Bibliographies thématiques / Hybrid AI

Littérature scientifique sur le sujet « Hybrid AI »

Auteur : Grafiati
Publié le 22 juin 2024

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Articles de revues sur le sujet "Hybrid AI"

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Hopgood, A. « Hybrid AI ». ITNOW 55, no 4 (26 novembre 2013) : 10–11. http://dx.doi.org/10.1093/itnow/bwt066.

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Ikegaya, Yuji. « Brain-AI hybrid ». Proceedings for Annual Meeting of The Japanese Pharmacological Society 97 (2023) : 3—B—SL16. http://dx.doi.org/10.1254/jpssuppl.97.0_3-b-sl16.

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Silva, Felipe Leno Da, Silvio Stanzani, Jefferson Fialho, Jorge Mondadori, Muriel Mazzetto, Felipe Sanches Couto et Raphael Cobe. « Designing a Hybrid AI Residency ». Proceedings of the AAAI Conference on Artificial Intelligence 35, no 17 (18 mai 2021) : 15640–46. http://dx.doi.org/10.1609/aaai.v35i17.17842.

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The industry demand for AI experts raised to unprecedented levels in the last years. However, the increasing demand was not met by the number of skilled professionals in this area. As an effort to mitigate this problem, many companies create AI residency programs to provide in-house practical training. However, we argue that the usual dynamics based on one-on-one mentorship in those programs is very hard to scale and insufficient to meet the demand for AI professionals. In this paper, we describe a hybrid AI residency program that connects educational institutions, partner companies, and prospective residents. This program is designed to be funded by partner companies.Residents are exposed to practical projects of industry interest and are instructed on AI techniques and tools. We describe how we implemented our program, the challenges involved, and the lessons learned after the conclusion of the first residency class. Our program was developed to be inclusive and scalable, and resulted in a high employment rate for our alumni. Furthermore, several partner companies invested in in-house AI teams after the residency, resulting in direct benefits for our local AI community.
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Siddique, Nazmul H., Balasundram P. Amavasai et Akira Ikuta. « Editorial : Hybrid Techniques in AI ». Artificial Intelligence Review 27, no 2-3 (mars 2007) : 77–78. http://dx.doi.org/10.1007/s10462-008-9085-2.

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Mateas, Michael. « Expressive AI : A Hybrid Art and Science Practice ». Leonardo 34, no 2 (avril 2001) : 147–53. http://dx.doi.org/10.1162/002409401750184717.

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Expressive AI is a new interdiscipline of AI-based cultural production, combining art practice and AI-research practice. This article explores expressive AI by comparing it with other AI discourses, describing how it borrows notions of interpretation and authorship from both art and AI research practice and providing preliminary desiderata for the practice.
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Monostori, L., Cs Egresits et B. Kádár. « Hybrid AI Approaches to Intelligent Manufacturing ». IFAC Proceedings Volumes 29, no 1 (juin 1996) : 571–76. http://dx.doi.org/10.1016/s1474-6670(17)57723-x.

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Khriapynskyi, Anton, Ihor Khmyrov, Ivo Svoboda, Mykhailo Shevchuk et Vira Iastrebova. « State information security strategies in conditions of hybrid threats ». Revista Amazonia Investiga 12, no 69 (30 septembre 2023) : 84–93. http://dx.doi.org/10.34069/ai/2023.69.09.7.

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Hybrid information threats under the conditions of modern development of digital technologies are currently becoming one of the major issues for a modern democracy. The amount of damage that hybrid threats bring to the world economy contributes to the establishment of effective legal mechanisms to combat them. The purpose of the study was to single out the information security strategies under conditions of hybrid threats, including the spread of disinformation and fake news. The application of the comparative analysis method mad it possible to identify the gaps in information security strategies for countering hybrid threats. The strategy of information security in the conditions of hybrid threats is a coordinated action plan aimed at countering and fighting hybrid threats to safeguard cyberspace and preserve a democracy. Information security against hybrid threats is based on such cornerstones as: availability, confidentiality, integrity of information data, and safety. Enhancement of information security under the conditions of hybrid threats should be carried out at the expense of digital transformation, increasing the level of digital literacy of society and establishing a fair responsibility measure for a purposeful spreading of disinformation. The perspective of further research is addressing information security strategies as well as elaborating practical guidelines for the formation of a secure information space.
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Almusaed, Amjad, Asaad Almssad, Ibrahim Yitmen et Raad Z. Homod. « Enhancing Student Engagement : Harnessing “AIED”’s Power in Hybrid Education—A Review Analysis ». Education Sciences 13, no 7 (21 juin 2023) : 632. http://dx.doi.org/10.3390/educsci13070632.

