AI 与机器学习numenta.com
千脑智能理论
The Thousand Brains Theory of Intelligence
2019 — 2022，它一次次回到首页
黑点是上首页（≥ 50 分）的那次投稿，空心点是没冲上去的投稿，点越大分数越高。
导读
Numenta 创始人 Jeff Hawkins 提出的“千脑智能理论”：新皮层中大量并行的皮质柱各自用参考系对同一对象建模，再通过投票机制形成统一感知。文章主张智能研究应回归对生物新皮层的逆向工程，而非单纯依赖深度学习。
为什么经久不衰：它提供了与主流深度学习截然不同、以神经科学为基础的智能理论框架，在深度学习局限逐渐显现的今天更具参考价值。
HN 评论精选
Numenta 走的是与深度学习相反的路线：先理解并逆向工程生物新皮层。
很高兴看到 Numenta 登上 HN 首页。他们做着极其困难而宏大的工作，却不处在深度学习的聚光灯下。他们采取哲学上截然不同的路径：与其寄希望于用海量数据把生物学上不合理的旧神经网络模型推向 AGI，创始人 Jeff Hawkins 认为应该更好地理解生物新皮层并对其进行逆向工程，因为它是大多数科学家公认的唯一智能来源。就像飞机没有翅膀，但我们必须先理解扑翼才能找到更好的飞行方式。
teabee89查看英文原文
I'm very pleased to see Numenta on HN front page, as they're doing incredibly difficult and ambitious work without deep learning's spotlight. They take a philosophically very different approach: instead of warming up biologically implausible neural models from the 60s and hope that with enough data we'll reach artificial general intelligence (AGI), Numenta founder Jeff Hawkins (way before Hinton's or Hassabis's recent declarations of DL reaching a deadend) thinks we shall understand our biological neocortex better and reverse engineer it because it's the only piece of hardware most scientists agree is at the source of intelligence. Although planes don't have wings, we had to understand wing-flapping first to find better ways to fly. If you're interested, I highly recommend you follow https://discourse.numenta.org/
质疑：千脑模型在工程上等价于受限的单一网络，缺乏实验证据和性能论证。
我不明白这个理论为何有吸引力。假设我们建 1000 个神经网络做目标检测，让它们投票，最佳投票机制大概就是一个最终的投票层。这个架构等价于一个在前 n-1 层禁止各部分之间连接的单一网络，优势何在？大脑可能这样做，但除非有实验证据或能论证性能提升，否则任何人都能提出一个 inferior 的架构并声称大脑就是这么做的。
mollerhoj查看英文原文
I don't really understand why this theory is attractive. I hope someone here can help explain: Suppose we build a 1000 NNs for object detection, and let them 'vote' for which model to believe. What would be the best voting mechanism? Probably a final voting layer, right? This architecture can be described as a single NN where we disallow connections between 1000 separate parts of the network in the first n-1 layers. What would be the advantage of this? I appreciate that the brain - might - do it, but unless you can show any indication of this with experiments, or argue for why it would result in a performance increase, what's the point? Anyone can come up with an inferior architecture and propose that it's what the brain does.
千脑理论与 Minsky 的“心智社会”理论有相似之处。
这个理论让我想起 Minsky 的“心智社会”理论：多个智能体以不同方式编码同一概念，并以投票/抑制机制分层级地处理输入。不过他的模型比这里看到的要理论化得多。
EliasY查看英文原文
This theory reminds me of Marvin Minsky's "society of mind" theory where multiple agents/parts of a certain brain center encode the same object/concept in different ways and have a vote/suppress mechanism for processing different inputs in a heterarchial manner. But his model was far more theoretical than what I see here.
用孩子快速泛化识别物体的例子印证了组合结构学习的独特性。
文中说三维物体表征是学习组合结构的基础，这点特别有意思。我年幼的女儿能极快地学会一个物体并识别它，比如看很粗糙的简笔画就能认出现实中的动物，这是计算机完全做不到的。我还能用她已认识的东西解释新事物——老虎就像狮子但没有鬃毛、有条纹——她马上就能认出来。这种教与学非常自然，和目前计算机视觉的僵硬相比显得格外‘人性化’。
jonplackett查看英文原文
> A three-dimensional representation of objects also provides the basis for learning compositional structure, i.e. how objects are composed of other objects arranged in particular ways I found this particularly interesting. I have a young daughter and I find it fascinating how quickly and reliably she can learn an object and then recognise others in a way a computer just can’t do at all. Eg see very rough line drawing of an object, then recognise that animal in real life. It’s also interesting the way I can explain new objects or animals based on one she knows and then have her recognise them easily. Eg a tiger is like a lion but has no mane and does have stripes. This comes very naturally when teaching and learning and feels very uniquely ‘human’ compared to the rigidity of computer vision I’ve seen so far.
历次讨论
同类还值得读
如何写一个拼写纠正器
How to Write a Spelling Corrector
Peter Norvig 用一个几十行的 Python 程序演示如何做拼写纠正：先用编辑距离（删除、插入、替换、换位）生成所有可能的候选词，再用从大量文本中统计出的词频概率，选出…
AI 与机器学习上首页 10 次 · 2007–2022norvig.com编辑精选面向黑客的概率编程与贝叶斯方法
Probabilistic Programming and Bayesian Methods for Hackers (2013)
《黑客的贝叶斯方法与概率编程》是一本开源的贝叶斯统计入门书，以 Python/PyMC 代码和交互式 Jupyter Notebook 为主线，强调“先实践、后数学”的学习路径，…
AI 与机器学习上首页 5 次 · 2013–2018camdavidsonpilon.github.io编辑精选神经网络黑客指南
Hacker's Guide to Neural Networks
Andrej Karpathy 写的神经网络入门教程，面向程序员，从最简单的表达式求导讲起，逐步推导出反向传播和梯度下降，最后引出神经网络。
AI 与机器学习上首页 4 次 · 2014–2019karpathy.github.io编辑精选神经网络能逼近任意函数的可视化证明
A visual proof that neural nets can approximate any function
这一章来自 Michael Nielsen 的在线书《Neural Networks and Deep Learning》，用 sigmoid 单元叠加成局部隆起/平滑阶跃的图示…
AI 与机器学习上首页 4 次 · 2014–2022neuralnetworksanddeeplearning.com编辑精选论乔姆斯基与统计学习的两种文化
On Chomsky and the Two Cultures of Statistical Learning
Norvig 回应 Chomsky 对统计语言模型批评的文章，围绕“统计学习能否解释语言”展开，比较生成与解释、少参数模型与 Shannon 式高参数统计模型。
AI 与机器学习上首页 4 次 · 2011–2025norvig.com编辑精选