The 70% factuality ceiling: why Google’s new ‘FACTS’ benchmark is a wake-up call for enterprise AI

According to the initial results, no model—including Gemini 3 Pro, GPT-5, or Claude 4.5 Opus—managed to crack a 70% accuracy ...
Microsoft

nnScaler: Constraint-Guided Parallelization Plan Generation for Deep Learning Training

With the growing model size of deep neural networks (DNN), deep learning training is increasingly relying on handcrafted search spaces to find efficient parallelization execution plans. However, our ...
IEEE

Evaluating Deep Learning Models for Brain Tumor Detection Utilizing Transfer Learning and Fine-Tuning Approach

Abstract: Brain tumor identification is a significant task in medical imaging, necessitating precise and fast diagnostic methods to enhance patient outcomes. Despite advances in medical imaging ...
IEEE

An Efficient Synchronous Training Integrated Model for Driving Decision-Making Based on Deep Reinforcement Learning

Abstract: Deep reinforcement learning (DRL) is a promising way to develop autonomous driving decision-making models. However, poor driving decisions and low sample efficiency for multiple DRL coupled ...
TechAnnouncer

Mastering Generative AI Training: Your Essential Guide for 2026

Repeatable training means training the AI over and over again in a way that you can do the exact same steps each time. This ...