Principles for human-centered interaction design, Part 2: Can humans and machines think together?

Bunch, Larry; Bradshaw, Jeffrey M.; Hoffman, Robert R.; Johnson, Matthew

doi:10.1109/mis.2015.44

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…At the receiver, the conventional decoding method and the proposed predictive decoding method were used to reconstruct images based on the received descriptions. Note that our focus is to test the effectiveness of the proposed side decoding in terms of rate-distortion performance, as compared to the conventional side decoding [51] as shown in (4) and (5). We implemented both the algorithms with the sublattice index number N = 31.…”

Section: Experimental Methodsmentioning

confidence: 99%

Predictive side decoding for human-centered multiple description image coding

2020

J Wireless Com Network

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Multiple description coding (MDC) provides a favorable solution for human-centered image communication, which takes into account people's varying watching situations as well as people's demand for real-time image display. As an effective technique for MDC, three-description lattice vector quantization (3D-LVQ) is considered for image coding in this paper. Based on intra-and inter-correlation in the 3D-LVQ index assignment as well as wavelet intra-subband correlation, a novel predictive decoding method for 3D-LVQ-based image coding is proposed to enhance side decoding performance, which attempts to predict lost descriptions (sublattice points) in a good way for better reconstructions of wavelet vectors (fine lattice points) in the side decoding. Experimental results validate effectiveness of the proposed decoding scheme in terms of rate-distortion performance.

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Section: Experimental Methodsmentioning

confidence: 99%

Predictive side decoding for human-centered multiple description image coding

2020

J Wireless Com Network

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“…However, after a first moment of doubt, most users started balancing pros and cons of current situation and the proposed one and considered the optimisation of time and resources that a DDVA would have for them resulted on a more positive perceived customer care relationship; (ii) Secondly, users also would like to have a backup person to talk to when the DDVA is not able to solve their problem, following the Human In-The-Loop practice [18]. These findings point out to the fact that people do not consider the DDVA will satisfy all their requests, and consider that they would not remain unsolved when combining human and AI forces [25]. This led to a higher level of satisfaction of the DDVA concept proposed, while establishing a potential scenario to merge both worlds in order to enhance customer service and strengthen the value proposition.…”

Section: Transferring Human Roles To Aimentioning

confidence: 99%

“…When the AI systems need autonomy to perform tasks and make decisions on behalf of users, it becomes essential to develop trust on the AI actions in order to use them and adopt them as assistants in people's daily lives [21]. Currently, a human is always there controlling cases of doubt or ambiguity, for no machine is perfect, as no human is [22], so there is no need to separate AI from humans, since various trials such as with Centaur Chess [23] and IBM Watson [24] proved that human minds and machines work better together than separately [25].…”

Section: Mistrusting Ai To Solve Complex Problemsmentioning

confidence: 99%

Building Trust Between Users and Telecommunications Data Driven Virtual Assistants

García

Lopez

2018

IFIP Advances in Information and Communication Technology

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When we think about traditional technology services, such as the Internet and popular smartphone apps, interaction or experience related words tend to come to our mind. However, when we shift to more disruptive and breakthrough technologies, such as big data, IoT or Artificial Intelligence (AI), trust and security become a key challenge as the environments in which the personal information is stored and used to extract value changes in ways that are difficult to understand for normal users. This study explores the current challenges to trust and rely on a Virtual Assistant and establishes a series of constructs to build trust in a Telecoms Data Driven Virtual Assistant by considering aspects from an embryonic idea of the AI to its final articulation as a digital service. It examines a series of multi-methodology research interactions to help shape and refine the service, not only to build trust in the AI but also in the organisation behind it.

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