· ai
AI models block 87% of single attacks, but just 8% when attackers persist
One malicious prompt gets blocked, while ten prompts get through. That gap defines the difference between passing benchmarks and withstanding real-world attacks...
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AI-native 6G: From networks to intelligence fabrics
“Network language models” will coordinate complex interactions among intelligent components, computational infrastructure, access points, data centers, and more...
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[Paper] HERMES: Heterogeneous Application-Enabled Routing Middleware for Edge-IoT Systems
The growth of the Internet of Things has enabled a new generation of applications, pushing computation and intelligence toward the network edge. This trend, how...
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Learning, Hacking, and Shipping ML
Vyacheslav Efimov on AI hackathons, data science roadmaps, and how AI meaningfully changed day-to-day ML Engineer work The post Learning, Hacking, and Shipping...
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[Paper] Integrating Artificial Intelligence and Mixed Integer Linear Programming: Explainable Graph-Based Instance Space Analysis in Air Transportation
This paper analyzes the integration of artificial intelligence (AI) with mixed integer linear programming (MILP) to address complex optimization challenges in a...
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OpenAGI emerges from stealth with an AI agent that it claims crushes OpenAI and Anthropic
A stealth artificial intelligence startup founded by an MIT researcher emerged this morning with an ambitious claim: its new AI model can control computers bett...
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[Paper] Revisiting Direct Encoding: Learnable Temporal Dynamics for Static Image Spiking Neural Networks
Handling static images that lack inherent temporal dynamics remains a fundamental challenge for spiking neural networks (SNNs). In directly trained SNNs, static...
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[Paper] Current Challenges of Symbolic Regression: Optimization, Selection, Model Simplification, and Benchmarking
Symbolic Regression (SR) is a regression method that aims to discover mathematical expressions that describe the relationship between variables, and it is often...
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[Paper] Morphling: Fast, Fused, and Flexible GNN Training at Scale
Graph Neural Networks (GNNs) present a fundamental hardware challenge by fusing irregular, memory-bound graph traversals with regular, compute-intensive dense m...
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[Paper] In-context Inverse Optimality for Fair Digital Twins: A Preference-based approach
Digital Twins (DTs) are increasingly used as autonomous decision-makers in complex socio-technical systems. Their mathematically optimal decisions often diverge...
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Why AI Alignment Starts With Better Evaluation
You can’t align what you don’t evaluate The post Why AI Alignment Starts With Better Evaluation appeared first on Towards Data Science....
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[Paper] Parallel Delayed Memory Units for Enhanced Temporal Modeling in Biomedical and Bioacoustic Signal Analysis
Advanced deep learning architectures, particularly recurrent neural networks (RNNs), have been widely applied in audio, bioacoustic, and biomedical signal analy...