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Hybrid learning is a complex combination of face-to-face and online learning. This model combines the use of multimedia materials with traditional classroom work. Virtual hybrid learning is employed alongside face-to-face methods. That aims to investigate using Artificial Intelligence (AI) to increase student engagement in hybrid learning settings. Educators are confronted with contemporary issues in maintaining their students’ interest and motivation as the popularity of online and hybrid education continues to grow, where many educational institutions are adopting this model due to its flexibility, student-teacher engagement, and peer-to-peer interaction. AI will help students communicate, collaborate, and receive real-time feedback, all of which are challenges in education. This article examines the advantages and disadvantages of hybrid education and the optimal approaches for incorporating Artificial Intelligence (AI) in educational settings. The research findings suggest that using AI can revolutionize hybrid education, as it enhances both student and instructor autonomy while fostering a more engaging and interactive learning environment.
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Yang, J. B. « Hybrid AI system for retaining wall selection ». Construction Innovation 4, no 1 (mars 2004) : 33–52. http://dx.doi.org/10.1108/14714170410814999.

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Yang, J. B. « Hybrid AI system for retaining wall selection ». Construction Innovation 4, no 1 (1 mars 2004) : 33–52. http://dx.doi.org/10.1191/1471417504ci065oa.

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Thèses sur le sujet "Hybrid AI"

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Piotrowski, Wiktor Mateusz. « Heuristics for AI planning in hybrid systems ». Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/heuristics-for-ai-planning-in-hybrid-systems(bbe2ba21-3449-4689-8bf8-6e441515cd10).html.

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The vast majority of real-world domains feature both discrete and continuous be-haviour to some extent. Translating them into Automated Planning problems is diÿcult, and requires a very expressive modelling language. Furthermore, solving problems in these hybrid domains is challenging for planners due to non-linear sys-tem dynamics, vast search spaces, and a wide range of domain features. Despite these obstacles, planning in hybrid domains has been a growing area in Artificial Intelligence. Eÿcient heuristics are key to solving problems in hybrid domains. This dissertation describes research into domain-independent heuristics designed specifically for mixed discrete-continuous planning problems defined in the PDDL+ modelling language with a particular focus on aerospace applications. To tackle hybrid planning problems, we exploit the planning-via-discretisation approach where the continuous dynamics of a model is approximated with uniform time steps and step-functions. Building on previous research in Automated Plan-ning and model checking, we define a set of domain-independent heuristics designed to reason with all aspects of the PDDL+ feature set as well as non-linear system dynamics. First, we present a relaxation-based heuristic, Staged Relaxed Planning Graph+ (SRPG+) inspired by the Relaxed Planning Graph (RPG) approach used in temporal and numerical planning. We also extend the SRPG+ to validation-free discretisation-based planning. Second, we describe the Policy Abstraction Database (PADB), an extension to the Pattern Database (PDB) heuristic for PDDL+ do-mains. It relies on solving an abstracted and relaxed version of the problem and uses the relaxed solution as a guide to solving the original problem. Next, we define the Polyhedra-based PDB (PolyPDB), an abstraction-based heuristic adapted from state-of-the-art model checking techniques and Pattern Databases. Finally, given that while the field of planning in hybrid domains is growing, the range of avail-able benchmark domains is significantly underdeveloped, we discuss the modelling of novel hybrid domains in PDDL+ and innovative uses for the PDDL+ language. The novel heuristics have been implemented in DiNo, a new heuristic PDDL+ planner. It is based on UPMurphi, a planner set in the planning-as-model-checking paradigm. Results show that our heuristics significantly improve the rate of explo-ration of the search space and facilitate eÿciently finding the goal on a range of novel and existing benchmark PDDL+ domains.
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Khan, Laiq. « Hybrid AI paradigms applied to power system damping controls ». Thesis, University of Strathclyde, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.273412.

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Lilla, Abdurahman Daleel. « AI-based hybrid optimisation of multi-megawatt scale permanent magnet synchronous generators for offshore wind energy capture ». Master's thesis, Faculty of Engineering and the Built Environment, 2019. https://hdl.handle.net/11427/31667.

