Yup, due to the FTX Collapse, the competition was no longer funded.
Oliver Z
Karma: 379
Intro to ML Safety virtual program: 12 June − 14 August
AI Safety Newsletter #3: AI policy proposals and a new challenger approaches
AI Safety Newsletter #2: ChaosGPT, Natural Selection, and AI Safety in the Media
Skill up in ML for AI safety with the Intro to ML Safety course (Spring 2023)
Yup! The bounty is still ongoing. We have been awarding prizes throughout the duration of the bounty and will post an update in January detailing the results.
Thanks for letting me know! I missed that it was until September. Will do!
The Center for AI Safety is hiring for four roles: Project Manager, Operations Manager, Research Engineer, and Chief of Staff.
Our mission is to promote safety and x-risk in the broader ML community. We envision a world in which most ML researchers are familiar with the arguments for risks from AI and a significant proportion of them are working directly on relevant safety problems.
To this end, we conduct empirical safety research and run various field-building projects (e.g., safety competitions, workshops, fellowships, and summer programs) to accelerate the growth of the safety community.
The following apply for all roles:
Deadline: We will be reviewing applications on a rolling basis
Ideal Start date: As soon as possible
Role: Full-time, salaried employee
Work location: San Francisco (in-person)
If you have questions, feel free to reach out to oliver@safe.ai
If you’re interested in joining us, please see the full job descriptions!
Announcing an Empirical AI Safety Program
Whether the program recurs likely depends on a few different factors including the results of the first iteration of the program. Assuming things all go well; however, we would be excited to run this again next year.
Yup, wanted to confirm here the ~100x in efficacy comes from getting 10x in relevance and 10x in ability (from selecting someone 10x better than the average research scientist).
Regarding the relative value of PhD vs scientist: the model currently values the average scientist at ~10x the average PhD at graduation (which seem broadly consistent with the selectivity of becoming a scientist and likely underrepresents the research impact as measured by citations—the average scientist likely has more than 10x the citation count as the average PhD). Then, the 5 years includes the PhD growing significantly as they gain more research experience, so the earlier years will not be as productive as their final year.