AI
Accuracy isn’t the same as reliability. Meet PaTAS—a “trust meter” that runs in parallel with neural networks to quantify how much you should trust each prediction, using Subjective Logic. * How it works: PaTAS adds Trust Nodes and Trust Functions that propagate trust about inputs, parameters, and activations. A
ReinforcementLearning
Reinforcement learning (RL) wins in games and simulators—but deploying it on real products is a different story. Gabriel Dulac‑Arnold, Daniel Mankowitz, and Todd Hester outline nine must-solve challenges before RL can safely power real-world systems. * Safety & constraints: avoid harmful actions while learning. * Sample efficiency: learn from limited,
AI
Predictive maintenance means fixing equipment before it fails. But in the auto industry, messy maintenance logs—full of typos, missing fields, near-duplicates, and wrong dates—can derail the machine-learning models that power those predictions. This study tests large language model (LLM) agents as smart cleaners for those logs. The team
AI
Struggling to compare or improve your automations? Opus offers a simple, quantitative way to score and optimize any Workflow—so you can pick what works, fix what doesn’t, and waste fewer resources. What’s inside * Opus Workflow Reward: a probabilistic score that blends likelihood of success, resource usage, and
AgenticAI
What is "Agentic AI" really? Sebastian Benthall and Andrew Clark argue it’s best seen as a software delivery mechanism—like SaaS—that lets an application work autonomously inside complex enterprises. * Applications first: Agentic AI systems should be judged as products, not as foundation models. * Validate with users: