A talk given at North Star AI conference 2018 by Dr Maxim Orlovsky uncovers technical details of the possible intersections between AI and blockchain technologies. In particular – how blockchain and our Pandora Boxchain project can help solve Byzantine fault tolerance problem in autonomous multi-agent environments and decrease strategic risks coming from the emerging artificial intelligence

1. How AI benefits from Blockchain
and Game Theory with
Scalable Censorship-resistant
Consensus
dr. Maxim Orlovsky & Pandora Foundation Team

2. Multi-agent systems
• AI is not singular – it is multi-agent:
autonomous vehicles, IoT, mobiles, etc.
• At some point in future(?)
agents will become interacting.
• Multiple V2V, V2E, E2E protocols will evolve,
which will face byzantine faults
and multiple security issues

3. Imagefromsitewww.artlebedev.ru

4. Distributed Systems &
Byzantine Fault Tolerance
• The Byzantine General’s Problem was first proposed by Lamport,
Shostak, and Pease in 1982.
• A problem of making a reliable system from unreliable parts
(Ethan Buchman, Cosmos Network)
• From 1982 to 1999, no one had invented a solution for the problem
• Non-blockchain based solutions (Paxos, Raft, DLS, BPFT) had limited
fault-tolerance and were used in trusted setups (space communications
etc).
• Bitcoin opened a way to enhance byzantine tolerance due to economical
stimuli and game theory. It gave rise to other blockchain technologies.

5. Blockchain:
Distributed Ledger Technology (DLT)
• Trusted Persistent Data Records
• Inside Trustless Environments
• Without Central Governance
• Secured By Economic Incentives

6. Blockchain Key Parameters
• Security: (sometimes called “immutability“ or “persistence“):
past history does not change.
Leads to audibility and accountability
• Liveness: availability to post new transactions
Liveness intrinsically means censorship-resistance
• Blockchain-based distributed systems achieve these qualities
by following the same consensus protocol
• Non-faulty behavior is supported by
economic incentives (like mining rewards)

7. Blockchain
• Replicative state machine
(type of distributed database)
+ asymmetric cryptography (accountability)
+ ordered immutable set
of historical state changes (audibility)

8. Consensus Protocol
• Required for public blockchains
(”private/federated blockchain” is an oxymoron)
• Adds rules by which distributed parts
of replicative state machine reaches consensus
on the current state
• Achieves that by adding economic incentives
for participants following the protocol utilizing
specially-designed game theory settings
(cryptocurrency)

9. Blockchain + Consensus Results:
• Solution to byzantine generals problems
(no other way to solve it)
• i.e. appearance of trust in trustless
environments (due to accountability and
audibility)

10. Technology
• Distributed database (ledger)
• Distributed computations
(state changes),
Turing-complete OR incomplete
• Peer-to-peer mesh network
• Cryptographically secured
Economics
• Multiagent economy
• Game theory
• Free open market
• Non-state decentralized
economies linked to particular
types of resources
or businesses
BLOCKCHAIN

11. Block
data Block = Block
{ header :: BlockHeader
, blockSize :: Int
, transactionCount :: Int
, transactions :: [Transaction]
}
data BlockHeader = BlockHeader
{ version :: Int
, prevBlockHeader :: Hash32
, merkleRoot :: Hash32
, timestamp :: Int32
, ...
}
Hash of Previous
Block Header
Merkle Root
Block 1
Transactions
Block 1
Header
Hash of Previous
Block Header
Merkle Root
Block 1
Transactions
Block 2
Header

12. Smart Contracts
• Proposed by Nick Szabo much before blockchain
• Can automate multi-agent AI-based systems
• Used since bitcoin in most of blockchains
to automate state transition changes according
to some rules
• Turing-incomplete & Turing-complete

13. Turing-incomplete
• Bitcoin (multisigs, lightning, sidechains)
and bitcoin clones
• Limited functionality
(but robust and easily formally verified)

14. Bitcoin Smart Contract
<expiry time>
OP_CHECKLOCKTIMEVERIFY
OP_DROP
OP_DUP
OP_HASH160
<pubKeyHash>
OP_EQUALVERIFY
OP_CHECKSIG
<sig>
<pubKey>

15. Turing-complete
• Ethereum & Ethereum Classic, Rootstock
(all EVM-based)
• Highly-vulnerable
• ”Single-threaded”, unscalable:
all computations are repeated by each node

16. EVM Smart Contract on Solidity
library StateMachineLib {
struct StateMachine {
bool initialized;
uint8 currentState;
mapping(uint8 => uint8[]) transitionTable;
}
function transitionToState(StateMachine storage _machine, uint8 _newState) internal {
// Checking if the state transition is allowed
bool transitionAllowed = false;
uint8[] storage allowedStates = _machine.transitionTable[uint8(_machine.currentState)];
for (uint no = 0; no < allowedStates.length; no++) {
if (allowedStates[no] == _newState) {
transitionAllowed = true;
}
}
require(transitionAllowed == true);
}
...

