Model Drift, Bias, and Explainability: Why AI Risk Gets More Complicated in Practice

Examining the Limitations of Transformer Models and the Gap to Human-Level AI

The article presents a skeptical perspective on claims about imminent Artificial General Intelligence (AGI), arguing that current transforme

Research on AI Failure Modes: How Misalignment Scales with Model Intelligence and Task Complexity

This research paper examines how AI system failures scale with model intelligence and task complexity, exploring whether failures manifest a

Study Shows Biased Feeds Can Steer AI Decisions, Especially in Smaller Models

This article discusses how biased data feeds can steer AI agents' decisions, particularly affecting smaller models more than larger ones. It

The Growing Need for Explainable AI in Large Language Models

The article discusses the growing importance of explainable AI (XAI) as large language models like ChatGPT and Gemini become more powerful a

When Capability Meets Consequence: How Business Risk, Not Technology, Dictates AI's Real Labor Market Impact

Why Enterprise AI Fails: The Gap Between Model Capability and Reliability Engineering

The article argues that enterprise AI adoption is failing not because the models lack capability, but because organizations lack the reliabi