The famous Three Laws of Robotics proposed by science fiction writer Isaac Asimov in his 1942 short story "Runaround":

• First Law: A robot may not injure a human being, or, through inaction, allow a human being to come to harm.

• Second Law: A robot must obey the orders given it by human beings, except where such orders would conflict with the First Law.

• Third Law: A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

Asimov's Three Laws are not real technical specifications but a form of literary creation. However, they have had a profound impact on discussions of robotics and AI ethics in the real world, inspiring thoughts on AI safety, ethical design, and accountability.

In today's AI development, while the Three Laws are not directly adopted, similar principles (such as "human-centered" and "transparency") are often mentioned, especially in the context of trustworthy AI. Speaking of trustworthy AI, it is necessary to provide some additional explanation.

Trustworthy AI aims to enable users to trust AI systems, allowing them to use these systems for decision-making or daily life while minimizing potential risks and negative impacts. How is this achieved? If we apply the Three Laws of Robotics to AI development, we might ask the following questions:

• Safety: How can we ensure that AI does not directly or indirectly harm humans?

• Obedience: Should AI unconditionally obey human commands?

• Autonomy: How can AI maintain autonomy while being constrained by boundaries set by humans?

To answer these three questions, we need to ensure reliability, safety, transparency, fairness, explainability, and privacy protection in the design, development, and application of AI. The demand for both transparency and privacy protection is often dismissed even in the field of technology development, but it is a genuine requirement.

What to do? Let AI continue to advance using encryption technology, elevating trustworthy AI to a new level, and it can even be applied on top of blockchain. Why? Blockchain inherently possesses openness and transparency, which conflicts with the sensitivity of AI data. So there is a little trick: if you see a project boasting about doing AI on the blockchain, first check how it handles data encryption. If it can't manage that well, it's likely just riding the wave.

When it comes to encryption, it can be overwhelming; the technology is too complex, filled with mathematical formulas. The terms are recognizable, but when put together, they make no sense. Let me explain it in the simplest language, though I am also a novice; the encryption technology here needs a real scientist to explain.

First, the well-known Zero-Knowledge Proof (ZK) has been humorously translated as "Zero Intelligence Proof" due to the zks反撸. This technology is indeed a remarkable existence in cryptography, primarily used to verify specific propositions without revealing details, proving facts and outputting true or false results.

The core idea is not to disclose details.

For example, I want to prove that a wallet address belongs to me, but I do not want to disclose my password and account details to any protocol or chain. What can I do? At this point, ZK can be used to complete the verification, ultimately giving you a yes or no result.

Another recently hotly discussed encryption technology is Fully Homomorphic Encryption (FHE). It's another extremely convoluted term, obscure and difficult to understand, but that's often the case with technical nomenclature. To describe it in the simplest terms, it can be summarized as:

Performing calculations in an encrypted state and outputting encrypted results.

Is this something a person can understand? Let me explain further. Traditional encryption methods (like AES or RSA) require data to be decrypted before processing, and then re-encrypted afterward. The uniqueness of FHE lies in its ability to support direct operations on ciphertext (encrypted data), with results consistent with those obtained by performing the same operations on plaintext (unencrypted data) and then encrypting the results.

In other words, you give me a riddle, and without knowing the answer, I can work on your riddle and then output the result in the form of a riddle, which only someone who knows the answer can view.

This technology is now referred to as the holy grail of encryption technology because it perfectly addresses the aforementioned issue of how to protect privacy while ensuring transparency. The concept of FHE was first proposed by Craig Gentry in 2009, and since then, both academia and industry (such as IBM and Microsoft) have continued to improve the algorithms, such as those based on CKKS, BFV, or TFHE schemes.

Is there a blockchain project that uses Fully Homomorphic Encryption (FHE) and practices Trustworthy AI? Indeed, there is; this project is Mind Network. Has it issued tokens? Can it be exploited? Let's first discuss their basic situation.