17. Problems
Why blockchains didn’t conquer the world

18. Consensus
How to define who has the right to sign
a new block of state changes (transactions)?
Randomness
• Physical process:
PoW – energy-consuming
• On-chain randomness:
early PoS – predictable
• Off-chain randomness:
PoS with oracles – centralised
Agreement
• BFT-algorithms (since 1980):
PoS – vulnerable
• Delegation/voting:
dPoS – centralised
• Most have problems with finality
(probabilistic at most)

19. Smart Contracts
• ‘Single-threaded’ global computing
(scalability)
• Consensus depends on contract correctness
• Value depends on contract correctness
• Rich contracts can’t be truly formally verified

20. Non-solutions:
objectives are substituted by means
• Blockchain is neither self-aim nor any kind of distributed
technology
• “Blockchain” without cryptocurrency:
no economical incentives layer ->
no protection from not following consensus
• Private/Federative “blockchain”:
just a distributed DB with unnecessary computational burden.
no true liveness and security
• Such “blockchains” are just a word
for PR/marketing/funding purposes

22. Prometheus: Hybrid Consensus for AI
• Smart contracts extended to generic computing
(suitable for AI models) that can run in parallel
in trustless decentralised network
• Computing results are proved by specially-designed
PoW consensus
• Value transferred by independent PoS-consensus
• Randomness is created by computing actual models
on actual data (decentralised oracle)

23. Proof of Reputation
(using reputation)
Validators
Proof of Computing Work
(producing reputation)
Full Node Workers Verifiers Arbiters
Reputation level
increase
Computing work /
reward ratio increase

24. Prometheus Layers
Proof of Cognitive Work:
P2P state channels
with Nash equilibrium achieved only for
a correct AI model training/inference
Poseidon
Proof of Reputation Blockchain:
State Machine/Settlement Layer with
three-tier scalability
• blockchain – Hyperion
• sidechains – Talassa
• state channels – Tethys

25. Poseidon
• Tasks (AI model training/inference) is split in batches.
Each batch computed in parallel by some Workers.
• Each task & batch is verified by three Verifier (using
testing set for training or repeating 10% of random
samples for inference).
• Both Workers & Verifiers have stake,
Verifiers additionally have reputation.
• If Verifiers confirm computing then task is complete
Worker and Verifiers get paid + Workers get mining reward
https://github.com/pandoraboxchain/pyrrha-consensus

26. Poseidon Arbitration
• If result is not confirmed then Worker either
can be penaltised with its stake OR it can apply
for Arbitration
• There are two-tier Arbitration procedure which provably
leads to correct result.
Nodes found faulty are penaltised with stake/reputation
and correct nodes are rewarded with reputation & paid.
• This leads to Nash equilibrium without repeating all
computations on each node of the network.

27. Hyperion: replicative state machine
for settlements of Poseidon computing
• Randomness is taken from PoCW
• No Turing-complete computing
• Provable finality & security
• Liveness and censorship-resistance
• Three-tier scalability
(blockchain, sidechains, state channels)

28. Imagefromsitewww.artlebedev.ru

29. MALICIOUS AGENT
DETECTION IN
AUTONOMOUS
AI-DOMINATED SYSTEMS

30. Other Key Properties
Unique:
• Censorship resistance
• Zero governance
• Scalability
• Hybrid consensus with
PoS/PoW model
Common:
• Open markets for models,
big data and computing
power
• Economic incentives for
participants
• Zero-knowledge

31. What Pandora Boxchain
will also give AI?
• Privacy & zero knowledge:
fixing “loss of privacy” problem
• Free, open markets (models, big data, computing
power): enabling faster progress and fair rewards
• Mitigating strategic risks from AI
with game theory, economics & byzantine fault
tolerance –instead of “ethics” which would not work

32. The Solution
Instead of:
AI regulations
Big data regulations
AI “Kill switch”
“Azimov laws”
Use:
Economic incentives
Zero knowledge
Game theory
Audibility & accountability
Blockchain

33. Maxim Orlovsky PhD, MD
orlovsky@pandora.foundation
/pandoraboxchain
github.com/pandoraboxchain
Special thanks to Dmitry Litvinov
for the presentation design
& Pandora Foundation Team
www.pandoraboxchain.ai

34. … one more thing

35. A bit of futurism

36. AI Today: Limited Progress
Engineering over science –
lack of new scientific paradigms:
• Practical progress comes from computational
resources and big data
• CNN, Deep learning, Reinforcement learning,
AGN etc represent engineering innovations

37. New Directions
Engineering:
• Multi-agent systems
• Distributed/
decentralised systems
• New biologically-
inspired cognitive
architectures
Science:
• Game theory
• Byzantine fault
tolerance
• Complexity science

38. ⊂ Blockchain
Engineering:
• Multi-agent systems
• Distributed/
decentralised systems
• New biologically-
inspired cognitive
architectures
Science:
• Game theory
• Byzantine fault
tolerance
• Complexity science

39. happens when
selection acts
on a diverging
population
while
modern neuronets
are singular
Evolution

40. •species exchanges genes via viruses
•cells emit chemical and electric signals
•animals communicate via mimics and emotions
•humans — via language
Information transfer:
What about #AI?

41. requires
Consensus
Persistence
Liveness
Information transfer:

42. •DNA translation will be the same for
bacteria as for a human being
•written text is understood in the same
way by all native speakers
Consensus:
each agent gets the same
sense of a signal as others

43. •DNA in chromosomes do not change during
the live of the most cells
•Egyptian hieroglyphs could still be
understood by researchers
Persistence:
the sense of the signal
does not diminish with time

44. Is perfect both for
consensus and persistence
rendering multi-agent #AI
as an evolving self-organized system
BLOCKCHAIN: