BigchainDB: Blockchains for Artificial Intelligence by Trent McConaghy
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The document discusses the integration of blockchain technology with artificial intelligence, highlighting its potential benefits such as decentralized data sharing, immutability, and the creation of new models. It suggests that a planetary-scale blockchain database like IPDB can enhance AI by providing trustworthy audit trails, a shared registry for training data, and an environment for decentralized autonomous organizations (DAOs) focused on AI. Additionally, it presents various use cases and examples, such as managing music rights and creating decentralized applications (dApps) for artistic AI generation.
BigchainDB: Blockchains for Artificial Intelligence by Trent McConaghy
- 1. Blockchains for Artificial Intelligence Trent McConaghy @trentmc0
- 3. Blockchain: A Special “Spreadsheet in the Sky” What’s special: - no one owns it - anyone can add to it - no one can delete from it - Writing to a blockchain is like etching in stone. - Which allows us to issue assets, and transfer them
- 4. The Internet of Everything needs a Ledger of Everything. The blockchain is a truly open, distributed, global platform that fundamentally changes what we can do online, how we do it, and who can participate. Call it the world wide ledger.
- 5. Blockchains are databases with “blue ocean” benefits Decentralized / shared control Immutability / audit trail Tokens / exchanges
- 6. A blockchain caveat or two Completely new code bases Reinventing consensus No sharding = no scaling No querying // single-node querying Let’s fix this...
- 7. Everyone uses databases. How do they scale to big data? Answer: Distribute storage across many machines (sharding) 7 0 50 100 150 200 250 300 350 0 200,000 400,000 600,000 800,000 1,200,000 175,000 367,000 537,000 1,100,000 Nodes Writes/s Example: Cassandra scaling. More nodes = more throughput, more capacity A “consensus” algorithm keeps distributed nodes in sync.
- 8. How to build a scalable blockchain database (BigchainDB) 1. Start with an enterprise-grade distributed DB, e.g. MongoDB 2. Engineer in blockchain characteristics • Each DB node is a federation node Decentralized / Shared Control • Hash Previous Blocks • Append-only Immutable / Audit Trails • “Own” = have private key • Asset lives on the database Native assets
- 9. IPDB = a public global blockchain database
- 18. More examples
- 19. Energy Value prop: manage $ flow in energy deregulation
- 20. Music rights Value prop: A streaming service owned by all
- 21. Education Credentials Value prop: reduce fraudulent degrees, lower HR friction
- 22. How can blockchains help AI?
- 23. Work off of each of the benefits… Decentralized / shared control Immutability / audit trail Tokens / exchanges
- 24. Decentralized / shared control encourages data sharing More data better models -10.3 + 7.08e-5 / id1 + 1.87 * ln( -1.95e+9 + 1.00e+10 / (vsg1*vsg3) + 1.42e+9 *(vds2*vsd5) / (vsg1*vgs2*vsg5*id2) ) 10^( 5.68 - 0.03 * vsg1 / vds2 - 55.43 * id1+ 5.63e-6 / id1 ) 90.5 + 190.6 * id1 / vsg1 + 22.2 * id2 / vds2 2.36e+7 + 1.95e+4 * id2 / id1 - 104.69 / id2 + 2.15e+9 * id2 + 4.63e+8 * id1 - 5.72e+7 - 2.50e+11 * (id1*id2) / vgs2 + 5.53e+6 * vds2 / vgs2 + 109.72 / id1 Merge Build models Build model Low accuracy High accuracy
- 25. Decentralized / shared control encourages data sharing Qualitatively new ecosystem-level data qualitatively new models Example: shared diamond certification houses data makes fraud id possible Merge All the diamond cert houses Alldiamonds forcerthouse1 Certhouse2 Certhouse3 Certhouse4 All the legit diamonds Build 1-class classifier Legit Fraudulent No single cert house has enough data to make an accurate classifier Build classifiers
- 26. Decentralized / shared control encourages data sharing Qualitatively new planet-level data qualitatively new models “IPDB is kibbles for AI” --David Holtzman
- 27. Immutability for An Audit Trail on Training/Testing Data & Models For greater trustworthiness of the data & models (Avoid garbage-in, garbage-out) Provenance in building models: • Sensor / input stream data • Training X/y data • Model building convergence Provenance in testing / in the field: • Testing X data • Model simulation • Testing yhat data Time-stamp/store Applications: • you can tell if a sensor is lying • you know the “story” of a model • catch leaks in the data chain
- 28. Another Opportunity: A shared global registry of training data & models All the Kaggle datasets All the Kaggle models All the ImageNet datasets All the ImageNet models ….…. “Models are owned by the planet”
- 29. Training/testing data & models as intellectual property assets Decentralized data & model exchanges Your datasets or models… …licensed to others Others’ datasets & models …licensed to you ….…. “EMX – European Model Exchange?”
- 30. Sell your CARTS?
- 31. One more app AI DAOs
- 32. Then you get decentralized processing. aka “smart contracts” What if you used a blockchain to store state of a state machine? State Virtual machine
- 33. Then you get decentralized processing. And you can build a world computer having decentralized processing, storage, and communications (e.g. Ethereum vision) What if you used a blockchain to store state of a state machine? State Virtual machine Decentralized applications (dapps) World computer
- 34. DAO: a computational process that • runs autonomously, • on decentralized infrastructure, • with resource manipulation. It’s code that can own stuff! DAO: Decentralized Autonomous Organization State Virtual machine DAO Dapp
- 35. AI entity is a feedback control system. That is, AGI. Its feedback loop would continue on its own, taking inputs, updating its state, and actuating outputs, with the resources to do so continually. AGI on a DAO? AI DAO World computer
- 36. Example: The ArtDAO Algorithm… 1. Run AI art engine to generate new image, using GP or deep 2. Claim attribution in blockchain, using ascribe 3. Create multiple editions, using ascribe 4. Post editions for sale onto a marketplace, using Getty (centralized), or OpenBazaar (decent.) 5. Sell the editions. $ goes to ArtDAO using built-in cryptocurrency like Ether. IP go from ArtDAO using ascribe. 6. Repeat! Create more art, sell it, get wealthier
- 37. Example: The ArtDAO Algorithm… 1. Run AI art engine to generate new image, using GP or deep 2. Claim attribution in blockchain, using ascribe 3. Create multiple editions, using ascribe 4. Post editions for sale onto a marketplace, using Getty (centralized), or OpenBazaar (decent.) 5. Sell the editions. $ goes to ArtDAO using built-in cryptocurrency like Ether. IP go from ArtDAO using ascribe. 6. Repeat! Create more art, sell it, get wealthier Over time, if ArtDAO makes more money from sales than from generating new art, then it will accumulate wealth. And, you can’t turn it off.
- 38. Angles to Making AI DAOs • DAO AI DAO. Start with DAO, add AI. E.g. Plantoid • AI AI DAO. Start with AI, add DAO. E.g. numer.ai • SaaS DAO AI DAO. Convert SaaS to DAO. Then add AI • Physical service AI DAO. E.g. Uber self-owning cars
- 39. Blockchains for Artificial Intelligence A planetary-scale blockchain database (IPDB) unlocks opportunities: 1. Data sharing Better models 2. Data sharing Qualitatively new models 3. Audit trails on data & models for more trustworthy predictions 4. Shared global registry of training data & models 5. Data & models as IP assets data & model exchange 6. AI DAOs – AI that can accumulate wealth, that you can’t turn off Trent McConaghy @trentmc0 bigchaindb.com ipdb.foundation