Gun violence is a multifaceted issue that continues to pose significant challenges in the United States. Each day 12 children die from gun violence in America. Another 32 are shot and injured, Since Columbine in 1999, more than 338,000 students in the U.S. have experienced gun violence at school and who survived facing mental health problems. Gun violence in schools refers to incidents involving the use of firearms that result in injury or death within educational institutions in the United States. It is a tragic and concerning issue that has gained significant attention due to its impact on the safety and well-being of students, teachers, and the broader community. Since we are developing in terms of science, technology and even planning to send people over Mars, but why can't we control or even stop gun violence using advance Artificial intelligence using thermal camara before valuable life lost. In this paper we would like to provide end to end solution and architecture, innovative idea using AI.

The paper “Large Language Models Bias Issues Solving Through SDRT” discusses the challenges and ethical concerns posed by large language models (LLMs) such as GPT-3 and GPT-4 in the realm of natural language processing (NLP) and artificial intelligence research. It proposes a solution in the form of Segmented Discourse Representation Theory (SDRT) to address these challenges. By integrating SDRT into existing transformer models and incorporating it into both encoders and decoders, the paper aims to reduce bias, enhance semantic understanding, and foster more meaningful and transparent conversations. This approach recognizes the importance of responsible LLM development and the need for solutions to mitigate issues like misinformation, biased content, and lack of contextual understanding. Through its technical details and architectural improvements, the paper contributes to the ongoing discourse on enhancing the capabilities and ethical use of large language models in complex NLP environments.

This study introduces a groundbreaking idea: using swallowable sensor capsules with augmented reality (AR) to conduct real-time, high-resolution examinations of the human body. These capsules, ingested like pills, transmit data as they pass through the body. Physicians can then use AR headsets to view this data as a 3D model of the organ or tissue being examined, along with sensor readings and a miniature endoscopic view. While there are technical challenges to overcome, this innovation has the potential to revolutionize non-invasive examinations of the digestive tract and internal cavities, improving the diagnosis of conditions like inflammation, infections, and cancers. With further technological development, swallowable sensor capsules with AR integration could become a valuable complement to traditional medical imaging methods, offering dynamic 3D visualizations of the body’s inner workings.