Sep 2, 2024

Simulation Operators: The Next Level of the Annotation Business

Ardy Haroen

Details

Summary

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 integrate the computer brain into a physical receptor/actuator (e.g. robots). As the demand for cyber-physical agents increases, so would be the need to train and align them.

We also bet on the scenario where frontier AI and robotics labs would not be able to handle all of the demands for training and aligning those agents, especially in specific domains, therefore leaving opportunities for other players to fulfill the requirements for training those agents: providing a dataset of scenarios to fine-tune the agents and providing the people to give feedback to the model for alignment.

Furthermore, we also bet that human intervention would still be required to supervise deployed agents, as demanded by various regulations. Therefore leaving opportunities to develop supervision platforms which might highly differ between different industries.

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@misc {

title={

Simulation Operators: The Next Level of the Annotation Business

author={

Ardy Haroen

date={

9/2/24

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Review

Reviewer Name

Constructive critique

Criteria 1:

Advancing interpretability (Possible questions to answer in this criteria: Does the project contribute to the field of mechanistic interpretability? Does it provide new insights into understanding or steering AI model behavior? How well does it move us towards reprogramming AI models? How original and innovative is the approach?)

2.5

Criteria 2:

Relevance for AI safety (Possible questions to answer in this criteria: How important is the contribution to advancing the field of AI safety? Do we expect the results to generalize beyond the specific case(s) presented in the submission? Does the approach introduce new safety mechanisms or enhance existing ones in innovative ways?))

2.5

Criteria 3:

Methodology & presentation (Possible questions to answer in this criteria: How well is the project executed from a technical standpoint?, Is the code well-structured, documented, and reproducible?, How effectively does it utilize Goodfire's SDK/API and other provided resources?, How clearly and effectively is the research presented in the paper?, Quality of visualizations and demos (if applicable), Clarity of methodology explanation and results interpretation))

2.5

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