Ai Magazine最新文献_第8页

Privacy-Preserving Convolutional Bi-LSTM Network for Robust Analysis of Encrypted Time-Series Medical Images 保护隐私的卷积Bi-LSTM网络用于加密时间序列医学图像的鲁棒分析

IF 0.9 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Ai Magazine

Pub Date : 2023-08-28 DOI: 10.3390/ai4030037

Manjur S. Kolhar, S. Aldossary

Deep learning (DL) algorithms can improve healthcare applications. DL has improved medical imaging diagnosis, therapy, and illness management. The use of deep learning algorithms on sensitive medical images presents privacy and data security problems. Improving medical imaging while protecting patient anonymity is difficult. Thus, privacy-preserving approaches for deep learning model training and inference are gaining popularity. These picture sequences are analyzed using state-of-the-art computer aided detection/diagnosis techniques (CAD). Algorithms that upload medical photos to servers pose privacy issues. This article presents a convolutional Bi-LSTM network to assess completely homomorphic-encrypted (HE) time-series medical images. From secret image sequences, convolutional blocks learn to extract selective spatial features and Bi-LSTM-based analytical sequence layers learn to encode time data. A weighted unit and sequence voting layer uses geographical with varying weights to boost efficiency and reduce incorrect diagnoses. Two rigid benchmarks—the CheXpert, and the BreaKHis public datasets—illustrate the framework’s efficacy. The technique outperforms numerous rival methods with an accuracy above 0.99 for both datasets. These results demonstrate that the proposed outline can extract visual representations and sequential dynamics from encrypted medical picture sequences, protecting privacy while attaining good medical image analysis performance.

深度学习(DL)算法可以改善医疗保健应用程序。DL改善了医学影像诊断、治疗和疾病管理。在敏感的医学图像上使用深度学习算法会带来隐私和数据安全问题。在保护病人匿名的同时提高医学成像水平是很困难的。因此，深度学习模型训练和推理的隐私保护方法越来越受欢迎。这些图像序列使用最先进的计算机辅助检测/诊断技术(CAD)进行分析。将医疗照片上传到服务器的算法会带来隐私问题。本文提出了一种卷积Bi-LSTM网络来评估完全同态加密(HE)时间序列医学图像。从秘密图像序列中，卷积块学习提取选择性空间特征，基于bi - lstm的分析序列层学习编码时间数据。加权单元和序列投票层使用不同权重的地理位置来提高效率并减少错误诊断。两个严格的基准——CheXpert和BreaKHis公共数据集——说明了该框架的有效性。该技术优于许多竞争对手的方法，两个数据集的精度都在0.99以上。实验结果表明，所提出的轮廓可以从加密的医学图像序列中提取视觉表征和序列动态，在保护隐私的同时获得良好的医学图像分析性能。

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引用次数: 0

Towards the ultimate brain: Exploring scientific discovery with ChatGPT AI 走向终极大脑：用ChatGPT AI探索科学发现

IF 0.9 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Ai Magazine

Pub Date : 2023-08-26 DOI: 10.1002/aaai.12113

Gerardo Adesso

This paper presents a novel approach to scientific discovery using an artificial intelligence (AI) environment known as ChatGPT, developed by OpenAI. This is the first paper entirely generated with outputs from ChatGPT. We demonstrate how ChatGPT can be instructed through a gamification environment to define and benchmark hypothetical physical theories. Through this environment, ChatGPT successfully simulates the creation of a new improved model, called GPT⁴, which combines the concepts of GPT in AI (generative pretrained transformer) and GPT in physics (generalized probabilistic theory). We show that GPT⁴ can use its built-in mathematical and statistical capabilities to simulate and analyze physical laws and phenomena. As a demonstration of its language capabilities, GPT⁴ also generates a limerick about itself. Overall, our results demonstrate the promising potential for human-AI collaboration in scientific discovery, as well as the importance of designing systems that effectively integrate AI's capabilities with human intelligence.

