ARBOx & ARENA

For July & August my focus has been up-skilling for AISafety. I was accepted on to ARBOx which involved a week of studying their prerequisites, 2 weeks attending an in-person crash course in Oxford and then self-studying the fairly extensive ARENA syllabus the course had utilised afterwards.

30/06/2025	ARBOx
07/07/2025	ARBOx
14/07/2025	Travel / Planning / ARBOx prereqs
21/07/2025	ARENA Prerequisites / Medical appointments
28/07/2025	Break – Family holidays
04/08/2025	SPAR/Anthropic Research Applications
11/08/2025	ARENA Ray Tracing + Pylance setup
18/08/2025	ARENA: Ray Tracing
25/08/2025	ARENA: CNNs

ARBOx – Oxford

ARBOx was a great use of my time. It was pretty motivational to be around people much smarter than myself, living out of student halls again, and to be able to focus 100% of the course material through pretty intensive pair programming session. I think much of the material I was lacking the foundations to really utilize but it was a great introduction and the pair programming opened my eyes to how other people work on these exercises (much faster than I do mostly).

I also met some great people there, working on this stuff alone can feel like an impossible task sometimes so having 19 other people going through the same learning process alongside you and chatting to them about all these niche AISafety topics you care about was really inspiring.

I did have a bit of imposter syndrome while there as I was usually the weaker pair in the pair programming exercises. My career experience of Swift, Unit testing and design patterns doesn’t really come in useful when going through Jupyter notebooks and most my partners had career or post-grad experience with deep-learning. They were amazingly patient though and once I put aside feeling guilty over slowing them down it was double valuable going through the material with people who can literally show me the things I don’t know.

ARENA – Post ARBOx

Since leaving ARBOx I’ve focussed on the ARENA prerequisite & Fundamentals materials as my lack of Pytorch, Linear Algebra and experience working with Tensors were my biggest blockers when going through the ARBOx syllabus.

ARENA sets pretty intense time-intervals on their exercises and has a lot of reading material that you generally skip or skim-read when pair programming with the aim to finish a notebook in a day. I think this is a good practice when familiarising yourself with a topic as I personally learn best when having to recall the topic when the time comes to utilise it in a project.

However the fundamentals I’ve decided to go over more slowly for two reasons. One being that everything else on this syllabus is going to rely on it so knowing it well will help me with subsequent exercises. The 2nd being that these foundational topics will not just be useful for AISafety but for most the ML related areas I wish to explore while doing this career transition. So I’m taking it slow, going over some of the additional reading material, writing up Anki flash cards and watching youtube videos visualising key topics.

I’ve created a fork of the ARENA syllabus to work out of for now but will probably take on a suggestion one of the ARBOx teachers gave me and invest a bit of time in to creating a few demos of my favourite exercises to start building up a portfolio and cement my knowledge in each of these areas.

SPAR – Supervised Alignment Research

I put the best part of three days in to applying for this years SPAR cycle. My main aim is to fit-test different career paths and getting hands on AI Alignment research experience would be a perfect fit-test.

However I’m getting cold feet for some of the more research heavy projects for a variety of reasons:

• I’m committing to 3 months of 10-30 hours a week involvement when I only plan on spending 12 months for my entire career transition and there are a lot of areas other than whatever niche of AI Safety alignment research my chosen projects involve.
• My biggest draw as a volunteer is my SWE experience yet its the ML that I need to improve on most.
• I’ll be in Bali for 2 of the 3 months meaning my time zone and involvement will be limited somewhat.
• After unpacking it research heavy roles might not be a good fit for me. I find it really hard to get motivated to; apply for funding, read papers, write reports and run experiments that involve scaffolding LLMs.
• It’s a very competitive field