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The finite nature of earth’s natural resources has become a post-industrial reality. Despite their alarming depletion, fossil fuels still dominated the global final energy landscape. Technological advances and rapid deployment of various renewable energy technologies have demonstrated their potential at reducing the worlds dependency on fossil fuels and their negative impacts. Presently, wind energy is the most cost-effective means of renewable energy conversion in the developed world and has currently has a price point that is in direct competition with fossil fuel. Coupled with the low price, the adoption of wind power has seen capacity increases in excess of 650% over the last ten years. Permanent Magnet Synchronous Generators (PMSGs) have become prominent in large wind energy system applications. The Radial Flux machine topology has become particularly attractive. In order to improve the competitiveness of large wind energy systems, the main focal point of current research is toward reducing the Levelised Cost of Energy (LCOE) of the systems. A proven method of reducing the LCOE of wind power generation is by upscaling RF-PMSGs to the multi mega-watt (MW) range. For the much wider adoption of wind power generation, the cost of energy (price/MWh) needs to be driven down further, by the development of more efficient and cost-effective ways to harvest the vast amounts of energy. The main objective of this dissertation is the drive-train selection, detailed design, sizing and optimisation of a 10.8 MW permanent magnet radial flux synchronous generator (RF-PMSG) to be used in the next generation of offshore wind farms. From an analytical viewpoint, the results suggested the use of a medium speed RF-PMSG utilizing a single-stage geared drivetrain, together with a MV voltage rating (3.3kV) for the 10.8 MW RF-PMSG designed in the thesis. Finally, this dissertation proposes a promising hybrid, analytical-numerical optimisation of a 10.8 MW RF-PMSG to be used for offshore Wind Energy Conversion. The hybrid optimisation utilises a two-stage optimisation strategy that incorporates both an analytical and a numerical (FEA) optimisation; using the DE algorithm and the Taguchi method respectively. Although the permanent magnet losses are neglected in the analytical loss calculations, they are included in the numerical FE portion of the hybrid optimisation. The initial stage (STAGE I) of the hybrid optimisation utilised the DE algorithm. The objective function was set to reduce the initial cost (!"#"$%&) of the RF-PMSG, by reducing the active material mass ('()$"*+) in the generator, i.e. NdFeB PM mass (',-), copper mass (').), and active steel in the stator lamination and rotor core ('/0$%&1$++&), while maintaining a pmsg efficiency (23456 ≥ 97%). The initial stage saw a reduction in initial cost by 25.5%, while maintaining an efficiency of 23456 = 97.8%. The final stage (STAGE II) of the hybrid optimisation utilising the Taguchi method, to make improvements on the performance of the machine, by optimising the Torque and back EMF characteristics while further reducing the NdFeB PM mass. The Magnet Fill Factor (APM), the Slot opening (bs0), the thickness of the permanent magnet poles (ℎ34) and the equivalent length of the air gap (?6) were used as optimisation variables. The final stage saw a decrease in cogging torque (@)06) by 53.4%, an increase in average torque (@%*) by 1.1%, a reduction in the total harmonic distortion of the back EMF (@AB) by 8.0%, a reduction in the required mass of the NdFeB permanent magnet material by 12.43%, while maintaining a torque ripple (@C"3) < 10%. The RF-PMSG characteristics optimised using the hybrid analytical-numerical optimisation were hypothesised to contribute in a reduction of the LCOE of offshore wind energy both in terms of Operational expenditure (OPEX) and Capital expenditure (CAPEX).
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ISAKSSON, LARS JOHANNES. « HYBRID DEEP LEARNING AND RADIOMICS MODELS FOR ASSESSMENT OF CLINICALLY RELEVANT PROSTATE CANCER ». Doctoral thesis, Università degli Studi di Milano, 2022. https://hdl.handle.net/2434/946529.

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Precision medicine holds the potential to revolutionize healthcare by providing every patient with personalized treatments and decisions tailored to his or her individual needs. This might be enabled by the large influx of potentially diagnostic information from new sources such as genetics and modern imaging techniques, provided the relevant information can be extracted. One such framework that has started to demonstrate promise in radiology, especially in the assessment of cancer, is radiomics; the practice of characterizing images by extracting a substantial amount of quantitative mathematical descriptors. This success has largely been enabled by artificial intelligence (AI) and machine learning developments that are capable of handling the big data arrays. Using radiomics, researchers have been able to build prediction models capable of assisting and informing doctors in important decisions such as risk assessment or the choice of treatment. But even though radiomics has shown promise in preliminary studies, there is still a long way to go before radiomics and related AI applications can become routine tools in clinics. The road from patient admission to release is long, and all its intricate steps need to be studied in detail to establish the AI models' benefits and safety. Deep learning is an incredibly powerful AI technique that has revolutionized many areas of science and industry such as recommender systems and protein folding. The technique has demonstrated particular capabilities in image analysis, such as the ability to drive cars autonomously and generate realistic-looking images from scratch. However, the recent advances in deep learning have largely been segregated from the radiomics domain, even though they can synergize with radiomics by performing complementary tasks such as image segmentation and denoising. There is considerable potential for DL and radiomics to cooperatively reinforce each other that so far has been majorly unexplored. This thesis investigates the application of radiomics and deep learning in the context of prostate cancer. It focuses on the clinical perspective of where machine learning implementations are most likely to have a beneficial real-world impact. A key contribution is the deployment aspect: the models are not simply proofs of concept but are conceived and applied in a practical scenario, from patient admission to treatment decision. The specific areas studied include automatic organ segmentation in medical images, automatic quality assurance of segmentations, image processing, and radiomic feature analysis. Finally, a comprehensive study is performed on predicting essential pathological variables with AI, which so far has not been studied previously. Taken together, the methods outlined in this thesis constitute a concrete pathway of how AI can be used to bolster the steps along the patient's clinical trajectory. Successful applications of these methods hold the potential to reduce the workload of clinicians and improve patient outcomes.
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Abdullah, Siti Norbaiti binti. « Machine learning approach for crude oil price prediction ». Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/machine-learning-approach-for-crude-oil-price-prediction(949fa2d5-1a4d-416a-8e7c-dd66da95398e).html.

