LLM
Researchers propose semantic faithfulness and entropy production measures as novel approaches to detect and manage hallucinations in large language models, advancing AI content reliability.
face detection
Ant International claims top honors at NeurIPS competition focused on fairness in AI face detection, addressing critical bias challenges in systems used for identity verification and deepfake detection.
AI Agents
Despite impressive demos, AI coding agents struggle with brittle context windows, broken refactors, and missing operational awareness. Here's why these technical limitations matter.
Machine Learning
Master the fundamentals of L1 (Lasso) and L2 (Ridge) regularization techniques that prevent overfitting in machine learning models, from deepfake detectors to video generation systems.
AI detection
Learn the technical methods and tools used to identify AI-generated text, images, and media as synthetic content becomes increasingly sophisticated and harder to distinguish from human-created work.
AI Politics
New research reveals AI systems will soon craft personalized political messages at scale, raising urgent questions about synthetic media in democracy and the need for content authenticity measures.
AI Security
Data poisoning threatens AI model integrity by corrupting training data. Learn attack vectors, detection methods, and defense strategies for protecting ML systems.
deepfake detection
Monash University partners with international institutions to develop advanced deepfake detection methods and combat AI-driven misinformation across digital platforms.
AI governance
New research presents a framework for automatically translating natural language AI policies into machine-executable rules, enabling real-time governance enforcement for AI systems.
AI detection
Researchers introduce DAMASHA, a segmentation-based approach to detect AI-generated content in mixed texts while providing human-interpretable explanations for its decisions.
AI Security
IBM's open-source ART framework lets developers systematically attack their own AI models to find vulnerabilities before bad actors do. Here's why robustness testing matters.
transformers
Deep technical comparison of transformer and mixture of experts architectures, exploring how MoE models achieve computational efficiency while maintaining performance in modern AI systems including video generation.