Regain information autonomy by bringing people closer to the source of truth.

Build brain-computer interfaces that enhance focus and rationality, provide this preferentially to AI alignment researchers to bridge the gap between capabilities and alignment research progress.

We started this project with the intent of identifying a specific issue with nnsight debugging and submitting a pull request to fix it. We found a minimal test case where an IndexError within a nnsight run wasn’t correctly propagated to the user, making debugging difficult, and wrote up a proposal for some pull requests to fix it. However, after posting the proposal in the discord, we discovered this page in their GitHub (https://github.com/ndif-team/nnsight/blob/2f41eddb14bf3557e02b4322a759c90930250f51/NNsight_Walkthrough.ipynb#L801, ctrl-f “validate”) which addresses the problem. We replicated their solution here (https://colab.research.google.com/drive/1WZNeDQ2zXbP4i2bm7xgC0nhK_1h904RB?usp=sharing) and got a helpful stack trace for the error, including the error type and (several stack layers up) the line causing it.

it's a basic line testing of my toy model

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.

This project proposes a cryptographic system for assigning unique identities to AI models and verifying their outputs to ensure accountability and traceability. By leveraging these techniques, we address the risks of AI misuse and untraceable actions. Our solution aims to enhance AI safety and establish a foundation for transparent and responsible AI deployment.