本文提出了一种利用OpenAI开发的人工智能（AI）环境ChatGPT进行科学发现的新方法。这是第一篇完全由ChatGPT输出的论文。我们展示了如何通过游戏化环境指导ChatGPT来定义和基准测试假设的物理理论。通过这种环境，ChatGPT成功地模拟了一种新的改进模型的创建，称为GPT4，它结合了人工智能中的GPT（生成预训练变换器）和物理学中的GPT（广义概率理论）的概念。我们证明GPT4可以使用其内置的数学和统计功能来模拟和分析物理定律和现象。为了展示其语言能力，GPT4还制作了一首关于自己的打油诗。总的来说，我们的研究结果证明了人类与人工智能在科学发现中的合作潜力，以及设计将人工智能的能力与人类智能有效集成的系统的重要性。

引用次数: 4

Comparison of Various Nitrogen and Water Dual Stress Effects for Predicting Relative Water Content and Nitrogen Content in Maize Plants through Hyperspectral Imaging 利用高光谱成像技术预测玉米植株相对水分和氮含量的氮水双重胁迫效应比较

IF 0.9 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Ai Magazine

Pub Date : 2023-08-18 DOI: 10.3390/ai4030036

H. Maki, Valerie Lynch, Dongdong Ma, M. Tuinstra, M. Yamasaki, Jian Jin

Water and nitrogen (N) are major factors in plant growth and agricultural production. However, these are often confounded and produce overlapping symptoms of plant stress. The objective of this study is to verify whether the different levels of N treatment influence water status prediction and vice versa with hyperspectral modeling. We cultivated 108 maize plants in a greenhouse under three-level N treatments in combination with three-level water treatments. Hyperspectral images were collected from those plants, then Relative Water Content (RWC), as well as N content, was measured as ground truth. A Partial Least Squares (PLS) regression analysis was used to build prediction models for RWC and N content. Then, their accuracy and robustness were compared according to the different N treatment datasets and different water treatment datasets, respectively. The results demonstrated that the PLS prediction for RWC using hyperspectral data was impacted by N stress difference (Ratio of Performance to Deviation; RPD from 0.87 to 2.27). Furthermore, the dataset with water and N dual stresses improved model accuracy and robustness (RPD from 1.69 to 2.64). Conversely, the PLS prediction for N content was found to be robust against water stress difference (RPD from 2.33 to 3.06). In conclusion, we suggest that water and N dual treatments can be helpful in building models with wide applicability and high accuracy for evaluating plant water status such as RWC.

水和氮是植物生长和农业生产的主要因子。然而，这些往往是混淆的，并产生重叠的植物胁迫症状。本研究的目的是通过高光谱模型验证不同水平的N处理是否影响水状态预测，反之亦然。以108株玉米为研究对象，在3级氮配3级水处理下进行温室栽培。采集这些植物的高光谱图像，然后测量相对含水量(RWC)和N含量作为地面真值。采用偏最小二乘(PLS)回归分析建立了RWC和N含量的预测模型。然后，分别针对不同的N处理数据集和不同的水处理数据集，比较其准确性和稳健性。结果表明，利用高光谱数据对RWC的PLS预测受到N应力差(性能偏差比;RPD从0.87到2.27)。此外，水氮双应力数据集提高了模型的精度和鲁棒性(RPD从1.69提高到2.64)。相反，PLS对氮含量的预测对水分胁迫差异(RPD从2.33到3.06)是稳健的。综上所述，水氮双重处理有助于建立适用范围广、精度高的植物水分状态(如RWC)评价模型。

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引用次数: 0

Evaluation of an Arabic Chatbot Based on Extractive Question-Answering Transfer Learning and Language Transformers 基于抽取式问答迁移学习和语言转换的阿拉伯语聊天机器人评价

