5,651 research outputs found
Mapping AI Arguments in Journalism Studies
This study investigates and suggests typologies for examining Artificial
Intelligence (AI) within the domains of journalism and mass communication
research. We aim to elucidate the seven distinct subfields of AI, which
encompass machine learning, natural language processing (NLP), speech
recognition, expert systems, planning, scheduling, optimization, robotics, and
computer vision, through the provision of concrete examples and practical
applications. The primary objective is to devise a structured framework that
can help AI researchers in the field of journalism. By comprehending the
operational principles of each subfield, scholars can enhance their ability to
focus on a specific facet when analyzing a particular research topic
Computer-assisted versus oral-and-written dietary history taking for diabetes mellitus
Background: Diabetes is a chronic illness characterised by insulin resistance or deficiency, resulting in elevated glycosylated haemoglobin A1c (HbA1c) levels. Diet and adherence to dietary advice is associated with lower HbA1c levels and control of disease. Dietary history may be an effective clinical tool for diabetes management and has traditionally been taken by oral-and-written methods, although it can also be collected using computer-assisted history taking systems (CAHTS). Although CAHTS were first described in the 1960s, there remains uncertainty about the impact of these methods on dietary history collection, clinical care and patient outcomes such as quality of life.
Objectives: To assess the effects of computer-assisted versus oral-and-written dietary history taking on patient outcomes for diabetes mellitus.
Search methods: We searched The Cochrane Library (issue 6, 2011), MEDLINE (January 1985 to June 2011), EMBASE (January 1980 to June 2011) and CINAHL (January 1981 to June 2011). Reference lists of obtained articles were also pursued further and no limits were imposed on languages and publication status.
Selection criteria: Randomised controlled trials of computer-assisted versus oral-and-written history taking in patients with diabetes mellitus.
Data collection and analysis: Two authors independently scanned the title and abstract of retrieved articles. Potentially relevant articles were investigated as full text. Studies that met the inclusion criteria were abstracted for relevant population and intervention characteristics with any disagreements resolved by discussion, or by a third party. Risk of bias was similarly assessed independently.
Main results: Of the 2991 studies retrieved, only one study with 38 study participants compared the two methods of history taking over a total of eight weeks. The authors found that as patients became increasingly familiar with using CAHTS, the correlation between patients' food records and computer assessments improved. Reported fat intake decreased in the control group and increased when queried by the computer. The effect of the intervention on the management of diabetes mellitus and blood glucose levels was not reported. Risk of bias was considered moderate for this study.
Authors' conclusions: Based on one small study judged to be of moderate risk of bias, we tentatively conclude that CAHTS may be well received by study participants and potentially offer time saving in practice. However, more robust studies with larger sample sizes are needed to confirm these. We cannot draw on any conclusions in relation to any other clinical outcomes at this stage
ChatGPT: ascertaining the self-evident. The use of AI in generating human knowledge
The fundamental principles, potential applications, and ethical concerns of
ChatGPT are analyzed and discussed in this study. Since ChatGPT emerged, it has
gained a rapidly growing popularity, with more than 600 million users today.
The development of ChatGPT was a significant mile-stone, as it demonstrated the
potential of large-scale language models to generate natural language responses
that are almost indistinguishable from those of a human. ChatGPT's operational
principles, prospective applications, and ability to advance a range of human
endeavours are discussed in the paper. However, much of the work discusses and
poses moral and other problems that rely on the subject. To document the
latter, we submitted 14 queries and captured the ChatGPT responses. ChatGPT
appeared to be honest, self-knowledgeable, and careful with its answers. The
authors come to the realization that since AI is already a part of society, the
pervasiveness of the ChatGPT tool to the general public has once again brought
to light concerns regarding AI in general. Still, they have moved from the
domain of scientific community collective reflection at a conceptual level to
everyday practice this time.Comment: 20 pages, 2 figure
GPT Models in Construction Industry: Opportunities, Limitations, and a Use Case Validation
Large Language Models(LLMs) trained on large data sets came into prominence
in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT
models from OpenAI have been released. These models perform well on diverse
tasks and have been gaining widespread applications in fields such as business
and education. However, little is known about the opportunities and challenges
of using LLMs in the construction industry. Thus, this study aims to assess GPT
models in the construction industry. A critical review, expert discussion and
case study validation are employed to achieve the study objectives. The
findings revealed opportunities for GPT models throughout the project
lifecycle. The challenges of leveraging GPT models are highlighted and a use
case prototype is developed for materials selection and optimization. The
findings of the study would be of benefit to researchers, practitioners and
stakeholders, as it presents research vistas for LLMs in the construction
industry.Comment: 58 pages, 20 figure
Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research
The final search query for the Systematic Literature Review (SLR) was
conducted on 15th July 2022. Initially, we extracted 1707 journal and
conference articles from the Scopus and Web of Science databases. Inclusion and
exclusion criteria were then applied, and 58 articles were selected for the
SLR. The findings show four dimensions that shape the AI explanation, which are
format (explanation representation format), completeness (explanation should
contain all required information, including the supplementary information),
accuracy (information regarding the accuracy of the explanation), and currency
(explanation should contain recent information). Moreover, along with the
automatic representation of the explanation, the users can request additional
information if needed. We have also found five dimensions of XAI effects:
trust, transparency, understandability, usability, and fairness. In addition,
we investigated current knowledge from selected articles to problematize future
research agendas as research questions along with possible research paths.
