World Congress 2024 Aug 20, 2024 Session details

Exploring 5 Key Applications of AI Abundance with Blockchain Assurance

Ed Marquez

Blockchain is the missing trust layer for generative AI. Discover five ways developers use decentralized ledgers to secure training pipelines, audit model lineage, and automate royalties.

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#1 about 3 min

Balancing artificial intelligence abundance with blockchain assurance capabilities

Blockchain technology provides necessary authenticity and scarcity to balance the exponential growth of artificial intelligence.

#2 about 4 min

Understanding the fundamental risks of artificial intelligence models

Decision-making black boxes and corrupted training environments demand resilient cryptographic data tracking systems.

#3 about 6 min

Ensuring data integrity for artificial intelligence model training

Decentralized messaging buses cryptographically timestamp training sets to prevent malicious bias injection in databases.

#4 about 4 min

Tracking artificial intelligence model lineage and operational history

Anchoring model components and datasets on distributed ledgers provides compliant audit trails for highly regulated industries.

#5 about 7 min

Enhancing blockchain data accessibility via retrieval augmented generation

Function calling and natural language plugins extract actionable insights from complex distributed ledger historical data.

#6 about 4 min

Securing human content ownership and traceability with blockchains

Tamper-proof digital asset tagging prevents the devaluation of human creativity by automating fair creator compensation workflows.

#7 about 2 min

Bringing transparency to artificial intelligence decisions and governance

Decentralized autonomous organizations transition model development decisions away from centralized control toward open community governance.

#8 about 2 min

Reviewing blockchain applications for artificial intelligence technologies

This concluding summary provides developer resources for combining distributed ledgers with modern machine learning architectures.

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Finding practical intersections between blockchain technology and artificial intelligence

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Expanding blockchain utility through asset tokenization and AI integration

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Lowering the barrier to entry for blockchain development

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Addressing data origins and control in decentralized AI

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Enabling autonomous AI agents with programmable blockchain budgets

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