AI Safety Collective - Crowdsourcing Solutions for Critical AI Safety Challenges
Lye Jia Jun, Dhruba Patra, Philipp Blandfort · Team AI Safety Collective
Submitted to Hackathon for Technical AI Safety Startups. Sprint projects are early-stage work by participants, not Apart Research publications.
The AI Safety Collective is a global platform designed to enhance AI safety by crowdsourcing solutions to critical AI Safety challenges. As AI systems like large language models and multimodal systems become more prevalent, ensuring their safety is increasingly difficult. This platform will allow AI companies to post safety challenges, offering bounties for solutions. AI Safety experts and enthusiasts worldwide can contribute, earning rewards for their efforts.
The project focuses initially on non-catastrophic risks to attract a wide range of participants, with plans to expand into more complex areas. Key risks, such as quality control and safety, will be managed through peer review and risk assessment. Overall, The AI Safety Collective aims to drive innovation, accountability, and collaboration in the field of AI safety.
Reviews
No public critique yet.
Cite this project
@misc{jun2024ai,
title = {{AI Safety Collective - Crowdsourcing Solutions for Critical AI Safety Challenges}},
author = {Lye Jia Jun and Dhruba Patra and Philipp Blandfort},
year = {2024},
month = sep,
note = {Submitted to Hackathon for Technical AI Safety Startups, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/ai-safety-collective-crowdsourcing-solutions-for-critical-ai-safety-challenges}},
url = {https://apartresearch.com/sprints/projects/ai-safety-collective-crowdsourcing-solutions-for-critical-ai-safety-challenges}
}More from Hackathon for Technical AI Safety Startups
- 1st place by peer reviewView project: DarkForest - Defending the Authentic and Humane Web
DarkForest - Defending the Authentic and Humane Web
DarkForest
DarkForest is a pioneering Human Content Verification System (HCVS) designed to safeguard the authenticity of online spaces in the face of increasing AI-generated content. By leveraging graph-based reinforcement …
- View project: Jailbreaking general purpose robots
Jailbreaking general purpose robots
Luax Labs
We show that state of the art LLMs can be jailbroken by adversarial multimodal inputs, and that this can lead to dangerous scenarios if these LLMs are used as planners in robotics. We propose finetuning small multimodal …
- View project: Simulation Operators: The Next Level of the Annotation Business
Simulation Operators: The Next Level of the Annotation Business
Ardy
We bet on agentic AI being integrated into other domains within the next few years: healthcare, manufacturing, automotive, etc., and the way it would be integrated is into cyber-physical systems, which are systems that …