6,709 research outputs found
Modeling Group Dynamics for Personalized Robot-Mediated Interactions
The field of human-human-robot interaction (HHRI) uses social robots to
positively influence how humans interact with each other. This objective
requires models of human understanding that consider multiple humans in an
interaction as a collective entity and represent the group dynamics that exist
within it. Understanding group dynamics is important because these can
influence the behaviors, attitudes, and opinions of each individual within the
group, as well as the group as a whole. Such an understanding is also useful
when personalizing an interaction between a robot and the humans in its
environment, where a group-level model can facilitate the design of robot
behaviors that are tailored to a given group, the dynamics that exist within
it, and the specific needs and preferences of the individual interactants. In
this paper, we highlight the need for group-level models of human understanding
in human-human-robot interaction research and how these can be useful in
developing personalization techniques. We survey existing models of group
dynamics and categorize them into models of social dominance, affect, social
cohesion, and conflict resolution. We highlight the important features these
models utilize, evaluate their potential to capture interpersonal aspects of a
social interaction, and highlight their value for personalization techniques.
Finally, we identify directions for future work, and make a case for models of
relational affect as an approach that can better capture group-level
understanding of human-human interactions and be useful in personalizing
human-human-robot interactions
The Globalization of Artificial Intelligence: African Imaginaries of Technoscientific Futures
Imaginaries of artificial intelligence (AI) have transcended geographies of the Global North and become increasingly entangled with narratives of economic growth, progress, and modernity in Africa. This raises several issues such as the entanglement of AI with global technoscientific capitalism and its impact on the dissemination of AI in Africa. The lack of African perspectives on the development of AI exacerbates concerns of raciality and inclusion in the scientific research, circulation, and adoption of AI. My argument in this dissertation is that innovation in AI, in both its sociotechnical imaginaries and political economies, excludes marginalized countries, nations and communities in ways that not only bar their participation in the reception of AI, but also as being part and parcel of its creation.
Underpinned by decolonial thinking, and perspectives from science and technology studies and African studies, this dissertation looks at how AI is reconfiguring the debate about development and modernization in Africa and the implications for local sociotechnical practices of AI innovation and governance. I examined AI in international development and industry across Kenya, Ghana, and Nigeria, by tracing Canadaâs AI4D Africa program and following AI start-ups at AfriLabs. I used multi-sited case studies and discourse analysis to examine the data collected from interviews, participant observations, and documents.
In the empirical chapters, I first examine how local actors understand the notion of decolonizing AI and show that it has become a sociotechnical imaginary. I then investigate the political economy of AI in Africa and argue that despite Western efforts to integrate the African AI ecosystem globally, the AI epistemic communities in the continent continue to be excluded from dominant AI innovation spaces. Finally, I examine the emergence of a Pan-African AI imaginary and argue that AI governance can be understood as a state-building experiment in post-colonial Africa. The main issue at stake is that the lack of African perspectives in AI leads to negative impacts on innovation and limits the fair distribution of the benefits of AI across nations, countries, and communities, while at the same time excludes globally marginalized epistemic communities from the imagination and creation of AI
Utilitarianism and the Social Nature of Persons
This thesis defends utilitarianism: the view that as far as morality goes, one ought to choose the option which will result in the most overall well-being. Utilitarianism is widely rejected by philosophers today, largely because of a number of influential objections. In this thesis I deal with three of them. Each is found in Bernard Williamsâs âA Critique of Utilitarianismâ (1973). The first is the Integrity Objection, an intervention that has been influential whilst being subject to a wide variety of interpretations. In Chapter Two I give my interpretation of Williamsâs Integrity objection; in Chapter Three I discuss one common response to it, and in Chapters Four and Five I give my own defence of utilitarianism against it. In Chapter Six I discuss a second objection: the problem of pre-emption. This problem is also found in Williams, but has received greater attention through the work of other authors in recent years. It suggests that utilitarianism is unable to deal with some of the modern worldâs most pressing moral problems, and raises an internal tension between the twin utilitarian aims of making a difference and achieving the best outcomes. In Chapter Seven I discuss a third objection: that utilitarianism is insufficiently egalitarian. I find this claim to be unwarranted, in light of recent social science and philosophy. My responses to Williamsâs objections draw upon resources from the socialist tradition â in particular, that traditionâs emphasis on the importance of social connections between individuals. Socialists have often been hostile to utilitarianism, in part for socialist-inflected versions of Williamsâs objections. Thus, in responding to these objections I aim to demonstrate that socialist thought contains the means to defuse not only mainstream philosophyâs rejection of utilitarianism but also its own, and thus to re-open the possibilities for a productive engagement between the two traditions
Fillers in Spoken Language Understanding: Computational and Psycholinguistic Perspectives
Disfluencies (i.e. interruptions in the regular flow of speech), are
ubiquitous to spoken discourse. Fillers ("uh", "um") are disfluencies that
occur the most frequently compared to other kinds of disfluencies. Yet, to the
best of our knowledge, there isn't a resource that brings together the research
perspectives influencing Spoken Language Understanding (SLU) on these speech
events. This aim of this article is to synthesise a breadth of perspectives in
a holistic way; i.e. from considering underlying (psycho)linguistic theory, to
their annotation and consideration in Automatic Speech Recognition (ASR) and
SLU systems, to lastly, their study from a generation standpoint. This article
aims to present the perspectives in an approachable way to the SLU and
Conversational AI community, and discuss moving forward, what we believe are
the trends and challenges in each area.Comment: To appear in TAL Journa
AQ-GT: a Temporally Aligned and Quantized GRU-Transformer for Co-Speech Gesture Synthesis
The generation of realistic and contextually relevant co-speech gestures is a
challenging yet increasingly important task in the creation of multimodal
artificial agents. Prior methods focused on learning a direct correspondence
between co-speech gesture representations and produced motions, which created
seemingly natural but often unconvincing gestures during human assessment. We
present an approach to pre-train partial gesture sequences using a generative
adversarial network with a quantization pipeline. The resulting codebook
vectors serve as both input and output in our framework, forming the basis for
the generation and reconstruction of gestures. By learning the mapping of a
latent space representation as opposed to directly mapping it to a vector
representation, this framework facilitates the generation of highly realistic
and expressive gestures that closely replicate human movement and behavior,
while simultaneously avoiding artifacts in the generation process. We evaluate
our approach by comparing it with established methods for generating co-speech
gestures as well as with existing datasets of human behavior. We also perform
an ablation study to assess our findings. The results show that our approach
outperforms the current state of the art by a clear margin and is partially
indistinguishable from human gesturing. We make our data pipeline and the
generation framework publicly available
More Than Machines?
