Startup presents a project aimed at exploring the capabilities of artificial intelligence (AI) through advanced machine learning techniques and deep neural networks. The developed mini‑app on the Telegram platform combines computer vision algorithms, image processing, and gamification, creating a unique experimental format for user interaction.
Scientific and Technical Foundation of the Project
At the core of the project is the application of Convolutional Neural Networks (CNNs), which utilize optimization algorithms such as Stochastic Gradient Descent (SGD) with adaptive methods (e.g., Adam), along with regularization techniques like dropout and L2 normalization. By employing transfer learning and fine-tuning on pre-trained models (for example, ResNet and Inception), the system demonstrates high effectiveness in object recognition even under challenging conditions such as complex backgrounds, variable lighting, and diverse viewing angles.
Incoming images are processed through a multi-stage pipeline architecture that includes preliminary normalization, data augmentation, convolution using nonlinear activation functions (ReLU, Leaky ReLU), and pooling layers to reduce the dimensionality of the feature space. Thanks to the use of backpropagation and dynamic parameter tuning, the system achieves high precision and recall while minimizing the risk of overfitting.
Gamified Experimental Platform
The innovation of the project lies not only in utilizing modern computer vision algorithms but also in integrating them into a gamified format. Users interacting with the app participate in an experiment to locate specific objects in the real world, representing a synthesis of virtual and physical realities. Upon successful object detection, verification protocols are activated via comparative embedding analysis, ensuring an accurate match between the object and the query and automatically initiating the crediting of digital tokens.
This platform serves as an experimental environment for research in reinforcement learning, where user interaction with the system enables the collection and processing of large volumes of data for further retraining and optimization of AI algorithms.
Perspectives and Directions for Future Research
The launch of the mini‑app opens new horizons in interdisciplinary research, merging fields such as artificial intelligence, natural language processing, image analysis, and cybernetic modeling. The empirical data gathered will contribute to the development of new neural network architectures and the improvement of existing methods for feature extraction and data augmentation. We are confident that the synergy between a scientific approach and a gamified format will serve as a catalyst for creating adaptive systems capable of real-time self-learning.