OAISI (the Oxford AI Safety Initiative) is excited to announce ARBOx, a two-week bootcamp to rapidly upskill in technical AI safety and machine learning. We’ll do a condensed variant of the ARENA syllabus, aiming to build familiarity with mechanistic interpretability and transformers, along with a short introduction to RL and RLHF.[1]
ARBOx is best suited for those who have a fairly strong technical background, but who lack experience in mechanistic interpretability or transformer models.[2] We expect basic familiarity with linear algebra, Python programming and AI safety.
Deadline to apply: end of day anywhere on earth, December 13th, 2024
Dates: January 6th-17th, 2025
What we provide:
Accommodation in central Oxford
Provided meals
Partial support for travel costs
Content: an abbreviated version of the ARENA syllabus
gpt-2-small from scratch
A paper replication or two (e.g. Redwood’s IOI paper)
We believe that understanding some basic ML/mechinterp is useful for a large range of roles in AI safety, beyond directly working in technical AI safety. Strong applicants who intend to work in more governance-related areas are particularly encouraged to apply.
Apply for ARBOx: an ML safety intensive [deadline 13 Dec ’24]
OAISI (the Oxford AI Safety Initiative) is excited to announce ARBOx, a two-week bootcamp to rapidly upskill in technical AI safety and machine learning. We’ll do a condensed variant of the ARENA syllabus, aiming to build familiarity with mechanistic interpretability and transformers, along with a short introduction to RL and RLHF.[1]
ARBOx is best suited for those who have a fairly strong technical background, but who lack experience in mechanistic interpretability or transformer models.[2] We expect basic familiarity with linear algebra, Python programming and AI safety.
Deadline to apply: end of day anywhere on earth, December 13th, 2024
Dates: January 6th-17th, 2025
What we provide:
Accommodation in central Oxford
Provided meals
Partial support for travel costs
Content: an abbreviated version of the ARENA syllabus
gpt-2-small from scratch
A paper replication or two (e.g. Redwood’s IOI paper)
A brief introduction to RLHF
See you there!
Apply here!Questions? Reach out to us at arbox@oaisi.org.
Our syllabus is inspired by CaMLAB, a similar programme run by our counterparts in Cambridge.
We believe that understanding some basic ML/mechinterp is useful for a large range of roles in AI safety, beyond directly working in technical AI safety. Strong applicants who intend to work in more governance-related areas are particularly encouraged to apply.