I read an analysis by @Mira_Network that was very straightforward: the real danger is actually the "bias" of AI. In simple terms, hallucinations are when AI talks nonsense, where the model speaks confidently but incorrectly, which is a form of "random answering"; but bias is different, it is a systemic "tendency error", a deep-rooted distortion. For example, it may always lean towards a certain political stance, or it may unconsciously be more "friendly" to certain groups of people. This is not just occasional mistakes, but a fundamental tilt. It's like watching an archer; hallucinations are like missing a few shots, while bias is when the entire set of arrows is skewed to one side. On the surface, hallucinations are more eye-catching, but bias is the real hidden issue that affects the direction of AI. Mira raised a key point: we are currently too eager to pursue "accuracy" while neglecting "precision". AI can provide answers that seem very logical, but if the direction is wrong from the start, then no amount of precision will help. Personally, I feel this is similar to how we used to tune algorithms, focusing too much on short-term performance while ignoring long-term impacts. AI's bias will ultimately subtly influence social cognition and decision-making, possibly more dangerously than hallucinations. Therefore, if AI is truly to become a tool for humanity, the issue of bias must be addressed even earlier than hallucinations. We cannot just look at how beautifully it speaks, but rather whether it is silently influencing the way we view the world. This may be the biggest challenge of the AI era. The key is that there is a leaderboard activity on @KaitoAI, and the projects on @arbitrum are also very popular, so you might want to check out $ENERGY, $SHOUT, $Bane, $Chao, $BOOM, and $666 on @shoutdotfun.