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Crude oil prices impact the world economy and are thus of interest to economic experts and politicians. Oil price’s volatile behaviour, which has moulded today’s world economy, society and politics, has motivated and continues to excite researchers for further study. This volatile behaviour is predicted to prompt more new and interesting research challenges. In the present research, machine learning and computational intelligence utilising historical quantitative data, with the linguistic element of online news services, are used to predict crude oil prices via five different models: (1) the Hierarchical Conceptual (HC) model; (2) the Artificial Neural Network-Quantitative (ANN-Q) model; (3) the Linguistic model; (4) the Rule-based Expert model; and, finally, (5) the Hybridisation of Linguistic and Quantitative (LQ) model. First, to understand the behaviour of the crude oil price market, the HC model functions as a platform to retrieve information that explains the behaviour of the market. This is retrieved from Google News articles using the keyword “Crude oil price”. Through a systematic approach, price data are classified into categories that explain the crude oil price’s level of impact on the market. The price data classification distinguishes crucial behaviour information contained in the articles. These distinguished data features ranked hierarchically according to the level of impact and used as reference to discover the numeric data implemented in model (2). Model (2) is developed to validate the features retrieved in model (1). It introduces the Back Propagation Neural Network (BPNN) technique as an alternative to conventional techniques used for forecasting the crude oil market. The BPNN technique is proven in model (2) to have produced more accurate and competitive results. Likewise, the features retrieved from model (1) are also validated and proven to cause market volatility. In model (3), a more systematic approach is introduced to extract the features from the news corpus. This approach applies a content utilisation technique to news articles and mines news sentiments by applying a fuzzy grammar fragment extraction. To extract the features from the news articles systematically, a domain-customised ‘dictionary’ containing grammar definitions is built beforehand. These retrieved features are used as the linguistic data to predict the market’s behaviour with crude oil price. A decision tree is also produced from this model which hierarchically delineates the events (i.e., the market’s rules) that made the market volatile, and later resulted in the production of model (4). Then, model (5) is built to complement the linguistic character performed in model (3) from the numeric prediction model made in model (2). To conclude, the hybridisation of these two models and the integration of models (1) to (5) in this research imitates the execution of crude oil market’s regulators in calculating their risk of actions before executing a price hedge in the market, wherein risk calculation is based on the ‘facts’ (quantitative data) and ‘rumours’ (linguistic data) collected. The hybridisation of quantitative and linguistic data in this study has shown promising accuracy outcomes, evidenced by the optimum value of directional accuracy and the minimum value of errors obtained.
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Jha, Alok Kumar. « Intelligent Control and Path Planning of Multiple Mobile Robots Using Hybrid Ai Techniques ». Thesis, 2016. http://ethesis.nitrkl.ac.in/7416/1/2016_PhD_AKJha_510ME109.pdf.

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This work reports the problem of intelligent control and path planning of multiple mobile robots. Soft computing methods, based on three main approaches i.e. 1) Bacterial Foraging Optimization Algorithm, 2) Radial Basis Function Network and 3) Bees Algorithm are presented. Initially, Bacterial foraging Optimization Algorithm (BFOA) with constant step size is analyzed for the navigation of mobile robots. Then the step size has been made adaptive to develop an Adaptive Bacterial Foraging Optimization (ABFO) controller. Further, another controller using radial basis function neural network has been developed for the mobile robot navigation. Number of training patterns are intended to train the RBFN controller for different conditions arises during the navigation. Moreover, Bees Algorithm has been used for the path planning of the mobile robots in unknown environments. A new fitness function has been used to perform the essential navigational tasks effectively and efficiently. In addition to the selected standalone approaches, hybrid models are also proposed to improve the ability of independent navigation. Five hybrid models have been presented and analyzed for navigation of one, two and four mobile robots in various scenarios. Comparisons have been made for the distance travelled and time taken by the robots in simulation and real time. Further, all the proposed approaches are found capable of solving the basic issues of path planning for mobile robots while doing navigation. The controllers have been designed, developed and analyzed for various situations analogous to possible applications of the robots in indoor environments. Computer simulations are presented for all cases with single and multiple mobile robots in different environments to show the effectiveness of the proposed controllers. Furthermore, various exercises have been performed, analyzed and compared in physical environments to exhibit the effectiveness of the developed controllers.
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« Representing Hybrid Transition Systems in an Action Language Modulo ODEs ». Master's thesis, 2017. http://hdl.handle.net/2286/R.I.44191.

