Blockchain, AI and Machine Learning

Copyright © SAS Institute Inc. All rights reserved.
Blockchain, AI and Machine Learning
Tao Wang
2019
This presentation is based on information in the public domain
Opinions expressed are solely my own, therefore may not represent the views of my employer

What is Blockchain?
A single-linked list, distributed/duplicated/decentralized
Image source: https://en.wikipedia.org/wiki/File:Bitcoin_Block_Data.svg
2

Part 1:
Bitcoin
3

Bitcoin = the king of cryptocurrencies
source: https://coinmarketcap.com
4
Running on the Bitcoin blockchain

History
• Satoshi Nakamoto
• Created the reference implementation with a
Genesis Block of 50 Bitcoins ($$$ from air)
• 2008
• August 18, Domain name "bitcoin.org"
registered
• October 31 (*Halloween), Bitcoin white paper
published
• November 09, Bitcoin project registered at
SourceForge.net
• 2009
• January 3, Genesis Block was created
• January 9, Bitcoin v0.1 released and announced
• January 12, First Bitcoin transaction, in block 170
5

Who is Satoshi Nakomoto?
A person, a group of people, an alien from the Next Dimension?
6

The Bitcoin paper
Bitcoin: A Peer-to-Peer Electronic Cash System
• Bitcoin blockchain = public/distributed ledger
• Transactions are stored in blocks with duplicates in each node
• Goal: $$$ goes from A to B without going through a financial institution
Source: NANCY LIAO, A BRIEF INTRODUCTION TO BLOCKCHAIN
Source: IBM, Making Blockchain Real for Business: Explained
7

#1 challenge for decentralized digital currency?
double-spending
Source: Huan Ke, Bitcoin/BlockChain for Experts
8

Solution to double-spending
• Timestamp transactions by hashing them into an ongoing chain of hash-
based proof-of-work (mining), forming a record that cannot be changed
without redoing the proof-of-work
Image source: https://en.wikipedia.org/wiki/File:Bitcoin_Block_Data.svg
Source: Huan Ke, Bitcoin/BlockChain for Experts
9

Merkle Tree
Source: https://en.wikipedia.org/wiki/Merkle_tree#/media/File:Hash_Tree.svg
10

Mining = Proof-of-Work
• Mining = finding a nonce value (integer)
• When hashed with a combined hash of all transactions in a block, the
resulting hash begins with pre-defined a number of zeros
• Why is mining so difficult?
• No smart way to find the nonce, you just need to try every integer
• More zeros required = more difficult
Source: Eugene Luzgin, BLOCKCHAIN: Evolution from bitcoin to smart contracts, December 2017
Source: Tong Li, Cryptocurrency, Blockchain, Mining? What?
11

How does it work really?
• New transactions are broadcasted → all nodes
• Each node picks up the info → a block
• Each node tries to do Proof of Work (PoW) for its block
• Consensus
• Mining is difficult!
• Proof of Work = Proof of Waste, sort of
• When a node finds the answer, it broadcasts the block to all
• All nodes will verify & accept if it is valid → verification is easy!
• All nodes create the next block in the chain, independently → see next
page for handling disagreement
12

What if there is a disagreement?
Let’s vote!
• Proof-of-work = CPUs are the voters, one CPU one vote
• Longest (most difficult) chain = greatest proof-of-work
• How to reach Consensus?
• most CPUs are honest
• honest chain will grow the fastest and outpace any competing/dishonest chain
Source: David V Duccini, Blockchain Technology: Bitcoin and Beyond
13

Why are we doing this (mining)?
Incentive
• First block carries 50 Bitcoins (great, money created from the air)
• All the following blocks carry certain amount of Bitcoins (25, 12.5, etc.)
• What are the expenses?
• CPU/GPU time
• Electricity
14

Where to obtain Bitcoins other than mining
Mining is very difficult (1 Bitcoin = 1,000+ years of your CPU time)
• Exchanges
Source: https://coinmarketcap.com/currencies/bitcoin/#markets
15

Privacy
• Money is sent to an account/address (public key)
• Your private key can open the digital wallet
Source: https://www.youtube.com/watch?v=UieiMU-ImvI&list=PLQVvvaa0QuDcq2QME4pfeh0cE71mkb_qz
16

