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Human-Like Learning of Social Reasoning via Analogy 通过类比进行类人社会推理学习
Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31284
Irina Rabkina
{"title":"Human-Like Learning of Social Reasoning via Analogy","authors":"Irina Rabkina","doi":"10.1609/aaaiss.v3i1.31284","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31284","url":null,"abstract":"Neurotypical adult humans are impeccably good social reasoners. Despite the occasional faux pas, we know how to interact in most social settings and how to consider others' points of view. Young children, on the other hand, do not. Social reasoning, like many of our most important skills, is learned. \u0000\u0000Much like human children, AI agents are not good social reasoners. While some algorithms can perform some aspects of social reasoning, we are a ways off from AI that can interact naturally and appropriately in the broad range of settings that people can. In this talk, I will argue that learning social reasoning via the same processes used by people will help AI agents reason--and interact--more like people do. Specifically, I will argue that children learn social reasoning via analogy, and that AI agents should, too. I will present evidence from cognitive modeling experiments demonstrating the former and AI experiments demonstrating the latter. I will also propose future directions for social reasoning research that both demonstrate the need for robust, human-like social reasoning in AI and test the utility of common approaches.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"10 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Toward Autonomy: Metacognitive Learning for Enhanced AI Performance 迈向自主：元认知学习提升人工智能性能
Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31270
Brendan Conway-Smith, Robert L. West
{"title":"Toward Autonomy: Metacognitive Learning for Enhanced AI Performance","authors":"Brendan Conway-Smith, Robert L. West","doi":"10.1609/aaaiss.v3i1.31270","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31270","url":null,"abstract":"Large Language Models (LLMs) lack robust metacognitive learning abilities and depend on human-provided algorithms and prompts for learning and output generation. Metacognition involves processes that monitor and enhance cognition. Learning how to learn - metacognitive learning - is crucial for adapting and optimizing learning strategies over time. Although LLMs possess limited metacognitive abilities, they cannot autonomously refine or optimize these strategies. Humans possess innate mechanisms for metacognitive learning that enable at least two unique abilities: discerning which metacognitive strategies are best and automatizing learning strategies. These processes have been effectively modeled in the ACT-R cognitive architecture, providing insights on a path toward greater learning autonomy in AI. Incorporating human-like metacognitive learning abilities into AI could potentially lead to the development of more autonomous and versatile learning mechanisms, as well as improved problem-solving capabilities and performance across diverse tasks.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"35 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Leveraging Generative Artificial Intelligence to Broaden Participation in Computer Science 利用生成式人工智能扩大计算机科学的参与范围
Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31262
Devang Jayachandran, P. Maldikar, Tyler S. Love, Jeremy Blum
{"title":"Leveraging Generative Artificial Intelligence to Broaden Participation in Computer Science","authors":"Devang Jayachandran, P. Maldikar, Tyler S. Love, Jeremy Blum","doi":"10.1609/aaaiss.v3i1.31262","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31262","url":null,"abstract":"Generative Artificial Intelligence (AI) was incorporated into a competitive programming event that targeted undergraduate students, including those with little programming experience. The competition incorporated a range of challenge design approaches that promoted meaningful interaction with generative AI system, even while keeping the challenge difficulty level to an appropriate level. An analysis of survey responses and competition data showed that this format lowered barriers to participation, successfully engaged students throughout the competition, and increased the likelihood that they would participate in a similar event. In an extension of this work, a professional development workshop for high school teachers is being developed, along with a contest for high school students. Participant surveys and logs of interaction with the contest and generative AI systems will be analyzed to measure the effect of generative AI on student self-efficacy and suggest ways to integrate generative AI instruction into computer science curriculum.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"21 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Faithful Reasoning over Scientific Claims 对科学主张的忠实推理
Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31209
N. Tan, Niket Tandon, David Wadden, Oyvind Tafjord, M. Gahegan, Michael Witbrock
