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Living at the Intersection of Blockchains and Machine Learning

Arpit Mathur
September 20, 2021

Living at the Intersection of Blockchains and Machine Learning

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

Arpit Mathur

September 20, 2021
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  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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