Content type tag (summary, discussion)
Scaling, Oracles and Data
AIoT Blockchain, democratization of blockchain, interdisciplinary, theoretical, smart contract
Description of why this would be an interesting post
I originally read this paper to enhance my background knowledge of AIoT Blockchain. I think that the ideas would help progress our AIoT Blockchain project incrementally.
To quote the brief summary I had made of it:
This Stanford article highlights the fact that the processes in machine learning are frequently cost-inefficient and difficult to access for many. Datasets are frequently privately-owned and expensive to create. Therefore, it’s all about using blockchain to democratize AI.
Blockchain offers trust and security. Through smart contracts, it also offers reliability in execution of code and transparency. Smart contracts are specifically what is used for contribution of data. Hosting an ML model requires a one-time fee for uploading and a small fee for anyone uploading data. The authors propose three methods for incentivizing people to upload “good” data: gamified (rewarding based on validation and acknowledgement from other contributors), prediction market-based (reward based on how the data submitted affects the model’s accuracy), and ongoing self-assessment (reward based on mutual validation and mutual payment for good data).
Right now, this framework is most useful for smaller models, and research in scaling still needs to be done. Overall, however, a promising area of research!
Links to background reading (0 to 3 items)
Research Summary - Convergence of Blockchain, IoT, and AI - #15 by Gearlad