14 results
Search Results
2. How a Decades-Old Technology and a Paper From Meta Created an AI Industry Standard.
- Author
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Lin, Belle
- Subjects
- *
GENERATIVE artificial intelligence , *ARTIFICIAL intelligence , *LANGUAGE models - Abstract
Vector databases, a technology that has been around for decades, are now becoming an industry standard for AI businesses. These databases allow businesses to link their private data with large-language models, enabling AI to perform data analysis and other tasks. Pinecone, an early entrant in the vector database AI space, has experienced significant growth and success. However, they are no longer alone in the market, as other startups and existing database vendors have entered the space. The global vector-database market is expected to grow significantly in the coming years. [Extracted from the article]
- Published
- 2024
3. A Critical Review of Emerging Technologies for Flash Flood Prediction: Examining Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, and Robotics Techniques.
- Author
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Al-Rawas, Ghazi, Nikoo, Mohammad Reza, Al-Wardy, Malik, and Etri, Talal
- Subjects
MACHINE learning ,SOCIAL media ,ARTIFICIAL intelligence ,COMPUTER vision ,FLOOD risk - Abstract
There has been growing interest in the application of smart technologies for hazard management. However, very limited studies have reviewed the trends of such technologies in the context of flash floods. This study reviews innovative technologies such as artificial intelligence (AI)/machine learning (ML), the Internet of Things (IoT), cloud computing, and robotics used for flash flood early warnings and susceptibility predictions. Articles published between 2010 and 2023 were manually collected from scientific databases such as Google Scholar, Scopus, and Web of Science. Based on the review, AI/ML has been applied to flash flood susceptibility and early warning prediction in 64% of the published papers, followed by the IoT (19%), cloud computing (6%), and robotics (2%). Among the most common AI/ML methods used in susceptibility and early warning predictions are random forests and support vector machines. However, further optimization and emerging technologies, such as computer vision, are required to improve these technologies. AI/ML algorithms have demonstrated very accurate prediction performance, with receiver operating characteristics (ROC) and areas under the curve (AUC) greater than 0.90. However, there is a need to improve on these current models with large test datasets. Through AI/ML, IoT, and cloud computing technologies, early warnings can be disseminated to targeted communities in real time via electronic media, such as SMS and social media platforms. In spite of this, these systems have issues with internet connectivity, as well as data loss. Additionally, Al/ML used a number of topographical variables (such as slope), geological variables (such as lithology), and hydrological variables (such as stream density) to predict susceptibility, but the selection of these variables lacks a clear theoretical basis and has inconsistencies. To generate more reliable flood risk assessment maps, future studies should also consider sociodemographic, health, and housing data. Considering future climate change impacts, susceptibility or early warning studies may be projected under different climate change scenarios to help design long-term adaptation strategies. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
4. AI-guided discovery of the invariant host response to viral pandemics
- Author
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Stephen A. Rawlings, David M. Smith, Jennifer M. Dan, Gajanan D. Katkar, Laura E. Crotty Alexander, Dennis R. Burton, Shane Crotty, Mahdi Behroozikhah, Vanessa Castillo, Pradipta Ghosh, Courtney Tindle, Nathan Beutler, Debashis Sahoo, Thomas F. Rogers, MacKenzie Fuller, Victor Pretorius, Amanraj Claire, Sydney I. Ramirez, Soumita Das, Sahar Taheri, Soni Khandelwal, and Jason M. Duran
- Subjects
