CATEGORY ARCHIVE · PITON-GLOBAL
AI, Robotics & AVs
Articles from the PITON-Global Knowledge Center in this archive.
30 articles
01Articles
Designing a Remote Operations Center for Robots, Drones and Autonomous Vehicles
Robots, delivery rovers, drones and autonomous vehicles still need people watching, guiding and stepping in. This is how…
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Designing Graders for RL Environments That Agents Cannot Game
An agent trained with reinforcement learning learns whatever its grader rewards. If the grader can be tricked, the…
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Building a Coding Agent Benchmark From Real Repositories
Public coding benchmarks are saturating and leaking into training data. Teams building coding agents increasingly need private benchmarks…
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Measuring Hallucination in Vision-Language Models: A Claim-by-Claim Method
Vision-language models describe images fluently, and sometimes describe things that are not there. A claim-by-claim method gives teams…
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Reward Hacking: How to Detect It Before Your Policy Learns It
Policies trained against reward models find their weaknesses fast: longer answers, confident tone, flattering language, tidy formatting. Reward…
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Offline Recommender Evaluation With Human Raters: When Clicks Are Not Enough
Click data says what people did, not what they wanted. Offline evaluation with human raters lets recommendation teams…
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Building a Transaction Categorization Taxonomy That Models and People Can Both Use
Transaction categories power budgeting apps, spend analytics, lending models and fraud detection. A weak taxonomy makes every one…
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Negation and Uncertainty in Clinical Text Annotation: Rules That Keep Healthcare Models Honest
Clinical notes are full of negation, uncertainty and history: “no evidence of,” “rule out,” “family history of.” Annotation…
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Diarization Error Rate Explained: What It Measures, What It Misses and How to Label for It
Diarization error rate is the standard way to score who-spoke-when systems, and it is easy to misread. This…
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How to Screen Developer-Annotators for Code Model Training Data
Code models learn from whoever labels their data. If annotators cannot run, debug and judge code in the…
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Table Annotation Guidelines for Document AI: Cells, Spans, Headers and the Hard Cases
Tables are where document AI fails most visibly: wrong totals, shifted columns, merged headers misread. Clear table annotation…
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Robot Demonstration Data: Why Diversity Beats Volume for Robot Foundation Models
Robot foundation models need real interaction data, and teams often chase episode counts. The evidence from practice is…
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