Aibek Musaev

This demo presents KimBilet.com, an educational platform that utilizes generative AI to create personalized educational content on demand. Catering to high-school and college students, instructors, job seekers, and lifelong learners, the system generates customized courses based on user prompts, covering any topic of interest. Each course may include a sequence of AI-created lessons and quizzes, providing detailed feedback for every quiz option to enhance understanding. The platform supports intuitive navigation through keyboard shortcuts and allows users to jump between course items seamlessly. It also maintains a history of completed quizzes to help users track their learning progress. Future enhancements include topic suggestions based on past interests, support for coding exercises, and multilingual support. This demo will showcase how KimBilet.com leverages AI to offer adaptive learning experiences, engage attendees through interactive exploration, and discuss its potential applications in educational settings. Participants will gain insights into integrating AI-driven tools into teaching and learning processes to address diverse educational needs.

The physical world evolves. The cyber world evolves and grows with big data, with social media as a major component of information growth. Classic ML models are limited by their static training data with implicit Complete and Timeless Knowledge assumptions. In an evolving world, static training data suffer from knowledge obsolescence due to truly novel timely information. Knowledge obsolescence introduces a widening distance between static ML models and the evolving world, called cyber-physical gap. Periodic retraining of new models may restore their accuracy temporarily, but subsequently their performance will deteriorate with widening cyber-physical gap. Knowledge obsolescence affects statically trained models of any size, including LLMs. Two major research challenges arise from cyber-physical gap: (1) collection and incorporation of space-time aware ground truth training data, and (2) understanding and capturing of the varying speed of information and knowledge evolution when the physical and cyber worlds evolve.

Document classification or document categorization is one of the most studied areas in computer science due to its importance. The problem is to assign a document using its text to one or more classes or categories from a predefined set. We propose a new approach for fast text classification using randomized explicit semantic analysis (RS-ESA). It is based on a state of the art approach for word sense disambiguation based on Wikipedia, the largest encyclopedia in existence. Our method reduces Wikipedia repository using a random sample approach resulting in a throughput, which is an order of magnitude faster than the original explicit semantic analysis. RS-ESA approach has been implemented as part of the LITMUS project due to a need in classifying data from Social Media into relevant and irrelevant items with respect to landslide as a natural disaster. We demonstrate that our approach achieves 96% precision when classifying Social Media landslide data collected in December 2014. We also demonstrate the genericity of the proposed approach by using it for separating factual texts from fictional based on Wikipedia articles and fan fiction stories, where we achieve 97% in precision.