0301 basic medicine ,Medicine (General) ,Boolean equivalent clusters ,medicine.medical_treatment ,Cytidine ,Transcriptome ,0302 clinical medicine ,Cricetinae ,Lung alveoli ,Databases, Genetic ,Receptors ,Pandemic ,Interleukin 15 ,Gene Regulatory Networks ,Receptor ,Lung ,Neutralizing ,Artificial intelligence/machine learning ,Interleukin-15 ,biology ,Receptors, Interleukin-15 ,General Medicine ,Infectious Diseases ,Cytokine ,Virus Diseases ,030220 oncology & carcinogenesis ,Public Health and Health Services ,Medicine ,Autopsy ,Angiotensin-Converting Enzyme 2 ,Antibody ,Infection ,Research Paper ,Genetic Markers ,Clinical Sciences ,Hydroxylamines ,Antiviral Agents ,Article ,Antibodies ,General Biochemistry, Genetics and Molecular Biology ,Virus ,Vaccine Related ,Databases ,Natural Killer (NK) cells ,03 medical and health sciences ,Rare Diseases ,R5-920 ,Immune system ,Genetic ,Interleukin 15 (IL15) ,Artificial Intelligence ,Clinical Research ,Biodefense ,Genetics ,medicine ,Animals ,Humans ,Immune response ,Pandemics ,Mesocricetus ,Animal ,business.industry ,Gene Expression Profiling ,Prevention ,COVID-19 ,medicine.disease ,Antibodies, Neutralizing ,Angiotensin converting enzyme (ACE)-2 ,COVID-19 Drug Treatment ,Gene expression profiling ,Disease Models, Animal ,Orphan Drug ,Good Health and Well Being ,Emerging Infectious Diseases ,030104 developmental biology ,Coronavirus COVID-19 ,Disease Models ,Immunology ,biology.protein ,Cytokine storm ,business - Abstract
We sought to define the host immune response, a.k.a, the “cytokine storm” that has been implicated in fatal COVID-19 using an AI-based approach. Over 45,000 transcriptomic datasets of viral pandemics were analyzed to extract a 166-gene signature using ACE2 as a ‘seed’ gene; ACE2 was rationalized because it encodes the receptor that facilitates the entry of SARS-CoV-2 (the virus that causes COVID-19) into host cells. Surprisingly, this 166-gene signature was conserved in all viral pandemics, including COVID-19, and a subset of 20-genes classified disease severity, inspiring the nomenclatures ViP and severe-ViP signatures, respectively. The ViP signatures pinpointed a paradoxical phenomenon wherein lung epithelial and myeloid cells mount an IL15 cytokine storm, and epithelial and NK cell senescence and apoptosis determines severity/fatality. Precise therapeutic goals were formulated and subsequently validated in high-dose SARS-CoV-2-challenged hamsters using neutralizing antibodies that abrogate SARS-CoV-2•ACE2 engagement or a directly acting antiviral agent, EIDD-2801. IL15/IL15RA were elevated in the lungs of patients with fatal disease, and plasma levels of the cytokine tracked with disease severity. Thus, the ViP signatures provide a quantitative and qualitative framework for titrating the immune response in viral pandemics and may serve as a powerful unbiased tool to rapidly assess disease severity and vet candidate drugs.One Sentence SummaryThe host immune response in COVID-19.PANEL: RESEARCH IN CONTEXTEvidence before this studyThe SARS-CoV-2 pandemic has inspired many groups to find innovative methodologies that can help us understand the host immune response to the virus; unchecked proportions of such immune response have been implicated in fatality. We searched GEO and ArrayExpress that provided many publicly available gene expression data that objectively measure the host immune response in diverse conditions. However, challenges remain in identifying a set of host response events that are common to every condition. There are no studies that provide a reproducible assessment of prognosticators of disease severity, the host response, and therapeutic goals. Consequently, therapeutic trials for COVID-19 have seen many more ‘misses’ than ‘hits’. This work used multiple (> 45,000) gene expression datasets from GEO and ArrayExpress and analyzed them using an unbiased computational approach that relies upon fundamentals of gene expression patterns and mathematical precision when assessing them.Added value of this studyThis work identifies a signature that is surprisingly conserved in all viral pandemics, including Covid-19, inspiring the nomenclature ViP-signature. A subset of 20-genes classified disease severity in respiratory pandemics. The ViP signatures pinpointed the nature and source of the ‘cytokine storm’ mounted by the host. They also helped formulate precise therapeutic goals and rationalized the repurposing of FDA-approved drugs.Implications of all the available evidenceThe ViP signatures provide a quantitative and qualitative framework for assessing the immune response in viral pandemics when creating pre-clinical models; they serve as a powerful unbiased tool to rapidly assess disease severity and vet candidate drugs.