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abstract: Several physical systems exist in the real world that involve continuous as well as discrete changes. These range from natural dynamic systems like the system of a bouncing ball to robotic dynamic systems such as planning the motion of a robot across obstacles. The key aspects of effectively describing such dynamic systems is to be able to plan and verify the evolution of the continuous components of the system while simultaneously maintaining critical constraints. Developing a framework that can effectively represent and find solutions to such physical systems prove to be highly advantageous. Both hybrid automata and action languages are formal models for describing the evolution of dynamic systems. The action language C+ is a rich and expressive language framework to formalize physical systems, but can be used only with physical systems in the discrete domain and is limited in its support of continuous domain components of such systems. Hybrid Automata is a well established formalism used to represent such complex physical systems at a theoretical level, however it is not expressive enough to capture the complex relations between the components of the system the way C+ does. This thesis will focus on establishing a formal relationship between these two formalisms by showing how to succinctly represent Hybrid Automata in an action language which in turn is defined as a high-level notation for answer set programming modulo theories (ASPMT) --- an extension of answer set programs in the first-order level. Furthermore, this encoding framework is shown to be more effective and expressive than Hybrid Automata by highlighting its ability in allowing states of a hybrid transition system to be defined by complex relations among components that would otherwise be abstracted away in Hybrid Automata. The framework is further realized in the implementation of the system CPLUS2ASPMT, which takes advantage of state of the art ODE(Ordinary Differential Equations) based SMT solver dReal to provide support for ODE based evolution of continuous components of a dynamic system.
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Masters Thesis Computer Science 2017
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Thurner, Thomas. « The influence factors of the patients’ usage intention of AI-based preliminary diagnosis tools : the case study of Ada ». Master's thesis, 2020. http://hdl.handle.net/10400.14/29804.

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At present Artificial Intelligence (AI) is transforming the mechanisms and limitations of numerous industries. The healthcare sector is particularly affected with regard to the informative value of processing and analysing patient data through AI-based technologies. Public fund cuts and structural inefficiencies among other reasons, further aggregate the necessity of effectively employing the provided patient information. The majority of healthcare facilities, however, lack the resources or technical knowhow to realize the entire potential of Artificial Intelligence as a mean. As a consequence, emerging companies, that can be theoretically classified as the intermediate form of public and private establishments, have developed new concepts. The structural adaptability of so-called hybrid organizations facilitates the offering of specialized products and services adapted to the needs of patients. In this regard AI-based preliminary mobile diagnostic applications represent a promising opportunity to empower patients and positively influence the average health quality. The influence factors determining the adoption and usage intention of patients are yet unexplored. This dissertation therefore examined the patient’s perspective on AI-based preliminary diagnostic tools, in order to firstly expand the scope of present literature within this subject area and to identify the relevant key elements for the marketing and strategy measures of hybrid organizations operating in this field. The implications of this research include the recognition of the patients intended purpose of utilizing similar mobile applications, the consequently deriving strategic inferences, and a guidance for the marketing and communication efforts of comparable vendors.
Atualmente, a inteligência artificial está a transformar os mecanismos e limitações de diversas indústrias. O sector da saúde é particularmente afetado pelo potencial informativo de processamento e análise de dados de pacientes através de tecnologias de inteligência artificial. Cortes orçamentais públicos e ineficiências a nível estrutural evidenciam a necessidade de, idealmente, empregar os dados de pacientes. Na sua maioria, as instalações de saúde carecem de recursos ou de conhecimento técnico para se inteirarem do potencial da inteligência artificial. Consequentemente, as empresas emergentes, que teoricamente podem ser classificadas como um formato intermédio entre estabelecimentos públicos e privados, definem um novo conceito. A adaptação estrutural das organizações híbridas facilita a oferta de produtos e serviços especializados às necessidades dos pacientes. Neste sentido, aplicações móveis de diagnóstico preliminar recorrendo a inteligência artificial, representam uma oportunidade promissora por conceder autonomia aos pacientes e influenciando positivamente a qualidade do sector da saúde. Os fatores determinantes da adoção e intenção de uso por parte dos pacientes está, ainda, por explorar. A presente dissertação examinou a perspetiva dos pacientes relativamente às ferramentas de diagnóstico preliminar com recurso à inteligência artificial, com o intuito inicial de expandir a literatura referente a esta temática e de identificar elementos fundamentais para as medidas de marketing e estratégia de organizações híbridas que operam neste meio. As implicações deste estudo incluem o reconhecimento de pacientes que tencionem recorrer a aplicações móveis semelhantes e suas subsequentes implicações estratégicas, assim como diretrizes a nível de marketing e estratégia para negócios equivalentes.
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Weißenburger, Julius Eric. « Disruption in HR : the impact of Artificial Intelligence and machine learning innovation on recruiting ». Master's thesis, 2020. http://hdl.handle.net/10400.14/31314.