IF 0.9 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Ai Magazine

Pub Date : 2023-08-16 DOI: 10.3390/ai4030035

Tahani N. Alruqi, Salha M. Alzahrani

Chatbots are programs with the ability to understand and respond to natural language in a way that is both informative and engaging. This study explored the current trends of using transformers and transfer learning techniques on Arabic chatbots. The proposed methods used various transformers and semantic embedding models from AraBERT, CAMeLBERT, AraElectra-SQuAD, and AraElectra (Generator/Discriminator). Two datasets were used for the evaluation: one with 398 questions, and the other with 1395 questions and 365,568 documents sourced from Arabic Wikipedia. Extensive experimental works were conducted, evaluating both manually crafted questions and the entire set of questions by using confidence and similarity metrics. Our experimental results demonstrate that combining the power of transformer architecture with extractive chatbots can provide more accurate and contextually relevant answers to questions in Arabic. Specifically, our experimental results showed that the AraElectra-SQuAD model consistently outperformed other models. It achieved an average confidence score of 0.6422 and an average similarity score of 0.9773 on the first dataset, and an average confidence score of 0.6658 and similarity score of 0.9660 on the second dataset. The study concludes that the AraElectra-SQuAD showed remarkable performance, high confidence, and robustness, which highlights its potential for practical applications in natural language processing tasks for Arabic chatbots. The study suggests that the language transformers can be further enhanced and used for various tasks, such as specialized chatbots, virtual assistants, and information retrieval systems for Arabic-speaking users.

聊天机器人是一种能够理解自然语言并对其做出反应的程序，它既能提供信息，又能吸引人。本研究探讨了在阿拉伯聊天机器人上使用变形器和迁移学习技术的当前趋势。所提出的方法使用了来自AraBERT、CAMeLBERT、AraElectra- squad和AraElectra (Generator/Discriminator)的各种变压器和语义嵌入模型。评估使用了两个数据集:一个有398个问题，另一个有1395个问题和来自阿拉伯语维基百科的365,568个文档。我们进行了大量的实验工作，通过使用置信度和相似性指标来评估人工制作的问题和整套问题。我们的实验结果表明，将变压器架构的功能与抽取式聊天机器人相结合，可以为阿拉伯语问题提供更准确和上下文相关的答案。具体来说，我们的实验结果表明，AraElectra-SQuAD模型始终优于其他模型。在第一个数据集上的平均置信度得分为0.6422，平均相似度得分为0.9773;在第二个数据集上的平均置信度得分为0.6658，平均相似度得分为0.9660。研究得出结论，AraElectra-SQuAD表现出卓越的性能、高置信度和鲁棒性，这凸显了其在阿拉伯聊天机器人自然语言处理任务中的实际应用潜力。该研究表明，语言转换器可以进一步增强并用于各种任务，例如专门的聊天机器人、虚拟助手和面向阿拉伯语用户的信息检索系统。

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引用次数: 2

AI and elections: An introduction to the special issue AI与选举：特刊简介

IF 0.9 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Ai Magazine

Pub Date : 2023-08-16 DOI: 10.1002/aaai.12110

Biplav Srivastava, Anita Nikolich, Tarmo Koppel

A vibrant democracy relies on engaged voters making informed decisions about their representatives and keeping them accountable employing reliable information and secure election infrastructure. Significant and continuous effort is needed in improving a democracy and elections are a key part of that. Democracy at a practical level means empowering the voter with a right to choose and providing multiple capabilities, including knowledge about candidates, campaign finance, voting, processing votes, and so forth.Artificial Intelligence and machine learning have transformed modern society. It also impacts how elections are conducted in democracies, with mixed outcomes. For example, digital marketing campaigns have enabled candidates to connect with voters at scale and communicate remotely during COVID-19, but there remains widespread concern about the spread of election disinformation as the result of AI-enabled bots and aggressive strategies.In response, we conducted the first workshop at Neurips 2021 to examine the challenges of credible elections globally in an academic setting with apolitical discussion of significant issues. The speakers, panels, and reviewed papers discussed current and best practices in holding elections, tools available for candidates, and the experience of voters. They highlighted gaps and experience regarding AI-based interventions and methodologies. To ground the discussion, the invited speakers and panelists were drawn from three International geographies: US—representing one of the world's oldest democracies; India—representing the largest democracy in the world; and Estonia—representing a country using digital technologies extensively during elections and as a facet of daily life. The workshop had contributions on all technological and methodological aspects of elections and voting.At AAAI 2023, we ran the second edition of the workshop. It focused on topics of interest to election candidates like organizing candidate campaigns and detecting, informing, and managing mis- and disinformation; for election organizers, identifying and validating voters and informing people about election information; for voters, knowing about election procedures, verifying individual and community votes, navigating candidates and issues; and cross-cutting.Issues like promoting transparency in the election process, technology for data management and validation, and case studies of success or failure, and the reasons thereof. This time, additional speakers discussed experiences from Brazil, Canada, and Ireland. The workshop discussed AI trends, security gaps in elections and the lack of a standard secure stack to build trusted data-driven applications for elections, how AI and technology are already being used to make the election process work and how to improve, the role of journalists with AI and what policy steps are needed to adopt technology for a better-informed citizen.This special issue on AI for

