1,720,962 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Not cheating on the Turing Test: towards grounded language learning in Artificial Intelligence
Full text linkThesis (MA)--Stellenbosch University, 2020.AFRIKAANSE OPSOMMING: Raadpleeg teks vir opsommingENGLISH ABSTRACT: In this thesis, I carry out a novel and interdisciplinary analysis into various complex factors involved in human natural-language acquisition, use and comprehension, aimed at uncovering some of the basic requirements for if we were to try and develop artificially intelligent(AI)agents with similar capacities. Inspired by a recent publication wherein I explored the complexities and challenges involved in enabling AI systems to deal with the grammatical(i.e. syntactic and morphological) irregularities and ambiguities inherent in natural language (Alberts, 2019), I turn my focus here towards appropriately inferring the content of symbols themselves—as ‘grounded’ in real-world percepts, actions, and situations. I first introduce the key theoretical problems I aim to address in theories of mind and language. For background, I discuss the co-development of AI and the controverted strands of computational theories of mind in cognitive science, and the grounding problem(or ‘internalist trap’) faced by them. I then describe the approach I take to address the grounding problem in the rest of the thesis. This proceeds in chapter I.To unpack and address the issue, I offer a critical analysis of the relevant theoretical literature in philosophy of mind, psychology, cognitive science and (cognitive) linguistics in chapter II. I first evaluate the major philosophical/psychological debates regarding the nature of concepts; theories regarding how concepts are
acquired, used, and represented in the mind; and, on that basis, offer my own account of conceptual structure, grounded in current (cognitively plausible) connectionist theories of thought. To further explicate how such concepts are acquired and communicated,I evaluate the relevant embodied (e.g. cognitive, perceptive, sensor imotor, affective, etc.) factors involved in grounded human (social) cognition, drawing from current scientific research in the areas of4E Cognition and social cognition. On that basis, I turn my focus specifically towards grounded theories of language, drawing from the cognitive linguistics programme that aims to develop a naturalised, cognitively plausible understanding of human concept/language acquisition and use. I conclude the chapter with a summary wherein I integrate my findings from these various disciplines, presenting a general theoretical basis upon which to evaluate more practical considerations for its implementation in AI—the topic of the following chapter.In chapter III, I offer an overview of the different major approaches(and their integrations)in the area of Natural Language Understanding in AI, evaluating their respective strengths and shortcomings in terms of specific models. I then offer a critical summary wherein I contrast and contextualise the different approaches in terms of the more fundamental theoretical convictions they seem to reflect.
On that basis,in the final chapter, Ire-evaluate the aforementioned grounding problem and the different ways in which it has been interpreted in different (theoretical and practical) disciplines, distinguishing between a stronger and weaker reading. I then present arguments for why implementing the stronger version in AI seems, both practically and theoretically, problematic. Instead, drawing from the theoretical insights I gathered, I consider some of the key requirements for ‘grounding’ (in the weaker sense) as much as possible of natural language use with robotic AI agents, including implementational constraints that might need to be put in place to achieve this. Finally, I evaluate some of the key challenges that may be involved, if indeed the aim were to meet all the requirements specified.Master
Not Cheating on the Turing Test: Towards Grounded Language Learning in Artificial Intelligence
Full text linkRecent hype surrounding the increasing sophistication of language processing
models has renewed optimism regarding machines achieving a human-like command
of natural language. Research in the area of natural language understanding
(NLU) in artificial intelligence claims to have been making great strides in
this area, however, the lack of conceptual clarity/consistency in how
'understanding' is used in this and other disciplines makes it difficult to
discern how close we actually are. In this interdisciplinary research thesis, I
integrate insights from cognitive science/psychology, philosophy of mind, and
cognitive linguistics, and evaluate it against a critical review of current
approaches in NLU to explore the basic requirements--and remaining
challenges--for developing artificially intelligent systems with human-like
capacities for language use and comprehension.Comment: Philosophy master's thesis (2020) available on the SUNScholar
research repository (https://scholar.sun.ac.za/handle/10019.1/109271
Designing social actors: an ethics of system-user interaction: The ethical design of systems and agents that interact with people
Full text linkAs computing systems become increasingly conversational, autonomous, and proactive, they evolve from mere tools into persistent social actors, shaping our experience and behaviour through dozens of everyday interactions. Automated systems with varying degrees of 'smart'-ness already address and talk to users directly, treating them at once as friends, customers, patients, and targets. Yet, in this drive toward more personable and chatty interfaces, it is crucial to understand how to navigate the nuances of tactful and appropriate social interaction. This thesis applies a social psychological lens to the evaluation of system-user interactions, highlighting the fundamentally contextual nature of appropriate social acting. Thereby, it brings into focus, and systematically unpacks, a key dimension of ethics that has as yet been overlooked in computing contexts. That is, more than the what: what a system says or does, or tries to get users to do, it emphasises the how: how a system treats a person in a given interaction or ongoing relationship, regardless of whether the phrasing or intentions appear benign or even beneficent in principle.