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Talent is increasingly important for organizations which makes corporate recruitment a significant ongoing function. Recruiting top talent cannot occur where there are inefficiencies, high costs and lack of innovation. At the same time, artificial intelligence (AI) and machine learning (ML) are disrupting industries and different areas of business practice. This technology has the potential to create unprecedented value in recruiting functions also, by positively impacting efficiency, costs and employee fit. Despite rapid developments in the field of AI, academic literature on AI in recruitment is thin. Researchers further call for more collaborative work between practitioners and academics. This thesis aims to tackle this gap, evaluating how AI and ML disrupt traditional recruitment processes and bring about potential new outcomes. By integrating the experiences of experts, executives and the perceptions of potential job applicants, this thesis elucidates practical implications for the adoption of AI and ML in recruiting. The thesis uses qualitative and quantitative data collection. The findings present opportunities and also the limitations of AI and ML in recruiting. Furthermore, the effects of the technology on recruiting efficiency and validity are evaluated. This creates the basis for practical recommendations for organizations regarding the adoption of this technology. Notably, in the more standardized aspects of the recruitment processes, this technology does create value in hiring.
O talento é cada vez mais importante para as organizações que utilizam o recrutamento corporativo como uma função contínua e significativa. O recrutamento dos melhores talentos não pode ocorrer onde existem ineficiências, altos custos e falta de inovação. Ao mesmo tempo, a inteligência artificial (IA) e machine learning (ML) estão rompendo indústrias e diferentes áreas de prática de negócios. Essa tecnologia tem o potencial de criar um valor sem precedentes nas funções de recrutamento, impactando positivamente a eficiência, os custos e a adequação dos funcionários. Apesar do rápido desenvolvimento no campo da IA, a literatura acadêmica sobre IA no recrutamento é escassa. Os pesquisadores gostariam que existisse mais trabalho colaborativo entre profissionais e acadêmicos. Esta tese visa abordar essa lacuna, avaliando como a IA e o ML modificam os processos tradicionais de recrutamento e trazem novos resultados potenciais. Ao integrar as experiências de especialistas, executivos e as percepções de possíveis candidatos a emprego, esta tese elucida implicações práticas para a adoção de IA e ML no recrutamento. A tese utiliza coleta de dados qualitativa e quantitativa. Os resultados apresentam oportunidades e também as limitações da IA e ML. Além disso, os efeitos da tecnologia no recrutamento eficiente e válido são avaliados. Isso cria a base para recomendações práticas para as organizações com relação à adoção desta tecnologia. Notavelmente, nos aspectos mais padronizados dos processos de recrutamento, essa tecnologia cria valor na contratação.
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Livres sur le sujet "Hybrid AI"

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1960-, Sun Ron, et Alexandre Frederic, dir. Connectionist-symbolic integration : From unified to hybrid approaches. Mahwah, N.J : Lawrence Erlbaum Associates, 1997.

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Jian, Lirong. Hybrid rough sets and applications in uncertain decision-making. Boca Raton : Auerbach Publications, 2010.

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Alexandrov, Eugeniu G. Hibrizii distanți ai viței de vie (Vitis vinifera L. x Muscadinia rotundifolia Michx.) : Aspecte biomorfologice și uvologice = Les hybrides interspécifiques de vigne (V. vinifera L. x M. rotundifolia Michx. : aspects biomorphologiques = Otdalennye gibridy vinograda (Vitis vinifera L. x Muscadinia rotundifolia Michx.) : biomorfologicheskie i uvologicheskie aspekty. Chișinău : Grădina Botanică (Institut) a AȘM, 2012.

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Sanjeevikumar, P., Sulabh Sachan et Sanchari Deb. AI-Based Solutions for Hybrid and Electric Vehicles. Wiley & Sons, Incorporated, John, 2023.

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Sanjeevikumar, P., Sulabh Sachan et Sanchari Deb. AI-Based Solutions for Hybrid and Electric Vehicles. Wiley & Sons, Incorporated, John, 2023.

