Posts
What the executive order means for openness in AI
Good news on paper, but the devil is in the details
AI leaderboards are no longer useful. It's time to s...
What spending $2,000 can tell us about evaluating AI agents
AI existential risk probabilities are too unreliable...
How speculation gets laundered through pseudo-quantification
Scientists should use AI as a tool, not an oracle
How AI hype leads to flawed research that fuels more hype
AI Snake Oil is now available to preorder
What artificial intelligence can do, what it can't, and how to tell the diffe...
AI safety is not a model property
Trying to make an AI model that can’t be misused is like trying to make a com...
A safe harbor for AI evaluation and red teaming
An argument for legal and technical safe harbors for AI safety and trustworth...
Adversarial Machine Learning: Defense Strategies
The growing prevalence of ML models in business-critical applications results...
Observability in LLMOps: Different Levels of Scale
Observability is invaluable in LLMOps. Whether we’re talking about pretrainin...
LLM Observability: Fundamentals, Practices, and Tools
Large Language Models (LLMs) have become the driving force behind AI-powered ...
How to Migrate From MLflow to Neptune
MLflow is a framework widely used for its experiment-tracking capabilities, b...
3 Takes on End-to-End For the MLOps Stack: Was It Wo...
As machine learning (ML) drives innovation across industries, organizations s...
