The purpose of this research paper is to critically analyze the recent claims made by Microsoft researchers on how GPT-4 shows signs of AGI. First, an introduction is given to AGI and GPT-4 and then a brief literature review is conducted on how GPT-4 is showing such signs, according to the Microsoft Research Paper. For the methodology, these claims will be critically analyzed to discover how it is showing such signs or if it is not the implications, challenges and limitations, and the future of AGI, including considering the possibility of newer intelligent conversational bots which could be showing AGI capabilities as well.
Keywords: AGI, GPT-4, Microsoft, Open AI

People desire to live sophisticated lives while enjoying all the amenities in today's modern environment. The advancement of science and technology is accelerating rapidly to satisfy these needs. Internet of Things (IoT) plays an important role in automating a diverse arena, including health monitoring, agricultural irrigation, traffic management, street lighting, classrooms etc. with the help of cutting-edge innovations. Street lights are now operated manually, which waste a significant amount of energy globally and has to be altered. Especially in Sri Lankan, with the country facing an economic crisis, the wastage in the process of street lighting may cause a huge additional loss to the economy. The best approach in plugging this additional expenditure is by implementing a smart street lighting system using Arduino to control the energy wastage. Also, this smart system does not need any humans to operate it as it is a fully automated system as well. The system is implemented in Tinkercad - an online platform allowing users to design and simulate 3D models - and it works perfectly. The system fills the gap of this research as well.
Keywords:Internet of Things (IoT), Tinkercad, Arduino, Street light

A category of artificial intelligence (AI) called ma- chine learning aids machines or educators. Machines make wise judgments based on all the prior facts. Automated learning involves gathering and keeping track of a wealth of data and organizing it into an organized knowledge base for usage in diverse disciplines. By implementing machine learning in the educational setting, instructors can save time in their extracurricular activities. Teachers can, for instance, employ virtual assistants who operate remotely from their homes for their students. This type of support can improve kids’ academic performance and progress, and student performance should be improved. Personalized learning is supported by machine learning in reference to distributing education. The field of education now includes machine learning as a new frontier. It may change not just how instruction is given but also how pupils are encouraged to study well. By giving real-time feedback based on unique student behavior and other characteristics, machine learning promises to give customized in-class instruction. The likelihood of better learning increases as a result. By eliminating biases, machine learning also contributes significantly to evaluations and assessments. Machine learning, one of the most powerful recent technologies, controls interactions between humans and artificial intelligence. As a result, machine learning enables computers to discover unprogrammed underlying information. Additionally, machine learning is a significant predictor. The authors of this work provide readers with having extensive knowledge of machine learning-based applications in the education system. A general introduction to machine learning is made, and its application in education domain in presented.
Keywords:Machine leaning, Supervised learning, Unsupervised learning, Reinforcement learning, Education

Accurately predicting students' academic trajectories is crucial for effective educational interventions. This research introduces a comprehensive predictive model for understanding and forecasting students' progress, leveraging advanced data mining techniques. Specifically, three powerful classifiers—Random Forest, Support Vector Machines (SVM), and Artificial Neural Network (ANN) are employed to explore the intricate dynamics of students' academic journeys. The study places significant emphasis on uncovering patterns related to student absence days, lecturer involvement, and punctuality within the e-learning management system. This study examines the influence of Random Forest, SVM, and ANN on students' educational achievement by utilizing Random Forest, SVM, and ANN. The model proposed here incorporating these classifiers, demonstrates a substantial improvement of up to 12% to 18% in accuracy compared to models lacking these influential features. This research contributes to the field by showcasing the effectiveness of Random Forest, SVM, and ANN in predicting academic trajectories, thereby facilitating targeted interventions and personalized strategies for student success. The findings underscore the importance of leveraging diverse classifiers to comprehensively capture the multifaceted aspects of students' academic progress.
Keywords: Educational Data Mining, Predictive Model, Random Forest, Support Vector Machines, Artificial Neural Network, Academic Trajectories.