查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting originating in Verona, Italy, by NewsRx journalists, research stated, “Artificial intelligence has become an increasingly powerful technological instrument in recent years, revolutionizing many sectors, including public health. Its use in this field will inevitably change clinical practice, the patient-caregiver relationship and the concept of the diagnosis and treatment pathway, affecting the balance between the patient’s right to self-determination and health, and thus leading to an evolution of the concept of informed consent.” The news reporters obtained a quote from the research from the University of Verona, “The aim was to characterize the guidelines for the use of artificial intelligence, its areas of application and the relevant legislation, to propose guiding principles for the design of optimal informed consent for its use. A classic review by keywords on the main search engines was conducted. An analysis of the guidelines and regulations issued by scientific authorities and legal bodies on the use of artificial intelligence in public health was carried out. The current areas of application of this technology were highlighted, divided into sectors, its impact on them, as well as a summary of current guidelines and legislation. The ethical implications of artificial intelligence in the health care system were assessed, particularly regarding the therapeutic alliance between doctor and patient, and the balance between the right to self-determination and health.”
查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting from Lappeenranta, Finland, by NewsRx journalists, research stated, “This paper presents a novel framework for intrusion detection specially designed for cyberattacks, such as Denial-of-Service, Distributed Denialof- Service, Distributed Reflection Denial-of-Service, Brute Force, Botnets, and Sniffing, on vehicles that are situated in the Internet of Vehicles environment. We propose an intrusion detection system based on machine learning that is capable of detecting abnormal behavior by examining network traffic to find unusual data flows.” Financial supporters for this research include Research Council of Finland, Jane and Aatos Erkko Foundation via STREAM project.
查看更多>>摘要：Fresh data on Robotics - Robotics and Automation are presented in a new report. According to news reporting originating in Beijing, People’s Republic of China, by NewsRx journalists, research stated, “Embodied agents navigating unknown environments face the challenge of optimizing exploration based on semantic information. Conventional methods, reliant on collected data or pre-defined rules, have limitations in scalability and applicability, while pretrained language models based methods focusing on textual modality encounter perceptual noise, which affects decision-making.” Financial support for this research came from Beijing University of Posts and Telecommunications China Mobile Research Institute Joint Innovation Center.
查看更多>>摘要：Investigators publish new report on Artificial Intelligence. According to news reporting originating in Dundee, United Kingdom, by NewsRx journalists, research stated, “The application of deep learning (DL) to diagnostic dermatology has been the subject of numerous studies, with some reporting skin lesion classification performance on curated datasets comparable to that of experienced dermatologists. Most skin disease images encountered in clinical settings are macroscopic, without dermoscopic information, and exhibit considerable variability.” Financial supporters for this research include National Institute for Health and Care Research, NHS Transformation Directorate, Engineering & Physical Sciences Research Council (EPSRC).
查看更多>>摘要：New study results on artificial intelligence have been published. According to news reporting from Guangdong, People’s Republic of China, by NewsRx journalists, research stated, “Swimming is a sport with a very significant exercise effect, so swimming courses in colleges and universities can promote the overall development of students’ physical fitness.” The news correspondents obtained a quote from the research from Guangzhou College of Commerce: “In this paper, students’ swimming data are collected using intelligent sensors, and the collected data are noise-reduced and normalized by a low-pass filter. After the completion of data preprocessing, the swimming posture data features are extracted. The features are dimensionality reduced by the PCA method, combined with the BP neural network for training and accurate swimming posture recognition, and on this basis, a diversified teaching system for swimming courses in smart colleges and universities is constructed. After the students’ swimming data set was collected, the recognition effect of each swimming stroke was analyzed, and a comparison experiment was set up to investigate the practical effect and strategy of this teaching mode.”
查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news reporting out of Brescia, Italy, by NewsRx editors, research stated, “The concept of learning has multiple interpretations, ranging from acquiring knowledge or skills to constructing meaning and social development. Machine Learning (ML) is considered a branch of Artificial Intelligence (AI) and develops algorithms that can learn from data and generalize their judgment to new observations by exploiting primarily statistical methods.” The news reporters obtained a quote from the research from Catholic University of the Sacred Heart: “The new millennium has seen the proliferation of Artificial Neural Networks (ANNs), a formalism able to reach extraordinary achievements in complex problems such as computer vision and natural language recognition. In particular, designers claim that this formalism has a strong resemblance to the way the biological neurons operate. This work argues that although ML has a mathematical/statistical foundation, it cannot be strictly regarded as a science, at least from a methodological perspective. The main reason is that ML algorithms have notable prediction power although they cannot necessarily provide a causal explanation about the achieved predictions. For example, an ANN could be trained on a large dataset of consumer financial information to predict creditworthiness. The model takes into account various factors like income, credit history, debt, spending patterns, and more. It then outputs a credit score or a decision on credit approval. However, the complex and multi-layered nature of the neural network makes it almost impossible to understand which specific factors or combinations of factors the model is using to arrive at its decision. This lack of transparency can be problematic, especially if the model denies credit and the applicant wants to know the specific reasons for the denial. The model’s “black box” nature means it cannot provide a clear explanation or breakdown of how it weighed the various factors in its decision-making process.”
