基于原始电磁波的新人工智能范式是否可行？
*基于原始电磁波的新人工智能范式是否可行？*<p>大家好，<p>我想提出一个新的理论人工智能范式，我称之为wAI（波动人工智能）。与传统的人工智能从人类可理解的数据（文本、图像、音频）中学习不同，wAI将直接从原始电磁波模式中学习。<p>其核心愿景是解锁人类感知之外的现实和信息维度。通过分析原始波动数据，wAI有可能解码动物和植物之间的交流，检测隐藏的生物信号以进行早期疾病诊断，甚至探索新的宇宙现象。这不仅仅是为了让人工智能变得更快；而是为了赋予智能一个全新的感知维度。<p>我知道这非常具有推测性。主要挑战是巨大的：
* 我们如何定义从无结构波动数据中“学习”，而不依赖于预定义的人类模型？
* 我们如何大规模地收集和处理这些信息？
* 什么理论框架将支配这样的系统？<p>这更像是一个思想实验，而不是一个技术提案，我真心希望听到你们的看法。你们认为这是人工智能一个可行的未来方向，还是一个有趣但最终不可行的概念？你们看到哪些技术或哲学上的障碍？<p>期待你们的见解。
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*Is a new AI paradigm based on raw electromagnetic waves feasible?*<p>Hi HN,<p>I’d like to propose a new, theoretical AI paradigm I'm calling wAI (Wave AI). Unlike traditional AI that learns from human-interpretable data (text, images, audio), wAI would learn directly from raw electromagnetic wave patterns.<p>The core vision is to unlock dimensions of reality and information that are invisible to human perception. By analyzing raw wave data, a wAI could potentially decode communication between animals and plants, detect hidden bio-signals for early disease diagnostics, or even explore new cosmic phenomena. This isn’t just about making a faster AI; it's about giving intelligence a completely new sensory dimension.<p>I know this is highly speculative. The main challenges are immense:
* How do we define "learning" from unstructured wave data without a predefined human model?
* How do we collect and process this information at scale?
* What theoretical framework would govern such a system?<p>This is more of a thought experiment than a technical proposal, and I'm genuinely curious to hear your thoughts. Do you think this is a plausible future direction for AI, or an interesting but ultimately unfeasible concept? What technical or philosophical hurdles do you see?<p>Looking forward to your insights.