CATEGORY ARCHIVE · PITON-GLOBAL

AI, Robotics & AVs

Articles from the PITON-Global Knowledge Center in this archive.

30 articles
01Articles
AI, ROBOTICS & AVS 8-min read 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… Read article → AI, ROBOTICS & AVS 7-min read 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… Read article → AI, ROBOTICS & AVS 7-min read 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… Read article → AI, ROBOTICS & AVS 7-min read 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… Read article → AI, ROBOTICS & AVS 7-min read 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… Read article → AI, ROBOTICS & AVS 7-min read 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… Read article → AI, ROBOTICS & AVS 7-min read 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… Read article → AI, ROBOTICS & AVS 7-min read 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… Read article → AI, ROBOTICS & AVS 7-min read 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… Read article → AI, ROBOTICS & AVS 7-min read 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… Read article → AI, ROBOTICS & AVS 7-min read 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… Read article → AI, ROBOTICS & AVS 7-min read 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… Read article →
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