Think twice about your privacy
• What is required
• Transactions should be private
• Identity should not be linked to a transaction
• What is the reality
• Transactions are public
• Some public keys are well-known (no privacy for them)
• Using ML techniques, more details can be discovered
BTC inputs to the famous pizza purchase address (red), depth=4
Source: https://cloud.google.com/blog/big-data/2018/02/bitcoin-in-bigquery-blockchain-analytics-on-public-data
17

Who Accepts Bitcoins?
• Overstock.com
• Newegg.com
• Microsoft XBOX Network
• Telsa Motors
• Time Inc (publisher)
• Virgin Galactic
• Wordpress
• BitPay claims 44,000 merchants!
18

Part 2:
Ethereum and beyond
19

Ethereum
• Ethereum
• Blockchain platform that goes beyond money
• The platform for many other alt-coins & ICOs (Initial Coin Offering)
• Vitalik Buterin
• SEC
- Bitcoin & Ethereum are not securities (will NOT be regulated by SEC?)
- Some ICOs may be
- STO is going to replace ICO?
20

Everything can be tokenized!
21
• Royal Albert Hall, from 1867
• Wimbledon debentures, from 1920
• SEC no-action letter 2006 WL 488513
• Solar farm? Services?

SEC & ICO
22

The Ethereum paper
A Next-Generation Smart Contract and Decentralized Application Platform
• Smart contract = automated process
• Insurance claim been denied by a dishonest provider? Use smart contract!
• Turing-complete
• EVM (Ethereum Virtual Machine)
• Decentralized Applications (DApps)
• Decentralized “Facebook”, “Twitter”, etc.
• Distributed Autonomous Organizations (DAOs)
23

Why is Ethereum so important?
• Decentralized Internet = Web 3.0
• Facebook scandal (centralized Internet)
• GDPR compliance and Sovereign Identity
• Unstoppable AI = Artificial General Intelligence
• Can you turn off all the machines on Ethereum
blockchains?
Source: https://www.youtube.com/watch?v=5Tr13l0O1Ws
24

Public, permissioned, and private blockchains
Source: https://www.youtube.com/watch?v=OLtsQk6qceE
25

Public blockchain vs. permissioned blockchain
Source: https://www.youtube.com/watch?v=Y177TCUc4g0
26

Blockchain for X
Industries what will be disrupted (not replaced) by Blockchain
• Financial (banking, exchanges, cross-border money transfers)
• Insurance
• Claims been denied by a dishonest provider? Use smart contract!
• Supply Chain
• Government
• DMV (car titles)
• County’s public record
• Healthcare
• Retail
• Real Estate
• Online gaming/dating
• Elections, voter ID (Sovereign Identity, can be verified everywhere)
• Your Industry
Source: https://venturebeat.com/2016/10/22/blockchain-tech-could-fight-voter-fraud-and-these-countries-are-testing-it/
Source: https://www.netscribes.com/blockchain-patent-landscape
27

Applications of blockchain
Blockchain for X, continued
Source: https://www.ccn.com/bitcoin-wedding-marriage-on-the-blockchain/ Source: https://www.coindesk.com/walmart-blockchain-pilot-china-pork-market
Source: https://www.coindesk.com/circles-uk-license-blockchain-impact Source: https://bitcoinist.com/visa-blockchain-solution-payments/
28

Blockchain for social network
Steemit
• Creator of Steemit: Daniel Larimer
• Idea: get paid if people like your article
29

Blockchain for banking
Take a look at JPM
30
Source: CNBC, Feb 14, 2019 (Happy Valentine's Day)
Source: J.P. MORGAN, 2018
Source: CNBC, 2017

Blockchain for X
Who is next?
31
Source: Twitter
Source: Bloomberg
Source: nytimes

Industry use cases of permissioned blockchain
Source: https://www.hyperledger.org/wp-content/uploads/2018/03/The-Hyperledger-Vision-11-1.pdf
32

Consensus algorithms
• PoW – Proof of Work
• PoS - Proof of Stake
• Delegated PoS
• PoA - Proof-of-Authority
• PoWeight: Proof-of-Weight
• …
33

