The Nonlinear Library EA - AI Alignment Research Engineer Accelerator (ARENA): call for applicants by TheMcDouglas
Nov 7, 2023
11:53
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: AI Alignment Research Engineer Accelerator (ARENA): call for applicants, published by TheMcDouglas on November 7, 2023 on The Effective Altruism Forum.
TL;DR
Apply here for the third iteration of ARENA (Jan 8th - Feb 2nd)!
Introduction
We are excited to announce the third iteration of ARENA (Alignment Research Engineer Accelerator), a 4-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 run from January 8th - February 2nd 2024[1], and will be held at the offices of the London Initiative for Safe AI. These offices are also being used by several safety orgs (BlueDot, Apollo, Leap Labs), as well as the current London MATS cohort, and several independent researchers. 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, work 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 4-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.Note - participants can optionally not attend the program during this week, and instead join us at the start of Chapter 1, if they'd prefer this option
and if we're confident that they are already comfortable with the material in this chapter.
Topics include:
PyTorch basics
CNNs, Residual Neural Networks
Optimization (SGD, Adam, etc)
Backpropagation
Hyperparameter search with Weights and Biases
GANs & VAEsDuration: 5 days
Chapter 1 - Transformers & Interpretability
In this chapter, you will learn all about transformers, and build and train your own. You'll also study LLM interpretability, a field which has been advanced by Anthropic's Transformer Circuits sequence, and open-source work by Neel Nanda. This chapter will also branch into areas more accurately classed as "model internals" than interpretability, e.g. recent work on steering vectors.
Topics include:
GPT models (building your own GPT-2)
Training and sampling from transformersTransformerLensIn-context Learning and Induction HeadsIndirect Object IdentificationSuperpositionSteering VectorsDuration: 5 days
Chapter 2 - Reinforcement Learning
In this chapter, you will learn about some of the fundamentals of RL, and work with OpenAI's Gym environment to run their own experiments.
Topics include:
Fundamentals of RLVanilla Policy GradientProximal Policy Gradient
RLHF (& finetuning LLMs with RLHF)
Gym & Gymnasium environmentsDuration: 5 days
Chapter 3 - Paper Replications
We will conclude this program with paper replications, where participants will get guidance and mentorship while they replicate a paper containing material relevant to this course. This should draw on much of the skills and knowledge participants will have accumulated over the last 3 weeks.Duration: 5 days
Below is a diagram of the curriculum as a whole, and the dependencies between sections. Note that this may change slightly in the lead-up to the program.Here is som...
TL;DR
Apply here for the third iteration of ARENA (Jan 8th - Feb 2nd)!
Introduction
We are excited to announce the third iteration of ARENA (Alignment Research Engineer Accelerator), a 4-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 run from January 8th - February 2nd 2024[1], and will be held at the offices of the London Initiative for Safe AI. These offices are also being used by several safety orgs (BlueDot, Apollo, Leap Labs), as well as the current London MATS cohort, and several independent researchers. 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, work 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 4-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.Note - participants can optionally not attend the program during this week, and instead join us at the start of Chapter 1, if they'd prefer this option
and if we're confident that they are already comfortable with the material in this chapter.
Topics include:
PyTorch basics
CNNs, Residual Neural Networks
Optimization (SGD, Adam, etc)
Backpropagation
Hyperparameter search with Weights and Biases
GANs & VAEsDuration: 5 days
Chapter 1 - Transformers & Interpretability
In this chapter, you will learn all about transformers, and build and train your own. You'll also study LLM interpretability, a field which has been advanced by Anthropic's Transformer Circuits sequence, and open-source work by Neel Nanda. This chapter will also branch into areas more accurately classed as "model internals" than interpretability, e.g. recent work on steering vectors.
Topics include:
GPT models (building your own GPT-2)
Training and sampling from transformersTransformerLensIn-context Learning and Induction HeadsIndirect Object IdentificationSuperpositionSteering VectorsDuration: 5 days
Chapter 2 - Reinforcement Learning
In this chapter, you will learn about some of the fundamentals of RL, and work with OpenAI's Gym environment to run their own experiments.
Topics include:
Fundamentals of RLVanilla Policy GradientProximal Policy Gradient
RLHF (& finetuning LLMs with RLHF)
Gym & Gymnasium environmentsDuration: 5 days
Chapter 3 - Paper Replications
We will conclude this program with paper replications, where participants will get guidance and mentorship while they replicate a paper containing material relevant to this course. This should draw on much of the skills and knowledge participants will have accumulated over the last 3 weeks.Duration: 5 days
Below is a diagram of the curriculum as a whole, and the dependencies between sections. Note that this may change slightly in the lead-up to the program.Here is som...