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Machine Learning for dApps

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Talk at the World Summit AI 2019 by Diwaker Gupta. Covers intersection of Blockchain and AI, and specifically how Blockchains might offer some solutions for bringing ML capabilities to decentralized applications.

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Machine Learning for dApps

  1. 1. Machine Learning for dApps
  2. 2. “Apps” today Identity: email, social, phone Data: Centralized, Opaque Logic: mix of client / server
  3. 3. dApps = decentralized Apps Sovereign identity using blockchain User controlled storage for privacy
  4. 4. 250+ Apps 3000+ Community Strong, engaged, global
  5. 5. Most ML today is centralized Data moats (often user generated) Expertise Resources
  6. 6. Most ML today is centralized Data moats (often user generated) Expertise Resources Democratize ML? Users control data Limited expertise Limited resources
  7. 7. Blockchains (source: https: //dilbert.com/strip/2017-10-17) • (mostly) Immutable ledger [good for transparency] • Coordinate without trust [good for markets] • Tokens + Smart Contracts [good for incentives]
  8. 8. Readily available datasets Off-the-shelf models Commercial APIs (e.g. image classification) Easy, right?
  9. 9. Readily available datasets Off-the-shelf models Commercial APIs (e.g. image classification) Easy, right? Kate Crawford and Trevor Paglen, “Excavating AI: The Politics of Training Sets for Machine Learning (September 19, 2019) https: //excavating.ai
  10. 10. MSR: Decentralized & Collaborative AI on Blockchain “framework to host and train publicly available machine learning models” — July 2019, https://github.com/Microsoft/0xDeCA10B
  11. 11. Cold Start Problem No Data and/or No Models Limited infrastructure Privacy limitations
  12. 12. Cold Start Problem No Data and/or No Models Limited infrastructure Privacy limitations Blockchains can facilitate effective marketplaces
  13. 13. Cold Start: No data (source: computable.io)
  14. 14. Comprehensive Marketplace for Data & Services An ecosystem for the data economy and associated services, with a tokenized service layer that securely exposes data, storage, compute and algorithms for consumption. (source: oceanprotocol.com)
  15. 15. • Jupyter instance at https: //datascience.oceanprotocol.com • Data Marketplace at https: //commons.oceanprotocol.com • Participate in the Data Challenge! https: // oceanprotocol.com/challenge/
  16. 16. Strange Loops dApps (leverage Blockchain) ML Challenges (data, models, resources) Blockchain-based Solutions
  17. 17. Thanks! Questions? @diwakergupta

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