一个充满活力的民主国家依赖于参与的选民对其代表做出知情的决定，并通过可靠的信息和安全的选举基础设施让他们负起责任。需要作出重大和持续的努力来改善民主，选举是其中的一个关键部分。实际层面的民主意味着赋予选民选择权，并提供多种能力，包括候选人、竞选资金、投票、处理选票等方面的知识。人工智能和机器学习已经改变了现代社会。它还影响民主国家的选举方式，结果喜忧参半。例如，在新冠肺炎期间，数字营销活动使候选人能够与选民进行大规模联系并进行远程沟通，但由于启用人工智能的机器人和激进策略，选举虚假信息的传播仍然受到广泛关注。作为回应，我们在Neurips 2021举办了第一次研讨会，在学术环境中研究全球可信选举的挑战，对重大问题进行非政治性讨论。发言者、小组讨论和审查文件讨论了举行选举的当前和最佳做法、候选人可用的工具以及选民的经验。他们强调了在基于人工智能的干预措施和方法方面的差距和经验。受邀的演讲者和小组成员来自三个国际地区：代表世界上最古老民主国家之一的美国；印度——代表着世界上最大的民主国家；以及爱沙尼亚——代表一个在选举期间广泛使用数字技术并将其作为日常生活的一个方面的国家。讲习班在选举和投票的所有技术和方法方面作出了贡献。在AAAI 2023上，我们举办了第二届研讨会。它专注于选举候选人感兴趣的话题，如组织候选人竞选活动以及发现、告知和管理虚假信息；对于选举组织者，识别和确认选民，并向人们通报选举信息；对于选民，了解选举程序，核实个人和社区选票，了解候选人和问题；以及交叉切割。诸如提高选举过程的透明度、数据管理和验证技术、成功或失败的案例研究及其原因等问题。这次，其他发言者讨论了巴西、加拿大和爱尔兰的经验。研讨会讨论了人工智能趋势、选举中的安全漏洞以及缺乏标准的安全堆栈来构建可信的选举数据驱动应用程序，人工智能和技术如何被用于使选举过程发挥作用以及如何改进，记者在人工智能方面的作用，以及需要采取哪些政策步骤来采用技术，让公民更知情。这期关于选举人工智能的特刊突出了两个研讨会的一些富有洞察力的观点。这包括对人工智能和核心选举流程的审查，如何使用聊天机器人来促进选民参与，了解选民两极分化的企图，检测选举舞弊，以及一种新的用户调查投票形式。我们希望他们能促进更多的社区参与，促进人工智能、安全、新闻、政治学和法律之间的多学科研究合作，为世界各地的民主国家服务。提交人声明不存在冲突。

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引用次数: 0

Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare 可解释人工智能(XAI):医疗保健领域的概念和挑战

IF 0.9 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Ai Magazine

Pub Date : 2023-08-10 DOI: 10.3390/ai4030034

Tim Hulsen

Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks, and deep learning. AI can be applied in many different areas, such as econometrics, biometry, e-commerce, and the automotive industry. In recent years, AI has found its way into healthcare as well, helping doctors make better decisions (“clinical decision support”), localizing tumors in magnetic resonance images, reading and analyzing reports written by radiologists and pathologists, and much more. However, AI has one big risk: it can be perceived as a “black box”, limiting trust in its reliability, which is a very big issue in an area in which a decision can mean life or death. As a result, the term Explainable Artificial Intelligence (XAI) has been gaining momentum. XAI tries to ensure that AI algorithms (and the resulting decisions) can be understood by humans. In this narrative review, we will have a look at some central concepts in XAI, describe several challenges around XAI in healthcare, and discuss whether it can really help healthcare to advance, for example, by increasing understanding and trust. Finally, alternatives to increase trust in AI are discussed, as well as future research possibilities in the area of XAI.

