AI Summit, 5th March 2020

Blockchain for AIHow Blockchain/Distributed Ledger Technology can support AI through a shared and trusted data model

Dr Oisín Boydell, CeADAR

Blockchain and AI?

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• Both blockchain and AI are currently hot topics in technology, but are not usually thought of as relevant to each other

• Overview • Key data challenges of AI

• How blockchain and Distributed Ledger Technology enables trust and validation of shared data

• Example: blockchain for shared, trusted data models to support AI in supply chains

• Limitations of blockchain and conclusions

Challenges in AI adoption

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AI Adoption in the Enterprise (O’Reilly)

Data Engineering, Preparation, and Labeling for AI 2019 (Cognilytica)

Source: Kaggle

Challenges in AI adoption - Data

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• Data quantity

• Machine Learning algorithms can require a lot of training data

• ImageNet - 14 million labelled images

• Google’s Open Images V6 – 9 million

• AudioSet – 5,800 hours of 10 second labelled audio clips

Challenges in AI adoption - Data

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• Data quality

• Training many types of AI models requires properly annotated training data

• Data cleaning/correcting/formatting is manually intensive

• Poor quality data leads to poor quality AI

Challenges in AI adoption - Data

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• Data accessibility

• Data is often siloed across different departments/databases within an organisation, or across multiple organisations (e.g. across a supply chain)

• It is often closely guarded and protected – it is valuable, and can be a liability (e.g. GDPR)

Blockchain

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• Data quantity, quality, accessibility… what has this got to do with Blockchain?

• Blockchain and Distributed Ledger Technology (DLT) is fundamentally about trust and validity of shared data

• The double spending problem…

Source: https://blog.goodaudience.com/grandmas-guide-to-blockchain-55e40b9b05b4

Blockchain and distributed databases

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Distributed Database Distributed Blockchain Ledger

Blockchain/DLT key features

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• Trust• Participants do not have to trust each other in order to trust the validity and

integrity of shared data

• Data integrity• Every node can be certain that the data they have is correct, and is the same

as everyone else’s copy

• Single ‘version of truth’

• Immutability• Data cannot be subsequently modified once written to the ledger

• Complete and immutable history

Blockchain/DLT key features

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• Data ownership and privacy• Participants can control who has access to their own data through

cryptographic security

• Security• Very secure and difficult to hack

• Example: Bitcoin has never been successfully hacked

• Smart contracts• In addition to static data, code representing certain functionality (a contract)

can be shared and executed on the distributed ledger

• For e.g. automatic validation of data and data formats

Towards a shared data model for Supply Chains

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• Huge potential for AI in supply chains• Better demand prediction

• Efficiency improvements

• Automated decision making

• Automation of manual processing tasks

• Siloed data

• Limited data sharing

• No complete end-to-end view

• Diverse data formats

• However…

Towards a shared data model for Supply Chains

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• Shared Blockchain data model

• Blockchain based distributed ledger does not require a trusted 3rd

party

• Entities still own and control their own data, but benefit from sharing

Towards a shared data model for Supply Chains

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• Data quantity • Much more data available across the full supply chain

• Data can be aggregated from different supply chain participants

• Data quality• A shared data model ensures consistency

• A single source of truth

• Smart contracts can validate data written to the blockchain

• Data accessibility • Participants can control which data they share, and to who

• AI solutions can have access to a full end-to-end view of the supply chain

Blockchain in the Technology Product Supply Chain

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• Blockchain in the Technology Product Supply Chain

• Funded through the Disruptive Technologies Innovation Fund by the Department of Business, Enterprise and Innovation and Enterprise Ireland

Limitations of Blockchain for AI

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• High computational overhead – not a replacement for database technology

• Have to still trust the correctness of data being written onto the blockchain

• Needs buy in from participants – a challenge in many blockchain applications

Conclusions

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• Many challenges with the adoption of AI are related to data –quantity, quality, accessibility

• Blockchain and Distributed Ledger Technology supports the trust and validity of shared data

• AI benefits from trusted, shared data models (single version of truth) that can be enabled by blockchain