Dynamically Typed

Distill: Multimodal Neurons

Distill #1: Multimodal Neurons in Artificial Neural Networks by Goh et al. (2021), which investigates CLIP, OpenAI’s multimodal neural network that learned to match images on the internet to text snippets that surround them. (Probably) unlike older image classification models, CLIP has neurons that “light up” for high-level concepts that were never explicitly part of any classification dataset. “These neurons don’t just select for a single object. They also fire (more weakly) for associated stimuli, such as a Barack Obama neuron firing for Michelle Obama or a morning neuron firing for images of breakfast.” The article has deep dives into three neuron families : (1) the Region Neurons family (like neurons for the USA or for Europe; these links take you to the neurons’ pages on OpenAI Microscope); (2) the Person Neurons family (including Lady Gaga and Ariana Grande); and (3) the Emotion Neurons family (including sleepy and happy). It also highlights a baker’s dozen other families, from holidays and religions to brands and fictional universes. There’s even an LGBTQ+ neuron that responds to things like rainbow flags and the word “pride”! Beyond this exploration, the article looks at how these abstractions in CLIP can be used: for understanding language, emotion composition, and typographic attacks. The authors also note that “CLIP makes it possible for end-users to ‘roll their own classifier’ by programming the model via intuitive, natural language commands — this will likely unlock a broad range of downstream uses of CLIP-style models.” Sound familiar? I wonder how long it’ll take until OpenAI launches a v2 of their API that uses CLIP (+ DALL·E?) for image processing and generation the way v1 uses GPT-3 for text.