On the track of Artificial Intelligence: Learning with Intelligent Personal Assistants

Canbek, Nil Göksel; Mutlu, Mehmet Emin

doi:10.14687/ijhs.v13i1.3549

Cited by 137 publications

(43 citation statements)

References 11 publications

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“…As defined by Hauswald et al [16], an Intelligent Personal Assistant is "an application that uses inputs such as the user's voice, vision (images), and contextual information to provide assistance by answering questions in natural language, making recommendations, and performing actions." These assistants are used in many domains from medicine to education or e-commerce [14]. While the performance in answering questions, making recommendations, and performing actions is important, the user experience is paramount.…”

Section: Intelligent Assistantsmentioning

confidence: 99%

User-in-the-loop adaptive intent detection for instructable digital assistant

Lair

Delgrange²,

Mugisha³

et al. 2020

Proceedings of the 25th International Conference on Intelligent User Interfaces

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People are becoming increasingly comfortable using Digital Assistants (DAs) to interact with services or connected objects. However, for non-programming users, the available possibilities for customizing their DA are limited and do not include the possibility of teaching the assistant new tasks. To make the most of the potential of DAs, users should be able to customize assistants by instructing them through Natural Language (NL). To provide such functionalities, NL interpretation in traditional assistants should be improved: (1) The intent identification system should be able to recognize new forms of known intents, and to acquire new intents as they are expressed by the user. ( 2) In order to be adaptive to novel intents, the Natural Language Understanding module should be sample efficient, and should not rely on a pretrained model. Rather, the system should continuously collect the training data as it learns new intents from the user. In this work, we propose AidMe (Adaptive Intent Detection in Multi-Domain Environments), a user-in-the-loop adaptive intent detection framework that allows the assistant to adapt to its user by learning his intents as their interaction progresses. AidMe builds its repertoire of intents and collects data to train a model of semantic similarity evaluation that can discriminate between the learned intents and autonomously discover new forms of known intents. AidMe addresses two major issues -intent learning and user adaptation -for instructable digital assistants. We demonstrate the capabilities of AidMe as a standalone system by comparing it with a one-shot learning system and a pretrained NLU module through simulations of interactions with a user. We also show how AidMe can smoothly integrate to an existing instructable digital assistant.

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Section: Intelligent Assistantsmentioning

confidence: 99%

User-in-the-loop adaptive intent detection for instructable digital assistant

Lair

Delgrange²,

Mugisha³

et al. 2020

Proceedings of the 25th International Conference on Intelligent User Interfaces

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“…Fourthly, Cognitive Assistants have service delegation systems (Garrido et al, 2010;Canbek and Mutlu, 2016;Ebling, 2016), which can invoke various apps to complete tasks such as setting a timer, sending messages, getting directions, creating reminders, reminding daily appointments, responding to questions (Canbek and Mutlu, 2016). A cognitive assistant for audit planning could complete tasks such as opening linked third-party applications for auditors when they need them during their risk assessment, such as a calculator, data analytical tools/programs used in the firm, calendar.…”

Section: Features Of Cognitive Assistants and Their Advantages In Impmentioning

confidence: 99%

Developing a Cognitive Assistant for the Audit Plan Brainstorming Session

Qiao¹,

Vasarhelyi²

2018

IJDAR

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“…The use of IoT devices has further increased the quantities of data that are being gathered, stored and analysed. Applying AI to this data has increased its value and enabled direct monetization via targeted and bespoke advertising and recommendations (Biljana, et al, 2017, Goksel, et al, 2016).…”

Section: Privacy and Voice Command Devicesmentioning

confidence: 99%

Alexa, Emotions, Privacy and GDPR

Furey

Blue

2018

Electronic Workshops in Computing

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We exist in a world where emotional expression is a central facet of what makes us human. It allows us to interact richly with others and aids us in functioning as a society. Affective computing, also known as artificial emotional intelligence is the area of study that seeks to enable the development of systems and devices with the capacity to understand and replicate these human affects. The race is on to develop intelligent computing systems that can mimic human interaction and also emulate and convince humans that they are human too, building a sense of trust. The Amazon Echo and its intelligent personal assistant, "Alexa", is currently one of the most popular and pervasive of these intelligent devices. The human name given to this technological entity alludes to its human-like conversation abilities. However, most adult humans would quickly establish that "Alexa" is not a real person. This is obviously intimated through the automated voice, but is further demonstrated by a lack of recognition and display of any emotion. The advent of VCDs like Alexa raises a plethora of significant standard privacy considerations. This paper provides further speculation into the privacy concerns relating to the ability of these devices to gather data relating to an individual"s emotional state. This is conducted with consideration for the new General Data Protection Regulation (GDPR) introduced in May, 2018.

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On the track of Artificial Intelligence: Learning with Intelligent Personal Assistants

Cited by 137 publications

References 11 publications

User-in-the-loop adaptive intent detection for instructable digital assistant

User-in-the-loop adaptive intent detection for instructable digital assistant

Developing a Cognitive Assistant for the Audit Plan Brainstorming Session

Alexa, Emotions, Privacy and GDPR

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