Machine Learning Experts - Margaret Mitchell

Machine Learning Experts - Sasha Luccioni

Machine Learning Experts - Lewis Tunstall

Director of Machine Learning Insights [Part 4]

Director of Machine Learning Insights

Director of Machine Learning Insights [Part 2: SaaS Edition]

Director of Machine Learning Insights [Part 3: Finance Edition]

Mixture of Experts Explained

The Age of Machine Learning As Code Has Arrived

Mixtral of experts

Beyond Point Predictions: A Practical Introduction to Bayesian Neural Networks

The article "Beyond Point Predictions: A Practical Introduction to Bayesian Neural Networks" discusses how Bayesian neural networks enable more informed decision-making by quantifying uncertainty in predictions. It introduces practical aspects of implementing these models and highlights their advantages over traditional point prediction approaches.

Concrete AI safety problems

We (along with researchers from Berkeley and Stanford) are co-authors on today’s paper led by Google Brain researchers, Concrete Problems in AI Safety. The paper explores many research problems around ensuring that modern machine learning systems operate as intended.

The Benchmarking Epistemology: Validity Theory for Evaluating Machine Learning Models

The article discusses how predictive benchmarking—evaluating machine learning models by their performance and ranking—serves as a core method in machine learning research. It argues that benchmark scores only reflect performance on specific datasets and learning problems, and that drawing broader scientific conclusions requires explicit assumptions. By adapting concepts from psychological validity theory, the authors propose validity conditions to make these assumptions clear, and demonstrate their application in two case studies (ImageNet and the Fragile Families Challenge) to illustrate how benchmark results can inform inferences about research progress and limits of predictability.