Living at the Intersection of Blockchains and Machine Learning

Presentation given at the Philadelphia AI (PHLAI) conference - Sept 2021

Transcript

1. CONFIDENTIAL
   Living at the Intersection of
   Blockchains and Machine
   Learning
   Arpit Mathur
   Principal Engineer
   

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2. AI and Blockchains?!
   AI Blockchain
   More data is better Less data is better
   Centralized systems Decentralized systems
   Black Box Radical Transparency
   Aren’t Blockchains Antithetical to AI?
   

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3. AI Challenges
   • Trust
   • Bias
   • Walled Gardens
   • Data Sharing
   

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4. Can Blockchains
   alleviate AI’s
   biggest
   challenges?
   

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5. Understanding Blockchains
   

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6. Blockchains as we know it
   • A linked list of Blocks, each block containing
   many transactions
   • Wallets and Smart Contracts announce
   transactions
   • Miners combine Transactions into blocks
   • Miners compete with others to write blocks
   to the chain via Proof of Work
   • The winning miner then writes the data to
   the chain
   

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7. From Web 2.0 to 3.0
   Web 2.x Web 3.0
   User
   DNS
   Application Server
   App
   Splunk
   

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8. Web 2.x Web 3.0
   (Ethereum version)
   User Wallet
   DNS ENS
   Application Server Blockchain
   App Contract
   Splunk Etherscan
   From Web 2.0 to 3.0
   

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9. Writing code for Ethereum
   

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10. Smart Contracts
    • State Machines
    • State changes via Transactions
    • Written in Solidity and other languages
    • Deployed as Bytecode
    • Must be deterministic
    

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11. A Hello World contract
    

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12. Remix and Metamask
    

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13. OpenZeppelin
    

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14. Coins and NFTs
    • Tokens = wallet addresses +
    balances stored in a contract
    • Standard contract that
    implements a certain interface
    

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15. ERC 20 vs ERC 721
    

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16. Blockchains and AI
    Data Marketplaces
    AI products as NFTs
    Inclusive organizations and opportunities
    

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17. Rise of Data Marketplaces
    

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18. Bring verifiable data into Blockchains with Oracles
    

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20. “A blockchain comes in and collects all the
    learnings. I emphasize. The learnings, and not
    the patient data … the parameters, for
    example, of the neural network weights in the
    machine learning model.
    So in this case, no patient data ever leaves an
    individual hospital”
    - Dr. Eng Lim Goh,
    Senior vice president and CTO of artificial intelligence at
    Hewlett Packard Enterprise.
    

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21. Renting/Selling Algorithms and ML models on the Blockchain
    

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22. … as NFTs?
    

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23. Licensing ML products as NFTs
    

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24. Democratizing Software Development with GitCoin
    

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25. Building unbiased performance-oriented organizations
    

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26. Are we there yet?
    • Performance and Environmental concerns
    • Motivation
    

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27. Ethereum 2.0
    • Goals
    o Better energy
    o Reduced hardware
    requirements
    o Stronger immunity to
    centralization
    o Stronger support for shard
    chains
    • 15 TPS to 100,000 TPS
    Note: Permissionless blockchains
    will always be slower than
    databases. That is not the goal.
    

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28. If you want to go fast, go alone. If you
    want to go far, go together
    – African proverb
    

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29. Thank you
    

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