🚀 Big News!
4️⃣ New MindV Hubs are launching on Dec 27 at 09:00 UTC
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We are redefining privacy and security.
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It's time to accelerate the #bioacc movement - from wetware, to software to dreamware 🧪
Join me in signing the manifesto and accelerating biotech ➠ https://t.co/JRx0xYgAyn
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We are going much, much higherrrrrr
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Pledge: https://t.co/cxXNJN0aKU
🦄 Mind Network @EFDevcon: Top Events to Attend!
FHECon: The Frontier of Digital Sovereignty
📆 Wednesday, November 13 | 10:30 AM - 5:00 PM
📍 The Great Room Park Silom (Level 29), Bangkok
🔗 RSVP: https://t.co/tVuVnqCvqu
Exploring digital sovereignty with pioneering leaders: https://t.co/j1hC27tG2V
Hosted by #MindNetwork and co-hosted by @zama_fhe, @Supermooncamp, @SuperAlignment_ and @comma3vc. Special thanks to our sponsors: @chainlink, @googlecloud, @bitazzaofficial, @ssv_network, @KIPprotocol and @PeanutTrade.
🌟 Mark Your Calendars: #MindNetwork Team Speaking at 16 Additional Side Events!
Heavy centralization, limitations on freedom, predatory practices targeting user liquidity, and poor
crypto-forward policymaking have made the crypto landscape unsafe.
In the midst of this, a three-headed dog—the Xerberus, is here to make crypto safer than traditional banking, a 🧵
Recent advancements in Crypto AI showcase a pressing need for
- robust computational resources
- innovative decentralized training methods
- emerging paradigms in privacy and data utilization
@pexuai explore the pivotal themes shaping the future of Crypto #AI
🔻Compute is king
It’s clear that every #Crypto AI startup needs access to compute, whether by building their own decentralized networks/marketplace, or by leveraging existing ones.
This leads us to an important question: will a single entity emerge to consolidate all compute resources, or will the landscape remain decentralized and fragmented?
Decentralized AI thrives on the ability to access compute without permission.
🔻Decentralized training
Much of the foundational research on distributed training has been conducted in academic settings; the next step is to test these concepts in real-world production environments and explore their limits.
While network latency (characterized by being slow and costly) remains a significant challenge, a distributed approach may reduce certain training expenses.
However, current edge devices, such as mobile phones, still lack the capacity to train AI at scale.
🔻Verifiable inference
The market for verifiable inference (zkMP, opML, etc.) is still uncertain, with few convincing use cases beyond the moral need for trustless verification.
That said, privacy is crucial, and its role in AI is becoming more important.
🔻Private data = better models
Private data, such as in healthcare, can lead to more valuable #AI models.
Personalization is key to the next AI unlock; stripping out personally identifiable information diminishes the data’s utility.
🔻Fully Homomorphic Encryption
#FHE allows AI models to train on personal data while keeping it private, but the computation cost is very steep.
FHE is still in the research phase, probably 3-5 years away from production-ready applications.
🔻AI Agents
Could prediction markets help AI agents make better decisions?
Use cases for on-chain agents are still up for debate.
Ideas like intent-based MEV and Uniswap hooks are being tossed around, but nothing’s concrete.
🎉Thrilled to be part of #CrossSpace, the ultimate SocialFi hub connecting and investing in top-notch content creators!
🔄 Join me on this alpha adventure
🌟 Use my invite code: 8yGmk6UC
@CSpaceOfficial#socialfi#web3
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