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关键词： Natural language processing systems  

摘要： The emergence of Large Language Models (LLMs) has significantly impacted the field of Natural Language Processing and has transformed conversational tasks across various domains because of their widespread integration in applications and public access. The discussion surrounding the application of LLMs in education has raised ethical concerns, particularly concerning plagiarism and policy compliance. Despite the prowess of LLMs in conversational tasks, the limitations of reliability and hallucinations exacerbate the need to guardrail conversations, motivating our investigation of RAG in computer science higher education. We developed Retrieval Augmented Generation (RAG) applications for the two tasks of virtual teaching assistants and teaching aids. In our study, we collected the ratings and opinions of faculty members in undergraduate and graduate computer science university courses at various levels, using our personalized RAG systems for each course. This study is the first to gather faculty feedback on the application of LLM-based RAG in education. The investigation revealed that while faculty members acknowledge the potential of RAG systems as virtual teaching assistants and teaching aids, certain barriers and features are suggested for their full-scale deployment. These findings contribute to the ongoing discussion on the integration of advanced language models in educational settings, highlighting the need for careful consideration of ethical implications and the development of appropriate safeguards to ensure responsible and effective implementation. © 2024, CC BY.

关键词： Students  

摘要： Enhancing interaction and feedback collection in a first-semester computer science course poses a significant challenge due to students' diverse needs and engagement levels. To address this issue, we created and integrated a command-based chatbot on the course communication server on Discord. The DiscordBot enables students to provide feedback on course activities through short surveys, such as exercises, quizzes, and lectures, facilitating stress-free communication with instructors. It also supports attendance tracking and introduces lectures before they start. The research demonstrates the effectiveness of the DiscordBot as a communication tool. The ongoing feedback allowed course instructors to dynamically adjust and improve the difficulty level of upcoming activities and promote discussion in subsequent tutor sessions. The data collected reveal that students can accurately perceive the activities' difficulty and expected results, providing insights not possible through traditional end-of-semester surveys. Students reported that interaction with the DiscordBot was easy and expressed a desire to continue using it in future semesters. This responsive approach ensures the course meets the evolving needs of students, thereby enhancing their overall learning experience. © 2024, CC BY-NC-ND.

关键词： Inclusivity   Popular culture   Educational settings   Body positivity   Fat phobia  

摘要： Purpose-This paper aims to address weight-based bullying as a persistent issue among adolescents. Fat phobia, rooted in societal biases against overweight individuals, leads to social exclusion and discrimination, negatively impacting mental health and equality. Educational settings suffer from the profound effects of fat phobia, creating a toxic atmosphere that distracts from learning and marginalizes students who don't conform to traditional physical norms. Design/methodology/approach-This paper combines historical analysis, literature review and critical discourse analysis. It examines the historical roots of fat phobia, explores the impact of societal influences, analyzes contemporary educational practices and the use of popular culture as a innovative tool. Findings-Leveraging popular culture in anti-fat phobia education effectively challenges stereotypes. Educators empower students to critically analyze media depictions, encouraging empathy and inclusivity. Current events serve as critical teaching tools, sparking discussions on intersecting discrimination forms and equity. Practical implications-This paper emphasizes the urgent need to comprehensively address fat phobia in education, advocating for a shift in societal attitudes toward body diversity. Popular culture can serve as an educational tool to create inclusive classrooms where all students are valued and free from weight-based discrimination. Social implications-This paper emphasizes the urgent need to comprehensively address fat phobia in education, advocating for a shift in societal attitudes toward body diversity. Popular culture can serve as an educational tool to create inclusive classrooms where all students are valued and free from weight-based discrimination. Originality/value-The originality and value of this paper lie in its multi-faceted approach to examining weight-based discrimination, its historical context, practical educational strategies, and the innovative use of popular culture as a

关键词： Technology   Machine learning   Computer science   Drug discovery  

摘要： Companies are using artificial intelligence tools to help scientists to query their data without the need for programming skills.

关键词： Machine learning   Technology   Computer science  

摘要： Generative AI models are now widely accessible, enabling everyone to create their own machine-made something. But these models can collapse if their training data sets contain too much AI-generated content.

关键词： Computer science   Mechanical engineering  

摘要： Legged robots are well-suited for deployment in unstructured environments but require a unique control scheme specific for their design. As controllers optimised in simulation do not transfer well to the real world (the infamous sim-to-real gap), methods enabling quick learning in the real world, without any assumptions on the specific robot model and its dynamics, are necessary. In this paper, we present a generic method based on Central Pattern Generators, that enables the acquisition of basic locomotion skills in parallel, through very few trials. The novelty of our approach, underpinned by a mathematical analysis of the controller model, is to search for good initial states, instead of optimising connection weights. Empirical validation in six different robot morphologies demonstrates that our method enables robots to learn primary locomotion skills in less than 15 minutes in the real world. In the end, we showcase our skills in a targeted locomotion experiment. The authors present a machine learning approach to enable modular robots to learn to walk and follow objects within a short time. The method is based on bioinspired central pattern generators and is validated on six robots with different body shapes.

关键词： Computational science   Computer science  

摘要： Stable diffusion revolutionized image creation from descriptive text. GPT-2 (ref. 1), GPT-3(.5) (ref. 2) and GPT-4 (ref. 3) demonstrated high performance across a variety of language tasks. ChatGPT introduced such language models to the public. It is now clear that generative artificial intelligence (AI) such as large language models (LLMs) is here to stay and will substantially change the ecosystem of online text and images. Here we consider what may happen to GPT-{n} once LLMs contribute much of the text found online. We find that indiscriminate use of model-generated content in training causes irreversible defects in the resulting models, in which tails of the original content distribution disappear. We refer to this effect as 'model collapse' and show that it can occur in LLMs as well as in variational autoencoders (VAEs) and Gaussian mixture models (GMMs). We build theoretical intuition behind the phenomenon and portray its ubiquity among all learned generative models. We demonstrate that it must be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of LLM-generated content in data crawled from the Internet. Analysis shows that indiscriminately training generative artificial intelligence on real and generated content, usually done by scraping data from the Internet, can lead to a collapse in the ability of the models to generate diverse high-quality output.