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
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?
3. AI Challenges • Trust • Bias • Walled Gardens • Data Sharing
4. Can Blockchains alleviate AI’s biggest challenges?
5. Understanding Blockchains
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
7. From Web 2.0 to 3.0 Web 2.x Web 3.0 User DNS Application Server App Splunk
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
9. Writing code for Ethereum
10. Smart Contracts • State Machines • State changes via Transactions • Written in Solidity and other languages • Deployed as Bytecode • Must be deterministic
11. A Hello World contract
12. Remix and Metamask
13. OpenZeppelin
14. Coins and NFTs • Tokens = wallet addresses + balances stored in a contract • Standard contract that implements a certain interface
15. ERC 20 vs ERC 721
16. Blockchains and AI Data Marketplaces AI products as NFTs Inclusive organizations and opportunities
17. Rise of Data Marketplaces
18. Bring verifiable data into Blockchains with Oracles
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.
21. Renting/Selling Algorithms and ML models on the Blockchain
22. … as NFTs?
23. Licensing ML products as NFTs
24. Democratizing Software Development with GitCoin
25. Building unbiased performance-oriented organizations
26. Are we there yet? • Performance and Environmental concerns • Motivation
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.
28. If you want to go fast, go alone. If you want to go far, go together – African proverb
29. Thank you