- Published
- 2021
5. AI/ML Chatbots' Souls, or Transformers: Less Than Meets the Eye.
- Author
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Lazzari, Edmund Michael
- Subjects
- *
CHATBOTS , *ARTIFICIAL intelligence , *MACHINE learning , *LINGUISTICS , *COMPUTATIONAL linguistics , *ARTIFICIAL neural networks - Abstract
Given the peculiarly linguistic approach that contemporary philosophers use to apply St. Thomas Aquinas's arguments on the immateriality of the human soul, this paper will present a Thomistic-inspired evaluation of whether artificial intelligence/machine learning (AI/ML) chatbots' composition and linguistic performance justify the assertion that AI/ML chatbots have immaterial souls. The first section of the paper will present a strong, but ultimately crucially flawed argument that AI/ML chatbots do have souls based on contemporary Thomistic argumentation. The second section of the paper will provide an overview of the actual computer science models that make artificial neural networks and AI/ML chatbots function, which I hope will assist other theologians and philosophers writing about technology, The third section will present some of Emily Bender's and Alexander Koller's objections to AI/ML chatbots being able to access meaning from computational linguistics. The final section will highlight the similarities of Bender's and Koller's argument to a fuller presentation of St. Thomas Aquinas's argument for the immateriality of the human soul, ultimately arguing that the current mechanisms and linguistic activity of AI/ML programming do not constitute activity sufficient to conclude that they have immaterial souls on the strength of St. Thomas's arguments. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
6. A review of Earth Artificial Intelligence
- Author
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Sun, Ziheng, Sandoval, Laura, Crystal-Ornelas, Robert, Mousavi, S Mostafa, Wang, Jinbo, Lin, Cindy, Cristea, Nicoleta, Tong, Daniel, Carande, Wendy Hawley, Ma, Xiaogang, Rao, Yuhan, Bednar, James A, Tan, Amanda, Wang, Jianwu, Purushotham, Sanjay, Gill, Thomas E, Chastang, Julien, Howard, Daniel, Holt, Benjamin, Gangodagamage, Chandana, Zhao, Peisheng, Rivas, Pablo, Chester, Zachary, Orduz, Javier, and John, Aji
- Subjects
Information and Computing Sciences ,Artificial Intelligence ,Machine Learning and Artificial Intelligence ,Data Science ,Networking and Information Technology R&D (NITRD) ,Bioengineering ,Geosphere ,Hydrology ,Atmosphere ,Artificial intelligence/machine learning ,Big data ,Cyberinfrastructure ,Earth Sciences ,Engineering ,Geochemistry & Geophysics ,Earth sciences ,Information and computing sciences - Abstract
In recent years, Earth system sciences are urgently calling for innovation on improving accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in many subdomains amid the exponentially accumulated datasets and the promising artificial intelligence (AI) revolution in computer science. This paper presents work led by the NASA Earth Science Data Systems Working Groups and ESIP machine learning cluster to give a comprehensive overview of AI in Earth sciences. It holistically introduces the current status, technology, use cases, challenges, and opportunities, and provides all the levels of AI practitioners in geosciences with an overall big picture and to “blow away the fog to get a clearer vision” about the future development of Earth AI. The paper covers all the majorspheres in the Earth system and investigates representative AI research in each domain. Widely used AI algorithms and computing cyberinfrastructure are briefly introduced. The mandatory steps in a typical workflow of specializing AI to solve Earth scientific problems are decomposed and analyzed. Eventually, it concludes with the grand challenges and reveals the opportunities to give some guidance and pre-warnings on allocating resources wisely to achieve the ambitious Earth AI goals in the future.
