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"Why Fake News Is Relevant" - Introduction to the Userfeeds Protocol


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The Userfeeds Protocol is an open protocol for establishing information relevance in crypto-economic networks.

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"Why Fake News Is Relevant" - Introduction to the Userfeeds Protocol

  1. 1. Introducing the Userfeeds Protocol An open protocol for establishing information relevance in crypto-economic networks. Why “Fake News” Is Relevant @maciejolpinski
  2. 2. Research Development Protocol + Software platform development: Userfeeds Relevance Platform Economics of attention & information relevance: Userfeeds Protocol What We Do?
  3. 3. What happened in 2017 Seed Funding Initial Idea + Prototype Platform + Apps Live Userfeeds Relevance Platform Link Exchange Team Ramped Up to 7 People (5 Software Engineers + 2 Founders) January 2017 January 2018
  4. 4. It didn’t solve the problem of information relevance. HTTP/TCP/IP/SMTP solved the problem of information transmission. Relevance is solved by the application layer companies today. The Problem How to solve the relevance problem for the Web 3.0 stack?
  5. 5. What is relevant information?
  6. 6. Naftali Tishby Fernando C. Pereira William Bialek The Information Bottleneck method
  7. 7. “We define the relevant information in a signal x ∈ X as being the information that this signal provides about another signal y ∈ Y .” Source: The Information Bottleneck method paper
  8. 8. “Examples include the information that face images provide about the names of the people portrayed, or the information that speech sounds provide about the words spoken.” Source: The Information Bottleneck method paper Mike
  9. 9. Relevant information in behaviours (x) of people on the Web (X) is the information that it provides about other behaviours (y) of other people on the Web. (Y) What if we could extrapolate this definition to the broader definition of “relevance” on the Web:
  10. 10. Increasing relevance on the Internet is all about pursuing the ability to elicit “predictable” behaviours in others. “Fake news”, digital addiction are natural consequences of companies seeking more relevance / predictability.
  11. 11. Single “Evaluation” is an Atomic Signal of Relevance in Networks Website Website Person Photo Bank Account Purchase Like Link “Anchor Text” Product
  12. 12. Aggregated evaluations can be “compressed” by algorithms to extract relevance (predictability) Platform Google Facebook Amazon Context Keyword Social Circle Consumer
  13. 13. Vertical Relevance Subjective Context Dependent Individual Feeds ‘Silicon Valley’ business model Personalization Algorithms In Space Likes, Links, Followers
  14. 14. selling advertising product The aggregator business model of the Web 1.0 and Web 2.0 Extract relevance from as many contexts as possible and convert to money buying advertising product $ $ $ $ $ $ $ $ $ $ $ $cash stock price
  15. 15. Horizontal Relevance Objective Context Independent Shared Ledgers ‘Wall Street’ business model Consensus Algorithms In Time Money, Assets
  16. 16. $ $ $ $ $ $ $ $ $ $ $ loss of information during transitions Silicon Valley Data Silo Wall Street Data Silo You are here You are here
  17. 17. Internet 3.0 will create an explosion of horizontal relevance domains via tokenization. Each of them will be individually evaluated by the market. This will open up a new, temporal axis for establishing future relevance of ideas and information. $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $
  18. 18. Horizontal Relevance Vertical Relevance Token Token Price Token Token Price Token Token Price Tokenisation ? Question: “how to go back from consensus to personalization?”
  19. 19. “Proof of evaluation” is a basic signal of relevance. One Message Type for Both Vertical and Horizontal Relevance A Bmetadata C Cryptographic Key Arbitrary StringArbitrary Metadata Userfeeds protocol
  20. 20. One message type for all evaluations. Like Photo Link URL “anchor text” Token Transfer BlockchainAddress Cryptographic Key
  21. 21. Ranking API Application Ranking AlgorithmRanking Algorithm IPFSEthereum Blockchain Amazon S3 Evaluation Display Rankings Aggregation Publication Protocol Layers
  22. 22. Aggregated Graph of ‘Proofs of Evaluation’ ImageTextVideo
  23. 23. Infinite Evaluation Cycle Relevance Layer as Integral Part of the Web 3.0 stack Ranking API Ranking API Observer Application Observer Ethereum Blockchain IPFS Amazon S3 Amazon S3 Ranking Algorithm Ranking Algorithm Ranking Algorithm Ranking Algorithm IPFS Ethereum Blockchain Amazon S3 Application
  24. 24. Data layer Processing layer Display layer Current Implementation of The Protocol Userfeeds Relevance Platform Ethereum Blockchain X Database AlgorithmsAlgorithms API InterfaceInterfaceInterface Userfeeds Relevance Platform Blockchain Y
  25. 25. Today Proprietary and Separated Evaluation Graphs In Space In Time
  26. 26. InSpace In Time Userfeeds Protocol Open and Integrated Evaluation Graphs
  27. 27. Personalization Consensus Userfeeds Protocol Relevance That Combines Consensus With Personalization
  28. 28. Use Cases
  29. 29. It’s like Google AdSense for Token-based Communities Link Exchange is Coming Soon Click on an ETH icon to see an early preview 18 3.9212 Boost 5 In slot 15% Pay by Metamask33.0001 E T H 0% 100% 72.3% Available: 15.032 ETH Token holders Publishers Advertisers
  30. 30. Link Exchange for Personal ERC20 Tokens
  31. 31. … and it works with Augmented / Mixed Reality too
  32. 32. “Social Network” for Cats … or any ERC721 token
  33. 33. State Of The Dapps & LinkExchange - coming soon Promote dApps with Ether
  34. 34. Thank You @maciejolpinski