Manus brings the dawn of AGI, but AI safety is also worth pondering ...
Manus achieved SOTA (State-of-the-Art) results in the GAIA benchmark, showing that its performance surpasses Open AI's large models of the same level. In other words, it can independently complete complex tasks, such as cross-border business negotiations, which involve contract clause decomposition, strategy prediction, solution generation, and even coordination with legal and financial teams. Compared with traditional systems, Manus's advantages lie in its dynamic target decomposition capabilities, cross-modal reasoning capabilities, and memory-enhanced learning capabilities. It can decompose large tasks into hundreds of executable subtasks, process multiple types of data at the same time, and use reinforcement learning to continuously improve its decision-making efficiency and reduce error rates.
Evolution Path of AI
While marveling at the rapid development of science and technology, Manus also once again sparked disagreements within the industry about the evolution path of AI: Will AGI dominate the world in the future, or will MAS be collaboratively dominant? This starts with Manus' design concept, which implies two possibilities: One is the AGI path, which continuously improves the intelligence level of individual units to make them close to the comprehensive decision-making ability of humans. Another is the MAS path, which acts as a super coordinator and directs thousands of vertical field agents to work together. On the surface, we are discussing different paths, but in fact, we are discussing the underlying contradiction in the development of AI: how should efficiency and safety be balanced?
Risks and Challenges
The closer the single intelligence is to AGI, the higher the risk of black-box decision-making; and although multi-agent collaboration can disperse risks, it may miss the key decision window due to communication delays. The evolution of Manus has invisibly magnified the inherent risks of AI development. For example, the data privacy black hole, algorithm bias trap, and adversarial attack vulnerability are significant challenges that need to be addressed.
Fully Homomorphic Encryption (FHE)
As the youngest encryption method, Fully Homomorphic Encryption (FHE) is also a powerful weapon to solve security problems in the AI era. Fully Homomorphic Encryption (FHE) is a technology that allows computing on encrypted data. It offers solutions at the data level, algorithm level, and collaborative level, enhancing security measures in various scenarios.
Web3 Security and Indirect Interests
Under the framework of Vitalik’s impossible triangle, web3 security plays a crucial role in safeguarding decentralization and scalability. Various encryption methods, including Fully Homomorphic Encryption (FHE), contribute to enhancing security measures. In the evolving landscape of AI and web3, security remains a top priority to protect against potential vulnerabilities and attacks.
uPort and NKN are projects focusing on security measures, aiming to establish robust defense mechanisms in the digital realm. The future of AI and security intertwines, emphasizing the need for continuous innovation and vigilance in safeguarding sensitive data and systems.
The convergence of AI and security technologies marks a pivotal moment in technological advancement, where the resilience of defense systems is imperative for the sustainable growth of AI capabilities. Fully Homomorphic Encryption (FHE) emerges as a critical component in fortifying AI systems and paving the way for a secure and efficient era of artificial intelligence.
