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“What happens when AI infrastructure stops chasing everything — and focuses on making intelligence actually reliable?” Sachi Takahara / 0699.eth joins Karan Sirdesai (Karan), Founder of Mira, for a deep dive into how Mira is taking a sharper path in decentralized AI: high-trust, reliable intelligence as the core product....

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BlockLayer Podcast

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🔍 Mira( ) enhances the reliability of AI outputs through several innovative strategies and technologies 1️⃣Consensus Mechanism · Multi-AI Consensus: Mira Network uses a strong consensus mechanism across multiple AI models. · Pre-Output Verification: AI-generated outputs are reviewed and approved by several models before reaching users. · Improved Accuracy: This collective validation helps prevent hallucinations and ensures more reliable results. 2️⃣ Reduction of Errors · Accuracy Improvement: Mira’s infrastructure has increased AI accuracy from ~70% to up to 97% in certain use cases. · Performance Boost: These improvements significantly enhance overall AI performance. · Trust Factor: High accuracy helps address skepticism caused by frequent errors in traditional AI models. 3️⃣Blockchain Integration: · On-Chain Consensus: Real-time verification is achieved through decentralized blockchain-based consensus. · Secure & Trustworthy: This structure enables secure transactions and increases trust in AI outputs. · Auditability: Each node's result is verified and recorded, creating an auditable trail that reinforces reliability. 4️⃣ Addressing Biases: · Bias Reduction: Mira addresses bias issues in AI systems. · Decentralized Verification: A decentralized network helps minimize bias and promote fairness. · Fair AI Outputs: Ensures equitable results, especially for diverse demographic applications.4o 📝 In conclusion Mira is positioned at the forefront of ensuring AI reliability by implementing a trust layer that effectively mitigates errors and biases, thereby enhancing the operational capabilities of AI across different sectors.
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