This research introduces an innovative artificial intelligence-driven educational concept designed to optimize self-directed learning through personalized course delivery and automated teaching assistance. The system leverages fine-tuned AI models to create an adaptive learning environment that encompasses customized roadmaps, automated presentation generation, and three-dimensional modeling for complex concept visualization. By integrating real-time virtual assistance for doubt resolution, the platform addresses the immediate educational needs of learners while promoting autonomous learning practices. This study explores the psychological advantages of self-directed learning and demonstrates how AI automation can enhance educational outcomes through personalized content delivery and interactive support mechanisms. The research contributes to the growing field of educational technology by presenting a comprehensive framework that combines automated content generation, visual learning aids, and intelligent tutoring to create an efficient, scalable solution for modern educational needs. Preliminary findings suggest that this approach not only accommodates diverse learning styles but also strengthens student engagement and knowledge retention through its emphasis on self-paced, independent learning methodologies.

We address the main problem of self-learning LLM: the question of what to learn. We propose a self-learning LLM framework that enables an LLM to independently learn previously unknown knowledge through self-assessment of their own hallucinations. Using the hallucination score, we introduce a new concept of Points in The Unknown (PiUs), along with one extrinsic and three intrinsic methods for automatic PiUs identification. It facilitates the creation of a self-learning loop that focuses exclusively on the knowledge gap in Points in The Unknown, resulting in a reduced hallucination score. We also developed evaluation metrics for gauging an LLM's self-learning capability. Our experiments revealed that 7B-Mistral models that have been finetuned or aligned are capable of self-learning considerably well. Our self-learning concept allows more efficient LLM updates and opens new perspectives for knowledge exchange. It may also increase public trust in AI.

AI has classically come in three forms, supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is where AI is given many example scenarios and the right answer for each one (such as images labeled as Cat or Dog). Unsupervised learning has been traditionally where AI learns to group items together by similarity (clustering), without explicit labels. Reinforcement learning is where AIs try out strategies (such as in a game) and attempt to optimize a reward function (such as points in the game). Many commercial AIs are based on supervised learning.

I was at work as a postman. I hated my job, but I had to do it to fund my way through college. Whenever I was on my post walks, I'd listen to interviews of people I admired. One day, I just so happened to be listening to a Bill Gates interview, and someone from the audience asked him what he'd be doing if he hadn't created Microsoft. He responded along the lines of "I'd be testing the limits with Natural Language data," and that he'd be an AI researcher.

MIT researchers aim to improve the quality of life for patients suffering from glioblastoma, the most aggressive form of brain cancer, with a machine-learning model that makes chemotherapy and radiotherapy dosing regimens less toxic but still as effective as human-designed regimens. Machine-learning system determines the fewest, smallest doses that could still shrink brain tumors. MIT researchers are employing novel machine-learning techniques to improve the quality of life for patients by reducing toxic chemotherapy and radiotherapy dosing for glioblastoma, the most aggressive form of brain cancer. Glioblastoma is a malignant tumor that appears in the brain or spinal cord, and prognosis for adults is no more than five years. Patients must endure a combination of radiation therapy and multiple drugs taken every month.

In recent artificial intelligence (AI) research, convolutional neural networks (CNNs) can create artificial agents capable of self-learning. Self-learning autonomous moving objects utilize machine vision techniques based on processing and recognizing objects in digital images. Afterwards, deep reinforcement learning (Deep-RL) is applied to understand and learn intelligent actions and controls. The objective of our research is to study methods and designs on how machine vision and deep machine learning algorithms can be implemented in a virtual world (e.g., a computer game) for moving objects (e.g., vehicles or aircrafts) to improve their navigation and detection of threats in real life. In this paper, we create a framework for generating and using data from computer games to be used in CNNs and Deep-RL to perform intelligent actions. We show the initial results of applying the framework and identify various military applications that may benefit from this research.