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Data, AI, and Tokens: Ocean Protocol

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This talk introduces Ocean protocol. It describes:
-how data drives AI (artificial intelligence)
-the gap between data-haves and AI-haves
-the data silo crisis
-how Ocean addresses these issues by creating a substrate to catalyze a flowering of data marketplaces
-the Ocean structured approach to token design, from values to stakeholders to software stack.

Video: https://www.youtube.com/watch?v=fMDD0aTVt4s

This talk was presented at "9984 Summit - Blockchain Futures for Developers, Enterprises, and Society" hosted by IPDB & BigchainDB.

Published in: Technology

Data, AI, and Tokens: Ocean Protocol

  1. 1. Trent McConaghy @trentmc0 Data, AI and Tokens: Ocean Protocol
  2. 2. AI & Data
  3. 3. 1000x more data The Unreasonable Effectiveness of Data
  4. 4. Deep Learning: Neural Networks * Moore’s Law ≈1950s algorithms on 1000x+ more storage & compute https://en.wikipedia.org/wiki/File:HTM_Hierarchy_example.png
  5. 5. Deep learning models with >> capacity Error 5% .. 0.01%Models with limited capacity Error 25% .. 5% Another 1000x more data Deep Learning Loves Data
  6. 6. Have data Have AI $$ Value
  7. 7. The Problem
  8. 8. Have lotsa data (1000 enterprises) Have lotsa AI expertise (1000 AI startups)
  9. 9. Have lotsa data: 1000+ enterprises Have lotsa AI: 1000+ AI startups Have both: Goog, FB, a handful $$
  10. 10. Mo’ data (and mo’ compute) Mo’ accuracy Mo’ $
  11. 11. Here’s your personal data
  12. 12. Opinionated Tech
  13. 13. Some tech has particularly powerful opinions 1. AI – goes to heart of “what is humanity”
  14. 14. Some tech has particularly powerful opinions 2. Crypto – defense is easier than offense (new)
  15. 15. Opinionated Tech • “Designed for ethics”  Thoughtful approach for ethics •“Ignored the ethics”  Not opinion-less. Simply bad design.
  16. 16. What Can You Do, As a Technologist? • Determine your values, as best as you can. Set your moral compass. “Know thyself.” • Use this to guide your work. Infuse your work with your values. • With each new collaborative project -- especially large-scale ones -- agree on values up-front. Example:
  17. 17. Towards a Solution
  18. 18. Schelling point: A common solution in absence of communication Q: Tomorrow you have to meet a stranger in NYC. Where and when do you meet? A: noon, at Grand Central Station
  19. 19. Silo Mo’ Data (and mo’ compute) Mo’ accuracy Mo’ $ This is the current Schelling point
  20. 20. Silo Pool Mo’ Data (and mo’ compute) Mo’ accuracy Mo’ $ Change the Schelling point?
  21. 21. Have lotsa data (1000 enterprises) Have lotsa AI (1000 AI startups)
  22. 22. Have lotsa data (1000 enterprises) Have lotsa AI (1000 AI startups)
  23. 23. Have lotsa data (1000 enterprises) Have lotsa AI (1000 AI startups)
  24. 24. Have lotsa data (1000 enterprises) Have lotsa AI (1000 AI startups)
  25. 25. Connect 1000+ enterprises With 1000+ AI startups All get lotsa data & AI
  26. 26. Connect 1000+ enterprises With 1000+ AI startups All get lotsa data & AI How? Data marketplace
  27. 27. Data marketplace Have AI (Want data) Have data Have AI (Want data) Have AI (Want data) Have data Have data Have AI (Want data) Have data Have data Have AI (Want data)
  28. 28. https://www.flickr.com/photos/atelier_flir/1118912668 A single data storefront?
  29. 29. A single data storefront = another centralized monolith (oops!) https://www.flickr.com/photos/walmartcorporate/5249869475
  30. 30. We want data marketplaces to flourish. Cathedral Bazaar How?
  31. 31. We want this. A substrate for a thousand marketplaces to bloom. “SABRE for data” Data marketplace DM DM DM DM DM DM DM DM DM DM DM DM DM DM DM DM DM DM DM Have AI (Want data) Have data Have AI (Want data) Have AI (Want data) Have data Have data Have AI (Want data) Have data Have data Have AI (Want data)
  32. 32. Complement data marketplaces with a data commons
  33. 33. The MVP (Minimum Viable Protocol)
  34. 34. Making it real With
  35. 35. Structured Approach to Token Design Steps: 1. Mission, Values, Governance. 2. Use Cases. Pricing. 3. Stakeholders. 4. Goals 5. Checklist (from goals) 6. Candidate designs. 7. Iterate to final
  36. 36. Step 1a. Mission Ocean aims to unlock data, for more equitable outcomes for owners and consumers of data, using a thoughtful application of both technology and governance.