- Published
- 2022
7. Treat AI Like an Intern: Why Feedback, Praise and Patience Is Key to Good Results.
- Author
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Snow, Jackie
- Subjects
- *
ARTIFICIAL intelligence , *GENERATIVE artificial intelligence , *CHATBOTS , *PATIENCE , *MACHINE learning , *PRAISE - Abstract
A new academic paper suggests that if AI chatbots are not given feedback to improve their responses, the content produced by society may become increasingly homogenized and biased. The researchers argue that relying too heavily on AI tools for writing tasks could lead to the disappearance of individuals' unique writing styles. The study also highlights the potential for a "death spiral" of homogenization, where it becomes increasingly difficult to get anything other than a bland answer from AI-generated content. The researchers recommend making it easier for users to communicate their preferences to AI systems and providing clear instructions and disclosures about the limitations and biases of AI tools. [Extracted from the article]
- Published
- 2024
8. A Critical Review of Emerging Technologies for Flash Flood Prediction: Examining Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, and Robotics Techniques
- Author
-
Ghazi Al-Rawas, Mohammad Reza Nikoo, Malik Al-Wardy, and Talal Etri
- Subjects
flash floods ,artificial intelligence/machine learning ,Internet of Things ,cloud computing ,susceptibility predictions ,early warnings ,Hydraulic engineering ,TC1-978 ,Water supply for domestic and industrial purposes ,TD201-500 - Abstract
There has been growing interest in the application of smart technologies for hazard management. However, very limited studies have reviewed the trends of such technologies in the context of flash floods. This study reviews innovative technologies such as artificial intelligence (AI)/machine learning (ML), the Internet of Things (IoT), cloud computing, and robotics used for flash flood early warnings and susceptibility predictions. Articles published between 2010 and 2023 were manually collected from scientific databases such as Google Scholar, Scopus, and Web of Science. Based on the review, AI/ML has been applied to flash flood susceptibility and early warning prediction in 64% of the published papers, followed by the IoT (19%), cloud computing (6%), and robotics (2%). Among the most common AI/ML methods used in susceptibility and early warning predictions are random forests and support vector machines. However, further optimization and emerging technologies, such as computer vision, are required to improve these technologies. AI/ML algorithms have demonstrated very accurate prediction performance, with receiver operating characteristics (ROC) and areas under the curve (AUC) greater than 0.90. However, there is a need to improve on these current models with large test datasets. Through AI/ML, IoT, and cloud computing technologies, early warnings can be disseminated to targeted communities in real time via electronic media, such as SMS and social media platforms. In spite of this, these systems have issues with internet connectivity, as well as data loss. Additionally, Al/ML used a number of topographical variables (such as slope), geological variables (such as lithology), and hydrological variables (such as stream density) to predict susceptibility, but the selection of these variables lacks a clear theoretical basis and has inconsistencies. To generate more reliable flood risk assessment maps, future studies should also consider sociodemographic, health, and housing data. Considering future climate change impacts, susceptibility or early warning studies may be projected under different climate change scenarios to help design long-term adaptation strategies.
- Published
- 2024
- Full Text
- View/download PDF
9. Automation of Bid Proposal Preparation Through AI Smart Assistant
- Author
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Manchanda, Sanjeev, Xhafa, Fatos, Series Editor, Sharma, Neha, editor, Chakrabarti, Amlan, editor, Balas, Valentina Emilia, editor, and Bruckstein, Alfred M., editor