HyperLedger
Source: https://www.hyperledger.org
34

Part 3:
A Unified Analytical Framework for
Trustable Machine Learning and
Automation Running with Blockchain
35

Goals
Fusion of blockchain and AI
• Blockchain
• Trusted data source for AI
• AI
• Make blockchain smarter
• Gartner: blockchain "is providing decentralized trust across a network of
untrusted participants. The potential ramifications for analytics use cases
are significant"
36

Which blockchains to use?
So many are out there…
• Public blockchains
• ETH
• TRX
• EOS
• …
• Permissioned blockchains
37

Where is your database?
Data storage on public blockchains can be very expensive
• IPDB (Interplanetary Database), IPFS (Interplanetary File System), others
38
Source: medium.com Source: ipfs.io

A Unified Framework for Trustable AI, Machine Learning and Analytics
AI
Analytics Machine Learning
Blockchain – power of trust
39

Problems and the solution
• Problems
• Trust
- ML uses data from mutable database (system admin & hacker can modify data)
• Automation
- Best timing to train/score/tune
• Solution
• Trust
- ML uses data from immutable blockchain
• Automation
- ML runs as Smart Contract (automated process) for automation
40

Existing work
https://singularitynet.io
• Shoot the moon: blockchain for AGI (Artificial General Intelligence)
- Dr. Ben Goertzel puts some stuff together (Hanson Robotics, OpenCog, etc.)
• Ideas: G2B (GitHub to Business), AI as a service for the AGI token
- Sharing-economy (think Uber) marketplace (GitHub <-> Business) for AI
- A set of Smart Contracts templates, access via the AGI token
• ICO
- Raised $36m in 60 seconds
41
Source: coinmarketcap.com, data retrieved in 2019

Existing work, continued
DanKu protocol
• Down to the earth: Neural Net implementation on Ethereum
• Ideas [6]
- Reward giver submits dataset, evaluation function, a reward amount
- Model providers download the dataset and model, submit the model
- The Ethereum blockchain evaluates the models and selects a winner
• Limitations
- Only works for the Ethereum blockchain
- Other limitations listed in the manuscript [6]
42

Proposed framework
A Unified Analytical Framework for Trustable ML Running with Blockchain [7]
43

Differences with existing work
Between “shoot the moon” and “down to the earth”
• Permissioned blockchain vs. public blockchain
• Proposed framework has little to do with token/money
• Proposed framework vs. SingularityNET
• Not a big fan of AGI (for now)
• Proposed framework vs. DanKu protocol
• Beyond NNET/Ethereum
44

Details
Core Machine Learning
• What is Core Machine Learning?
• Implementation of ML algorithm in its native form
• Single-thread-single-machine
• Components
• Model Initialization
• Model Training
• Model Validation
• Model Scoring
• Model Evaluation
• Model Serialization
• Model Clean-up
45

Details
Server Layer Implementation
46
• What is a Server Layer?
• Think CAS (SMP, MPP)
• Core ideas
• Unified framework to convert “Core ML” to
SMP & MPP
• Data comes from blockchain in “one shot”
through SQL and APIs
• Use case: build model based on the entire
blockchain data, can do heavy-lifting job

Pseudocode Snippet
Server Layer Implementation
Function Thread_main()
{
status = OK; push_exception_handler_to_stack();
wait_for_thread_event {
If (thread_event ==MODEL_INITIALIZATION) status = Model_initialization_at_thread_level(thread_event);
Else if (thread_event ==MODEL_TRAINING) status = Model_training_at_thread_level(thread_event);
Else if (thread_event ==MODEL_VALIDATION) status = Model_validation_at_thread_level(thread_event);
Else if (thread_event ==MODEL_SCORING) status = Model_scoring_at_thread_level(thread_event);
Else if (thread_event ==MODEL_EVALUATION) status = Model_evaluation_at_thread_level(thread_event);
Else if (thread_event ==MODEL_SERIALIZATION) {
status = Model_serialization_at_thread_level(thread_event);
If (status == OK) status = Model_serialization_at_machine_level_for_all_threads(); }
Else if (thread_event ==MODEL_CLEAN_UP) status = Model_clean_up_at_thread_level(thread_event);
Else Break; }
Finish:
pop_exception_handler_from_stack();
Return status;
}
47