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Sachan. AI-Based Solutions for Hybrid and Electric Vehicle S. Wiley & Sons, Limited, John, 2023.

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Romanelli, Ricardo, Antonio Pannullo et Marco Zanello. Endurance WEC : Dalle Gruppo C Ai Prototipi Ibridi/ from Group C to Hybrid Prototypes. Giorgio Nada Editore, 2021.

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(Editor), Ron Sun, et Frederic Alexandre (Editor), dir. Connectionist-Symbolic Integration : From Unified to Hybrid Approaches. Lawrence Erlbaum, 1997.

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Liu, Sifeng, Yi Lin et Lirong Jian. Hybrid Rough Sets and Applications in Uncertain Decision-Making. Taylor & Francis Group, 2018.

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Biswas, Gautam, et Sheila McIlraith. Hybrid Systems and AI - Modeling Analysis and Control of Discrete Plus Continuous Systems : Papers from the AAAI Spring Symposium. AAAI Press, 1999.

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Chapitres de livres sur le sujet "Hybrid AI"

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Henning, Klaus. « The Age of Hybrid Intelligence ». Dans Gamechanger AI, 61–70. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52897-3_7.

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Mainzer, Klaus, et Reinhard Kahle. « Prospects for Hybrid AI ». Dans Technik im Fokus, 113–50. Berlin, Heidelberg : Springer Berlin Heidelberg, 2024. http://dx.doi.org/10.1007/978-3-662-68290-6_5.

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Bloch, Isabelle. « Subsymbolic, hybrid and explainable AI ». Dans KI-Kritik / AI Critique, 179–96. Bielefeld, Germany : transcript Verlag, 2023. http://dx.doi.org/10.14361/9783839467664-010.

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Vassilev, Vassil, Sylvia Ilieva, Iva Krasteva, Irena Pavlova, Dessisslava Petrova-Antonova et Wiktor Sowinski-Mydlarz. « AI-Based Hybrid Data Platforms ». Dans Data Spaces, 147–70. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98636-0_8.

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AbstractThe current digital transformation of many businesses and the exponential growth of digital data are two of the key factors of digital revolution. For the successful meeting of high expectations, the data platforms need to employ the recent theoretical, technological, and methodological advances in contemporary computing and data science and engineering. This chapter presents an approach to address these challenges by combining logical methods for knowledge processing and machine learning methods for data analysis into a hybrid AI-based framework. It is applicable to a wide range of problems that involve both synchronous operations and asynchronous events in different domains. The framework is a foundation for building the GATE Data Platform, which aims at the application of Big Data technologies in civil and government services, industry, and healthcare. The platform implementation will utilize several recent distributed technologies such as Internet of Things, cloud, and edge computing and will integrate them into a multilevel service-oriented architecture that supports services along the entire data value chain, while the service orchestration guarantees a high degree of interoperability, reusability, and automation. The platform is designed to be compliant with the open-source software, but its open architecture supports also mixing with commercial components and tools.
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Davidzon, Guido A., et Henry Li. « AI for Decision Support in Molecular Neuroimaging ». Dans Hybrid PET/MR Neuroimaging, 67–78. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82367-2_8.

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Hnich, Brahim, Roberto Rossi, S. Armagan Tarim et Steven Prestwich. « A Survey on CP-AI-OR Hybrids for Decision Making Under Uncertainty ». Dans Hybrid Optimization, 227–70. New York, NY : Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-1644-0_7.

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Wall, Riley, et Parimala Thulasiraman. « An Island Model Genetic Algorithm Approach to Tuning AI Bots ». Dans Hybrid Intelligent Systems, 617–26. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73050-5_60.

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Zannos, Iannis, et Haruka Hirayama. « Towards an Aesthetic of Hybrid Performance Practice ». Dans Music in the AI Era, 111–21. Cham : Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35382-6_10.

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Barke, Daniel. « AI as a Driver of Hybrid Forms of Employment ». Dans Work and AI 2030, 151–58. Wiesbaden : Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-40232-7_17.

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Achterberg, Tobias, et Timo Berthold. « Hybrid Branching ». Dans Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, 309–11. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01929-6_23.

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Actes de conférences sur le sujet "Hybrid AI"

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Krishna, Siddanth, Siri S, Saif Kamalsha, Sai Amruth et Shruti Jadon. « PRIVATE-AI : A Hybrid Approach to privacy-preserving AI ». Dans 2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science (BCD). IEEE, 2023. http://dx.doi.org/10.1109/bcd57833.2023.10466330.

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Scala, Enrico. « AI Planning for Hybrid Systems ». Dans Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California : International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/805.