- Published
- 2021
- Full Text
- View/download PDF
10. Objectives and curriculum for a graduate business analytics capstone: Reflections from practice.
- Author
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Anand, Tej and Mitchell, Daniel
- Subjects
EXPERIENTIAL learning ,BUSINESS analytics ,TEAMS in the workplace ,BUSINESS students ,CAPSTONE courses ,SELF-managed learning (Personnel management) - Abstract
Many higher education institutions have responded to the significant shortage of professionals with strong analytical expertise by offering graduate programs in business analytics and data science. These programs are typically designed to be practitioner focused and many of them offer a Capstone course that gives students the opportunity to conduct a real‐world analytics project. In this article, we describe the innovative design of a Capstone course offered to cohorts of approximately 110 graduate students in the business school at the University of Texas, Austin. This course is designed to deliver self‐directed and experiential learning from interacting with business stakeholders and successfully completing team‐based, business analytics projects within a commercial firm using the firm's data. The course design includes student and sponsor engagement, the formation of diverse balanced project teams, matching of teams with projects, and scaffolding, which includes consistent structured mentoring, team reflections, interim deliverables and in‐class learning for practical skills not covered elsewhere in the curriculum. This course has now been offered successfully to two cohorts. In this article, we will also describe changes made for the second cohort related to team formation, project matching, mentoring and in‐class learning. These changes were based on feedback from the first cohort. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
11. A review of Earth Artificial Intelligence
- Author
-
Ziheng Sun, Laura Sandoval, Robert Crystal-Ornelas, S. Mostafa Mousavi, Jinbo Wang, Cindy Lin, Nicoleta Cristea, Daniel Tong, Wendy Hawley Carande, Xiaogang Ma, Yuhan Rao, James A. Bednar, Amanda Tan, Jianwu Wang, Sanjay Purushotham, Thomas E. Gill, Julien Chastang, Daniel Howard, Benjamin Holt, Chandana Gangodagamage, Peisheng Zhao, Pablo Rivas, Zachary Chester, Javier Orduz, and Aji John
- Subjects
Geochemistry & Geophysics ,Big data ,Engineering ,Geosphere ,Atmosphere ,Information and Computing Sciences ,Earth Sciences ,Cyberinfrastructure ,Hydrology ,Computers in Earth Sciences ,GeneralLiterature_MISCELLANEOUS ,Artificial intelligence/machine learning ,Information Systems - Abstract
In recent years, Earth system sciences are urgently calling for innovation on improving accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in many subdomains amid the exponentially accumulated datasets and the promising artificial intelligence (AI) revolution in computer science. This paper presents work led by the NASA Earth Science Data Systems Working Groups and ESIP machine learning cluster to give a comprehensive overview of AI in Earth sciences. It holistically introduces the current status, technology, use cases, challenges, and opportunities, and provides all the levels of AI practitioners in geosciences with an overall big picture and to “blow away the fog to get a clearer vision” about the future development of Earth AI. The paper covers all the majorspheres in the Earth system and investigates representative AI research in each domain. Widely used AI algorithms and computing cyberinfrastructure are briefly introduced. The mandatory steps in a typical workflow of specializing AI to solve Earth scientific problems are decomposed and analyzed. Eventually, it concludes with the grand challenges and reveals the opportunities to give some guidance and pre-warnings on allocating resources wisely to achieve the ambitious Earth AI goals in the future.
- Published
- 2022
12. Significance and possibility of artificial intelligence in institutional research for education
- Subjects
predictive model ,教学IR ,機械学習 ,explanatory model ,説明モデル ,institutional research for education ,予測モデル ,Artificial intelligence/machine learning - Abstract
教学IRにおいては、従来は説明モデルによる解析や可視化にもとづく意思決定支援が主要な機能であったが、近年では予測モデルにもとづく種々の予測に関してその重要性が高まっているといわれている。そこで本稿では、教学IRにおける機械学習の意義と可能性について、われわれの経験を題材として検討した。われわれの経験では、機械学習を用いることで、大学における中途退学や学力進捗を予測できる可能性があることが明らかになっている。このことから、いわゆる教学データを用いた機械学習により、今までなし得なかった教学上の種々の予測が可能となり、今後のわが国の教学IRが飛躍的に進展する可能性が示唆された。, In institutional research (IR) for education, the decision-making support based on the analysis and visualization by the explanation model was the main function in the past. However, the importance of various predictions based on predictive models is currently increasing in IR for education. Therefore, this paper examined the significance and possibility of artificial intelligence/machine learning (AI/ML) in IR for education using our experience as subjects. Our experience reveals that using AI/ML can predict dropouts and academic progress in university and college. Thus, it is suggested that using students' educational data, AI/ML could make various predictions in higher education that were not possible earlier, leading to dramatic progress in Japan's IR for education.
