blocks|key|4122477|text|The+Numenta+Platform+for+Intelligent+Computing。他们正在实现Jeff+Hawkins的“论智能”中描述的神经元类型。为了了解其重要性，他们正在研究软件神经元，这种神经元可以在视觉上识别大约200步的物体，而不是现在需要的成千上万步。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4122478|编辑:显然，SDK的1.6.1版本现已可用。学习软件的激动人心的时刻！|4122479|entityMap|0|LINK|mutability|MUTABLE|url|http://www.numenta.com/^0|0|1A|0|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@$A|O|B|P|1|Q]]|C|$]]|$1|D|3|E|5|6|7|R|8|@]|9|@]|C|$]]|$1|F|3|-4|5|6|7|S|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]]]

<a href="http://www.numenta.com/" rel="noreferrer">The Numenta Platform for Intelligent Computing</a>. They are implementing the type of neuron described in "On Intelligence" by Jeff Hawkins. For an idea of the significance, they are working on software neurons that can visually recognize objects in about 200 steps instead of the thousands and thousands necessary now.

Edit: Apparently version 1.6.1 of the SDK is available now. Exciting times for learning software!!

blocks|key|4122519|text|这不是AI本身，但OpenCyc+(可能是它的商业老大哥Cyc)可以提供AI应用程序真正理解它们所在的世界所需的“常识”。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4122520|例如，Cyc可以提供足够的一般知识，以便它可以开始“阅读”百科全书内容并对其进行推理，例如Wikipedia，或者作为代理浏览“语义Web”以开发一些特定于领域的知识库。|4122521|entityMap|0|LINK|mutability|MUTABLE|url|http://opencyc.org/^0|9|7|0|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@$A|O|B|P|1|Q]]|C|$]]|$1|D|3|E|5|6|7|R|8|@]|9|@]|C|$]]|$1|F|3|-4|5|6|7|S|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]]]

This isn't AI itself, but <a href="http://opencyc.org/" rel="nofollow noreferrer">OpenCyc</a> (and probably it's commercial big brother, Cyc) could provide the "common sense" AI applications need to really understand the world in which they exist. 

For example, Cyc could provide the enough general knowledge that it could begin to "read" and reason about encyclopedic content such as Wikipedia, or surf the "Semantic Web" acting as an agent to develop some domain-specific knowledge base.

blocks|key|4122557|text|w：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4122558|4122559|阿瑟·L·塞缪尔(1901+-+1990)是计算机游戏和人工智能领域的先驱。塞缪尔跳棋游戏程序似乎是世界上第一个自学程序...|blockquote|4122560|4122561|塞缪尔设计了各种机制，通过这些机制，他的程序可以变得更好。在他所谓的死记硬背学习中，程序记住了它已经看到的每个位置，以及奖励函数的终端值。该技术有效地扩展了每个位置的搜索深度。Samuel后来的程序根据输入的专业游戏重新评估了奖励函数。他还让它自己玩了数千场游戏，作为另一种学习方式。通过所有这些工作，塞缪尔的程序达到了令人尊敬的业余水平，并且是第一个以如此高的水平玩棋盘游戏的人。|4122562|4122563|Samuel:+Some+Studies+in+Machine+Learning+Using+the+Game+of+Checkers+(21页pdf文件)。Singularity近在眼前！:)|4122564|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Arthur_Samuel|1|http://www.research.ibm.com/journal/rd/033/ibmrd0303B.pdf|2|http://en.wikipedia.org/wiki/Technological_singularity^0|0|1|0|0|0|0|0|0|0|0|1V|1|27|B|2|0^^$0|@$1|2|3|4|5|6|7|Z|8|@]|9|@$A|10|B|11|1|12]]|C|$]]|$1|D|3|-4|5|6|7|13|8|@]|9|@]|C|$]]|$1|E|3|F|5|G|7|14|8|@]|9|@]|C|$]]|$1|H|3|-4|5|6|7|15|8|@]|9|@]|C|$]]|$1|I|3|J|5|6|7|16|8|@]|9|@]|C|$]]|$1|K|3|-4|5|6|7|17|8|@]|9|@]|C|$]]|$1|L|3|M|5|6|7|18|8|@]|9|@$A|19|B|1A|1|1B]|$A|1C|B|1D|1|1E]]|C|$]]|$1|N|3|-4|5|6|7|1F|8|@]|9|@]|C|$]]]|O|$P|$5|Q|R|S|C|$T|U]]|V|$5|Q|R|S|C|$T|W]]|X|$5|Q|R|S|C|$T|Y]]]]

<a href="http://en.wikipedia.org/wiki/Arthur_Samuel" rel="nofollow noreferrer">w</a>: 

<blockquote>
 Arthur L. Samuel (1901 – July 29,
 1990) was a pioneer in the field of
 computer gaming and artificial
 intelligence. The Samuel
 Checkers-playing Program appears to be
 the world's first self-learning
 program...
 
