Applying Blockchain Technology to AI Systems
June 25, 2018
The founder of Ocean Protocol and BigchainDB, Trent McConaghy, has written a detailed piece for the BigchainDB’s Blog titled, “Blockchains for Artificial Intelligence: from Decentralized Model Exchanges to Model Audit Trails.” In it, the engineer explains what blockchain technology offers the AI field. See the article for his philosophy, an interesting history lesson on AI and data, and his assertion that the performance issues inherent in blockchain tech are no big deal. Not yet, anyway.
After this thorough introduction, the piece spells out six opportunities McConaghy foresees for this blessed union: Data sharing for better models; Data sharing for qualitatively new models, including “new planet-level data for new planet-level insights” (more on that in a moment); Audit trails on data and models for more trustworthy predictions; a Shared global registry of training data and models; Data and models as IP assets for data and model exchange; and AI DAOs (Decentralized Autonomous Organizations), or “code that owns itself.” See the piece for details on each of these ideas.
Back to the planet-level data concept, which I found interesting. McConaghy references the Interplanetary Database, or IPDB, in his explanation. We’re told:
“IPDB is structured data on a global scale, rather than piecemeal. Think of the World Wide Web as a file system on top of the internet; IPDB is its database counterpart. (I think the reason we didn’t see more work on this sooner is that semantic web work tried to go there, from the angle of upgrading a file system. But it’s pretty hard to build a database by ‘upgrading’ a file system! It’s more effective to say from the start that you’re building a database, and designing as such.) ‘Global variable’ gets interpreted a bit more literally. …
I also noted this statement:
“Overall, we get a whole new scale for diversity of datasets and data feeds. Therefore, we have qualitatively new data. Planetary level structured data. From that, we can build qualitatively new models, that make relations which among inputs & outputs which weren’t connected before. With the models and from the models, we will get qualitatively new insights. I wish I could be more specific here, but at this point it’s so new that I can’t think of any examples. But, they will emerge!”
We are curious to see what does emerge, and to what purposes the technology is applied. Stay tuned.
Cynthia Murrell, June 25, 2018
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