We know that robots are just machines. Why then do we often talk about them as if they were alive? Laura Voss explores this fascinating phenomenon, providing a rich insight into practices of animacy (and inanimacy) attribution to robot technology: from science-fiction to robotics R&D, from science communication to media discourse, and from the theoretical perspectives of STS to the cognitive sciences. Taking an interdisciplinary perspective, and backed by a wealth of empirical material, Voss shows how scientists, engineers, journalists - and everyone else - can face the challenge of robot technology appearing »a little bit alive« with a reflexive and yet pragmatic stance
Augmented reality for minimally invasive spinal surgery
BackgroundAugmented reality (AR) is an emerging technology that can overlay computer graphics onto the real world and enhance visual feedback from information systems. Within the past several decades, innovations related to AR have been integrated into our daily lives; however, its application in medicine, specifically in minimally invasive spine surgery (MISS), may be most important to understand. AR navigation provides auditory and haptic feedback, which can further enhance surgeonsâ capabilities and improve safety.PurposeThe purpose of this article is to address previous and current applications of AR, AR in MISS, limitations of today's technology, and future areas of innovation.MethodsA literature review related to applications of AR technology in previous and current generations was conducted.ResultsAR systems have been implemented for treatments related to spinal surgeries in recent years, and AR may be an alternative to current approaches such as traditional navigation, robotically assisted navigation, fluoroscopic guidance, and free hand. As AR is capable of projecting patient anatomy directly on the surgical field, it can eliminate concern for surgeon attention shift from the surgical field to navigated remote screens, line-of-sight interruption, and cumulative radiation exposure as the demand for MISS increases.ConclusionAR is a novel technology that can improve spinal surgery, and limitations will likely have a great impact on future technology
Evaluating footwear âin the wildâ: Examining wrap and lace trail shoe closures during trail running
Trail running participation has grown over the last two decades. As a result, there have been an increasing number of studies examining the sport. Despite these increases, there is a lack of understanding regarding the effects of footwear on trail running biomechanics in ecologically valid conditions. The purpose of our study was to evaluate how a Wrap vs. Lace closure (on the same shoe) impacts running biomechanics on a trail. Thirty subjects ran a trail loop in each shoe while wearing a global positioning system (GPS) watch, heart rate monitor, inertial measurement units (IMUs), and plantar pressure insoles. The Wrap closure reduced peak foot eversion velocity (measured via IMU), which has been associated with fit. The Wrap closure also increased heel contact area, which is also associated with fit. This increase may be associated with the subjective preference for the Wrap. Lastly, runners had a small but significant increase in running speed in the Wrap shoe with no differences in heart rate nor subjective exertion. In total, the Wrap closure fit better than the Lace closure on a variety of terrain. This study demonstrates the feasibility of detecting meaningful biomechanical differences between footwear features in the wild using statistical tools and study design. Evaluating footwear in ecologically valid environments often creates additional variance in the data. This variance should not be treated as noise; instead, it is critical to capture this additional variance and challenges of ecologically valid terrain if we hope to use biomechanics to impact the development of new products
Speakers Raise their Hands and Head during Self-Repairs in Dyadic Conversations
People often encounter difficulties in building shared understanding during everyday conversation. The most common symptom of these difficulties are self-repairs, when a speaker restarts, edits or amends their utterances mid-turn. Previous work has focused on the verbal signals of self-repair, i.e. speech disfluences (filled pauses, truncated words and phrases, word substitutions or reformulations), and computational tools now exist that can automatically detect these verbal phenomena. However, face-to-face conversation also exploits rich non-verbal resources and previous research suggests that self-repairs are associated with distinct hand movement patterns. This paper extends those results by exploring head and hand movements of both speakers and listeners using two motion parameters: height (vertical position) and 3D velocity. The results show that speech sequences containing self-repairs are distinguishable from fluent ones: speakers raise their hands and head more (and move more rapidly) during self-repairs. We obtain these results by analysing data from a corpus of 13 unscripted dialogues, and we discuss how these findings could support the creation of improved cognitive artificial systems for natural human-machine and human-robot interaction
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