My seminar lecture session on Decentralised AI through Distributed Ledger Technologies in the second National Seminar on Machine Intelligence organised by University of Kerala, Department of Computer Science on 24th January 2020. I have covered the foundations of distributed ledger technologies, decentralisation roadmap, decentralised AI and decentralised data exchanges in this session.

1. DECENTRALISED AI THROUGH
DISTRIBUTED LEDGERS
National Seminar on Machine Intelligence 2020 -
University of Kerala, Department of Computer Science,

2. The current context of centralisation in AI
• During the last few years, AI has
evolved in centralised models across
different aspects of the lifecycle of AI
models.
• Increasing gap between the AI
innovations in large companies and
small scale startups
• Big companies control large datasets .
Large datasets are used to create AI
models. AI models generate high quality
data

3. Computation Challenge for Decentralised AI
• Decentralised distributed ledgers such
as Blockchain protocols are still very
limited in capability to execute
computations such as the ones
required by deep learning models.
• Decentralised AI networks require off-
chain computation models which
requires scalable infrastructure.

4. Incentive Challenge for Decentralised AI
• Decentralised AI models need to rely on
incentive mechanisms to motivate
different parties to participate in a
network
• Smart Contracts and Tokenisation
implementations on Distributed Ledgers
can help to realise incentives and rewards
• However the incentive structures should
be safeguarded against attack vectors
from bad actors to manipulate the
behaviour of a network

5. Decentralisation Roadmap
• Decentralisation of Datasets
• Decentralisation of Data Pipelines
• Decentralisation of Training Algorithms
• Decentralisation of Automation Algorithms
• Decentralisation of Activation Functions
• Decentralisation of Training Infrastructure
• Decentralisation of Deployment Infrastructure

6. The challenges for centralised AI

7. A contemporary dream on a society of minds !

8. Key Components of Decentralised AI

9. WHAT IS DISTRIBUTED LEDGER
TECHNOLOGY ?

10. A DIGITAL SYSTEM FOR RECORDING THE
TRANSACTION OF ASSETS IN WHICH THE
TRANSACTIONS ARE RECORDED IN
MULTIPLE PLACES AT THE SAME TIME.

11. THERE IS NO CENTRAL ADMINISTRATOR
OR CENTRALISED DATA STORAGE
REQUIRED IN A DISTRIBUTED LEDGER.

12. A CONSENSUS OF REPLICATED, SHARED, AND
SYNCHRONISED DIGITAL DATA
GEOGRAPHICALLY SPREAD ACROSS MULTIPLE
SITES, COUNTRIES, OR INSTITUTIONS.

