Data annotation & labeling blog.

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Maxim Dupont

Maxim Dupont is the founder of LabelVisor, a scientist & software engineer. His hobby of writing tech reviews has become his new way of life. His belief is that together we can achieve the best world.

Symbolic Reasoning for AI Decision Validation

Symbolic Reasoning for AI Decision Validation

AI is increasingly being used for decision-making in areas where accuracy and trust are important. With the emergence of such AI systems, the need for methods that allow validating the decision-making process, detecting logical contradictions, and ensuring the explainability of results is growing. One key area that addresses this problem

Logic-Based Neural Network Architectures

Logic-Based Neural Network Architectures

Most Artificial Intelligence systems fail to explain their decisions in critical scenarios. This gap between computational power and human understanding has sparked a revolution in AI design. Consequently, systems have emerged that integrate symbolic reasoning with adaptive learning, ensuring the transparency of decisions without losing accuracy. Unlike traditional approaches, which

Knowledge Graph-Enhanced Model Training for AI

Knowledge Graph-Enhanced Model Training for AI

Unlike traditional neural networks, which work only with statistical regularities, models enriched with knowledge graphs rely on structured connections between concepts. This enables them to perform more in-depth semantic analysis, improve the accuracy of predictions, and help avoid logical inconsistencies. During the training process, the knowledge graph serves as a

Mobile Annotation Apps for Field Data Collection

Mobile Annotation Apps for Field Data Collection

Today, artificial intelligence is fueled not by studio or laboratory data but by reality. To train high-precision models, whether for autonomous vehicles or agricultural monitoring systems, field data is required. That is, information is collected in an unpredictable and dynamic world. This is where the question arises: How to transform

Cloud-Based Annotation: AWS, Google Cloud, Azure

Cloud-Based Annotation: AWS, Google Cloud, Azure

Modern machine learning systems require not only large amounts of data, but also accurate, structured annotations. To achieve this on an industrial scale, companies are transitioning to cloud annotation, a model where the annotation process occurs in the cloud. Utilizing cloud platforms such as AWS, Google Cloud, and Azure ML

Embodied AI Data Collection for Robotics

Embodied AI Data Collection for Robotics

Embodied AI in robotics is a direction in which AI is combined with the physical body of a robot, enabling it to learn and act in the real world. Unlike traditional models that work only with visual or textual data, embodied AI perceives the environment through sensors, cameras, LiDAR, or

Attribution of Functions for AI-Based Business Intelligence Models

Attribution of Functions for AI-Based Business Intelligence Models

Traditional business intelligence tools struggle to decipher why models prioritize specific data patterns over others. This is where modern interpretation techniques become essential for reliable AI. Sophisticated analysis techniques reveal how specific variables influence outcomes. Global impact metrics quantify the overall role of each data point in learning, while instance-level