人工智能(AI)描述了能够执行通常需要人类智能的任务的计算机系统，例如视觉感知、语音识别、决策和语言翻译。人工智能技术的例子有机器学习、神经网络和深度学习。人工智能可以应用于许多不同的领域，如计量经济学、生物计量学、电子商务和汽车行业。近年来，人工智能也进入了医疗保健领域，帮助医生做出更好的决定(“临床决策支持”)，在磁共振图像中定位肿瘤，阅读和分析放射科医生和病理学家写的报告，等等。然而，人工智能有一个很大的风险:它可能被视为一个“黑匣子”，限制了人们对其可靠性的信任，这在一个决定可能意味着生死的领域是一个非常大的问题。因此，术语“可解释人工智能”(XAI)得到了发展势头。XAI试图确保人工智能算法(以及由此产生的决策)能够被人类理解。在这篇叙述性综述中，我们将介绍XAI中的一些核心概念，描述XAI在医疗保健领域面临的几个挑战，并讨论它是否真的可以帮助医疗保健向前发展，例如，通过增加理解和信任。最后，讨论了增加对人工智能信任的替代方案，以及未来在XAI领域的研究可能性。

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引用次数: 9

On safe and usable chatbots for promoting voter participation 关于促进选民参与的安全可用的聊天机器人

IF 0.9 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Ai Magazine

Pub Date : 2023-08-07 DOI: 10.1002/aaai.12109

Bharath Muppasani, Vishal Pallagani, Kausik Lakkaraju, Shuge Lei, Biplav Srivastava, Brett Robertson, Andrea Hickerson, Vignesh Narayanan

Chatbots, or bots for short, are multimodal collaborative assistants that can help people complete useful tasks. Usually, when chatbots are referenced in connection with elections, they often draw negative reactions due to the fear of mis-information and hacking. Instead, in this work, we explore how chatbots may be used to promote voter participation in vulnerable segments of society like senior citizens and first-time voters. In particular, we have built a system that amplifies official information while personalizing it to users' unique needs transparently (e.g., language, cognitive abilities, linguistic abilities). The uniqueness of this work are (a) a safe design where only responses that are grounded and traceable to an allowed source (e.g., official question/answer) will be answered via system's self-awareness (metacognition), (b) a do-not-respond strategy that can handle customizable responses/deflection, and (c) a low-programming design-pattern based on the open-source Rasa platform to generate chatbots quickly for any region. Our current prototypes use frequently asked questions (FAQ) election information for two US states that are low on an ease-of-voting scale, and have performed initial evaluations using focus groups with senior citizens. Our approach can be a win-win for voters, election agencies trying to fulfill their mandate and democracy at large.

聊天机器人，简称机器人，是一种多模式的协作助手，可以帮助人们完成有用的任务。通常，当聊天机器人与选举有关时，由于担心信息错误和黑客攻击，它们经常会引起负面反应。相反，在这项工作中，我们探索了如何使用聊天机器人来促进社会弱势群体的选民参与，如老年人和首次投票者。特别是，我们建立了一个系统，放大官方信息，同时透明地根据用户的独特需求（如语言、认知能力、语言能力）对其进行个性化设置。这项工作的独特性在于（a）一种安全的设计，只有基于并可追溯到允许来源的回应（例如，官方问题/答案）才会通过系统的自我意识（元认知）得到回答，（b）一种可以处理可定制回应/偏离的不回应策略，以及（c）基于开源Rasa平台的低编程设计模式，可为任何地区快速生成聊天机器人。我们目前的原型使用了美国两个州的常见问题（FAQ）选举信息，这两个州在易投票性方面较低，并使用老年人焦点小组进行了初步评估。我们的方法对选民、试图履行职责的选举机构和整个民主来说都是双赢的。

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引用次数: 0

Looking back, looking ahead: Strategic initiatives in AI and NSF's AI Institutes Program 回顾过去，展望未来：人工智能和美国国家科学基金会人工智能研究院项目的战略举措

IF 0.9 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Ai Magazine

Pub Date : 2023-08-07 DOI: 10.1002/aaai.12107

James Donlon, Ashok Goel

We introduce U.S. National Science Foundation's groundbreaking National AI Research Institutes Program. The AI institutes are interdisciplinary collaborations that continue the program's emphasis on tackling larger-scale, longer-time horizon challenges in both foundational and use-inspired AI research, and act as nexus points to address some of society's grand challenges.

