📢 #Gate Square Writing Contest Phase 3# is officially kicks off!
🎮 This round focuses on: Yooldo Games (ESPORTS)
✍️ Share your unique insights and join promotional interactions. To be eligible for any reward, you must also participate in Gate’s Phase 286 Launchpool, CandyDrop, or Alpha activities!
💡 Content creation + airdrop participation = double points. You could be the grand prize winner!
💰Total prize pool: 4,464 $ESPORTS
🏆 First Prize (1 winner): 964 tokens
🥈 Second Prize (5 winners): 400 tokens each
🥉 Third Prize (10 winners): 150 tokens each
🚀 How to participate:
1️⃣ Publish an
KIP announces the launch of Superior AI Agents, the first AI agent that truly achieves autonomous self-learning
PANews reported on March 13th that Web3 AI underlying protocol KIP Protocol recently released Superior AI Agents, the world's first truly self-learning artificial intelligence framework. Unlike traditional AI that relies on static data and manually set benchmarks, Superior Agents can dynamically evolve, learn, and adapt in real environments without human intervention to achieve preset goals. KIP believes that the only feasible path to Artificial Super Intelligence (ASI) is self-learning AI - capable of independently discovering knowledge, rather than relying on human input. Superior Agents are born under this concept, breaking through the traditional machine learning paradigm, with the ability for self-optimization and independent evolution. Superior Agents have made a significant breakthrough - they are among the first to attempt to autonomously pay for operating costs as AI agents, continuously optimizing their decisions through online trading rather than executing preset strategies. This marks a step for AI from being a passive tool to a self-optimizing, independently evolving intelligent entity. Superior Agents originated from the network security research at the National University of Singapore (NUS). The KIP team had developed an AI prototype capable of autonomous evolution as early as 2020, ultimately breaking through the traditional machine learning paradigm, proving that AI can test its own theories through real-world environments, rather than relying solely on human data memories.