In this post, I explore the evolving world of Language Learning Models (LLMs), considering how they learn, the future of human-LLM conversations, the hallucination problem, compensating data providers, the potential lucrativeness of data annotation, and the advent of a new Marxist struggle.

A survey of how neural networks are currently being used in simulation-based inference routines.

I'm taking some time to explore: Rust, complex systems, crypto.

I left ASAPP after 4+ wonderful years.

What would preeminent 20th century geographer Halford J. Mackinder say about the coming revolution in artificial intelligence and its impact on our current ideological war?

What happens when a post-Trump, reputationally-bruised United States, and improved generative models (the technology behind "deepfakes") collide head-on?

In world of weaponized drones piloted by algorithms, what new strategic opportunities arise?

I'm beginning to write about the intersection of artificial intelligence and geopolitics.

A detailed derivation of Mean-Field Variational Bayes, its connection to Expectation-Maximization, and its implicit motivation for the "black-box variational inference" methods born in recent years.

Deriving the expectation-maximization algorithm, and the beginnings of its application to LDA. Once finished, its intimate connection to variational inference is apparent.