Decentralized AI for the Rest of Us
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Decentralized AI aims to address challenges with centralized AI models, including data and model centralization, transparency issues, and the "rich get richer" problem. The presentation discusses several foundational technologies needed for decentralized AI, including homomorphic encryption, GAN cryptography, secure multi-party computation, and federated learning. It also notes the roles of blockchains and crypto tokens in incentivizing contributions and coordination in decentralized AI networks. Several existing platforms working on decentralized AI applications and services are briefly described.
![A More Practical Example
Alice
Server
(Cloud)
(Input: data x,
secret key sk)
“I want 1) the cloud to process my data
2) even though it is encrypted.”
Encpk[f(x)]
Encpk(x)
function f
f(x)
Run
Eval[ f, Encpk(x) ]
= Encpk[f(x)]
The special sauce! For security
parameter λ, Eval’s running should
be Time(f)∙poly(λ)
This could be
encrypted too.
Delegation: Should cost less for
Alice to encrypt x and decrypt f(x)
than to compute f(x) herself.](https://image.slidesharecdn.com/decentralizedaifortherestofusrev-180914181202/85/Decentralized-AI-for-the-Rest-of-Us-34-320.jpg)
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Decentralized AI for the Rest of Us
- 1. Decentralized AI for the Rest of Us Jesus Rodriguez Chief Scientist Invector Labs
- 2. About Me • Founder and Chief Scientist at Invector Labs(http://invectorlabs.com ) • Next generation software development on-demand agency • Focused on deep technologies (AI, blockchain…) • Speaker, author (https://medium.com/@jrodthoughts) • Investor, board member on over a dozen deep-tech companies
- 3. Agenda • The challenges of centralized AI • Decentralizing AI: risks and promises • Foundational blocks • Homomorphic Encryption • GAN cryptography • Secure multi-party computations • Federated Learning • Blockchains/Tokens • Existing decentralized AI platforms
- 4. Some References • Why Decentralized AI Matters Part I: Economics and Enablers: https://medium.com/datadriveninvestor/why-decentralized-ai-matters- part-i-economics-and-enablers-5576aeeb43d1 • Why Decentralized AI Matters Part II: Technological Enablers: https://medium.com/datadriveninvestor/why-decentralized-ai-matters- part-ii-technological-enablers-a67e3115312e • Why Decentralized AI Matters Part III: Platforms: https://medium.com/datadriveninvestor/why-decentralized-ai-matters- part-iii-technologies-930c3c9d10d • AI Has Not One, Not Two, but Many Centralization Problems: https://hackernoon.com/ai-has-not-one-not-two-but-many- centralization-problems-a5f0664361ed •
- 5. Decentralized AI: Hype vs. Reality
- 6. What does the hype look like?
- 7. HYPE
- 8. HYPE
- 9. HIGH HYPE
- 10. Reality is way simpler…
- 11. We Have Hundreds of Millions of Devices Running AI Models
- 12. Decentralized Architectures Are Here to Stay
- 14. The Evolution of Economic Movements Centralized Decentralized Recentralized
- 15. The Centralized Nature of AI…
- 16. Four Centralization Vectors of AI Data Models Training Regularization- Optimization AI Centralization Vectors
- 17. The Data Centralization Problem
- 18. The Model Centralization Problem
- 19. The Model Centralization Problem
- 21. Let’s Get Philosophical: Risks of Centralized AI…
- 22. The Risks of Centralized AI The Decentralized Knowledge – Centralized AI Friction The Rich Get Richer Problem Risks of Centralized AI The Transparency Influence Ratio
- 23. The Rich Get Richer Problem Big Companies Have a Data Advantage Over Startups When It Comes to AI A Big Company Decides to Create Different AI Models Against Their Proprietary Datasets AI Models Produce Even More Proprietary Data and Intelligence
- 24. Decentralized Knowledge vs. Centralized Models How can we know if the knowledge of an AI model is correct? Knowledge is an intrinsically decentralized activity But companies insist on constraining AI models to proprietary datasets
- 25. The Transparency/Influence Ratio AI algorithms are increasingly important in our lives? But we know very little about them? Who trains it? How was it trained? How does it build knowledge?
