Editing AI Governance DAO (section)

=== Decentralization ===
Many groups are aware of the threat to humanity of technological disruption. And every major center for development of AI has instituted some process for analyzing the ethics of their deployment of the technology, to a greater or lesser degree. However, almost all of these major initiatives are centralized, since the major power behind AI development is for-profit exploitation of the technology.

Therefore, unfortunately, at the moment, the ultimate values and goals that are guiding the development of AI are largely limited to monetary profit. Because of the centralized corporate deployment of the leading AI tools, a primary application of AI today is to improve the effectiveness of advertising. This is having negative effects on society as social media is being engineered to increase engagement at the expense of social harmony.

Further, we cannot establish wise regulation for such a complex problem as AI governance without improving our governance mechanisms, without improving democracy and human communication.

aiGovDAO uses the structure of DAOs to foster collaboration to develop AI tools in the service of the common good. Decentralized organization is used to empower as many people as possible who are conscious of the problem and wise with respect the the consequences of governing the progress of AI. We use the architecture of a DAO to filter information at the edge, to promote the best ideas from the globe to guide the development of AI.

A decentralized AI stack includes research and tooling conversations around data storage, compute, hardware, AI models, and governance. Therefore, in alignment with the goal of decentralizing the governance of AI, we center our focus on the use of small processors<ref>Large NNs dominate public attention. They currently are superior to small NNs, yet are inaccessible to the average researcher due to limitations on shared compute time on the few large networks. This perspective argues against the success of a decentralized AI governance group. But it is not insurmountable. 

Small NNs are surprisingly powerful, even today with the limited attention they have been given. Small NNs can be competitive with--even superior to--large models, since they allow more experimentation from a greater number of researchers. See, e.g., [https://www.semianalysis.com/p/google-we-have-no-moat-and-neither?open=false#%C2%A7large-models-arent-more-capable-in-the-long-run-if-we-can-iterate-faster-on-small-models this discussion]. And the processing power available to P2P networks exceeds that held in centrally owned corporations.</ref>, P2P networks, open source principles, the use of transparent training data, and democratic decision making.