing/Generation, and the Semantic Web. Museum visitors will be able to use a chatbot application that will be created through the MuBot platform, to chat with a ‘smart’ exhibit. They will be able to ask questions through text or voice (in natural language) and receive audible or written answers. The more the visitors ask, the more MuBot will learn and store new knowledge in its knowledge base. The paper presents a preliminary design of the proposed MuBot platform, experimenting with first prototype implementations using the well-known Dialogflow framework, as well as using a Knowledge Graph-based approach.
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Voice assistants are software agents that can interpret human speech and respond via synthesized voices. Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana, and Google’s Assistant are the most popular voice assistants and are embedded in smartphones or dedicated home speakers. Users can ask their assistants questions, control home automation devices and media playback via voice, and manage other basic tasks such as email, to-do lists, and calendars with verbal commands. This column will explore the basic workings and common features of today’s voice assistants. It will also discuss some of the privacy and security issues inherent to voice assistants and some potential future uses for these devices. As voice assistants become more widely used, librarians will want to be familiar with their operation and perhaps consider them as a means to deliver library services and materials.
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This paper reviews a cross-section of international developments in smart cities and their implications for the cultural heritage sector. A main focus of the paper is an assessment of selected case studies in the cultural heritage sector exploring the use of smart platforms and visualisation technologies. The results highlight a particular set of current challenges, as well as providing scope for identifying future opportunities in developing smart cultural heritage services. Smart cities. Smart heritage. Smart culture. Smart museums. Visualisation. Internet of Things. Augmented reality.
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By all accounts, 2016 is the year of the chatbot. Some commentators take the view that chatbot technology will be so disruptive that it will eliminate the need for websites and apps. But chatbots have a long history. So what's new, and what's different this time? And is there an opportunity here to improve how our industry does technology transfer?
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In this paper, we look at the possibility of a machine having a sense of humour. In particular, we focus on actual machine utterances in Turing test discourses. In doing so, we do not consider the Turing test in depth and what this might mean for humanity, rather we merely look at cases in conversations when the output from a machine can be considered to be humorous. We link such outpourings with Turing’s “arguments from various disabilities” used against the concept of a machine being able to think, taken from his seminal work of 1950. Finally we consider the role that humour might play in adding to the deception, integral to the Turing test, that a machine in practice appears to be a human.
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Much has been written about the Turing Test in the last few years, some of it preposterously off the mark. People typically mis-imagine the test by orders of magnitude. This essay is an antidote, a prosthesis for the imagination, showing how huge the task posed by the Turing Test is, and hence how unlikely it is that any computer will ever pass it. It does not go far enough in the imagination-enhancement department, however, and I have updated the essay with a new postscript.
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We use speech shadowing to create situations wherein people converse in person with a human whose words are determined by a conversational agent computer program. Speech shadowing involves a person (the shadower) repeating vocal stimuli originating from a separate communication source in real-time. Humans shadowing for conversational agent sources (e.g., chat bots) become hybrid agents ("echoborgs") capable of face-to-face interlocution. We report three studies that investigated people's experiences interacting with echoborgs and the extent to which echoborgs pass as autonomous humans. First, participants in a Turing Test spoke with a chat bot via either a text interface or an echoborg. Human shadowing did not improve the chat bot's chance of passing but did increase interrogators' ratings of how human-like the chat bot seemed. In our second study, participants had to decide whether their interlocutor produced words generated by a chat bot or simply pretended to be one. Compared to those who engaged a text interface, participants who engaged an echoborg were more likely to perceive their interlocutor as pretending to be a chat bot. In our third study, participants were naïve to the fact that their interlocutor produced words generated by a chat bot. Unlike those who engaged a text interface, the vast majority of participants who engaged an echoborg did not sense a robotic interaction. These findings have implications for android science, the Turing Test paradigm, and human-computer interaction. The human body, as the delivery mechanism of communication, fundamentally alters the social psychological dynamics of interactions with machine intelligence.
When judging the capabilities of technology, different humans can have very different perspectives and come to quite diverse conclusions over the same data set. In this paper we consider the capabilities of humans when it comes to judging conversational abilities, as to whether they are conversing with a human or a machine. In particular the issue in question is the importance of human judges interrogating in practical Turing tests. As supportive evidence for this we make use of transcripts which originated from a series of practical Turing’s tests held 6-7 June 2014 at the Royal Society London. Each of the tests involved a 3-participant simultaneous comparison by a judge of two hidden entities, one being a human and the other a machine. Thirty different judges took part in total. Each of the transcripts considered in the paper resulted in a judge being unable to say for certain which was the machine and which was the human. The main point we consider here is the fallibility of humans in deciding whether they are conversing with a machine or a human, hence we are concerned specifically with the decision-making process
As the movie The Imitation Game celebrates British mathematician Alan Turing's contributions to the Allied victory in World War II, the artificial intelligence (AI) community is rethinking another of his legacies: the Turing Test. At a 25 January workshop at the 29th Association for the Advancement of Artificial Intelligence conference in Austin, researchers will discuss proposals for a new Turing Championship composed of three to five research challenges. In contrast with Turing's single litmus test, the proposed challenges acknowledge that intelligence has multiple dimensions—from language comprehension to social awareness—that are best tackled piece by piece. The new Turing Championship would motivate researchers to develop machines with a deeper understanding of the world, argue the workshop organizers. By early 2016, they hope to stage a set of trial competitions that will be revised and repeated regularly.