  37. 37. Step 1b. Values • Unlock data— make data ever more accessible to a broader range of organizations. • Human right to personal data privacy & consent • Spread of power— democratic ideals, transparent governance. Strive to decouple capital from governance. • Spread of value— avoid becoming new oligarchs, better to spread wealth creation among the community. Rewards based on actions consistent with values. Discourage speculation, encourage building & community. As well as… • Identity & reputation over anonymity. • Sustainability— can last the ages • Resilience— not contingent on any one party. • Work with the law, not against the law
  38. 38. Step 1c. Governance 1. Governance of network. How protocol gets updated 2. Governance of Ocean Foundation. Growing the ocean community
  39. 39. Step 2. Use Cases Target verticals 1. Autonomous vehicles. Drives: Reconcile data commons & proprietary data. 2. Medical data. Drives: solve privacy, data regulations 3. (And another 20 after that)
  40. 40. Step 3. Pricing 1. Free Data. For the data commons. 2. Non-Free Fungible Data. Easy: an exchange 3. Non-Free Non-Fungible Data. Reconcile via a mix: • Fixed price • Auctions • Royalties • Programmable
  41. 41. Step 4. Stakeholders Data providers Data consumers Keepers Developers “I can sell my data with security, control, privacy and flexible pricing.” “I can discover and buy data.” ”I believe in the sustainability of the network and will be rewarded" “I want to build on Ocean Protocol” Marketplaces ”I have a new channel to sell data around the world.”
  42. 42. Step 5. Goals Token design = designing incentives for stakeholders to grow the value of the network. The main goal of Ocean network is a large supply of data. • For non-free data: incentive for supplying more? Referring? • For non-free data: good spam prevention? • For free data: incentive for supplying more? Referring? • For free data: good spam prevention? • Does token give higher marginal value to users of the network, vs external investors? • Are people incentivized to run keepers? • Is it simple? Is onboarding low-friction? Use incentives/crypto over legal recourse?
  43. 43. Step 6. Write checklist 7. Propose candidate designs, compare on checklist 8. Iterate
  44. 44. Software stack Ocean Ecosystem Ocean network Ocean core software: Registry of users, data, prices Open-source reference data market Data marketplace 1 Data providersData consumers, e.g. AI folks Data marketplace 2 … Data marketplace 3 DM DM DM … DM DM DM DM … DM … … … … Storage& Compute
  45. 45. Ocean Ecosystem Ocean network Inside Ocean Core Software Ocean core software Data marketplace DM … API. TX model is COALA IP for data. “ORC 20” Data curation: tokenized curation market Data access registry: tokenized read permissions User registry: tokenized registry Data pricing: registry of bids & asks, etc DM Storage& Compute Database, tokens: BigchainDB+IPDB Blob storage: on-prem, FileCoin, .. Biz logic Processing: on-prem, ..
  46. 46. Data access: tokenized read permissions Via role based access control (RBAC) { “asset”: { “ns”: “ipdb.testapp.vehicle”, “link”: “<hash>” }, “id”: “<hash>” } Image Source: Pixabay. CC0 license
  47. 47. class TokenCuratedRegistry def propose(data) def challenge(proposal) def vote(challenge) User registry: tokenized registry A whitelist of good actors, incentivized to grow the list Propose new user 300 tokens (stake) Challenge to proposal User status Alice “Challenged” Bob “Proposed” Mallory “OK” Trent “OK” 30 tokens (stake)
  48. 48. What unlocking data unlocks
  49. 49. Squeeze out the centralized personal data silos
  50. 50. >100x more data for health care research
  51. 51. Tokenize the enterprise, for benefit of community. Step 0 is to tokenize the data.
  52. 52. Self-driving cars: fewer accidents, more mobility
  53. 53. Self-driving, self-owning cars
  54. 54. Fleets of self-owning cars = car swarms
  55. 55. Self-owning power grid
  56. 56. Nature 1.0: soil, trees, wind, flocks of birds Nature 2.0: internet, metal, AI, swarms of cars Planetary infrastructure for us all
  57. 57. Conclusion
  58. 58. MOOD VISUALS Data is siloed with no incentive to share. Let’s equalize the opportunity for data access, while retaining privacy How? Opinionated AI*crypto tech. Ocean: a protocol for data marketplaces & data commons. Result: Equal opportunity for data, $ based on actual value Trent McConaghy @trentmc0

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