Posts

AI Snake Oil is now available to preorder

What artificial intelligence can do, what it can't, and how to tell the diffe...

Tech policy is only frustrating 90% of the time

That’s what makes it worthwhile

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...

On the Societal Impact of Open Foundation Models

Adding precision to the debate on openness in AI

Will AI transform law?

The hype is not supported by current evidence

Generative AI’s end-run around copyright won’t be re...

Output similarity is a distraction

Are open foundation models actually more risky than ...

A policy brief on open foundation models

Model alignment protects against accidental harms, n...

The hand wringing about failures of model alignment is misguided

What the executive order means for openness in AI

Good news on paper, but the devil is in the details

How Transparent Are Foundation Model Developers?

Introducing the Foundation Model Transparency Index

Evaluating LLMs is a minefield

Annotated slides from a recent talk

Reinforcement Learning From Human Feedback (RLHF) Fo...

Reinforcement Learning from Human Feedback (RLHF) has turned out to be the ke...

LLM For Structured Data

It is estimated that 80% to 90% of the data worldwide is unstructured. Howeve...

Strategies For Effective Prompt Engineering

When I first delved into machine learning, prompt engineering seemed like a n...

LLM Evaluation For Text Summarization

Text summarization is a prime use case of LLMs (Large Language Models). It ai...