Cannabis is now entering a similar inflection point, where algorithms may shape what gets grown, how it’s sold, and who wins in a rapidly growing industry.

By Eric Butterman SHARE From autonomous cars to video games, reinforcement learning (machine learning through interaction ...

New AI model decodes brain signals captured noninvasively via EEG opens the possibility of developing future neuroprosthetics ...

As the International Olympic Committee (IOC) embraces AI-assisted judging, this technology promises greater consistency and ...

AI is searching particle colliders for the unexpected ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

Neel Somani has built a career that sits at the intersection of theory and practice. His work spans formal methods, mac ...

From fine-tuning open source models to building agentic frameworks on top of them, the open source world is ripe with ...

MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to ...

The inversion of the one-dimensional wave spectrum from dual-polarized synthetic aperture radar (SAR) data is performed using machine learning methods, namely Random Forest (RF), eXtreme Gradient ...

Abstract: This study presents a comprehensive survey on Quantum Machine Learning (QML) along with its current status, challenges, and perspectives. QML combines quantum computing and machine learning ...