 Samuel designed various
 mechanisms by which his program could
 become better. In what he called rote
 learning, the program remembered every
 position it had already seen, along
 with the terminal value of the reward
 function. This technique effectively
 extended the search depth at each of
 these positions. Samuel's later
 programs reevaluated the reward
 function based on input professional
 games. He also had it play thousands
 of games against itself as another way
 of learning. With all of this work,
 Samuel’s program reached a respectable
 amateur status, and was the first to
 play any board game at this high of
 level.
</blockquote>

<a href="http://www.research.ibm.com/journal/rd/033/ibmrd0303B.pdf" rel="nofollow noreferrer">Samuel: Some Studies in Machine Learning Using the Game of Checkers</a> (21 page pdf file). <a href="http://en.wikipedia.org/wiki/Technological_singularity" rel="nofollow noreferrer">Singularity</a> is near! :)

blocks|key|55993|text|我自己最喜欢的是Donald+Michie在1960年的项目:威胁-火柴盒可教育的Naughts和Crosses。在这个项目中，Michie使用了一系列带有彩色珠子的火柴盒，他教这些火柴盒玩Tic-Tac-Toe。这是为了证明机器在某种意义上可以从以前的成功和失败中学习。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|55994|更多信息以及实验的计算机模拟都在这里：http://www.adit.co.uk/html/menace_simulation.html|offset|length|55995|entityMap|0|LINK|mutability|MUTABLE|url|http://www.adit.co.uk/html/menace_simulation.html^0|0|J|1D|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|O|8|@]|9|@$D|P|E|Q|1|R]]|A|$]]|$1|F|3|-4|5|6|7|S|8|@]|9|@]|A|$]]]|G|$H|$5|I|J|K|A|$L|M]]]]

One of my own favorites is Donald Michie's 1960, Project: MENACE - Matchbox Educable Naughts and Crosses Engine. In this project Michie used a collection of matchboxes with colored beads that he taught to play Tic-Tac-Toe. This was to demonstrate that machines could in some sense learn from their previous successes and failures. 

More information as well as a computer simulation of the experiment are here: <a href="http://www.adit.co.uk/html/menace_simulation.html" rel="nofollow noreferrer">http://www.adit.co.uk/html/menace_simulation.html</a>

blocks|key|891136|text|http://alice.pandorabots.com/+-这个机器人能够与我们进行非常智能的对话。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|891137|entityMap|0|LINK|mutability|MUTABLE|url|http://alice.pandorabots.com/^0|0|T|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

<a href="http://alice.pandorabots.com/" rel="nofollow noreferrer">http://alice.pandorabots.com/</a>
- This bot is able to have pretty intelligent conversation with us.

blocks|key|891256|text|http://www.triumphpc.com/johnlennon/|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|891257|重现约翰·列农的个性和思想。你可以在这个网站上和他聊天。|891258|entityMap|0|LINK|mutability|MUTABLE|url^0|0|10|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@$A|N|B|O|1|P]]|C|$]]|$1|D|3|E|5|6|7|Q|8|@]|9|@]|C|$]]|$1|F|3|-4|5|6|7|R|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|4]]]]

<a href="http://www.triumphpc.com/johnlennon/" rel="nofollow noreferrer">http://www.triumphpc.com/johnlennon/</a>

recreating the personality and thoughts of John Lennon.. you can have a chat with him on this site.

blocks|key|896259|text|http://AngelCog.org非常有趣。该项目基于这样一个想法，即要创建一个真正的AI，您必须分三个阶段进行：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|896260|1)尝试处理一般的逻辑，并且能够描述任何东西。|896261|2)逻辑处理代码，处理真实世界的“故事”。|896262|3)逻辑地处理它自己的代码，并与人交谈。|896263|该项目基于这样一个想法，即一旦一个程序在逻辑上处理它自己的代码，它就已经是一个AI。当然，它还需要能够理解“真实世界”。那是“另一半”。|896264|据我所知，没有其他人的项目基于这样的假设，即要制作一个合适的人工智能，人工智能必须理解它所用的语言。因此，假设AI是用C%2B%2B编写的。那么它必须精通C%2B%2B，能够读写和修改C%2B%2B程序，尤其是它自己！|896265|然而，它现在仍然是一个“玩具”，仍然处于开发的“第一阶段”。(“尝试处理一般的逻辑，并且能够描述任何东西。”)。但开发人员正在寻求帮助。|896266|entityMap|0|LINK|mutability|MUTABLE|url|http://angelcog.org/^0|0|J|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@$A|Y|B|Z|1|10]]|C|$]]|$1|D|3|E|5|6|7|11|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|12|8|@]|9|@]|C|$]]|$1|H|3|I|5|6|7|13|8|@]|9|@]|C|$]]|$1|J|3|K|5|6|7|14|8|@]|9|@]|C|$]]|$1|L|3|M|5|6|7|15|8|@]|9|@]|C|$]]|$1|N|3|O|5|6|7|16|8|@]|9|@]|C|$]]|$1|P|3|-4|5|6|7|17|8|@]|9|@]|C|$]]]|Q|$R|$5|S|T|U|C|$V|W]]]]

<a href="http://AngelCog.org" rel="nofollow">http://AngelCog.org</a> is quite interesting. The project is based around the idea that to make a true AI, you must do it in three stages:

1) Try to process logic in general, and be able to describe anything.

2) Logically process code, and process "Stories" about the real world.

3) Logically process it's own code, and talk to people.

The project is based around the idea, that once a program is logically processing it's own code, it is already an AI. Of course it also needs to be able to understand the "Real world". That's the "other half".

As far as I'm aware, no one else has a project based on the assumption that to make a proper AI, the AI must understand the language in which it is written. So lets say an AI is written in C++. Well then it must master C++ and be able to read and write and alter C++ programs, especially itself!!

It's still a "toy" right now however, and is still in the "First stage" of development. ("Try to process logic in general, and be able to describe anything."). But the developer is looking for help.

As I learn more about Computer Science, AI, and Neural Networks, I am continually amazed by the cool things a computer can do and learn. I've been fascinated by projects new and old, and I'm curios of the interesting projects/applications other SO users have run into.

What is the coolest AI project you've heard of?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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