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When planning the tasks of some physical entities that need to perform actions in the world (e.g., a Robot) it is necessary to take into account quite complex models for ensuring that the plan is actually executable. Indeed the state of these systems evolves according to potentially non-linear dynamics where interdependent discrete and continuous changes happen over the entire course of the task. Systems of this kind are typically compactly represented in planning using languages mixing propositional logic and mathematics. However, these languages are still poorly understood and exploited. What are the difficulties for planning in these settings? How can we build systems that can scale up over realistically sized problems? What are the domains which can benefit from these languages? This short paper shows the main two ingredients that are needed to build a heuristic search planner, outline the main impact that such techniques have on application, and provide some open challenges. These models and relative planners hold the promise to deliver explainable AI solutions that do not rely on large amounts of data.
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Wang, Haoran. « Freeing hybrid distributed AI training configuration ». Dans ESEC/FSE '21 : 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. New York, NY, USA : ACM, 2021. http://dx.doi.org/10.1145/3468264.3473104.

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Taran, Ekaterina, Veronika Malanina et Fabio Casati. « Crowd Science for Hybrid AI Applications ». Dans 2021 IEEE International Conference on Service-Oriented System Engineering (SOSE). IEEE, 2021. http://dx.doi.org/10.1109/sose52839.2021.00027.

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Azam, Md Ali, Md Abir Hossen et Md Hafizur Rahman. « Hybrid Ant Swarm-Based Data Clustering ». Dans 2021 IEEE World AI IoT Congress (AIIoT). IEEE, 2021. http://dx.doi.org/10.1109/aiiot52608.2021.9454238.

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Freitag, Marina. « Revolutionizing Indoor Energy Harvesting : From Advanced Materials to AI Integration ». Dans International Conference on Hybrid and Organic Photovoltaics. València : FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2024. http://dx.doi.org/10.29363/nanoge.hopv.2024.096.

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Diniz Junqueira Barbosa, Gabriel, et Simone Diniz Junqueira Barbosa. « Towards Diverse AI : Can an AI-Human Hybrid Council Prevent Future Apartheids ? » Dans 17th IFIP TC.13 International Conference on Human-Computer Interaction. Cardiff University Press, 2020. http://dx.doi.org/10.18573/book3.aa.

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Aghamohseni, Akram, et Rasool Ramezanian. « An efficient hybrid approach based on K-means and generalized fashion algorithms for cluster analysis ». Dans 2015 AI & Robotics (IRANOPEN). IEEE, 2015. http://dx.doi.org/10.1109/rios.2015.7270727.

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Pelosi, Andrea, Claudio Felicioli, Andrea Canciani et Fabio Severino. « A Hybrid-DLT Based Trustworthy AI Framework ». Dans 2023 IEEE International Conference on Enabling Technologies : Infrastructure for Collaborative Enterprises (WETICE). IEEE, 2023. http://dx.doi.org/10.1109/wetice57085.2023.10477792.

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Fang, Tao, Jingwei Li, Tongyu Wu, Ming Cheng et Xiaowen Dong. « Efficient training for the hybrid optical diffractive deep neural network ». Dans AI and Optical Data Sciences III, sous la direction de Ken-ichi Kitayama et Bahram Jalali. SPIE, 2022. http://dx.doi.org/10.1117/12.2607567.

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Rapports d'organisations sur le sujet "Hybrid AI"

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Wang, Jiali, Rao Kotamarthi, Virendra Ghate, Bethany Lusch, Prasanna Balaprakash, Justin Wozniak, Xingqiu Yuan et al. A Hybrid Climate Modeling System Using AI-assisted Process Emulators. Office of Scientific and Technical Information (OSTI), février 2021. http://dx.doi.org/10.2172/1769645.

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Djordjevich, Donna D., Patrick Gordon Xavier, Nathan Gregory Brannon, Brian E. Hart, Derek H. Hart, Charles Quentin Little, Fred John III Oppel, John Michael Linebarger et Eric Paul Parker. LDRD project final report : hybrid AI/cognitive tactical behavior framework for LVC. Office of Scientific and Technical Information (OSTI), janvier 2012. http://dx.doi.org/10.2172/1034891.

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Mohanty, Subhasish, et Joseph Listwan. A Hybrid AI/ML and Computational Mechanics Based Approach for Time-Series State and Fatigue Life Estimation of Nuclear Reactor Components. Office of Scientific and Technical Information (OSTI), septembre 2020. http://dx.doi.org/10.2172/1688432.

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Mohanty, Subhasish. Hybrid AI-ML and FE-based Digital Twin Predictive Modeling Framework for a PWR Coolant System Components : Updates on Multi-Time-Series-3D-Location Dependent Usages Factor Prediction. Office of Scientific and Technical Information (OSTI), juin 2022. http://dx.doi.org/10.2172/1874565.

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