A Polish mathematician was left surprised after an AI system solved a problem he had spent 20 years developing.

After years of creating highly specialized software, researchers used supercomputer clusters to finally solve the "100,000-body problem.

Abstract: Real-world constrained multiobjective optimization problems (CMOPs) are prevalent and often come with stringent time-sensitive requirements. However, most contemporary constrained ...

The annotation, recruitment, grounding, display, and won gates determine which content AI engines trust and recommend. Here’s how it works.

So, you want to get better at those tricky LeetCode Python problems, huh? It’s a common goal, especially if you’re aiming for tech jobs. Many people try to just grind through tons of problems, but ...

A new AI framework called THOR is transforming how scientists calculate the behavior of atoms inside materials. Instead of relying on slow simulations that take weeks of supercomputer time, the system ...

Although the potential applications of quantum computing are widespread, a new feasibility study suggests quantum computers ...

The AI adverse event problem nobody is talking about reveals risks in FDA-cleared surgical devices lacking robust clinical trials.

DeepMind’s AlphaProof system solved four out of six problems at the 2024 International Mathematical Olympiad, generating ...

Practical Application: The authors propose QFI-Informed Mutation (QIm), a heuristic that adapts mutation probabilities using diagonal QFI entries. QIm outperforms uniform and random-restart baselines, ...

Abstract: Solving constrained multi-objective optimization problems (CMOPs) is a challenging task due to the presence of multiple conflicting objectives and intricate constraints. In order to better ...

Most of you have used a navigation app like Google Maps for your travels at some point. These apps rely on algorithms that ...