Excited about @inference_labs 🚀 Their Zealy campaign lets the community earn rewards while exploring verifiable AI. A great way to engage with Web3 AI and see trust in actio
@inference_labs As AI systems become more capable and interconnected, trust can’t rely on reputation alone. Independent verification is essential for confidence at scale.
@inference_labs We’re moving from teaching AI to recognize pictures to teaching it to understand how the world works. That is a much bigger leap than it first appears
@inference_labs
1/ Computer vision can detect the aircraft. The harder question is whether the detection can be trusted later, when someone asks which model ran, what it saw, and why the output mattered.
In aviation, visibility is not enough. The AI decision needs a record.
@inference_labs Computer vision can detect aircraft. The real value is proving what it saw, which model ran, and why the result mattered. Accountability matters.
Full zero-knowledge verification of large ML models is slow, memory-intensive, and often overkill. DSperse solves this by targeting high-value subcomputations, or “slices”, reducing proof time and memory while keeping verifiability where it matters.
@inference_Labs
Full zero-knowledge verification of large ML models is slow, memory-intensive, and often overkill. DSperse solves this by targeting high-value subcomputations, or “slices”, reducing proof time and memory while keeping verifiability where it matters.
@inference_Labs
@inference_labs That’s the bigger challenge. Getting AI into production is one thing. Being able to audit its decisions and understand what happened when something goes wrong is what really builds trust.
@inference_labs
@inference_labs Exactly. Speed means little without accountability. If no one can reconstruct an agent’s actions, trust, compliance, and ownership all break down.
Full zero-knowledge verification of large ML models is slow, memory-intensive, and often overkill. DSperse solves this by targeting high-value subcomputations, or “slices”, reducing proof time and memory while keeping verifiability where it matters.
@inference_Labs
Full zero-knowledge verification of large ML models is slow, memory-intensive, and often overkill. DSperse solves this by targeting high-value subcomputations, or “slices”, reducing proof time and memory while keeping verifiability where it matters.
@inference_Labs
Excited about @inference_labs 🚀 Their Zealy campaign lets the community earn rewards while exploring verifiable AI. A great way to engage with Web3 AI and see trust in actio
AI-generated incident reports show how fast agents are moving into operational security work.
But a report is only useful if teams can trust how it was created.
Inference Labs is thinking about proof as the missing layer between automation and accountability.
https://t.co/Xg3Ro0easr
@inference_labs AI can generate reports in seconds, but trust comes from proving how every conclusion was reached. That’s the layer that turns automation into accountability.