That admission is what some in the field call recursive self-improvement (RSI), the point at which large language models ...

Combinatorial optimization problems are often encountered in real-world applications, including logistics, scheduling and ...

Abstract: Many existing surrogate-assisted optimization algorithms are limited to designing antennas with continuous variables only. However, numerous challenges emerge when tackling antenna ...

UTokyo and Kubota develop a drone potato yield prediction method combining multispectral imagery, AI, and growth models.

Solving complex optimization problems is central to many modern technologies, from logistics and financial modeling to chip ...

Large-scale recommendation systems are becoming harder to improve because they no longer operate as isolated models. Modern ...

As energy companies push AI deeper into industrial operations, success increasingly depends on governance, trusted data, and ...

In recent years, the frequency of weather-related natural disasters—cyclones, torrential rains, floods—has increased as a consequence of global warming. These disasters cause billions of dollars in ...

Arbor separates strategy from execution using isolated git worktrees, so engineering teams can finally trace which optimization actually moved the needle.

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...