{"title":"Faithful Reasoning over Scientific Claims","authors":"N. Tan, Niket Tandon, David Wadden, Oyvind Tafjord, M. Gahegan, Michael Witbrock","doi":"10.1609/aaaiss.v3i1.31209","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31209","url":null,"abstract":"Claim verification in scientific domains requires models that faithfully incorporate relevant knowledge from the ever-growing, vast existing literature. \u0000Unfaithful claim verifications can lead to misinformation such as those observed during the COVID-19 pandemic. Fact-checking systems often fail to capture the complex relationship between claims and evidence, especially with ambiguous claims and implicit assumptions. Relying only on current LLMs poses challenges due to hallucinations and information traceability issues. To address these challenges, our approach considers multiple viewpoints onto the scientific literature, enabling the assessment of contradictory arguments and implicit assumptions. Our proposed inference method adds faithful reasoning to large language models by distilling information from diverse, relevant scientific abstracts. This method provides a verdict label that can be weighted by the reputation of the scientific articles and an explanation that can be traced back to sources. Our findings demonstrate that humans not only perceive our explanation to be significantly superior to the off-the-shelf model, but they also evaluate it as faithfully enabling the tracing of evidence back to its original sources.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"77 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Dataset for Estimating Participant Inspiration in Meetings toward AI-Based Meeting Support System to Improve Worker Wellbeing 估算与会者在会议中的灵感的数据集，实现基于人工智能的会议支持系统，提高工人的幸福感
Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31231
Soki Arai, Yuki Yamamoto, Yuji Nozaki, Haruka Matsukura, Maki Sakamoto
{"title":"A Dataset for Estimating Participant Inspiration in Meetings toward AI-Based Meeting Support System to Improve Worker Wellbeing","authors":"Soki Arai, Yuki Yamamoto, Yuji Nozaki, Haruka Matsukura, Maki Sakamoto","doi":"10.1609/aaaiss.v3i1.31231","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31231","url":null,"abstract":"Various meetings are carried out in intellectual production activities and workers have to spend much time to create ideas. In creative meetings, it is sometime difficult for the meeting moderators and facilitators to efficiently conduct the meetings because the participants are required to come up with new ideas one after another and some participants hesitate to express unconventional ideas. Therefore, we propose to develop an AI-based meeting support system that estimates participants’ inspiration and helps to generate comfortable meeting environments for improvement of worker wellbeing. Participants’ inspiration is assumed to be estimated based on their speech and micro behaviors including smiles and nods. In this paper, a dataset we collected for the development of the proposed system is reported. The dataset consists of participants’ brain blood flows measured near-infrared spectrometers, micro behavior annotated from video recording, and inspiration the participants reported with buttons. The data for 1020 min was collected by conducting simulation meetings. In future work, we plan to train an LSTM (long short-term memory) based neural network model to realize the proposed system.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"84 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141123068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Resource-aware Federated Data Analytics in Edge-Enabled IoT Systems 边缘物联网系统中的资源感知联合数据分析
Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31219
Hana Khamfroush
{"title":"Resource-aware Federated Data Analytics in Edge-Enabled IoT Systems","authors":"Hana Khamfroush","doi":"10.1609/aaaiss.v3i1.31219","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31219","url":null,"abstract":"In a resource constrained environment like Internet-of-Things (IoT) systems, it is critical to make optimal decisions on how much resources\u0000to allocate pre-processing and how much to allocate to model training, and which specific combination of preprocessing and learning should be selected. \u0000This talk first, provides an overview of some initial steps we took towards developing federated data pre-processing in IoT environments, and then a\u0000visionary overview of potential research problems related to developing an integrated resource-aware and Quality-of-Service (QoS)-aware data pre-processing and model training system is provided.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"99 35","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Semantic Verification in Large Language Model-based Retrieval Augmented Generation 基于大型语言模型的检索增强生成中的语义验证
Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31199
Andreas Martin, Hans Friedrich Witschel, Maximilian Mandl, Mona Stockhecke
