Acknowledgments We are deeply grateful to Sandhini Agarwal, Daniela Amodei, Dario Amodei, Tom Brown, Jeff Clune, Steve Dowling, Gretchen Krueger, Brice Menard, Reiichiro Nakano, Aditya Ramesh, Pranav Shyam, Ilya Sutskever and Martin Wattenberg. Author Contributions Gabriel Goh: Research lead. Gabriel Goh first discovered multimodal neurons, sketched out the project direction and paper outline, and did […]

Contents This article is part of the Differentiable Self-organizing Systems Thread, an experimental format collecting invited short articles delving into differentiable self-organizing systems, interspersed with critical commentary from several experts in adjacent fields. Self-classifying MNIST Digits Adversarial Reprogramming of Neural Cellular Automata Neural Cellular Automata (NCA We use NCA to refer to both Neural Cellular

This article is part of the Circuits thread, an experimental format collecting invited short articles and critical commentary delving into the inner workings of neural networks. Curve Circuits Branch Specialization Introduction The problem of understanding a neural network is a little bit like reverse engineering a large compiled binary of a computer program. In this

Author Contributions As we mentioned in Curve Detectors, our first investigation into curve neurons, it’s hard to separate author contributions between different papers in the Circuits project. Much of the original research on curve neurons came before we decided to separate the publications into the behavior of curve neurons and how they are built. In

This article is part of the Circuits thread, an experimental format collecting invited short articles and critical commentary delving into the inner workings of neural networks. Naturally Occurring Equivariance in Neural Networks Curve Circuits Introduction Some of the neurons in vision models are features that we aren’t particularly surprised to find. Curve detectors, for example,

This article is part of the Circuits thread, an experimental format collecting invited short articles and critical commentary delving into the inner workings of neural networks. Curve Detectors High-Low Frequency Detectors Contents Convolutional neural networks contain a hidden world of symmetries within themselves. This symmetry is a powerful tool in understanding the features and circuits

Contents In this article, we apply interpretability techniques to a reinforcement learning (RL) model trained to play the video game CoinRun . Using attribution combined with dimensionality reduction as in , we build an interface for exploring the objects detected by the model, and how they influence its value function and policy. We leverage this

Computing has changed how people communicate. The transmission of news, messages, and ideas is instant. Anyone’s voice can be heard. In fact, access to digital communication technologies such as the Internet is so fundamental to daily life that their disruption by government is condemned by the United Nations Human Rights Council . But while the

Thread: Differentiable Self-organizing Systems How can we construct robust, general-purpose self-organising systems? Self-organisation is omnipresent on all scales of biological life. From complex interactions between molecules forming structures such as proteins, to cell colonies achieving global goals like exploration by means of the individual cells collaborating and communicating, to humans forming collectives in society such

Contents This article is part of the Differentiable Self-organizing Systems Thread, an experimental format collecting invited short articles delving into differentiable self-organizing systems, interspersed with critical commentary from several experts in adjacent fields. Growing Neural Cellular Automata Self-Organising Textures Growing Neural Cellular Automata demonstrated how simple cellular automata (CAs) can learn to self-organise into complex