我们介绍美国国家科学基金会开创性的国家人工智能研究院项目。人工智能研究所是跨学科合作，继续强调该项目在基础和应用启发的人工智能研究中应对更大规模、更长时间范围的挑战，并充当应对社会一些重大挑战的连接点。

引用次数: 1

“Minimum Necessary Rigor” in empirically evaluating human–AI work systems 经验评估人工智能工作系统的“最低必要严格性”

IF 0.9 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Ai Magazine

Pub Date : 2023-08-07 DOI: 10.1002/aaai.12108

Gary Klein, Robert R. Hoffman, William J. Clancey, Shane T. Mueller, Florian Jentsch, Mohammadreza Jalaeian

The development of AI systems represents a significant investment of funds and time. Assessment is necessary in order to determine whether that investment has paid off. Empirical evaluation of systems in which humans and AI systems act interdependently to accomplish tasks must provide convincing empirical evidence that the work system is learnable and that the technology is usable and useful. We argue that the assessment of human–AI (HAI) systems must be effective but must also be efficient. Bench testing of a prototype of an HAI system cannot require extensive series of large-scale experiments with complex designs. Some of the constraints that are imposed in traditional laboratory research just are not appropriate for the empirical evaluation of HAI systems. We present requirements for avoiding “unnecessary rigor.” They cover study design, research methods, statistical analyses, and online experimentation. These should be applicable to all research intended to evaluate the effectiveness of HAI systems.

人工智能系统的开发代表着对资金和时间的重大投资。为了确定投资是否有回报，评估是必要的。对人类和人工智能系统相互依赖完成任务的系统进行经验评估，必须提供令人信服的经验证据，证明工作系统是可学习的，技术是可用的。我们认为，对人工智能（HAI）系统的评估必须有效，但也必须高效。HAI系统原型的台架测试不需要复杂设计的大量大规模实验。传统实验室研究中施加的一些约束不适合对HAI系统进行实证评估。我们提出了避免“不必要的严谨”的要求。这些要求包括研究设计、研究方法、统计分析和在线实验。这些应适用于旨在评估HAI系统有效性的所有研究。

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引用次数: 1

Civicbase: An open-source platform for deploying Quadratic Voting for Survey Research Civicbase：一个用于部署调查研究的二次投票的开源平台

IF 0.9 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Ai Magazine

Pub Date : 2023-08-07 DOI: 10.1002/aaai.12103

Madeline E. Bassetti, Gustavo Dias, Daniel L. Chen, Alan Mortoni, Ritesh Das

Civic engagement is increasingly becoming digital. The ubiquity of computing increases our technologically mediated interactions. Governments have instated various digitization efforts to harness these new facets of virtual life. What remains to be seen is if citizen political opinion, which can inform the inception and effectiveness of public policy, is being accurately captured. Civicbase is an open-source online platform that supports the application of Quadratic Voting Survey for Research (QVSR), a novel survey method. In this paper, we explore QVSR as an effective method for eliciting policy preferences, optimal survey design for prediction, Civicbase's functionalities and technology stack, and Personal AI, an emerging domain, and its relevance to modeling individual political preferences.

公民参与正日益数字化。计算的普遍性增加了我们技术中介的互动。各国政府已开展各种数字化工作，以利用虚拟生活的这些新方面。还有待观察的是，能够为公共政策的制定和有效性提供信息的公民政治意见是否得到了准确的反映。Civicbase是一个开源的在线平台，支持研究二次投票调查（QVSR）的应用，这是一种新颖的调查方法。在本文中，我们探讨了QVSR作为一种获取政策偏好的有效方法、预测的最佳调查设计、Civicbase的功能和技术堆栈，以及个人人工智能这一新兴领域，及其与建模个人政治偏好的相关性。

引用次数: 0