{"title":"Semantic Verification in Large Language Model-based Retrieval Augmented Generation","authors":"Andreas Martin, Hans Friedrich Witschel, Maximilian Mandl, Mona Stockhecke","doi":"10.1609/aaaiss.v3i1.31199","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31199","url":null,"abstract":"This position paper presents a novel approach of semantic verification in Large Language Model-based Retrieval Augmented Generation (LLM-RAG) systems, focusing on the critical need for factually accurate information dissemination during public debates, especially prior to plebiscites e.g. in direct democracies, particularly in the context of Switzerland. Recognizing the unique challenges posed by the current generation of Large Language Models (LLMs) in maintaining factual integrity, this research proposes an innovative solution that integrates retrieval mechanisms with enhanced semantic verification processes. The paper outlines a comprehensive methodology following a Design Science Research approach, which includes defining user personas, designing conversational interfaces, and iteratively developing a hybrid dialogue system. Central to this system is a robust semantic verification framework that leverages a knowledge graph for fact-checking and validation, ensuring the correctness and consistency of information generated by LLMs. The paper discusses the significance of this research in the context of Swiss direct democracy, where informed decision-making is pivotal. By improving the accuracy and reliability of information provided to the public, the proposed system aims to support the democratic process, enabling citizens to make well-informed decisions on complex issues. The research contributes to advancing the field of natural language processing and information retrieval, demonstrating the potential of AI and LLMs in enhancing civic engagement and democratic participation.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"16 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The Arithmetic of Machine Decision : How to Find the Symmetries of Complete Chaos 机器决策的算术：如何找到完全混沌的对称性
Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31171
O. Bartheye, L. Chaudron
{"title":"The Arithmetic of Machine Decision : How to Find the Symmetries of Complete Chaos","authors":"O. Bartheye, L. Chaudron","doi":"10.1609/aaaiss.v3i1.31171","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31171","url":null,"abstract":"This present work is deliberately placed in the context capable of defining the requirements expressed by machine decision-making calculations. The informational nature of\u0000a decision requires abandoning any invariant preserving the structure but on the contrary switching into total chaos, a necessary and sufficient condition for exploiting the symmetries\u0000allowing the calculation to converge. Decision arithmetic is the best way to precisely define the nature of these symmetries.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"8 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Centering Humans in Artificial Intelligence  人工智能以人为本
Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31170
Cecilia O. Alm
{"title":"Centering Humans in Artificial Intelligence ","authors":"Cecilia O. Alm","doi":"10.1609/aaaiss.v3i1.31170","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31170","url":null,"abstract":"AI systems are breaking into new domains and applications, and it is pivotal to center humans in contemporary AI systems and contemplate what this means. This discussion considers three perspectives or human roles in AI as users, contributors, and researchers-in-training, to illustrate this notion.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"100 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multi-Modal Instruction-Tuning Small-Scale Language-and-Vision Assistant for Semiconductor Electron Micrograph Analysis 用于半导体电子显微图像分析的多模式指令调整小型语言和视觉助手
Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31205
Sagar Srinivas Sakhinana, Geethan Sannidhi, Venkataramana Runkana
{"title":"Multi-Modal Instruction-Tuning Small-Scale Language-and-Vision Assistant for Semiconductor Electron Micrograph Analysis","authors":"Sagar Srinivas Sakhinana, Geethan Sannidhi, Venkataramana Runkana","doi":"10.1609/aaaiss.v3i1.31205","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31205","url":null,"abstract":"We present a novel framework for analyzing and interpreting electron microscopy images in semiconductor manufacturing using vision-language instruction tuning. The framework employs a unique teacher-student approach, leveraging pretrained multimodal large language models such as GPT-4 to generate instruction-following data for zero-shot visual question answering (VQA) and classification tasks, customizing smaller multimodal models (SMMs) for microscopy image analysis, resulting in an instruction tuned language-and-vision assistant. Our framework merges knowledge engineering with machine learning to integrate domain-specific expertise from larger to smaller multimodal models within this specialized field, greatly reducing the need for extensive human labeling. Our study presents a secure, cost-effective, and customizable approach for analyzing microscopy images, addressing the challenges of adopting proprietary models in semiconductor manufacturing.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":"80 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141123153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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