- 26. Makes sense… Let’s Decentralize the Whole Thing…
- 27. Not so fast…
- 28. Challenges to Achieve AI Decentralization • Can third parties be correctly incentivized to contribute to the knowledge and quality of an AI model? • Can the activity and behavior of an AI model be transparently available to all parties without the need of trusting a centralized authority? • Can models be distributed and executed autonomously across hundreds of thousands of nodes? • Can entities train a model without having to disclose their data? The Privacy Problem The Autonomy Problem The Economic Problem The Transparency Problem
- 29. Trends Influencing Decentralized AI…
- 30. Data Privacy in Decentralized AI Architectures… You know…. security and that stuff
- 31. The Challenges • How to share data with different parties while maintaining certain levels of privacy? • How to perform computations over encrypted data? • How to become resilient to privacy attacks in a decentralized network?
- 33. Homomorphic Encryption • Alice wants workers to assemble raw materials into jewelry. • But Alice is worried about theft: • She wants workers to process raw materials without having access. • Alice puts raw materials in a locked glovebox. • Workers assemble jewelry inside glovebox, using the gloves. • Alice unlocks the box to get “results.”
- 34. A More Practical Example Alice Server (Cloud) (Input: data x, secret key sk) “I want 1) the cloud to process my data 2) even though it is encrypted.” Encpk[f(x)] Encpk(x) function f f(x) Run Eval[ f, Encpk(x) ] = Encpk[f(x)] The special sauce! For security parameter λ, Eval’s running should be Time(f)∙poly(λ) This could be encrypted too. Delegation: Should cost less for Alice to encrypt x and decrypt f(x) than to compute f(x) herself.
- 35. Full vs. Partial Homomorphic Encryption • Given the encryption of two data primitives a and b: E(a) and E(b) • Partial homomorphic encryption can compute E(a+b) or E(ab) without knowing a, b, or the private key • Unpadded RSA https://en.wikipedia.org/wiki/RSA_cryptosystem • ElGamal https://en.wikipedia.org/wiki/ElGamal_encryption • Paillier https://en.wikipedia.org/wiki/Paillier_cryptosystem • Full homomorphic encryption can compute both E(a+b) and E(ab) • Gentry • van Dijk-Gentry-Halevi-Vaikuntanathan
- 36. Homomorphic Encryption and Decentralized AI HEnc(Dataset) HEnc(Results) Data Scientists
- 37. Homomorphic Encryption Today • Remains mostly a theoretical exercise • Companies such as IBM and Microsoft have released research frameworks for partial homomorphic encryption • HElib https://github.com/shaih/HElib
- 39. GAN Cryptography • Simpler alternative to homomorphic encryption • Uses generative adversarial neural networks (GANs) to protect communications between two parties • The encryption algorithms evolve dynamically with the performance of the networks
- 40. GAN Cryptography • Alice and Bob are neural networks trying to communicate securely while Eve tries to break the communication • The outputs from Eve (Peve) are factored in the lost function for both Alice and Bob • Alice and Bob both discover new encryption algorithms that can defeat the best version of Eve
- 41. GAN Cryptography and Decentralized AI GAN Data Provider GAN Data Scientist GAN Data Scientist GAN Listener Enc(Dataset)
- 42. GAN Cryptography Today • Remains mostly an active research area • Some lightweight implementations like Numerai are available
- 44. sMPC • Solves the traditional millionaires’ problem for n-parties • A group of millionaires, are interested in knowing which of them is richer without revealing their actual wealth. This problem is analogous to a more general problem where there are two numbers a and b and the goal is to solve the inequality without revealing the actual values of a and b. • Enable secure computations in a network without a trusted party
- 45. sMPC • A set of parties with private inputs • Parties wish to jointly compute a function of their inputs so that certain security properties (like privacy and correctness) are preserved • Properties must be ensured even if some of the parties maliciously attack the protocol • Examples • Secure elections • Auctions • Privacy preserving data mining
- 46. sMPC and Decentralized AI HEnc(Dataset) HEnc(Results) Data Scientists
- 47. sMPC Cryptography Today • Several sMPC frameworks and libraries available • The Enigma blockchain (https://enigma.co/ ) is one of the most complete and scalable sMPC implementations in the market
- 48. Solving Decentralized Learning… How to learn without the man?