Details
Streaming Layer Implementation
48
• What is a Streaming Layer?
• Think ESP (moving windows, streaming)
• Core ideas
• Unified framework to make “Core ML” to
handle streaming data
• Data comes from blockchain “on-the-fly”
through streaming and APIs
• Use case: build online model based on the
ever-growing blockchain data, can do
incremental updates

Pseudocode Snippet
Streaming Layer Implementation
Function Model_validation_in_sliding_window(data_stream)
{
status = OK;
If (data_stream == ERROR || data_stream == EMPTY) goto Finish;
If (OK != Allocate_memory_for_validation_in_sliding_window())
status = OUT_MEM and goto Finish;
While (obtain_validation_data_from_data_stream() == OK)
status = model_validation();
Finish:
If (data_stream == ERROR) status = ERROR;
return status;
}
49

Details
Smart Contract Implementation (contribution lies here)
50
• What is a Smart Contract?
• Fancy word for “Automated Process”
• Running on blockchain
• Core ideas
• Unified framework convert “Core ML” to Smart
Contract
• Blockchain native
• Use case: build model running on blockchain in a
native way as smart contract

Pseudocode Snippet
Smart Contract Implementation
Function Reward_smart_contract()
{
status = OK;
If (is_my_digital_wallet_OK() == FALSE) status = BAD_WALLET and goto Finish;
If (winning_inquiry() == TRUE && total_reward() > 0 && can_collect_reward() == TRUE )
If (share_reward_with_others() == TRUE)
status = collect_divided_reward();
Else
status = collect_all_reward();
Finish:
return status;
}
51

Experiment
• Kaleido permissioned blockchain (an early start-up). Tech issues remain to be solved.
• Consensus algorithm: quorum/raft.
• 3 pharmacies Pharmacy-A, B, C, each logs transaction (or pointer/hash) onto the blockchain.
• All the data are synthetic and all the drugs are opioid (randomly grabbed from the Internet).
• The data was retrieved using SQL from the Kaleido blockchain to a Sever Layer (CAS).
• ARM is applied with: support >= 20%, confidence >= 70%, number of items in a rule <= 3.
52

Results
More results to come…
• 16 rules are found
• “actiq & fentora ==> meperidine”, means that if a patient is prescribed with Actiq and Fentora, this
patient is likely to be prescribed with Meperidine later
• This result does not make sense because the entire dataset is synthetic, and all the drugs are opioids
• However, if given real prescription data, ARM should be able to find trustable rules such as:
1. Some “gateway” non-opioids which may lead to opioids prescriptions.
2. Frequent combinations of opioid and non-opioids which are often prescribed together.
3. Other interesting or surprising rules/analysis.
53

Use AI to make smarter blockchains?
• IIN (Interbank Information Network) from JPMorgan
• AI happens first (fraud/anomaly detection, etc.)
• Then, blockchain does its job
• AI-enabled consensus?
• Smart contract: tuning, auditing, etc.?
• The Impossible Triangle?
• Optimize your position (AI=Optimization)
54

Blockchain moment for AI?
Will it come? When will it come?
• CV moment for AI
• 2006 - 2018
• NLP moment for AI
• 2017 - present
• Blockchain moment for AI?
• TBD
55

Closing remarks
1. Use blockchain for trustable-AI and AutoML
2. Use AI to make blockchain smarter
3. A Unified Analytical Framework for Trustable ML and Automation
56

Key References
• [1] S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System”, retrieved in
2018.
• [2] V. Buterin, “A Next Generation Smart Contract & Decentralized Application
Platform”, retrieved in 2018.
• [3] CoinMarketCap, https://coinmarketcap.com/all/views/all/
• [4] Eugene Luzgin, BLOCKCHAIN: Evolution from bitcoin to smart contracts,
December 2017.
• [5] David V Duccini, Blockchain Technology: Bitcoin and Beyond.
• [6] A. Kurtulmus and K. Daniel, Trustless Machine Learning Contracts;
Evaluating and Exchanging Machine Learning Models on the Ethereum
Blockchain, arXiv:1802.10185, 2018.
• [7] T. Wang, A Unified Analytical Framework for Trustable Machine Learning
and Automation Running with Blockchain, IEEE Big Data Workshops, 2018.
57

58
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