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AI stacking errors are a crisis for truth. When models build on flawed outputs, errors don’t just add up but explode through the system, corrupting every decision that follows. @_ninadn’s chat with Anil Lulla during Delphi Digital’s CryptoxAI month on how we’re solving it👇

33,641 Aufrufe • vor 1 Jahr •via X (Twitter)

6 Kommentare

Profilbild von Julezz (💎,💎)
Julezz (💎,💎)vor 1 Jahr

@_ninadn @anildelphi @Delphi_Digital who else to solve it but mira

Profilbild von Dan 🧪
Dan 🧪vor 1 Jahr

@_ninadn @anildelphi @Delphi_Digital Very well said by him

Profilbild von Beamable
Beamablevor 1 Jahr

@_ninadn @anildelphi @Delphi_Digital Luckily we have people and projects working to solve these problems 🙏

Profilbild von Pyker
Pykervor 1 Jahr

@_ninadn @anildelphi @Delphi_Digital

Profilbild von Eric Freeman
Eric Freemanvor 1 Jahr

@_ninadn @anildelphi @Delphi_Digital Scam=Mira, give my money back #ScamWarning #Mira

Profilbild von 808.eth 🦇
808.eth 🦇vor 1 Jahr

@_ninadn @anildelphi @Delphi_Digital we want nothing but the truth

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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.

Wecryptotogether (Theo/acc)

63,154 Aufrufe • vor 1 Jahr