Consequently, a comprehensive framework of XAI and its possible effects on user
behavior has been developed
EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria
By simply composing prompts, developers can prototype novel generative
applications with Large Language Models (LLMs). To refine prototypes into
products, however, developers must iteratively revise prompts by evaluating
outputs to diagnose weaknesses. Formative interviews (N=8) revealed that
developers invest significant effort in manually evaluating outputs as they
assess context-specific and subjective criteria. We present EvalLM, an
interactive system for iteratively refining prompts by evaluating multiple
outputs on user-defined criteria. By describing criteria in natural language,
users can employ the system's LLM-based evaluator to get an overview of where
prompts excel or fail, and improve these based on the evaluator's feedback. A
comparative study (N=12) showed that EvalLM, when compared to manual
evaluation, helped participants compose more diverse criteria, examine twice as
many outputs, and reach satisfactory prompts with 59% fewer revisions. Beyond
prompts, our work can be extended to augment model evaluation and alignment in
specific application contexts
Falling for fake news: investigating the consumption of news via social media
In the so called ‘post-truth’ era, characterized by a loss of public trust in various institutions, and the rise of ‘fake news’ disseminated via the internet and social media, individuals may face uncertainty about the veracity of information available, whether it be satire or malicious hoax. We investigate attitudes to news delivered by social media, and subsequent verification strategies applied, or not applied, by individuals. A survey reveals that two thirds of respondents regularly consumed news via Facebook, and that one third had at some point come across fake news that they initially believed to be true. An analysis task involving news presented via Facebook reveals a diverse range of judgement forming strategies, with participants relying on personal judgements as to plausibility and scepticism around sources and journalistic style. This reflects a shift away from traditional methods of accessing the news, and highlights the difficulties in combating the spread of fake news
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4
Large language models (LLMs) are a special class of pretrained language
models obtained by scaling model size, pretraining corpus and computation.
LLMs, because of their large size and pretraining on large volumes of text
data, exhibit special abilities which allow them to achieve remarkable
performances without any task-specific training in many of the natural language
processing tasks. The era of LLMs started with OpenAI GPT-3 model, and the
popularity of LLMs is increasing exponentially after the introduction of models
like ChatGPT and GPT4. We refer to GPT-3 and its successor OpenAI models,
including ChatGPT and GPT4, as GPT-3 family large language models (GLLMs). With
the ever-rising popularity of GLLMs, especially in the research community,
there is a strong need for a comprehensive survey which summarizes the recent
research progress in multiple dimensions and can guide the research community
with insightful future research directions. We start the survey paper with
foundation concepts like transformers, transfer learning, self-supervised
learning, pretrained language models and large language models. We then present
a brief overview of GLLMs and discuss the performances of GLLMs in various
downstream tasks, specific domains and multiple languages. We also discuss the
data labelling and data augmentation abilities of GLLMs, the robustness of
GLLMs, the effectiveness of GLLMs as evaluators, and finally, conclude with
multiple insightful future research directions. To summarize, this
comprehensive survey paper will serve as a good resource for both academic and
industry people to stay updated with the latest research related to GPT-3
family large language models.Comment: Preprint under review, 58 page
Human-Machine Communication: Complete Volume. Volume 6
his is the complete volume of HMC Volume 6
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