Introduction to Deep Evidential Regression for Uncertainty Quantification

Researchers at Tilde Research have released Aurora, a new optimizer for training neural networks that addresses a structural flaw in the widely-used Muon optimizer. The flaw quietly kills off a significant fraction of MLP neurons during training and keeps them permanently dead. Aurora comes with a 1.1B parameter pretraining experiment, a new state-of-the-art result on […] The post Tilde Research Introduces Aurora: A Leverage-Aware Optimizer That Fixes a Hidden Neuron Death Problem in Muon appeared first on MarkTechPost.

A software developer claims to have reverse-engineered Google DeepMind's SynthID system, showing how AI watermarks can be stripped from generated images or manually inserted into other works. A claim that, according to Google, isn't true. The developer, going by the username Aloshdenny, has open-sourced their work on GitHub and documented his process, claiming all it required was 200 Gemini-generated images, signal processing, and "way too much free time." A little weed also seemed to help. "No neural networks. No proprietary access," Aloshdenny said on Medium. "Turns out if you're unemployed and average enough 'pure black' AI-generated im … Read the full story at The Verge.

AI is consuming staggering amounts of energy—already over 10% of U.S. electricity—and the demand is only accelerating. Now, researchers have unveiled a radically more efficient approach that could slash AI energy use by up to 100× while actually improving accuracy. By combining neural networks with human-like symbolic reasoning, their system helps robots think more logically instead of relying on brute-force trial and error.