Uploaded byMike Slinn
Smart Contracts That Learn
This document discusses how smart contracts may incorporate machine learning capabilities in the future. It notes that while smart contracts themselves cannot literally learn, machine learning could be used with oracles, off-chain smart contracts, and other systems that interact with smart contracts. The document also cautions that security will need to improve and that non-determinism from machine learning may cause issues for blockchain consensus. It provides examples of how machine learning could be applied, such as for fraud detection, customer service agents, and medical diagnosis.
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Smart Contracts That Learn
- 1. Smart Contracts thatLearn Mike Slinn April 3, 2018 Global Blockchain Conference
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- 4. About Mike Slinn â˘Distinguished engineer ⢠Contributor to Ethereum Java and Scala libraries ⢠Operates ScalaCourses.com ⢠Publisher of Java / Node.js Ethereum courses (www.cryptocourses.tech) ⢠Author of EmpathyWorks (artificial personality) ⢠Expert witness ⢠Twitter: mslinn
- 5. Key Facts aboutMike Slinn ⢠Focuses on generating business value by applying people, process and technology ⢠Wrote 3 books on distributed computing ⢠Created hundreds of online lectures on advanced computing concepts ⢠Uses many computer languages (âpolyglotâ)
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- 7. The most popularlanguage or framework used to write smart contracts in 10 years probably has not been conceived of yet. -- Mike Slinn, March 3, 2018 --
- 8. Machine Learning &Smart Contracts ⢠Machine learning (ML) will be commonly used with smart contracts in 10 years. ⢠Highly specialized languages will evolve to define and verify contracts in various industries. ⢠ML will have features added to guarantee deterministic results for decentralized apps. ⢠Security will surely have to improve
- 9. Espionage ⢠In 10years time corporate and nation/state espionage will include: o Sabotaging training data o Spoofing smart contract events o Seeding smart contract templates with security weaknesses
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- 11. Dogma and ideologyare not good for business Dial Back the Koolaid
- 12. Confession Onchain smart contractscannot actually learn However, these can learn: ⢠User interfaces for on-chain distributed applications (ÄApps) ⢠Oracles ⢠Gateway contracts ⢠Off-chain smart contracts (systems integrated via json- rpc)
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- 14. Beware Non-Deterministic Behavior â˘Blockchain requires determinism for consensus ⢠An AI-driven product may not have deterministic behavior and may produce counter-intuitive results ⢠A personalized recommender system may produce different results for a user action after learning additional preferences
- 15. ML is CurrentlyOffchain ML is currently centralized offchain, because significant computation and storage are required ⢠Centralized, offchain processes can respond to onchain events and initiate activity via json-rpc or IPC to Ethereum clients ⢠ML likely not decentralized for several years
- 16. âDecentralizationâ is Misleading â˘Decentralization uses consensus before output is accepted from multiple EVMs, which then leads to transactions. ⢠Centralization does not use consensus; note that the web3 definition of centralization includes distributed applications that employ DNS with geolocation routing and failover.
- 17. Security Issues ⢠Discussionlater in this talk o Features/Complexity versus Security o Solidity is misnamed
- 18. Solidity is NotRequired ⢠Centralization is optional ⢠System integration strategies o ML systems can indirectly interact with the blockchain using json-rpc or IPC to an Ethereum client such as geth or Parity o Native apps can combine ML with blockchain ⢠Application decentralization is optional ⢠Solidity is optional
- 19. Issues With Solidity: â˘Primitive type system. ⢠Compiler bugs (more surely exist). ⢠Few software tools available. ⢠Expensive to work with. ⢠Very expensive to maintain. ⢠Shelf life for this technology will be short.
- 20. Avoid Solidity IfPossible ⢠Write the smart contract in the language of your choice, and use json-rpc calls as desired. ⢠Resulting code will be well understood by all. ⢠Audits will be more reliable. ⢠Costs will be much less using a common language instead of Solidity. ⢠Talent will be much easier to find.
- 21. Ethereum Is NotSymmetric ⢠Onchain smart contracts are distributed and use consensus ⢠Offchain smart contracts (using Ethereum clients) are centralized and do not use consensus ⢠This will likely evolve over the next several years
- 22. Transpiling ⢠Process ofconverting a program written in one language into another language. ⢠Solidity could be transpiled to json-rpc calls from node.js and JVM languages (Java, Scala, JRuby, Jython, Groovy, etc). ⢠⌠but donât bother because you endure all the problems with Solidity and get none of the benefits of native contracts
- 23. Use Cases With ArchitectureDiagrams
- 24. Self-Optimizing Contracts ⢠Optimizetransactions for greatest margin, minimal waste, constant deal flow, or other criteria ⢠Results would improve over time ⢠This is an onchain example
- 25. Genetic algorithms Evolution strategies Evolutionaryprogramming Simulated annealing Gaussian adaptation Hill climbing Swarm intelligence Integer linear programming
- 26. Fraudulent Event Detection â˘Smart contracts currently act on all events ⢠Fraud detection often employs machine learning ⢠Incorporating ML into smart contracts could make them resistant to fraud ⢠This is an onchain example
- 28. Automated Customer ServiceAgents ⢠Chatbots and voice interfaces (Alexa) ⢠Much more natural to use ⢠Can be built into devices ⢠This is an onchain example
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- 30. Medical Diagnosis ExpertSystem ⢠Smart contract mediates access to an expert system (oracle, incorporates machine learning) ⢠Accepts anonymous patient data ⢠Passes data to an expert system that performs analysis ⢠Returns diagnostic results ⢠Charges for the service ⢠This is an offchain example
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- 32. Supply Chain ⢠SOLIDITYIS NOT REQUIRED ⢠Native application (JVM, .NET, C++, whatever) uses json-rpc to interact with the blockchain ⢠Solidity could be transpiled to json-rpc calls, but donât bother as previously discussed ⢠This is an offchain example
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- 35. Traditional Contracts⌠⢠Outlinethe terms of a relationship ⢠According to a specific jurisdiction ⢠So that the specified government can enforce the terms
- 36. Smart Contracts⌠⢠Enablerule-based autonomous actions in response to events. ⢠Work within and between organizations and the rest of society, world-wide.
