Google is fundamentally changing how Android handles app updates in version 16. The new approach moves optimization scripts earlier in the installation process, potentially eliminating update delays that frustrate users. This represents a significant improvement in mobile software management.
The Hidden Data Footprint of Intelligent Systems As AI systems evolve from passive responders to active agents that perceive, plan,…
Researchers have developed a novel quantum-inspired algorithm that significantly improves financial risk prediction accuracy. The hybrid approach reportedly outperforms conventional methods by at least 9% across key evaluation metrics.
Financial institutions and investors may soon benefit from a revolutionary new approach to risk prediction that combines quantum computing principles with biological inspiration, according to recent research published in Scientific Reports. The newly developed Quantum-Inspired Chimpanzee Optimization Algorithm with Kernel Extreme Learning Machine (QChOA-KELM) framework reportedly addresses longstanding limitations in computational efficiency and predictive performance that have plagued traditional financial risk models.
German DeepTech firm etalytics has extended its Series A funding to €16 million with Microsoft’s M12 leading the €8 million extension. The AI energy intelligence platform helps industrial clients reduce energy consumption by up to 50% according to company reports. The funding will accelerate global expansion, particularly into the U.S. market.
German energy technology startup etalytics has successfully closed an €8 million Series A extension, bringing its total Series A funding to €16 million, according to recent reports. The Darmstadt-based DeepTech company, which specializes in AI-powered energy intelligence solutions, announced that the extension is led by M12, Microsoft’s Venture Fund, with continued participation from existing investors Alstin Capital, ebm-papst, and BMH.
Researchers are tackling fundamental weaknesses in artificial neural networks through innovative metalearning approaches. The framework provides machines with targeted incentives and practice opportunities to overcome cognitive limitations that have plagued AI development for decades.
Recent breakthroughs in metalearning approaches are addressing longstanding challenges that have limited the capabilities of artificial neural networks compared to human cognition, according to reports in Nature Machine Intelligence. The research focuses on providing machines with both incentives to improve specific skills and opportunities to practice those skills, creating an explicit optimization framework that contrasts with conventional AI training methods.