13. A PEER TO PEER NETWORK AND A
CONSENSUS ALGORITHM IS REQUIRED TO
ENSURE REPLICATION ACROSS NODES.

14. It is important to understand the difference
between centralised and distributed ledger

15. There is a definite differentiation between
centralised, decentralised and distributed ledgers

16. Legends of Ledgers

17. Recent Innovations in Ledger Technology

18. Focused History of Distributed Ledgers

19. An extended history of distributed ledgers

22. Principles of Distributed Ledgers

23. Blockchain Network and Node Anatomy

24. A simple view on blockchain data structure

25. Hash Pointers in a Blockchain Data Structure

26. The end to end transaction in a Bitcoin Network

27. Connection of Blocks and
Collection of Transactions

28. Structure of Blockchain in Ethereum Network

29. The Internals of Hashing Algorithms in
Blockchain Data Structure

30. Development of Wallet Address through
Cryptographic Algorithms

31. Key Management in a Bitcoin Blockchain

33. The message padding in secure hashing algorithm

34. Essential Properties of Hash Functions

35. Message Blocks and Block Ciphers in SHA - 256

36. Block Data Hashing in a Bitcoin Blockchain

37. Fundamentals of Elliptical Curve Cryptography
based Digital Signature Algorithm

38. Depiction of Elliptical Curve
Signing and Verification Actions

39. End to End Steps in Digital Signature Verification

40. Transformation of Data in a Digital Signature

41. Blockchain Protocol Components and Categories

42. Architecture Layers - Important Views

43. Architecture Layers - Integration Models

44. Consensus Algorithms - Reference Architecture

45. Consensus Algorithms - Security Models

46. Consensus Algorithms - Important Milestones

47. Consensus Algorithms - Protocols and Platforms

48. Consensus Algorithms - Deep Dive

49. Consensus Algorithms - A Comparison

50. Nakamoto Consensus Algorithm - Components

51. Nakamoto Consensus Algorithm - A Deep Dive

52. PBFT Consensus - Hyperledger Implementation

53. Client Interaction and Data Storage on a
Blockchain Protocol Implementation

54. Additional Hybrid and Private Networks on
Blockchain Protocol and Data Model

55. Enterprise Blockchain Reference Architecture

56. A Cloud based Production Scale
Blockchain Deployment Architecture

57. Healthcare Provider Patient
Data Management on Blockchain Protocol

58. A Wireless Sensor Network on Blockchain Protocol

59. Big Data Integration Architecture for
Blockchain Driven Data Marketplaces

60. Blockchain for AI Algorithms
• SingularityNET - SingularityNET is a
distributed AI platform on the
Ethereum blockchain, with each
blockchain node backing up an AI
algorithm.
• Intuition Fabric - The goal of Intuition
Fabric is to democratise access to AI
through a network of deep learning
models that are stored on the
interplanetary file system and accessed
through the Ethereum blockchain.

61. Blockchain for AI Algorithms
• Cerebrum - Cerebrum is a decentralised
platform for crowdsourced machine
learning. Cerebrum enables anyone to
encrypt their data and host machine
learning competitions to utilise crowd-
sourced machine learning models.
• OpenMined - OpenMined is a
community focused on building open-
source technology for the decentralised
ownership of data and intelligence.
With OpenMined, AI can be trained on
data that it never has access to.

62. Blockchain for AI Algorithms
• Effect.ai - Effect.ai is a blockchain-
powered, decentralised platform for
Artificial Intelligence development and
AI related services.
• Decentralised Machine Learning -
DML aims to create a blockchain-based
decentralised machine learning protocol
and ecosystem.
• Raven Protocol - Raven Protocol is a
decentralised and distributed deep-
learning training protocol.

63. Blockchain for AI Algorithms
• Thought Network - Thought's
blockchain-enabled Fabric
fundamentally changes applications by
embedding artificial intelligence into
every bit of data making it agile,
actionable and inherently secure.
• Matrix AI - Thought's blockchain-
enabled Fabric fundamentally changes
applications by embedding artificial
intelligence into every bit of data
making it agile, actionable and
inherently secure.

64. Blockchain for AI Algorithms
• Pandora Boxchain - A marketplace for AI
kernels, computations and big data
powered by Proof of Cognitive Work
(PoCW).
• Fitchain - Fitchain is a blockchain-based
machine learning factory that allows data
scientists to train models, tracing them from
inception and throughout their lifetime.
• Cortex Labs - Cortex Labs is a
decentralised AI platform with a virtual
machine that allows you to execute AI
programs on-chain.

65. Blockchain for Datasets
• Ocean Protocol - Ocean Protocol is a
decentralised data exchange protocol
that lets people share and monetise data
while guaranteeing control, auditability,
transparency and compliance to all
actors involved. Its network handles
storing of the metadata (i.e. who owns
what), links to the data itself, and more.
• Neuromation - Distributed synthetic
data platform for Deep Learning
Applications.

66. Blockchain for Datasets
• Synapse AI - A decentralised global
data marketplace built on the
Ethereum blockchain.
• Bottos AI - A decentralised AI data
sharing network that uses a consensus-
based application platform to allow AI
projects to gather training data.
• Computable - A decentralised data
marketplace for artificial intelligence
applications.

67. Blockchain for Computation
• Neureal - Open source, peer-to-peer AI
supercomputing powered by blockchain.
• TrueBit - TrueBit gives Ethereum smart
contracts a computational boost.
• DeepBrain Chain - A decentralised AI
computing platform that supplies processing
power to companies looking to develop A.I.
technologies.
• AI Crypto - AI Crypto is a blockchain-based
ecosystem where AI resources such as GPU,
models, and data are distributed in order to
lower the cost of AI development.

