TL;DR

Apply here for the second iteration of ARENA!

Introduction

We are excited to announce the second iteration of ARENA (Alignment Research Engineer Accelerator), a 6-week ML bootcamp with a focus on AI safety. Our mission is to prepare participants for full-time careers as research engineers in AI safety, e.g. at leading organizations or as independent researchers.

The program will commence on May 22nd, 2023, and will be held at the Moorgate WeWork offices in London. This will overlap with SERI MATS, who are also using these offices. We expect this to bring several benefits, e.g. facilitating productive discussions about AI safety & different agendas, and allowing participants to form a better picture of what working on AI safety can look like in practice.

ARENA offers a unique opportunity for those interested in AI safety to learn valuable technical skills, engage in their own projects, and make open-source contributions to AI safety-related libraries. The program is comparable to MLAB or WMLB, but extends over a longer period to facilitate deeper dives into the content, and more open-ended project work with supervision.

For more information, see our website.

Outline of Content

The 6-week program will be structured as follows:

Chapter 0 - Fundamentals

Before getting into more advanced topics, we first cover the basics of deep learning, including basic machine learning terminology, what neural networks are, and how to train them. We will also cover some subjects we expect to be useful going forwards, e.g. using GPT-3 and 4 to streamline your learning, good coding practices, and version control.

Topics include:

PyTorch basics
CNNs, Residual Neural Networks
Optimization
Backpropagation
Hyperparameter search with Weights and Biases
Model training & PyTorch Lightning

Duration: 5 days

Chapter 1 - Transformers & Mechanistic Interpretability

In this chapter, you will learn all about transformers, and build and train your own. You'll also learn about Mechanistic Interpretability of transformers, a field which has been advanced by Anthropic’s Transformer Circuits sequence, and open-source work by Neel Nanda.

Topics include:

GPT models (building your own GPT-2)
Training and sampling from transformers
TransformerLens
In-context Learning and Induction Heads
Indirect Object Identification
Superposition

Duration: 9 days

Chapter 2 - Reinforcemen

...

AugusteB

AugusteB

AugusteB

AugusteB