SOTA-AI December Contest

Welcome to the State-of-the-Art AI (SOTA-AI) Monthly Challenge. In an industry flooded with API wrappers and prompt engineers, we are searching for the true builders. SOTA-AI is a student-driven initiative founded by medalists from the International Olympiad in Artificial Intelligence (IOAI). We operate on a simple belief: true AI mastery requires versatility—the ability to handle structured data, natural language, and visual information with equal rigour. This is not a hackathon where you stitch together pre-made services. This is a Kaggle-style engineering marathon designed to test your depth, not just your speed. The Triad: Three Domains, One Engineer To conquer this challenge, you must tackle three distinct tasks. We are testing your ability to generalize your skills across the entire AI landscape. ⚠️ Important Note: The exact problem statements, datasets, and evaluation metrics for the tasks below will be revealed on Day 1 of the challenge, both here on Devpost and on our Discord server. 📊 Task 1: Classical Machine Learning The Domain: Structured / Tabular Data. The Challenge: It all starts here. You will face a raw dataset requiring extensive feature engineering, handling of missing values, and the application of statistical learning models. This is about mathematical intuition, not just importing libraries. The Goal: Prove that you understand the fundamentals—Regression, Classification, and the bias-variance tradeoff—before you attempt deep learning. 💬 Task 2: Natural Language Processing (NLP) The Domain: Text & Sequence Data. The Challenge: Move beyond simple API calls. You will be tasked with building, fine-tuning, or optimizing models that understand context and semantics. Whether the task involves sentiment analysis or sequence generation, you must own the architecture. The Goal: Demonstrate your technical understanding of Embeddings, RNNs, or Transformer attention mechanisms. 👁️ Task 3: Computer Vision (CV) The Domain: Image & Pixel Data. The Challenge: Teach a machine to "see." You will work with image datasets to perform classification, segmentation, or object detection. You will need to handle data augmentation and architecture selection. The Goal: Showcase your grasp of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs).