Balancing AI Security and Efficiency: Reflections on the Manus Model

Recently, the Manus model has made breakthrough progress in the GAIA benchmark tests, surpassing large language models of the same tier in performance. This achievement means that Manus is capable of independently handling complex tasks, such as multinational business negotiations, involving contract analysis, strategy formulation, and proposal generation among various aspects. The advantages of Manus lie in its dynamic goal decomposition capability, cross-modal reasoning ability, and memory-enhanced learning capability. It can break down complex tasks into multiple executable sub-tasks while handling various data types, and continuously improve decision-making efficiency and reduce error rates through reinforcement learning.

The emergence of Manus has once again sparked discussions on the development path of artificial intelligence: should we move towards a single General Artificial Intelligence (AGI), or a collaborative Multi-Agent System (MAS)? This question actually reflects the core contradiction of how to balance efficiency and safety in AI development. As individual intelligence approaches AGI, the risk of opacity in its decision-making process also increases; while multi-agent collaboration can disperse risks, it may miss critical decision-making opportunities due to communication delays.

The progress of Manus has also amplified the inherent risks in the development of AI. For example, in medical scenarios, AI systems need access to sensitive genomic data of patients; in financial negotiations, there may be undisclosed corporate financial information involved. Additionally, AI systems may exhibit algorithmic bias, such as providing unfair salary suggestions to specific groups during hiring processes or having a high error rate in assessing terms for emerging industries during legal contract reviews. More seriously, AI systems may face adversarial attacks, such as hackers disrupting the AI's judgment ability through specific voice frequencies.

These challenges highlight a concerning trend: the more intelligent AI systems become, the broader their potential attack surface.

In the field of cryptocurrency and blockchain, security has always been a core concern. Inspired by the "impossible triangle" theory proposed by Ethereum founder Vitalik Buterin, various encryption technologies have emerged in this field:

1. Zero Trust Security Model: This model is based on the principle of "never trust, always verify" and requires strict authentication and authorization for each access request.

2. Decentralized Identity (DID): This is an identity recognition standard that does not require a centralized registration authority, providing new ideas for identity management in the Web3 era.

3. Fully Homomorphic Encryption (FHE): This technology allows data computation to be performed in an encrypted state, which is crucial for protecting privacy in scenarios such as cloud computing and data outsourcing.

Fully homomorphic encryption, as an emerging technology, is expected to become a key tool in addressing security issues in the AI era. It can play a role in the following aspects:

• Data layer: All information input by users (including biometric features, voice, etc.) is processed in an encrypted state, and even the AI system itself cannot decrypt the original data.

• Algorithm Level: Achieve "encrypted model training" through FHE, so that even developers cannot directly understand the AI's decision-making process.

• Collaborative Level: Communication between multiple AI agents uses threshold encryption, so even if a single node is compromised, it will not lead to global data leakage.

Although Web3 security technology may not have a direct connection to ordinary users, it has an indirect impact on everyone. In this challenging digital world, continuously enhancing security capabilities is key to avoiding becoming "chives".

As AI technology continues to approach human intelligence levels, we increasingly need advanced defense systems. Fully homomorphic encryption not only addresses current security issues but also lays the foundation for a stronger AI era in the future. On the road to artificial general intelligence, FHE is no longer optional but a necessary condition to ensure the safe development of AI.