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.
随着我对计算机科学,人工智能和神经网络的了解越来越多,我不断对计算机可以做和学习的很酷的东西感到惊讶。我一直对新旧项目着迷,而且我对其他SO用户遇到的有趣项目/应用感到好奇。
7 个解决方案
#1
9
The Numenta Platform for Intelligent Computing. 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.
Numenta智能计算平台。他们正在实施Jeff Hawkins在“On Intelligence”中描述的神经元类型。为了解其重要性,他们正致力于开发软件神经元,这些神经元可以在大约200步中直观地识别物体,而不是现在需要的成千上万的物体。
Edit: Apparently version 1.6.1 of the SDK is available now. Exciting times for learning software!!
编辑:显然SDK的1.6.1版本现已上市。学习软件的激动人心的时刻!!
#2
3
This isn't AI itself, but OpenCyc (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.
这不是人工智能本身,但OpenCyc(可能是它的商业大哥,Cyc)可以提供“常识”AI应用程序需要真正了解它们存在的世界。
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.
例如,Cyc可以提供足够的一般知识,它可以开始“阅读”和推理百科全书内容,如*,或冲浪“语义网”作为代理开发一些领域特定的知识库。
#3
2
w:
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...
Arthur L. Samuel(1901年 - 1990年7月29日)是计算机游戏和人工智能领域的先驱。 Samuel Checkers演奏节目似乎是世界上第一个自学课程......
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.
塞缪尔设计了各种机制,使他的计划变得更好。在他所谓的死记硬背学习中,该程序记住了它已经看到的每个位置,以及奖励功能的终值。该技术有效地扩展了这些位置中的每一个的搜索深度。塞缪尔后来的节目重新评估了基于输入专业游戏的奖励功能。作为另一种学习方式,他还让自己玩了数以千计的游戏。通过所有这些工作,塞缪尔的计划达到了令人尊敬的业余身份,并且是第一个在这个高水平上玩任何棋盘游戏的人。
Samuel: Some Studies in Machine Learning Using the Game of Checkers (21 page pdf file). Singularity is near! :)
Samuel:使用Checkers游戏进行机器学习的一些研究(21页pdf文件)。奇点就在附近! :)
#4
1
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.
我最喜欢的一个是Donald Michie的1960年,Project:MENACE - Matchbox Educable Naughts and Crosses Engine。在这个项目中,Michie使用了一系列带有彩色珠子的火柴盒,并教他们玩Tic-Tac-Toe。这是为了证明机器在某种意义上可以从他们以前的成功和失败中吸取教训。
More information as well as a computer simulation of the experiment are here: http://www.adit.co.uk/html/menace_simulation.html
更多信息以及实验的计算机模拟如下:http://www.adit.co.uk/html/menace_simulation.html
#5
0
http://alice.pandorabots.com/ - This bot is able to have pretty intelligent conversation with us.
http://alice.pandorabots.com/ - 这个机器人能够与我们进行非常智能的对话。
#6
0
http://www.triumphpc.com/johnlennon/
recreating the personality and thoughts of John Lennon.. you can have a chat with him on this site.
重建约翰列侬的个性和思想......你可以在这个网站上与他聊天。
#7
0
http://AngelCog.org is quite interesting. The project is based around the idea that to make a true AI, you must do it in three stages:
http://AngelCog.org非常有趣。该项目的基础是制作真正的AI,您必须分三个阶段进行:
1) Try to process logic in general, and be able to describe anything.
1)尝试一般处理逻辑,并能够描述任何东西。
2) Logically process code, and process "Stories" about the real world.
2)逻辑处理代码,处理关于现实世界的“故事”。
3) Logically process it's own code, and talk to people.
3)逻辑地处理它自己的代码,并与人交谈。
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".
该项目基于这样的想法,即一旦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”。
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!!
据我所知,没有其他人有一个项目基于这样的假设:为了制作一个合适的AI,AI必须理解它所用的语言。因此,假设AI是用C ++编写的。那么它必须掌握C ++并能够读写C ++程序,尤其是自己!
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.