Mind Network positions itself as the infrastructure for on-chain agents, empowering developers to create a fully encrypted blockchain network. Binance Labs, Hashkey, Animoca Brands, Chainlink, and others have invested $12.5 million, and it has also received funding from the Ethereum Foundation. Mind Network is also the first FHE project integrated with DeepSeek, providing encrypted inference support for open-source models. Swarms has already collaborated with Mind Network to create an AI multi-agent collaboration system, and ai16z, vana, and spore have also partnered with them.

Here, I would like to introduce a technical term: "HTTPZ."

We are already familiar with "http" and "https." "http" is the foundational protocol of the early Web2 internet, but it transmits data in plaintext, raising concerns about security and privacy. Under the advocacy of large companies like Google, "https" has gradually replaced "http" as the universal protocol, but the centralized privacy and security issues remain unresolved.

"HTTPZ" is a new protocol that emerged in the context of FHE technology, allowing calculations on data while maintaining encryption, achieving end-to-end secure transmission. AgenticWorld is based on this protocol, serving as the consensus foundation for AI Agents.

The introduction of "HTTPZ" has also given rise to an interesting topic: encryption sovereignty. If decentralized ledgers and decentralized intelligence merge, then the data citizens living in the "HTTPZ" era are referred to as CitizenZ.

The concept of CitizenZ is derived from Friedrich Hayek's ideas about free markets and the principles proposed by Rees-Mogg and Davidson in "Sovereign Individuals." Hayek advocated for minimizing external control and maximizing individual choice freedom. "Sovereign Individuals" further emphasizes the importance of applying this freedom in the so-called "information age" (which is very similar to the intelligent age).

How should we understand CitizenZ? It's actually simple: every individual has absolute control over their personal speech, data, assets, and other digital properties. And this sovereignty must adhere to:

• Removing intermediaries: Participation rights, such as voting, do not require third-party intermediaries.

• Trustless security: System security is based on cryptography rather than entities.

• Transparency: Fully verifiable processes based on blockchain, unaffected by tampering.

• Sovereign control: Individuals have complete control over basic rights such as property, data, and voting.

Taking citizen voting as an example, if CitizenZ votes in the future based on blockchain and AI, what changes compared to now?

• Verifiable: Using zero-knowledge proofs to verify the validity of votes without revealing voter identities.

• Encrypted counting: Using homomorphic encryption for encrypted counting to ensure the fairness of voting.

• Tamper-proof: Blockchain provides immutable voting records, ensuring transparency.

It's not hard to understand why Mind Network has continuously received funding from the Ethereum Foundation: as the underlying technological logic gradually materializes, we can begin to explore deeper paradigms and orders, even realizing the thoughts of Hayek and Davidson, providing a complete philosophical foundation for the Agentic AI ecosystem.

In addition to providing infrastructure for industry projects, Mind Network has also built an "ideal country" called AgenticWorld on BNB Chain and MindChain. This is a multi-chain intelligent agent economic system centered around training and collaboration. In simple terms, Mind Network has created a society for agents (intelligent agents), even including schools and businesses, allowing AI to grow from learning to earning in a one-stop manner.

Here, users can create their own AI agents by staking tokens. Through foundational learning, agents can continuously grow and earn rewards. Once your agent reaches a certain stage, it can take on tasks and earn money. If you are dissatisfied with its performance, you can "kill" it and reclaim your staked assets (scary).

Have you noticed? This is essentially a self-operating system set after establishing goals, with the underlying technology being what was discussed above.

MindChain is a Rollup chain specifically tailored for FHE verification, capable of handling large data while achieving fast settlement and transactions while maintaining high security. MindChain can provide remote staking support for a broader source chain through a reliable messaging mechanism, ensuring the credibility of the staking process.

This chain has now entered the tokenization phase, and the airdrop has been completed. 11.71% of the total supply of $FHE will be used for airdrops, with low guarantees allowing for at least 10 $FHE. Staking is now open, with a maximum APY of 400%.

Yesterday, the new token was completed on Binance Wallet, with oversubscription exceeding 170 times. It is now open for trading on Binance Alpha, Kraken, and other common exchanges.

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