Ai Magazine最新文献

{"title":"What Is the Role of AI for Digital Twins?","authors":"F. Emmert-Streib","doi":"10.3390/ai4030038","DOIUrl":"https://doi.org/10.3390/ai4030038","url":null,"abstract":"The concept of a digital twin is intriguing as it presents an innovative approach to solving numerous real-world challenges. Initially emerging from the domains of manufacturing and engineering, digital twin research has transcended its origins and now finds applications across a wide range of disciplines. This multidisciplinary expansion has impressively demonstrated the potential of digital twin research. While the simulation aspect of a digital twin is often emphasized, the role of artificial intelligence (AI) and machine learning (ML) is severely understudied. For this reason, in this paper, we highlight the pivotal role of AI and ML for digital twin research. By recognizing that a digital twin is a component of a broader Digital Twin System (DTS), we can fully grasp the diverse applications of AI and ML. In this paper, we explore six AI techniques—(1) optimization (model creation), (2) optimization (model updating), (3) generative modeling, (4) data analytics, (5) predictive analytics and (6) decision making—and their potential to advance applications in health, climate science, and sustainability.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"2 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75160778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An analysis of Watson vs. BARD vs. ChatGPT: The Jeopardy! Challenge","authors":"Daniel E. O'Leary","doi":"10.1002/aaai.12118","DOIUrl":"https://doi.org/10.1002/aaai.12118","url":null,"abstract":"<p>The recently released BARD and ChatGPT have generated substantial interest from a range of researchers and institutions concerned about the impact on education, medicine, law and more. This paper uses questions from the Watson Jeopardy! Challenge to compare BARD, ChatGPT, and Watson. Using those, Jeopardy! questions, we find that for high confidence Watson questions the three systems perform with similar accuracy as Watson. We also find that both BARD and ChatGPT perform with the accuracy of a human expert and that the sets of their correct answers are rated highly similar using a Tanimoto similarity score. However, in addition, we find that both systems can change their solutions to the same input information on subsequent uses. When given the same Jeopardy! category and question multiple times, both BARD and ChatGPT can generate different and conflicting answers. As a result, the paper examines the characteristics of some of those questions that generate different answers to the same inputs. Finally, the paper discusses some of the implications of finding the different answers and the impact of the lack of reproducibility on testing such systems.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"44 3","pages":"282-295"},"PeriodicalIF":0.9,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12118","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50155705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-Preserving Convolutional Bi-LSTM Network for Robust Analysis of Encrypted Time-Series Medical Images","authors":"Manjur S. Kolhar, S. Aldossary","doi":"10.3390/ai4030037","DOIUrl":"https://doi.org/10.3390/ai4030037","url":null,"abstract":"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.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"35 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77541093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards the ultimate brain: Exploring scientific discovery with ChatGPT AI","authors":"Gerardo Adesso","doi":"10.1002/aaai.12113","DOIUrl":"https://doi.org/10.1002/aaai.12113","url":null,"abstract":"<p>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.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"44 3","pages":"328-342"},"PeriodicalIF":0.9,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50144698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Various Nitrogen and Water Dual Stress Effects for Predicting Relative Water Content and Nitrogen Content in Maize Plants through Hyperspectral Imaging","authors":"H. Maki, Valerie Lynch, Dongdong Ma, M. Tuinstra, M. Yamasaki, Jian Jin","doi":"10.3390/ai4030036","DOIUrl":"https://doi.org/10.3390/ai4030036","url":null,"abstract":"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.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"61 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91028386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of an Arabic Chatbot Based on Extractive Question-Answering Transfer Learning and Language Transformers","authors":"Tahani N. Alruqi, Salha M. Alzahrani","doi":"10.3390/ai4030035","DOIUrl":"https://doi.org/10.3390/ai4030035","url":null,"abstract":"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.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"47 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79936160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI and elections: An introduction to the special issue","authors":"Biplav Srivastava,&nbsp;Anita Nikolich,&nbsp;Tarmo Koppel","doi":"10.1002/aaai.12110","DOIUrl":"https://doi.org/10.1002/aaai.12110","url":null,"abstract":"&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;This special issue on AI for ","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"44 3","pages":"216-217"},"PeriodicalIF":0.9,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50143024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare","authors":"Tim Hulsen","doi":"10.3390/ai4030034","DOIUrl":"https://doi.org/10.3390/ai4030034","url":null,"abstract":"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.","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"13 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72967473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On safe and usable chatbots for promoting voter participation","authors":"Bharath Muppasani,&nbsp;Vishal Pallagani,&nbsp;Kausik Lakkaraju,&nbsp;Shuge Lei,&nbsp;Biplav Srivastava,&nbsp;Brett Robertson,&nbsp;Andrea Hickerson,&nbsp;Vignesh Narayanan","doi":"10.1002/aaai.12109","DOIUrl":"https://doi.org/10.1002/aaai.12109","url":null,"abstract":"<p>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.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"44 3","pages":"240-247"},"PeriodicalIF":0.9,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50123472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Looking back, looking ahead: Strategic initiatives in AI and NSF's AI Institutes Program","authors":"James Donlon,&nbsp;Ashok Goel","doi":"10.1002/aaai.12107","DOIUrl":"https://doi.org/10.1002/aaai.12107","url":null,"abstract":"<p>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.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"44 3","pages":"345-348"},"PeriodicalIF":0.9,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.12107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50123693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}