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Data, AI, and Tokens: Ocean Protocol

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This talk describes the problem of data silos, and the root cause which is lack of incentive to share. Ocean Protocol aims to democratize data for use by AI, by leveraging blockchain incentives. It uses a Proofed Curation Market construction, which combines cryptographic proof (e.g. proof of availability) with curation markets.

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Data, AI, and Tokens: Ocean Protocol

  1. 1. Trent McConaghy @trentmc0 Data, AI and Tokens: Ocean Protocol
  2. 2. Audio radar
  3. 3. 1000x more data The Unreasonable Effectiveness of Data
  4. 4. Deep learning models with >> capacity Error 5% .. 0.01%Models with limited capacity Error 25% .. 5% Another 1000x more data Deep Learning Loves Data
  5. 5. The Problem
  6. 6. Mo’ data (and mo’ compute) Mo’ accuracy Mo’ $
  7. 7. Here’s your personal data
  8. 8. Eroding the silos
  9. 9. Silo Pool Mo’ Data (and mo’ compute) Mo’ accuracy Mo’ $
  10. 10. Have lotsa data (1000 enterprises) Have lotsa AI (1000 AI startups)
  11. 11. Have lotsa AI (1000 AI startups) Have lotsa data (1000 enterprises)
  12. 12. A new data economy Have lotsa AI (1000 AI startups) Have lotsa data (1000 enterprises) DM DM DM DM DM DM DM DM
  13. 13. A new data economy Have lotsa AI (1000 AI startups) Have lotsa data (1000 enterprises) DM DM DM DM DM DM DM DM Ocean
  14. 14. Economic incentives
  15. 15. Blockchain Superpower: Get people to do stuff By rewarding with tokens
  16. 16. Bitcoin goal: maximize security of network Token rewards if: run compute to secure network
  17. 17. Economic Incentive for Bitcoin Objective: Maximize security of network • Where “security” = compute power • Therefore, super expensive to roll back changes to the transaction log E(Ri) α Hi * T E() = expected value # tokens (BTC) dispensed each block block rewards hash power of actor = contribution to “security”
  18. 18. Ocean goal: maximize supply of relevant data Token rewards if: supply data, and curate it
  19. 19. Economic Incentive for Ocean Objective: Maximize supply of relevant data • This means: reward curating data + making it available • Where “curating” = betting on data. Reward taste-making. E(Rij) α log10(Sij) * log10(Dj) * T *Ri Expected reward for user i on dataset j Dj = proofed popularity = # times made dataset available Sij = predicted popularity = user’s curation market stake in dataset j # tokens during interval
  20. 20. From AI data to AI services Motivations: • Privacy, so compute on-premise or decentralized • Data is heavy, so compute on-premise • Link in emerging decentralized AI compute Objective function: Maximize supply of relevant services =reward curating services + proving that it was delivered E(Rij) α log10(Sij) * log10(Dj) * T *Ri proofed popularity of service predicted popularity of service
  21. 21. Ocean is a network of curated services. An AI services pipeline. Availability Consumption Privacy GovernanceProduction commons Inter- Operability Discovery *Note: logos shown are examples and do not imply partnerships or integrations
  22. 22. Prototypes
  23. 23. Sandbox with Singapore Data Authority
  24. 24. What unlocking AI data & services unlocks
  25. 25. Self-driving cars: fewer accidents, more mobility
  26. 26. >100x more data for health care research
  27. 27. Erode the data silos
  28. 28. Erode the data silos
  29. 29. Erode the data silos
  30. 30. Conclusion
  31. 31. AI data is siloed. AI services are siloed. Let’s change the rules of the game with incentives. Let’s democratize access to AI data & services! Trent McConaghy @trentmc0

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