Smarter Buildings: Forecasting Energy Demand with AI
A new deep learning framework improves the accuracy of building load forecasting by intelligently fusing historical data and adapting to fluctuating energy usage.
Science
Predicting Machine Lifespans with AI: A New Approach to Industrial Maintenance
A novel deep learning model leverages the power of CNNs, LSTMs, and attention to accurately forecast when industrial equipment will fail, prioritizing safety and minimizing downtime.
The Rise of Agent Skills: Promise and Peril
Reusable skills for AI agents are streamlining automation, but public skill registries are creating new security vulnerabilities.
The Fragile Network: How Declining Redundancy Fuels Systemic Risk
New research reveals that the geometry of global production networks is creating a permanently fragile system increasingly susceptible to cascading failures.
Mapping Crypto Risk: A New Geometric Approach
Researchers are applying techniques from topology to understand the stability of cryptocurrency price movements and improve risk management strategies.
The Algorithmic Pulse: How AI is Shaping Population Health
![Generative AI usage is not uniform across racial and ethnic groups; while Black adults demonstrate the highest rates of application in health-related pursuits (30%) and entertainment (31%), individuals identifying with “Other/2+” ethnicities lead in utilizing the technology for internet searches (54%) and educational purposes (32%), suggesting that assessments of AI exposure require nuanced measurement of <i>how</i> the technology is employed, rather than simply <i>whether</i> it is used-though data from the AmeriSpeak survey ([latex]N=1{,}163[/latex]) indicates some estimates are based on small sample sizes and should be interpreted with caution.](https://arxiv.org/html/2604.14086v1/x2.png)
A new framework proposes applying epidemiological principles to understand and measure the pervasive impact of artificial intelligence on the wellbeing of communities.
Tracing Errors: A New Path to System Reliability
A comprehensive review reveals that combining log analysis with execution trace modeling offers the most effective approach to pinpointing and classifying faults in complex distributed systems.
The Fragile Logic of AI: When Tiny Errors Cause Big Problems
![Despite a vast spectrum of singular values-ranging from [latex]\sigma_1 = 615.3[/latex] to approximately zero-the maximum stable perturbation magnitude remains remarkably consistent at approximately [latex]10^{-{10}}[/latex], demonstrating that instability is a pervasive characteristic of the entire embedding manifold.](https://arxiv.org/html/2604.13206v1/x6.png)
New research reveals that large language models are surprisingly susceptible to numerical instability, potentially leading to unpredictable outputs and systemic failures.
Fighting Fire with Algorithms: A New Approach to Stopping Fake News
Researchers are combining the power of artificial intelligence and social network modeling to develop strategies for curbing the spread of misinformation online.
Predicting IT Chaos: How Machine Learning Tames Change-Related Incidents
A new study demonstrates how machine learning can proactively identify and mitigate IT incident risk stemming from system changes, moving beyond traditional rule-based approaches.