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Paul Pokotylo

Paul Pokotylo

Paul Pokotylo is an Account Executive, leading strategic initiatives in accelerating AI development. He specializes in custom data solutions for various business applications that drive value.

RLHF Annotation Services: Training AI Models with Human Feedback

RLHF Annotation Services: Training AI Models with Human Feedback

Traditional language models rely on text patterns. Modern systems need to understand intent, context, and ethics. This requires a three-phase process: basic pretraining, supervised refinement, and reinforcement learning optimization. Each phase integrates expert human judgment to align results with real-world needs. Combining computational power with practical application through precise feedback

Tools and Tips for Remote Collaboration in Annotation Teams

Tools and Tips for Remote Collaboration in Annotation Teams

Remote collaboration in annotation teams has become essential as organizations increasingly rely on distributed workforces. Annotation projects often involve complex datasets that require precise communication and real-time feedback. Clear communication protocols, regular check-ins, and defined roles foster accountability and smooth workflows. Training sessions and quality checks support consistent standards across

Reducing Manual Labeling Effort Through Smart Sample Selection

Reducing Manual Labeling Effort Through Smart Sample Selection

Reducing the manual labeling required in machine learning pipelines has become an increasingly important goal as data continues to scale in volume and complexity. One promising approach to alleviate this burden is strategic data sampling, where only the most informative or influential examples are selected for labeling. This approach, rather

Establishing Performance Benchmarks for Annotation Teams

Establishing Performance Benchmarks for Annotation Teams

A large number of AIs fail due to poor data integrity. This confirms the important role of performance benchmarks in data annotation. Establishing performance benchmarks for annotation teams is imperative. They show annotators' progress on projects and help track their performance. Clear metrics and standards increase the efficiency of

Combining Real and Synthetic Data for Optimal Model Performance

Combining Real and Synthetic Data for Optimal Model Performance

Data integration in AI training involves combining diverse data sets into a single framework supporting robust and generalizable learning. Rather than relying on a single narrow data set, integration allows for including data from sensors, logs, user interactions, knowledge bases, and more. Each data stream typically has its format, structure,

Annotating Data for AR/VR Applications: Creating Immersive Experiences

Annotating Data for AR/VR Applications: Creating Immersive Experiences

Data annotation forms the backbone of the systems that power these technologies in augmented reality (AR) and virtual reality (VR) applications. From object recognition to scene understanding, data annotation ensures that virtual content matches the user's physical environment. The process involves tagging images and videos to identify objects,

Outsourcing vs. In-House Labeling: A Cost-Benefit Analysis

Outsourcing vs. In-House Labeling: A Cost-Benefit Analysis

Data labeling is a fundamental step in machine learning and artificial intelligence that directly affects models' performance. Companies must choose between outsourcing this task to specialized vendors or building internal teams to perform it themselves. Budget, project scope, timeline, and available expertise often influence this decision. While both options