68. Blockchain for
AI in Finance
• Numerai - Numerai is a hedge fund
powered by a network of anonymous data
scientists that build machine learning
models to operate on encrypted data and
stake cryptocurrency to express confidence
in their models.
• Erasure - Erasure is a decentralised
protocol and data marketplace for financial
predictions.
• Cindicator - Cindicator is a crowd-sourced
prediction engine for financial and crypto
indicators.

69. Blockchain for
AI in Medicine
• Doc.ai - doc.ai aims to decentralise
precision medicine on the blockchain by
using AI.
• BurstIQ - Healthcare data marketplace with
granular ownership and granular consent of
data. By using on-chain storage on a custom
blockchain, BurstIQ can comply with
HIPAA, GDPR, and other regulations.
• Vitalyx - Vytalyx is a health technology
company that plans to use AI and
blockchain to store and analyse medical
data.

70. SINGULARITYNET

71. THE SINGULARITYNET PLATFORM WAS INITIALLY
CONCEIVED BY BEN GOERTZEL, SIMONE’ GIACOMELLI
AND DAVID HANSON IN A SERIES OF BRAINSTORMING
SESSIONS AT HANSON ROBOTICS IN HONG KONG
DURING THE FIRST HALF OF 2017.

72. Ben Goertzel on
Artificial General
Intelligence
Intelligence is the ability to detect patterns in the
world and in the agent itself, measurable in
terms of emergent behaviour of "achieving
complex goals in complex environments”. A
"baby-like" artificial intelligence is initialised,
then trained as an agent in a simulated or virtual
world such as Second Life to produce a more
powerful intelligence.

73. Ben Goertzel on
Artificial General
Intelligence
Knowledge is represented in a network whose
nodes and links carry probabilistic truth values as
well as "attention values", with the attention
values resembling the weights in a neural
network. Several algorithms operate on this
network, the central one being a combination of
a probabilistic inference engine and a custom
version of evolutionary programming.

74. SingularityNET Architecture Overview

75. SingularityNET Ecosystem

76. SingularityNET MicroServices

77. SingularityNET Heterogenous Node Model

78. SingularityNet - Collaborative Training Model

79. SingularityNET Workflow Architecture

80. OPENCOG PLATFORM

81. OPENCOG PRIME IS AN ARCHITECTURE FOR ROBOT AND
VIRTUAL EMBODIED COGNITION THAT DEFINES A SET OF
INTERACTING COMPONENTS DESIGNED TO GIVE RISE TO
HUMAN-EQUIVALENT ARTIFICIAL GENERAL INTELLIGENCE (AGI)
AS AN EMERGENT PHENOMENON OF THE WHOLE SYSTEM.

82. OpenCog Design Overview
• A graph database that hold terms, formulaes, proofs, sentences, relationships
together with interpretations
• A collection of pre-defined knowledge representations
• A collection of pre-defined type sub system
• A collection of pre-defined imperative and functional programming compilers
• A collection of pre-defined satisfiability modulo theory solvers
• A generic rule engine including a forward chainer and backward chainer, that is
able to chain rules together
• Inference engines and reasoning systems, such as Bayesian Inference, Fuzzy logic,
Constraint Solvers, Motion Planners
• An attention allocation subsystem based on economic theory
• A probabilistic reasoning engine based on probabilistic inference network
• A probabilistic generic program evolver called MOSES
• An NLP system consisting of Link Grammar, inspired by Meaning Text Theory and
Word Grammar in relation to Dependency Grammar
• OpenPsi - An implémentation of Psi- Theory for handling emotional states

83. OpenCog Cognitive Architecture

84. OpenCog Language and Perception Model

85. OpenCog Technology Architecture

86. OpenCog Concept Hierarchy

87. OpenCog Atomspace Graph Database

88. OpenCog Memory Architecture

89. OpenCog Cognitive State Graphs

90. OpenCog Cognitive State Graphs

94. OCEAN PROTOCOL

95. Ocean Protocol Data Architecture