Artificial Intelligence Functions Research Articles

ChatGPT's Capabilities for Use in Anatomy Education and Anatomy Research

ChatGPT's Capabilities for Use in Anatomy Education and Anatomy Research

AI Language Models as Educational Allies: Enhancing Instructional Support in Higher Education

Incorporating artificial intelligence (AI) to support academic faculty is the main topic of the systematic literature review. The purpose of the study is to examine the potential advantages and disadvantages of such implementation, with a particular emphasis on how the use of AI in educational settings may impact teaching methods, individualised learning experiences, and administrative procedures. For the literature review, the Science Direct, Taylor & Francis, and Emerald Insight databases were carefully examined. In order to thoroughly analyse the integration of AI in instructional faculty support, each article was evaluated for its quality of content and relevance to the research questions. According to the findings, AI-driven tools like ChatGPT and intelligent tutoring systems have the potential to significantly improve instruction and foster adaptive learning, which would result in better educational outcomes. The use of AI can enhance administrative procedures and promote personalised learning for students. By highlighting its advantages and drawbacks, this study advances knowledge of AI's function in instructional faculty support for education. By highlighting critical gaps and difficulties, this study lays the groundwork for further research and the development of best practices for AI integration in educational contexts.

Security Issues of GPUs and FPGAs for AI-powered near & far Edge Services

Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs) are widely applied to cloud and embedded applications in which such devices are applied to near and far edge computing operations. This pool of available devices has a wide range of power/size specifications to support servers ranging from big data centres to small cloudlets, or even down to embedded systems and IoT boards. Overall, the most prominent devices and vendors in the market today are the following Xilinx for FPGA-based accelerators, Nvidia and AMD for GPUs, Intel for FPGA- /GPU-based accelerators. Decreasing the latency and increasing the throughput of Artificial Intelligence Functions (AIF), either for network automation or user applications, requires some sort of parallelization inside such purpose-built hardware acceleration. The AI@EDGE project is developing a Connect-Compute Platform (CCP) in which hardware accelerators (1 Nvidia GPU Tesla V100 (near edge device) and 1 Jetson AGX and 1 Jetson Nano (far edge devices), as well as 2 Xilinx FPGAs Alveo U280+U200 (near edge devices) and 1 Versal VCK190 and 2 Zynq ZCU104) are placed inside a server node and execute edge computing scenarios involving multiple nodes of diverse compute capabilities each, to test various integration approaches, to study orchestration techniques measure AIF deployment efficiency, all while developing certain FPGA/GPU code to accelerate representative AIFs of AI@EDGE. In this paper we compare the power/size/performance specifications of all accelerators and highlight the security issues associated with the cloud and embedded accelerators. This study presents the security issues announced by the vendors with the results of our tests and proposes tests and security functions (policies and objectives) which will be applied to the CCP to increase the security level of CCP. It also considers security issues related with the hardware set-up (accelerators inside server nodes) from the network point of view.

Развитие российских методов стратегического анализа и целесообразность замены SWOT-модели

Theoretical problems of developing methods and technologies of strategic analysis are considered. The scientific criticism of the SWOT approach widely used today in management practice and in education is given and the inexpediency of its use is shown. Instead of the SWOT approach, a new model of strategic analysis is proposed, based on new generalized variables, creating a target orientation to solving a set of development problems, completely eliminating the defects and miscalculations of the SWOT approach. A new technology of strategic analysis “Search for reserves and resources for creating competitive advantages and eliminating obstacles to solving development problems” has been developed, which is advisable to use in managing the development of enterprises, regional industries, the economy of the Russian Federation as a whole and other complex systems, and in the future can be used for inclusion of artificial intelligence functions (AI) in the sphere of strategic analysis.

Human-Machine Convergence and Disruption of Socio-Cognitive Capabilities

People who have access to smartphone or computers are already using AI. A complex set of algorithm works in search engines, social networking sites, voice recognition applications, navigation applications, OTT platforms and video games. Human-machine convergence has been made possible because of the science and technology. Artificial Intelligence and Machine Learning are the contemporary technologies which is being used in several sectors for better precisions like medical diagnostics, remote sensing etc. There is a tremendous amount of potential for AI and ML to help individuals develop their mental and interpersonal skills. Since quite some time, researchers have been looking at what effect, if any, artificial intelligence may have on the decision-making process of humans. In the same way that it is used in the decision-making processes of the scientific and corporate communities, artificial intelligence is also being employed in the legal and gambling industries. Software and algorithms based on artificial intelligence (AI) have the potential to simulate human behaviour and predict its effects. Many people are of the opinion that artificial intelligence will improve the quality of human judgement and decision making, particularly in high-stakes circumstances like war. Investigating the structural, relational, and cognitive bases of artificial intelligence's function as a facilitator of decision-making is the goal of this line of study.

An Innovative Approach for Pore Pressure Prediction and Drilling Optimization in an Abnormally Subpressured Basin

Summary Thus far, an indirect generalized method to predict pore pressure under subpressured conditions has not been reported in the literature. In this work, an innovative procedure is presented for estimation of pore pressure and optimization of wells drilled in the abnormally subpressured Deep Basin of the Western Canada Sedimentary Basin (WCSB). The procedure starts with detailed evaluation of five wells drilled in a township that covers the study area. Pore pressure was calculated from sonic logs and the modified D exponent by the use of Eaton's method (Eaton 1975), which proved to be the most effective approach for abnormally subpressured conditions over a variety of methods tested (Contreras et al. 2011). The optimization procedure was carried out by use of the apparent-rock-strength log (ARSL), which is an effective indicator of formation drillability and is very sensitive to the pore pressure. Next, optimization of individual sections in each well was carried out to determine the optimum types of bits and operational parameters for the lowest cost of drilling. An artificial-intelligence function was implemented to set up the optimum combination of parameters in such a way that the rate of penetration (ROP) (m/h) was increased after a number of simulation runs while controlling the bit wear. Special attention was focused on tight gas reservoirs for selection of the most suitable parameters that increase the quality of drill cuttings. It was concluded that the roller-cone bit IADC 547 (with at least 0.73 hp in the bit per square inch) provides the best-quality cuttings for the Nikanassin Group. This is of paramount importance for increasing accuracy in the quantitative determination of permeability and porosity from cuttings particularly in those tight gas reservoirs where the amount of cores is very limited. It is concluded that wells in the Deep Basin of the WCSB can be drilled efficiently with seven bit runs while maintaining the cuttings quality, bit-wear level, and well stability at a significantly high average ROP of 13 m/h. Another conclusion is that the normal trend methods from sonic logs are the most effective approach when dealing with an abnormally subpressured basin.