A New Way to Make Strategy Happen: Novogain Turns Bottom-Up Behavior into Measurable Results

For decades, organizations have tried to execute strategy through top-down control. Targets are set, KPIs defined, incentives aligned, and results monitored. Yet despite better analytics and ...

AI captures particle accelerator behavior to optimize machine performance

Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...

These 4 critical AI vulnerabilities are being exploited faster than defenders can respond

From prompt injection to deepfake fraud, security researchers say several flaws have no known fix. Here's what to know about them.
Psychology Today

Why the New Artificial Intelligence Is So Powerful

AI became powerful because of interacting mechanisms: neural networks, backpropagation and reinforcement learning, attention, training on databases, and special computer chips.
FinanceFeeds

Crypto Machine Learning Algorithms Explained for Beginners

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...

Arcee's U.S.-made, open source Trinity Large and 10T-checkpoint offer rare look at raw model intelligence

San Francisco-based AI lab Arcee made waves last year for being one of the only U.S. companies to train large language models (LLMs) from scratch and release them under open or partially open source ...
IEEE

Model-Free Offline Reinforcement Learning for Linear Quadratic Control

Abstract: This paper investigates the linear quadratic control problem with both process and measurement noise using reinforcement learning. Instead of requiring a system model or real-time controller ...
Frontiers

Deep Reinforcement Learning for complex hydropower management: evaluating Soft Actor-Critic with a learned system dynamics model

Introduction: Optimizing the operation of interconnected hydropower systems presents significant challenges due to complex non-linear dynamics, hydrological uncertainty, and the need to balance ...
IEEE

Model-Free Reinforcement Learning in Microgrid Control: A Review

Abstract: The global shift toward distributed energy resources (DERs) has accelerated the deployment of microgrids (MGs), introducing unprecedented control challenges that traditional strategies often ...
EurekAlert!

Reinforcement learning world models for catalyst surface reconstruction: state-of-the-art review

This work presents an AI-based world model framework that simulates atomic-level reconstructions in catalyst surfaces under dynamic conditions. Focusing on AgPd nanoalloys, it leverages Dreamer-style ...