书籍介绍
For all of neuroscience's advances, we've made little progress on its biggest question: How do simple cells in the brain create intelligence?
Jeff Hawkins and his team discovered that the brain uses maplike structures to build a model of the world-not just one model, but hundreds of thousands of models of everything we know. This discovery allows Hawkins to answer important questions about how we perceive the world, why we have a sense of self, and the origin of high-level thought.
A Thousand Brains heralds a revolution in the understanding of intelligence. It is a big-think book, in every sense of the word.
AI导读
核心看点
- 提出千脑理论，大脑通过海量模型构建世界认知
- 揭示参考系是智能核心，解释感知与自我意识起源
- 批判深度学习局限，主张基于脑原理实现通用人工智能
适合谁读
- 对脑科学、认知机制及人工智能底层逻辑感兴趣的读者
- 希望深入理解人类智能本质及未来科技走向的探索者
- 杰夫·霍金斯前作读者及关注AGI发展路径的技术人员
读前提醒
- 前半部分理论干货密集，后半部分哲学推演较多需耐心
- 作者观点大胆且具前瞻性，部分假说尚缺实验验证需辩证看
- 建议结合《人工智能的未来》对比阅读，理解理论演进脉络
读者共识
- 核心概念醍醐灌顶，对智能本质的解释极具启发性与颠覆性
- 前半部分科学性强，后半部分关于人类未来的论述略显牵强
- 作者风格自信甚至傲慢，但理论框架宏大，值得反复研读
本导读基于书籍简介、目录、原文摘录、短评和书评生成，不等同于全文精读。
精彩摘录
- "Knowledge in the brain is distributed. Nothing we know is stored in one place, such as one cell or one column. ... This is why we call it the Thousand Brains Theory: knowledge of any particular item is distributed among thousands of complementary models."
- "I believe the future of AI will be based on brain principles. Truly intelligent machines, AGI, will learn models of the world using maplike reference frames just like the neocortex."
- "The secret ingredient, if you will, is that intelligence is created through thousands of small models of the world, where each model uses reference frames to store knowledge and create behaviors."
- "If we could sense all frequencies of electromagnetic radiation, then we would see radio broadcasts and radar and would have X-ray vision. With different sensors, the same universe would lead to different perceptual experiences. The two important points are that the brain only knows about a subset of"
- "But our emergent intelligence has had a consequence that is not necessarily in the best interest of genes. ... Because of our knowledge and intelligence, we can consider acting in ways that are not in the best interest of genes, such as using birth control or modifying genes that we don’t like. ... "
- "“Uploading your brain” is a misleading phrase. What you have really done is split yourself into two people. ... Uploading your brain at first sounds like a great idea. Who wouldn’t want to live forever? But making a copy of ourselves by uploading our brain into a computer will not achieve immortalit"
- "迷你皮质柱中，会有多个神经元对同一输入模式产生应激反应。它们就像在起跑线上的选手，都在等待相同的信号。如果它们都获得了输入，就都会发射脉冲信号。但如果有一个或几个神经元已经处于预测状态，根据我们的理论，只有这些神经元才会发射脉冲信号，其他神经元则会被抑制。因此，当一个未预测到的输入到达时，多个神经元会同时被激发，但如果输入是预测到的，那么将只有处于预测状态的神经元会发射脉冲信号。这是从新皮质中观察到的一个常见现象：未预测到的输入通常会比预测到的输入引起更大的刺激"
- "芒卡斯尔认为，每根皮质柱中都存在一种通用的算法，但他不知道这种算法是什么。弗朗西斯·克里克认为，我们需要一个新的框架来理解大脑，然而他也不知道这个框架应该是什么。2016年的那一天，我手里握着咖啡杯，突然意识到芒卡斯尔的算法和克里克的框架都基于参考系。虽然我还没有弄清楚神经元是如何做到这一点的，但我知道这一定是真的。参考系正是其中缺失的成分，是揭开新皮质之谜和理解智能的关键。"
作者简介
Jeff Hawkins is the cofounder of Numenta, a neuroscience research company; founder of the Redwood Neuroscience Institute; and one of the founders of the field of handheld computing. He is a member of the National Academy of Engineering and author of On Intelligence.
用户评论
还可以，但实操性不强，有好些观点不太认同
作者把之前几篇论文的想法罗列了下，单纯说想法是很新奇的——但是，作为一本书太单薄了。如果作者能多了解下现在认知哲学里与之相似的范式，以及神经科学里其他研究，这种想法应该能完善。毕竟作者只是一名计算机工作者，可应用的人工智能才是其主要目标——但还是太乐观些。
断断续续读完。前半是科普脑科学和认知学科一些研究成果，试图给AI找一条新路；后半部是卡尔萨根附体。可以一读。
一篇blog可以说明白非要写一本书，真是服了。 old brain 不就是爬行脑吗？machine intellegence 当然无法做到爬行脑的功能. 另外，作者的研究方法（如果有的话）类似在解剖鸟造飞机. 没看到一个证明他理论的实验.
一本关于intelligence的书，作者把自己的观点表达得很清楚，就是扯得有些远了。。。对他的理论，表示谨慎的半信半疑，过几年再判断吧。作者提出人类传承，智力和知识比基因传承更有意义，倒是很有趣。或许上帝在暗笑，你们还能改了我的规则。
几百年前我们还不知道地球的边界。现在的我们还无法理解宇宙的边界，但是再过几百年呢？理解大脑，建立模拟大脑智能的工具，或许这样人类就能在灭亡的宿命之前穿越星际，去向真正的星辰大海和永恒。
就感觉能讲的很短的东西写了这么长。。
Mind-blowing book
没劲，感觉絮絮叨叨的，没啥新东西
看这本书也相当于在给我的new brain添加新认知
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