- 37. Smart Contracts ThreatenTradition ⢠Enforce a relationship with cryptographic code ⢠Without regard to the jurisdiction of any government
- 38. System Integration ⢠Collectinginputs and outputs to smart contracts requires system integration. ⢠Approaches to system integration vary widely according to the nature of the input sources, output destinations, volumes of data, required reliability, fairness (near-constant latency), etc.
- 39. Smart Contracts ⌠â˘Also known as cryptocontracts ⢠Are computer programs ⢠Directly control the transfer of blockchain-based digital currencies or assets ⢠Define the rules and penalties for an agreement ⢠Might automatically enforce those obligations
- 40. Smart Contract Capabilities Managerelationship between parties by: ⢠Maintaining virtual ledgers ⢠Reading/writing arbitrary on-chain data ⢠Reading/writing off-chain data ⢠Forwarding events to other contracts ⢠Acting as a software library
- 41. Smart Contract Requirements â˘Observability ⢠Verifiability ⢠Privity ⢠Enforceability
- 42. Oracles ⢠Smart contractsneed oracles to resolve details that cannot be precisely known at the time the contract is written. ⢠Oracles provide reference information for smart contracts. ⢠An oracle is a REST API connected to a data source. ⢠Using oracles generally decreases security
- 43. Smart Contract Platforms â˘Bitcoin ⢠Cardano (eventually) ⢠DFinity ⢠EOSIO ⢠Ethereum ⢠Hyperledger ⢠Lisk ⢠Pact on Kadena ⢠NEO ⢠Quorum ⢠Qtum ⢠RSK ⢠Stratis ⢠Wanchain
- 44. Ethereum Virtual Machine(EVM) ⢠Deterministic. ⢠Each Ethereum node has an EVM instance. ⢠EVM has a similar execution model to both the Java and the .NET virtual machines. ⢠All these VMs are stack machines executing bytecode. ⢠EVM adds storage and its bytecode is more suited for contracts.
- 45. Viable Smart ContractLanguages All compile to EVM byte code except Chaincode ⢠AxLang ⢠Chaincode (Hyperledger) ⢠LLL ⢠Pact (Kadena) ⢠Solidity
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- 47. Solidity ⢠Solidity contractsare difficult to secure. ⢠Formal verification could help. ⢠Most Solidity contracts ignore security recommendations. ⢠Solidityâs support for types is rather primitive.
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- 50. AxLang ⢠Smart contractlanguage designed to support formal verification. ⢠Cross-compiled Scala DSL for Ethereum ⢠Designed to scale ⢠Not yet ready
- 51. Pact / Kadena â˘Functional, interpreted Lisp-like syntax ⢠Features type inference ⢠Similar to database stored procedures in an online transaction processing (OLTP) system ⢠Not Turing complete ⢠Runs on the Kadena blockchain
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- 53. json-rpc ⢠Popular communicationsprotocol ⢠Communicates between off-chain applications and an Ethereum client
- 54. Some json-rpc Libraries â˘web3.js â for node.js o Can also transpile Solidity to JavaScript ⢠web3j â for Java ⢠Can also transpile Solidity to Java ⢠web3j-scala â for Scala o (I wrote this one) o Idiomatic Scala wrapper around web3j
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- 56. How do ComputersLearn? ⢠Trial and error with feedback ⢠Training
- 57. Types of MachineLearning Classifier systems Reinforcement Representation Rule-based Similarity and metric Sparse dictionary Support vector machines Association rule Artificial neural networks Bayesian networks Clustering Decision tree Deep Genetic algorithms Inductive logic programming
- 58. How Might SmartContracts Learn? ⢠âLearningâ computation must occur off-chain ⢠Enforced by the Ethereum fee structure
- 59. Machine Learning Applications Machineperception, including computer vision and object recognition Optimization and metaheuristic Recommendation systems Robot locomotion (autonomy) Sentiment analysis Speech and handwriting recognition Structural health monitoring Syntactic pattern recognition User behavior analytics Translation Automated theorem proving Adaptive websites Bioinformatics Classifying DNA sequences Detecting credit-card fraud General game playing Information retrieval Internet fraud detection Insurance Linguistics Medical diagnosis
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- 61. Security / ComplexityTradeoff
- 62. Solidity and Security â˘Contracts written in Solidity are difficult to make secure. o Formal verification could help. ⢠Solidityâs support for types is primitive
- 63. Security Cannot BeRetrofitted ⢠Secure systems can only be designed that way from the start o Trying to secure an existing platform can only give marginal improvements ⢠Need orders of magnitude of improvements to smart contract security o Not possible without a fresh start
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- 65. Thank you! Questions? MikeSlinn mslinn@micronauticsresearch.com 650-678-2285