查看更多>>摘要：Research findings on artificial intelligence are discussed in a new report. According to news originating from Huaqiao University by NewsRx correspondents, research stated, “Sustainable Development 8 proposes the promotion of full and productive employment for all. Intelligent production factors, such as robots, the Internet of Things, and extensive data analysis, are reshaping the dynamics of labour supply and demand.” Our news reporters obtained a quote from the research from Huaqiao University: “In China, which is a developing country with a large population and labour force, analysing the impact of artificial intelligence technology on the labour market is of particular importance. Based on panel data from 30 provinces in China from 2006 to 2020, a two-way fixed-effect model and the two-stage least squares method are used to analyse the impact of AI on employment and to assess its heterogeneity. The introduction and installation of artificial intelligence technology as represented by industrial robots in Chinese enterprises has increased the number of jobs. The results of some mechanism studies show that the increase of labour productivity, the deepening of capital and the refinement of the division of labour that has been introduced into industrial enterprises through the introduction of robotics have successfully mitigated the damaging impact of the adoption of robot technology on employment. Rather than the traditional perceptions of robotics crowding out labour jobs, the overall impact on the labour market has exerted a promotional effect. The positive effect of artificial intelligence on employment exhibits an inevitable heterogeneity, and it serves to relatively improves the job share of women and workers in labour-intensive industries. Mechanism research has shown that virtual agglomeration, which evolved from traditional industrial agglomeration in the era of the digital economy, is an important channel for increasing employment.”
查看更多>>摘要：Data detailed on Robotics have been presented. According to news originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Establishing manufacturability design criteria for multidimensional complex parts can significantly reduce the production cost, shorten the manufacturing cycle, and improve the production quality of directed energy deposition. Therefore, there is an urgent need to establish a high-performance manufacturing design strategy for complex parts.” Funders for this research include National Natural Science Foundation of China (NSFC), China Postdoctoral Science Foundation, China Postdoctoral Science Foundation, State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Postdoctoral Research Foundation of Chaoyang District.
查看更多>>摘要：Researchers detail new data in Artificial Intelligence. According to news reporting originating in Edinburgh, United Kingdom, by NewsRx journalists, research stated, “Artificial intelligence (AI)-based surrogate reservoir models (SRMs) can provide computationally feasible and accurate approximations to numerical simulations. An AI-based SRM is trained to a set of parameters that significantly reduces its variance.” Funders for this research include Petroleum Technology Development Fund (PTDF), Heriot-Watt University Gas Condensate Research Group. The news reporters obtained a quote from the research from Heriot-Watt University, “This can be done by either supervised or semi-supervised learning. The latter involves regularization of the model’s parameters using non-physics-based, physics-based or a combination of both regularization terms. Effective enforcement of the physics-based and non-physics-based regularizations can significantly reduce the variance of AI-based SRMs. Little study has been reported on the application and effects of regularization terms. Also, for highly compressible subsurface flow where strong nonlinearities exist, well-constructed composite AI-based architectures and regularizations are necessary for learning. This paper applies and studies the effects of various regularization terms for highly compressible subsurface flow; it proposes unique and effective techniques in AI-based surrogate development and training. The learning utilizes the discretized domain and boundary physics with derivatives obtained from both finite difference methods (FDM) and algorithmic differentiation (AD).The regularizations are partly enforced as a hard constraint in the network architecture using a trainable layer and as soft constraints using a multi-cost function. The soft constraints exploit a tank material balance and time-discretization numerical errors, in addition to the domain, boundary and non-physics-based L2 regularization terms. The timely-trained AI-based surrogate predictions agree with those obtained from a numerical simulator. The regularization terms separately contribute to improved learning. The non-physics-based L2 norm if used in the right order of magnitude, improves grid block predictions. The tank material balance regularization term constrains the AI-based surrogate parameters to net domain accumulation, ensuring its reliability. The trainable hard enforcement layer enforces the initial condition and improves the predictions compared to other hard enforcement techniques. The discretized domain equation and time-discretization numerical errors allow for learning of variable timesteps, which give the best rounding-truncation error trade-off and improve the predictions compared to those of fixed timesteps.”
查看更多>>摘要：Investigators publish new report on Artificial Intelligence. According to news reporting originating in Starkville, Mississippi, by NewsRx editors, the research stated, “Connecting unemployed people to job openings has been a challenge post-pandemic.” The news reporters obtained a quote from the research from Mississippi State University, “With the help of artificial intelligence and big data, we addressed this issue by creating a deep learning model to provide realistic job recommendations for unemployed people based on the employment history of each individual. First, the transfer learning model was applied to match job titles and O*NET Standard Occupational Classification (OSOC) codes using data on job seekers from the Mississippi Department of Employment Security, where OSOC is a standard occupational classification-based system used by U.S. federal agencies to classify workers into occupational categories.” According to the news reporters, the research concluded: “Next, a Long Short -Term Memory (LSTM) model was created for career pathway prediction, to generate the top three OSOC job recommendations based on the individual’s employment history. .”
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