In 2020, this investigation started from the more general premise that technologies are social actors, recontextualising pioneering insights of the 'Computers Are Social Actors’ research paradigm of the '90s in the current digital landscape. The aim then became to systematically investigate the implications of people experiencing system-human communications as a social actor 'talking to' them, whether in text, voice or other modalities: from basic mobile push notifications to advanced dialogue agents. During this time, two major developments in computing pushed my research towards more specific domains. One was the boom in digital conversational agents during the COVID pandemic, at its peak during the time, initiating a trend in replacing human service providers with chatbots. The second was the development and proliferation of large language models (LLMs), and the popularising idea of 'agentic AI’ as proactive LLM-based agents that take real-world actions and maintain ongoing dialogues with the same person over time. In this context, the aim of understanding appropriate automated social behaviour started becoming more relevant than ever, and the implications even more profound.
In simple terms, this thesis explores different angles to support the argument that we should evaluate interactive systems as social actors, and carefully analyse how they treat people. That is, how the same interaction patterns can be experienced differently between contexts, and how different forms of treatment may impact a person's behaviour and psychological wellbeing. Based on my background in cognitive linguistics and language philosophy, I anticipated that shifting the focus from the universal to the pragmatics of situated interaction would not only be useful, but essential as systems become increasingly proactive and autonomous in their conversational ability.
Combining interdisciplinary literature reviews, qualitative studies with end-users, and technical experiments with LLMs, this thesis argues that bad social acting is not just a matter of being obviously "toxic", cruel, or offensive, just as good social acting is not a matter of being maximally kind, honest, or helpful. Instead, appropriate social acting is an art: too much friendliness can seem invasive, too much flattery can seem manipulative, too much honesty can seem rude, too much helpfulness can seem patronising. Navigating this space requires more tact than simply avoiding risky topics or radiating positivity, as people are more socially intelligent and complex than designers have often given them credit for.
From a normative perspective, this research critically engages with interaction design practices that normalise treating users as things to predict, steer and optimise. Instead, it advocates for a person-centred approach where the goal is not merely to make systems more efficient, effective or engaging, but to ensure that they empower and treat people with due regard. As digital platforms and agents proliferate and evolve, this thesis lays the groundwork for a culture in which computing systems approach their role in our social world with appropriate caution, tact, and respect
Meeting them halfway : altering language conventions to facilitate human-robot interaction
Full text linkCITATION: Alberts, L. 2019. Meeting them halfway: Altering language conventions to facilitate human-robot interaction. Stellenbosch Papers in Linguistics Plus, 56:97-122. doi:10.5842/56-0-799The original publication is available at https://spilplus.journals.ac.za/pubENGLISH ABSTRACT: This article considers the remaining hindrances for natural language processing technologies in achieving open and natural (human-like) interaction between humans and computers. Although artificially intelligent (AI) systems have been making great strides in this field, particularly with the development of deep learning architectures that carry surface-level statistical methods to greater levels of sophistication, these systems are yet incapable of deep semantic analysis, reliable translation, and generating rich answers to open-ended questions. I consider how the process may be facilitated from our side, first, by altering some of our existing language conventions (which may occur naturally) if we are to proceed with statistical approaches, and secondly, by considering possibilities in using a formalised artificial language as an auxiliary medium, as it may avoid many of the inherent ambiguities and irregularities that make natural language difficult to process using rule-based methods. As current systems have been predominantly English-based, I argue that a formal auxiliary language would not only be a simpler and more reliable medium for computer processing, but may also offer a more neutral, easy-to-learn lingua franca for uniting people from different linguistic backgrounds with none necessarily having the upper hand.https://spilplus.journals.ac.za/pub/article/view/799Publisher’s versio
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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