The Age of Machine Learning As Code Has Arrived

The Era of No-Code AI: What You Need to Know

If you are a programmer and you don't feel "special" anymore, you are not alone The post The Era of No-Code AI: What You Need to Know appeared first on Towards Data Science .

Reasoning Beyond Prediction: From Data-Driven to Causal Software Engineering

arXiv:2606. 27960v1 Announce Type: cross Abstract: Software engineering is an intellectually demanding, creative discipline that juggles a web of interdependent tasks to design, build, and assure the quality of increasingly complex systems.

AI and compute

We’re releasing an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3. 4-month doubling time (by comparison, Moore’s Law had a 2-year doubling period)[^footnote-correction].

Vibe Coding Ate My Homework: An evaluation of AI approaches to greenfield software engineering and programming

arXiv:2606. 18293v1 Announce Type: cross Abstract: Thanks to rapid developments in generative AI, we are in the midst of a paradigm shift that may change how we interact with computers forever.

Director of Machine Learning Insights

Machine Learning Experts - Sasha Luccioni

Machine Learning Experts - Margaret Mitchell

On the Relation between Code Quality and Machine Learning Performance: A Large-scale Empirical Study

The study examined 265,363 Kaggle notebooks to explore how code quality relates to machine learning performance. Using Pylint and SonarQube, it found that general Python code quality shows negligible correlation with performance, while ML‑specific violations have a small negative association with performance. Popularity and author expertise do not predict code quality or performance, though competition expertise correlates with better performance and fewer ML‑specific violations.

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.

Scientific computing in the age of agentic AI

A new field report shows how scientists use AI coding agents to modernize scientific computing, accelerating software development and discovery in genomics and beyond.

Director of Machine Learning Insights [Part 4]

Training CodeParrot 🦜 from Scratch