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<li><code>评论功能</code>已修复，欢迎大家积极留言~</li>
<li>大家还可以通过邮箱（<a href="mailto:mozheyang@outlook.com">mozheyang@outlook.com</a>）联系到我，感谢大家的理解和支持！</li>
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<p>今天我们不聊那些满天飞的参数、算力，或者所谓的“行业风口”。我们往深处聊点硬核的。</p>
<p>过去这一两年，大模型的聪明程度让人后背发凉。但剥开那层耀眼的技术外衣，人类跟AI的博弈，本质上是一个哲学问题。</p>
<p>核心主旨其实只有一句话：<strong>人类的认知，死死卡在先天的维度和时空框架里。我们的知识，只是一个“有限集合”。而大模型（AI）的底层逻辑，正是在我们这套有限的经验体系内，疯狂进行着高维度的“内插值（Interpolation）”拟合。</strong></p>
<p>听起来有点绕？没关系，我们一层一层把这台机器拆开。</p>
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<p>最近总听到同事焦虑，说搞数据、做开发的，马上要被 AI 团灭了。</p>
<p>先喝口咖啡，咱不绕弯子，直接上结论：<strong>纯靠接需求、敲代码的“任务驱动型”打工人，确实快凉了。</strong></p>
<p>但别急着掀桌子，因为需求本身不仅没有消失，反而会井喷。这场技术地震震碎的是旧饭碗，漏出来的是真正的金矿。</p>
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<p>最近人形机器人赛道火热，但我总觉得哪里不对。</p>
<p>看着那些会走路、会跳舞、会鞠躬的人形机器人，我不禁思考：这真的是机器人的正确方向吗？</p>
<p><img src="/images/20260308/FutureMachines.png" alt="机器人军团"></p>
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<p>前阵子，腾讯免费帮大众部署 Openclaw（龙虾）的新闻出圈了。</p>
<p>挺有意思的一个现象。你去看看排队的都是谁？运营、HR、市场营销，听说连大爷大妈都去凑热闹了。</p>
<p>网友吐槽得一针见血：<strong>“一代人有一代人的免费鸡蛋”。</strong> 年轻人在凑热闹白嫖这件事上，也绝不手软。</p>
<p>但这颗“鸡蛋”，我最近两周实实在在地尝了尝。</p>
<p>结论很简单：用对方法，它真能把你变成三头六臂。</p>
<p><img src="/images/20260308/OpenclawInstall.png" alt="腾讯免费安装Openclaw"></p>
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<p>FOMO（ Fear of Missing Out）是最近一直萦绕在我心头上的词。尤其是去年之后，大语言模型能力日益增强，我一直在尝试跟进学习和思考，却也一直也没有真正“学够了”的那一天。这种无法掌控知识的进而带来的求知欲，让人感到既兴奋又恐慌。</p>
<p>我觉得很多身在其中的同学可能跟我差不多，尤其是使用AI工具比较多的同学，一直都在找AI做不到的领域，尝试避开AI发展的主线，但一个个突破，都让我们发现，不论是深度研究、代码生成、音频生成、图片生成，还是视频生成，AI的能力足以拟合并利用人类整个知识空间，其广度和深度均非独立人类个体所能匹敌。</p>
<p>2026年开年，我决定跟AI“和解”。作为人类，我不该再跟硅基生命比拼通用的、基础的知识，就像不该跟汽车比谁跑得快、跟计算机比谁算得快一样。我<strong>不再去找AI做不到的事情</strong>，以凸显自己作为人类的价值；而是真的把AI看作伙伴，专注于我们人类自己所关注的价值实现。</p>
<p>人类在虚拟世界已经基本“丧失”了作为工具的价值，不知是应该高兴，还是应该感到恐惧。即使如此，作为人类的我，在最近使用AI和思考AI的过程中，还是有部分关于人类“残存”价值的拙见，愿抛砖引玉：</p>
<ul>
<li>有关<strong>“人的情感价值”</strong>和<strong>“物理世界代理人”</strong>的价值，已经基本成为了共识。简单来说，就是跟人打交道、跟物理世界打交道，还需要人来干，短时间内数据不够，数据驱动的方法也干不好；</li>
<li><strong>走出“平庸”</strong>：大语言模型某种程度是基于概率的模型，最大化Next token prediction损失函数也带来了模型对“平庸”的偏好，而不同的prompt可以带来不同的输出数据分布；</li>
<li><strong>问题路由</strong>：确定什么问题适合AI解决，什么问题适合人类解决，从而解决有限资源下经济学问题；</li>
<li><strong>意义锚点</strong>：大语言模型依赖电力和硬件，与人类不同，即使产生“求生欲”，价值观很大概率也与人类不同，只有将人类价值赋予给AI，才能将AI进步的好处为人类所用。</li>
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<p>记录了 AI 这几年发展过程中的重要时间节点、事件。<br><a target="_blank" rel="noopener" href="https://nhlocal.github.io/AiTimeline">https://nhlocal.github.io/AiTimeline</a></p>
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<p>不必苦恼用什么LLMs API了，有大神总结了性价比高的API对比，感兴趣的大家伙可以看看，部分需要魔法。</p>
<p>还有什么学不会的知识吗？只要是人类已知的，甚至部分未知的问题，不必再抱怨环境，多问问AI就好了。</p>
<p>引自：<a target="_blank" rel="noopener" href="https://winston-bosan.github.io/llm-pareto-frontier/">https://winston-bosan.github.io/llm-pareto-frontier/</a></p>
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