- Published
- 2021
13. Automatic Discovery of Families of Network Generative Processes
- Author
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Telmo Menezes, Camille Roth, Centre Marc Bloch (CMB), Ministère de l'Europe et des Affaires étrangères (MEAE)-Bundesministerium für Bildung und Forschung-Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.)-Centre National de la Recherche Scientifique (CNRS), This paper has been partially supported by the 'Algodiv'' grant (ANR-15-CE38-0001) funded by the ANR (French National Agency of Research)., ANR-12-CORD-0018,Algopol,Politique des algorithmes(2012), and ANR-15-CE38-0001,ALGODIV,Algodiv: Recommandation algorithmique et diversité des informations du web(2015)
- Subjects
Computer science ,Network science ,Genetic Programming ,Complex networks ,Genetic programming ,[INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] ,01 natural sciences ,[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI] ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] ,03 medical and health sciences ,[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] ,0103 physical sciences ,Selection (linguistics) ,Evolutionary computations ,010306 general physics ,Artificial Intelligence/Machine Learning ,030304 developmental biology ,Network model ,0303 health sciences ,[SHS.STAT]Humanities and Social Sciences/Methods and statistics ,[SHS.SOCIO]Humanities and Social Sciences/Sociology ,business.industry ,Complex network ,Machine Learning ML ,Network formation ,Computational social sciences ,Artificial intelligence ,Social network Analysis SNA ,business ,Symbolic regression ,Generative grammar - Abstract
International audience; Designing plausible network models typically requires scholars to form a priori intuitions on the key drivers of network formation. Oftentimes, these intuitions are supported by the statistical estimation of a selection of network evolution processes which will form the basis of the model to be developed. Machine learning techniques have lately been introduced to assist the automatic discovery of generative models. These approaches may more broadly be described as "symbolic regression", where fundamental network dynamic functions, rather than just parameters, are evolved through genetic programming. This chapter first aims at reviewing the principles, efforts and the emerging literature in this direction, which is very much aligned with the idea of creating artificial scientists. Our contribution then aims more specifically at building upon an approach recently developed by us [Menezes & Roth, 2014] in order to demonstrate the existence of families of networks that may be described by similar generative processes. In other words, symbolic regression may be used to group networks according to their inferred genotype (in terms of generative processes) rather than their observed phenotype (in terms of statistical/topological features). Our empirical case is based on an original data set of 238 anonymized ego-centered networks of Facebook friends, further yielding insights on the formation of sociability networks.
- Published
- 2019
14. The Certification Challenges of Connected and Autonomous Vehicles
- Author
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Bonnin, Hugues and bonnin, hugues
- Subjects
safety critical ,certification ,autonomous and connected vehicle ,software ,data-centric ,cloud computing ,Artificial Intelligence/Machine Learning ,big-data ,[INFO.INFO-ES] Computer Science [cs]/Embedded Systems - Abstract
— In the context of connected cars, some technologies or approaches are unavoidable: data-centric and big data; AI/ML-based systems; cloud computing and agility. These points are not limited to connected cars, but are trends in all transportation fields at least. In this paper, we analyze the interest of these technologies. Then, we analyze that these techniques are not completely in phase with traditional ways of developing safety critical software because the standards used for this purpose do not rely fundamentally on the same approaches to develop software.
- Published
- 2018
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