Long-form analysis on AI governance, data science, and financial technology.
The Federal Reserve's November 2025 Statement of Supervisory Operating Principles signals a seismic shift — from checkbox compliance to material risk. Here's what changed and why it matters.
A comprehensive framework for analyzing open-source GenAI across near, mid, and long-term development stages — and why the benefits generally outweigh the risks when governance keeps pace.
LLMs used as evaluators show an average 40% bias in their outputs and a 49.6% RBO score misalignment with human preferences. The COBBLER benchmark quantifies exactly how and where these biases emerge.
An EDA of 2007–2011 lending data to identify the driving factors behind loan defaults — amount-to-income ratios, revolving utilisation, derogatory records, and loan purpose all tell a story.
Regulatory frameworks, model risk, and what compliance means in the age of large language models.
The Federal Reserve's November 2025 Statement of Supervisory Operating Principles signals a seismic shift — from checkbox compliance to material risk. Here's what changed and why it matters.
A comprehensive framework for analyzing open-source GenAI across near, mid, and long-term development stages — and why the benefits generally outweigh the risks when governance keeps pace.
LLMs used as evaluators show an average 40% bias in their outputs and a 49.6% RBO score misalignment with human preferences. The COBBLER benchmark quantifies exactly how and where these biases emerge.
Technical deep-dives on machine learning, NLP, and applied statistical methods.
Fast.ai uncovered something strange in LLM fine-tuning: training loss dropped suddenly after just one pass through the data — suggesting models can memorize inputs almost immediately. Here's what it means.
Re-training LLMs from scratch when new data arrives is prohibitively expensive. Three simple strategies — LR re-warming, LR re-decaying, and minimal data replay — match the performance of full re-training at a fraction of the cost.
Traditional ensemble methods fail when correct answers are in the minority. AoR introduces hierarchical reasoning chain evaluation and dynamic sampling to fix this — and consistently outperforms standard approaches.
xLSTM revisits the classic LSTM architecture with exponential gating and new memory structures — then scales it to 300B tokens. The results are more competitive than most expected.
LLM agents hit 94% success on basic web tasks — but drop to 25% on compositional tasks that combine multiple steps. The CompWoB benchmark exposes exactly where and why this happens.
Gradio lets you wrap any Python function in a browser UI in under 10 lines. Here's how the Interface class works, what components are available, and why it's the fastest way to build an ML proof of concept.
A practical walkthrough of building regularised regression models for house price prediction — from raw data preprocessing to comparing Ridge and Lasso, with residual diagnostics to validate the result.
End-to-end analyses of real financial and business problems.
An EDA of 2007–2011 lending data to identify the driving factors behind loan defaults — amount-to-income ratios, revolving utilisation, derogatory records, and loan purpose all tell a story.
How do you detect fraudulent users at scale when they look like everyone else? A walkthrough of using search-to-communication ratios, geographic patterns, and temporal behaviour to surface bot activity.