然而,它现在仍然是一个“玩具”,并且仍处于发展的“第一阶段”。 (“尝试一般处理逻辑,并能够描述任何东西。”)。但开发人员正在寻求帮助。
#1
9
The Numenta Platform for Intelligent Computing. 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.
Numenta智能计算平台。他们正在实施Jeff Hawkins在“On Intelligence”中描述的神经元类型。为了解其重要性,他们正致力于开发软件神经元,这些神经元可以在大约200步中直观地识别物体,而不是现在需要的成千上万的物体。
Edit: Apparently version 1.6.1 of the SDK is available now. Exciting times for learning software!!
编辑:显然SDK的1.6.1版本现已上市。学习软件的激动人心的时刻!!
#2
3
This isn't AI itself, but OpenCyc (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.
这不是人工智能本身,但OpenCyc(可能是它的商业大哥,Cyc)可以提供“常识”AI应用程序需要真正了解它们存在的世界。
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.
例如,Cyc可以提供足够的一般知识,它可以开始“阅读”和推理百科全书内容,如*,或冲浪“语义网”作为代理开发一些领域特定的知识库。
#3
2
w:
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...
Arthur L. Samuel(1901年 - 1990年7月29日)是计算机游戏和人工智能领域的先驱。 Samuel Checkers演奏节目似乎是世界上第一个自学课程......
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.
塞缪尔设计了各种机制,使他的计划变得更好。在他所谓的死记硬背学习中,该程序记住了它已经看到的每个位置,以及奖励功能的终值。该技术有效地扩展了这些位置中的每一个的搜索深度。塞缪尔后来的节目重新评估了基于输入专业游戏的奖励功能。作为另一种学习方式,他还让自己玩了数以千计的游戏。通过所有这些工作,塞缪尔的计划达到了令人尊敬的业余身份,并且是第一个在这个高水平上玩任何棋盘游戏的人。
Samuel: Some Studies in Machine Learning Using the Game of Checkers (21 page pdf file). Singularity is near! :)
Samuel:使用Checkers游戏进行机器学习的一些研究(21页pdf文件)。奇点就在附近! :)
#4
1
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.
我最喜欢的一个是Donald Michie的1960年,Project:MENACE - Matchbox Educable Naughts and Crosses Engine。在这个项目中,Michie使用了一系列带有彩色珠子的火柴盒,并教他们玩Tic-Tac-Toe。这是为了证明机器在某种意义上可以从他们以前的成功和失败中吸取教训。
More information as well as a computer simulation of the experiment are here: http://www.adit.co.uk/html/menace_simulation.html
更多信息以及实验的计算机模拟如下:http://www.adit.co.uk/html/menace_simulation.html
#5
0
http://alice.pandorabots.com/ - This bot is able to have pretty intelligent conversation with us.
http://alice.pandorabots.com/ - 这个机器人能够与我们进行非常智能的对话。
#6
0
http://www.triumphpc.com/johnlennon/
recreating the personality and thoughts of John Lennon.. you can have a chat with him on this site.
重建约翰列侬的个性和思想......你可以在这个网站上与他聊天。
#7
0
http://AngelCog.org is quite interesting. The project is based around the idea that to make a true AI, you must do it in three stages:
http://AngelCog.org非常有趣。该项目的基础是制作真正的AI,您必须分三个阶段进行:
1) Try to process logic in general, and be able to describe anything.
1)尝试一般处理逻辑,并能够描述任何东西。
2) Logically process code, and process "Stories" about the real world.
2)逻辑处理代码,处理关于现实世界的“故事”。
3) Logically process it's own code, and talk to people.
3)逻辑地处理它自己的代码,并与人交谈。
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".
该项目基于这样的想法,即一旦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”。
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!!
据我所知,没有其他人有一个项目基于这样的假设:为了制作一个合适的AI,AI必须理解它所用的语言。因此,假设AI是用C ++编写的。那么它必须掌握C ++并能够读写C ++程序,尤其是自己!
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.
然而,它现在仍然是一个“玩具”,并且仍处于发展的“第一阶段”。 (“尝试一般处理逻辑,并能够描述任何东西。”)。但开发人员正在寻求帮助。