# ML Engineering for AI Safety & Robustness - Catherine Olsson and 80,000 Hours (2018-11)

Source: https://80000hours.org/articles/ml-engineering-career-transition-guide/
Authors: Catherine Olsson and the 80,000 Hours team
Date: Published November 2018; update note visible Feb 2022
Fetch-status: excerpted from HTML via browser.
Use: source-graph evidence from Spinning Up's "Other Resources" section; useful for research-engineer skill acquisition, less central to research taste.

## Why this matters for agents

This source is more about becoming useful on ML research teams than choosing research ideas. Its most relevant claim is that implementing and debugging foundational algorithms is a high-value learning path, with easy environments, metrics, and reference-code scrutiny.

## Quotes

> Technical AI safety is a multifaceted area of research, with many sub-questions in areas such as reward learning, robustness, and interpretability.

> Not all of these questions are best tackled with abstract mathematics research; some can be approached with concrete coding experiments and machine learning (ML) prototypes.

> Once you know the 101-level basics of ML, the next thing to learn is how to implement and debug ML algorithms.

> Breadth of experience is not important here: you don’t need to read all the latest papers, or master an extensive reading list. You also don’t need to do novel research or come up with new algorithms.

> What you do need is to get your hands dirty implementing and debugging ML algorithms, and to build evidence for job interviews that you have some experience doing this.

> The most straightforward way to gain this experience is to choose a subfield of ML relevant to a lab you’re interested in. Then read a few dozen of the subfield’s key papers, and reimplement a few of the foundational algorithms that the papers are based on or reference most frequently.

> For each algorithm, they would first test on very easy environments, and then move to more difficult environments.

> Once the algorithm was partially working, they would attain higher performance by looking for remaining bugs, both by reviewing the code carefully, and by collecting metrics such as average policy entropy to perform sanity-checks, rather than just tune hyperparameters.

> Most importantly, he was able to implement and debug ML algorithms, going from math in a paper to running code.

## Source graph

This page was linked from Spinning Up's "Other Resources" section. It points to Josh Achiam's Key Papers in Deep RL list and a Daniel Ziegler self-study path. It is useful background for training agents to value implementation and debugging practice, but probably secondary for a dedicated research-taste skill.
