Machine Learning
280 articles in this category (Page 8 of 12)
TII Abu-Dhabi Released Falcon H1R-7B: A New Reasoning Model Outperforming Others in Math and Coding
Technology Innovation Institute (TII) released Falcon-H1R-7B, a 7B parameter model achieving performance comparable to 14B-47B models in math, code, and reasoning benchmarks.
Generative Simulation Benchmarking for precision oncology clinical workflows with inverse simulation verification
A novel methodology combining generative simulation and inverse verification addresses the limitations of traditional AI benchmarking in oncology, improving clinical decision support.
Self-Supervised Temporal Pattern Mining for Wildfire Evacuation Logistics Networks Under Real-Time Policy Constraints
This article details a novel approach to wildfire evacuation logistics, leveraging self-supervised learning to improve adaptability to changing conditions, achieving a more robust system than traditional supervised methods.
Self-Supervised Temporal Pattern Mining for circular manufacturing supply chains with embodied agent feedback loops
A novel system combining self-supervised learning and embodied agents achieves a 42% improvement in predicting component return volumes in circular manufacturing.
A Coding Implementation on Building Self-Organizing Zettelkasten Knowledge Graphs and Sleep-Consolidation Mechanisms
This tutorial demonstrates building a “Zettelkasten” memory system using Gemini, achieving dynamic knowledge graph organization and sleep-based memory consolidation.
Stanford & Harvard Paper Decodes Agentic AI's Demo-vs-Reality Gap
A new paper from Stanford, Harvard, UC Berkeley, and Caltech proposes a unified framework for understanding adaptation in Agentic AI systems, explaining why they often excel in demos but struggle in real-world applications.
Meta-Optimized Continual Adaptation for autonomous urban air mobility routing with ethical auditability baked in
This article details a framework, MOCA-E², for autonomous urban air mobility routing that achieves a 37% improvement in delivery time while incorporating ethical considerations and auditability.
QConAI: Balancing Probabilistic and Deterministic Systems for Reliable Agentic AI
Aaron Erickson at QCon AI NYC 2025 advocated for treating agentic AI as an engineering challenge, emphasizing the need for deterministic systems to constrain probabilistic models and improve reliability.