Proceedings of the AAAI Symposium Series最新文献_第2页

Federated Learning of Things - Expanding the Heterogeneity in Federated Learning 联合物联网学习--扩大联合学习的异质性

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31221

Scott Kuzdeba

{"title":"Federated Learning of Things - Expanding the Heterogeneity in Federated Learning","authors":"Scott Kuzdeba","doi":"10.1609/aaaiss.v3i1.31221","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31221","url":null,"abstract":"The Internet of Things (IoT) has revolutionized how our devices are networked, connecting multiple\u0000aspects of our life from smart homes and wearables to smart cities and warehouses. IoT’s strength\u0000comes from the ever-expanding diverse heterogeneous sensors, applications, and concepts that are all\u0000centered around the core concept collecting and sharing data from sensors. Simultaneously, deep\u0000learning has changed how our systems operate, allowing them to learn from data and change the way\u0000we interface with the world. Federated learning moves these two paradigm shifts together, leveraging\u0000the data (securely) from the IoT to train deep learning architectures for performant edge applications. \u0000However, today’s federated learning has not yet benefited from the scale of diversity that the IoT and\u0000deep learning sensors and applications provide. This talk explores how we can better tap into the\u0000heterogeneity that surrounds the potential of federated learning and use it to build better models. This\u0000includes the heterogeneity from device hardware to training paradigms (supervised, unsupervised,\u0000reinforcement, self-supervised).","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120646","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

Comparing Human Behavior to an Optimal Policy for Innovation 将人类行为与最佳创新政策相比较

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31291

Bonan Zhao, Natalia Vélez, Thomas L. Griffiths

引用次数: 0

K-PERM: Personalized Response Generation Using Dynamic Knowledge Retrieval and Persona-Adaptive Queries K-PERM：利用动态知识检索和角色自适应查询生成个性化回复

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31203

Kanak Raj, Kaushik Roy, Vamshi Bonagiri, Priyanshul Govil, K. Thirunarayan, Raxit Goswami, Manas Gaur

{"title":"K-PERM: Personalized Response Generation Using Dynamic Knowledge Retrieval and Persona-Adaptive Queries","authors":"Kanak Raj, Kaushik Roy, Vamshi Bonagiri, Priyanshul Govil, K. Thirunarayan, Raxit Goswami, Manas Gaur","doi":"10.1609/aaaiss.v3i1.31203","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31203","url":null,"abstract":"Personalizing conversational agents can enhance the quality of conversations and increase user engagement. However, they often lack external knowledge to appropriately tend to a user’s persona. This is crucial for practical applications like mental health support, nutrition planning, culturally sensitive conversations, or reducing toxic behavior in conversational agents. To enhance the relevance and comprehensiveness of personalized responses, we propose using a two-step approach that involves (1) selectively integrating user personas and (2) contextualizing the response by supplementing information from a background knowledge source. We develop K-PERM (Knowledge-guided PErsonalization with Reward Modulation), a dynamic conversational agent that combines these elements. K-PERM achieves state-of-the- art performance on the popular FoCus dataset, containing real-world personalized conversations concerning global landmarks.We show that using responses from K-PERM can improve performance in state-of-the-art LLMs (GPT 3.5) by 10.5%, highlighting the impact of K-PERM for personalizing chatbots.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120369","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

Advancing Neuro-Inspired Lifelong Learning for Edge with Co-Design 通过协同设计推进神经启发的边缘终身学习

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31226

Nicholas Soures, Vedant Karia, D. Kudithipudi

引用次数: 0

Constructing Deep Concepts through Shallow Search 通过浅层搜索构建深层概念

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31292

Bonan Zhao, Christopher G Lucas, Neil R. Bramley

{"title":"Constructing Deep Concepts through Shallow Search","authors":"Bonan Zhao, Christopher G Lucas, Neil R. Bramley","doi":"10.1609/aaaiss.v3i1.31292","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31292","url":null,"abstract":"We propose bootstrap learning as a computational account for why human learning is modular and incremental, and identify key components of bootstrap learning that allow artificial systems to learn more like people. Originated from developmental psychology, bootstrap learning refers to people's ability to extend and repurpose existing knowledge to create new and more powerful ideas. We view bootstrap learning as a solution of how cognitively-bounded reasoners grasp complex environmental dynamics that are far beyond their initial capacity, by searching ‘locally’ and recursively to extend their existing knowledge. Drawing from techniques of Bayesian library learning and resource rational analysis, we propose a computational modeling framework that achieves human-like bootstrap learning performance in inductive conceptual inference. In addition, we demonstrate modeling and behavioral evidence that highlights the double-edged sword of bootstrap learning, such that people processing the same information in different batch orders could induce drastically different causal conclusions and generalizations, as a result of the different sub-concepts they construct in earlier stages of learning.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141118854","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 Generative AI-Based Virtual Physician Assistant 基于生成式人工智能的虚拟医生助理

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31182

Geoffrey W. Rutledge, Alexander Sivura

引用次数: 0

What Is a Correct Output by Generative AI From the Viewpoint of Well-Being? – Perspective From Sleep Stage Estimation – 从幸福的角度看什么是生成式人工智能的正确输出？- 从睡眠阶段估计的角度看人工智能

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31250

K. Takadama

引用次数: 0

Evaluating Large Language Models with RAG Capability: A Perspective from Robot Behavior Planning and Execution 评估具有 RAG 功能的大型语言模型：机器人行为规划与执行视角

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31254

Jin Yamanaka, Takashi Kido

{"title":"Evaluating Large Language Models with RAG Capability: A Perspective from Robot Behavior Planning and Execution","authors":"Jin Yamanaka, Takashi Kido","doi":"10.1609/aaaiss.v3i1.31254","DOIUrl":"https://doi.org/10.1609/aaaiss.v3i1.31254","url":null,"abstract":"After the significant performance of Large Language Models (LLMs) was revealed, their capabilities were rapidly expanded with techniques such as Retrieval Augmented Generation (RAG). Given their broad applicability and fast development, it's crucial to consider their impact on social systems. On the other hand, assessing these advanced LLMs poses challenges due to their extensive capabilities and the complex nature of social systems.\u0000\u0000In this study, we pay attention to the similarity between LLMs in social systems and humanoid robots in open environments. We enumerate the essential components required for controlling humanoids in problem solving which help us explore the core capabilities of LLMs and assess the effects of any deficiencies within these components. This approach is justified because the effectiveness of humanoid systems has been thoroughly proven and acknowledged. To identify needed components for humanoids in problem-solving tasks, we create an extensive component framework for planning and controlling humanoid robots in an open environment. Then assess the impacts and risks of LLMs for each component, referencing the latest benchmarks to evaluate their current strengths and weaknesses. Following the assessment guided by our framework, we identified certain capabilities that LLMs lack and concerns in social systems.","PeriodicalId":516827,"journal":{"name":"Proceedings of the AAAI Symposium Series","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141121105","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

Enhancing Knowledge Graph Consistency through Open Large Language Models: A Case Study 通过开放式大型语言模型增强知识图谱的一致性：案例研究

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31201

Ankur Padia, Francis Ferraro, Tim Finin

引用次数: 0

Teaching Functions with Gaussian Process Regression 利用高斯过程回归教学函数

Proceedings of the AAAI Symposium Series Pub Date : 2024-05-20 DOI: 10.1609/aaaiss.v3i1.31277

Maya Malaviya, Mark K. Ho

引用次数: 0