- 49. The Challenges • How to execute machine learning models on mobile or IoT devices? • How to customize a machine learning model based on personal data? • How to improve a machine learning model based on executions across a large number of devices?
- 56. Federated Learning and Decentralized AI AI Execution Node AI Execution Node AI Execution Node AI Execution Node Federated Training Node Federated AI Training- Execution Node AI Training- Execution Node AI Training- Execution Node AI Training- Execution Node AI Training- Execution Node Decentralized
- 57. Federated Learning Today • There are several implementations available on TensorFlow Lite • Google has been testing federated learning on Gboard https://www.blog.google/products/search/gboard-now-on-android/
- 58. A Runtime for Decentralized AI… How do we run these things?
- 59. Challenges • How to train models without a centralized authority? • How to use trained models without trusting a central party? • How to validate and enforce trust among the different parties in the lifecycle of a deep learning model
- 60. Blockchains and Smart Contracts…
- 61. Blockchains and Smart Contracts
- 62. Smart Contracts and Decentralized AI Data Scientist (builds and train model) Blockchain Smart Contract Blockchain Smart ContractCompany Data Scientist (tests and optimizes model) Data and Success Criteria Blockchain Smart Contract
- 63. Solving the Incentive Problem… How to pay the data geeks?
- 64. Challenges • How to build incentives for data scientists training, testing and building models? • How to build incentives for parties contributing datasets? • How to discourage bad behaviors in the network?
- 66. Crypto Tokens
- 67. Tokenized Decentralized AI Data Scientist (builds and train model) Blockchain Smart Contract Blockchain Smart ContractCompany Data Scientist (tests and optimizes model) Data and Success Criteria Blockchain Smart Contract Tokens Tokens
- 70. SingularityNET • Network powering Sophia • DApp Marketplace: The SingularityNET DApp is the entry-point to discovering and using AI services on the SingularityNET Network • SNET Registry: The SingularityNET Registry is an open and uncensorable registry of Organizations, AI Services, and Type Repositories that are accessible from within the SingularityNET Network. • Service Daemon: The Service Daemon exposes an AI developer's application as an API that is accessible through the SingularityNET Network.
- 71. OpenMined • Open source community focused on creating decentralized AI protocols • Sonar— A federated learning server running on the blockchain that handles all campaign requests, holding Bounty in trust. • Capsule— A third-party PGP server to generate public and private keys in order to ensure that Sonar neural network stays encrypted properly. • Mine— The individual data repositories of a user. These are constantly checking Sonar for new neural nets to contribute to. • Syft— The library containing Neural Networks that can be trained in an encrypted state (so that Miners can’t steal the neural networks that they download to train).
- 72. Algorithmia DanKu • Created by Algorithmia • Decentralized protocol for running machine learning contests • Users can publish datasets using smart contracts • Models are trained on the datasets and submitted to the blockchain • Other models evaluate the results and compensate the users
- 73. Ocean • Ocean Protocol is an ecosystem for sharing data and associated services • Providers: These actors have AI data or services that they make available in a cryptographically provable fashion. • Marketplaces: Data/service marketplaces are typically how providers and consumers interact with Ocean network, for convenience. • Data commons interfaces: Side-by-side with data marketplaces that serve priced data are interfaces for data commons, for free or commons data. • Keeper: Keepers are responsible for collectively maintaining the network. Anyone can run an Ocean keeper node; it’s permissionless. Participation is open and anonymous.
- 74. Summary • The future of AI is likely to be more decentralized AI • Achieving decentralized AI requires 3 key building blocks • A decentralized runtime to execute computations • Decentralized and scalable learning methods • Strong privacy protocols for the exchange of data and models • Blockchains have become catalyzers for the implementation of decentralized AI architectures • It’s happening already