AI + Blockchain

This is a roundup of actions and issues
for the value of combining AI and Blockchain

Below are 3 sections:

A) Examples of AI + Blockchain in the market

B) Unique Capabilities of Blockchain that Add Value to AI

C) AI Alternatives Beyond Blockchain

FIRST, THIS IS THE TAKEAWAY:

When AI Should / Shouldn't Choose Blockchain:

  • Choose blockchain if you need transparent data records, secure digital identity verification, or an ecosystem with monetizable assets (like models or datasets). It’s ideal for sectors that require transparency, collaboration across entities, and decentralized control.
  • Opt for non-blockchain alternatives if you’re focused on privacy, speed, or highly confidential data that doesn’t require decentralized collaboration, like in federated learning or encryption applications.

Here are more references to help your conversations:

- Listen to the AMA - Ask Me Anything session on X Spaces about "Real-World Disruptive Use-Cases for AI and Blockchain" with me and International Marketing Strategist, Francesco Pagano


Circular Protocol Is building a layer one blockchain-enabled toolset for developers. They produce a new community AMA every Friday at noon (Eastern).

Email David for the link: david@davidcutler.net

- Read our POV on the intersections of these compatible technologies at https://bit.ly/AI_BC_Power

- Summary of the recent State of AI report on Crypto (not Blockchain)
       GAI Insights has a video breakdown

- Summary of the a16z State of Crypto 2024 Report (not Blockchain)



A) Examples of AI + Blockchain in the market

- IBM Food Trust

  • Metrics: Reduced food recalls by 25%, saved retailers millions in costs associated with product recalls.
  • Overview: A blockchain-based platform that tracks food products from farm to fork.
  • Use Case: Improves food safety by providing transparency into supply chains, enabling rapid identification and recall of contaminated products. AI is used to analyze data from sensors and IoT devices to predict potential quality issues.
  • URL: https://www.ibm.com/products/supply-chain-intelligence-suite/food-trust

- SingularityNET

  • Metrics: Over 100,000 AI services listed on the platform, with a growing community of developers and users.
  • Overview: A decentralized AI platform that allows developers to create, share, and monetize AI services.
  • Use Case: Enables the development of AI applications in various fields, such as healthcare, finance, and robotics. Blockchain ensures secure and transparent transactions between AI service providers and consumers.
  • URL: https://singularitynet.io/

- Ocean Protocol

  • Metrics: Over $100 million worth of data has been tokenized on the platform, facilitating data sharing and monetization.
  • Overview: A decentralized data exchange protocol that allows data owners to monetize their data while maintaining control over its usage.
  • Use Case: Facilitates the sharing of data for AI training and development, addressing privacy concerns through cryptographic techniques. AI algorithms can be used to analyze and extract insights from the shared data.
  • URL: https://oceanprotocol.com/press

- Provenance

  • Metrics: Increased trust in supply chains, leading to higher consumer confidence and increased sales for participating businesses.
  • Overview: A blockchain-based platform that provides transparency and traceability for supply chains.
  • Use Case: Enables consumers to verify the authenticity and sustainability of products, from food to fashion. AI is used to analyze data from sensors and IoT devices to track product journeys and identify potential issues.
  • URL: https://www.provenance.org/news-insights/blockchain-the-solution-for-transparency-in-product-supply-chains

- Hedera Hashgraph

  • Metrics: Over 100 million transactions per second, with a low transaction cost and fast consensus times.
  • Overview: A distributed ledger technology that offers faster transaction speeds and lower energy consumption compared to traditional blockchains.
  • Use Case: Supports a wide range of applications, including supply chain management, identity verification, and gaming. AI can be used to analyze data stored on the network to identify patterns and trends.
  • URL: https://hedera.com/

B) Unique Capabilities of Blockchain that Add Value to AI

  1. Data Integrity and Immutability: Blockchain’s decentralized ledger ensures that AI’s inputs, outputs, and decision-making processes are secure, tamper-proof, and transparent. This can address AI’s “black-box” problem by providing an audit trail for each decision or dataset, especially important in finance and healthcare.
  2. Decentralized AI Networks: Blockchain can help create decentralized AI ecosystems, reducing dependency on centralized servers and single points of failure. This is especially valuable for projects needing collaboration across borders without trusting a single authority (e.g., medical research, public data repositories).
  3. Smart Contracts for Secure AI Transactions: Smart contracts allow secure, automated transactions for AI-generated data, ensuring fair, automated payments, especially in data marketplaces where AI data or models are shared, bought, or sold.
  4. Tokenization for Fair Compensation: Blockchain’s tokenization capabilities allow AI models and data to be monetized through tokens on decentralized marketplaces, providing direct compensation to creators and ensuring intellectual property rights protection. This creates a new revenue model for data and algorithm ownership, especially in creative or proprietary industries.
  5. Digital Identity Verification: Blockchain’s identity solutions can verify user credentials and data ownership. In AI applications, this is essential for validating data sources and reducing data biases caused by unverified sources.

C) AI Alternatives Beyond Blockchain

Blockchain’s capabilities are impressive, but there are some alternatives for AI that can solve similar issues or offer different benefits:
  1. Federated Learning for Privacy-Preserving AI: In federated learning, data stays decentralized, and AI models learn from data without needing a central repository. This is ideal for privacy-sensitive applications like healthcare or finance, where data can be used without being exposed.
  2. Privacy-Preserving Computation (MPC and Homomorphic Encryption): Secure computation techniques like Multi-Party Computation (MPC) and homomorphic encryption allow AI models to work on encrypted data without decrypting it. This is highly effective for confidential applications but requires computational power and specialized expertise.
  3. Edge Computing for Data Locality: Edge computing enables AI to run on local devices rather than central servers, minimizing latency and securing data at the point of creation. This is great for IoT devices and settings where real-time responses are critical, such as autonomous vehicles and smart cities.
  4. Differential Privacy: Differential privacy techniques ensure that AI systems aggregate and analyze data without compromising individual privacy. Companies like Apple and Google use this approach to protect user data while still deriving insights from large datasets.
  5. Trusted Execution Environments (TEEs): TEEs use secure, isolated computing environments (e.g., Intel’s SGX) to perform sensitive computations, allowing AI to process data securely without exposure to external threats. TEEs are effective for applications where trust is critical but decentralization is not essential.

Contact me to discuss your options... And get an alert for our Friday AMAs.







617-331-7852

Growth Actions - DavidCutler.net 
Web3 Applied - TruthRefinery.com 
Circular Partnerships - CircularLabs.io 



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