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/*翻译中 WangYueScream LemonDemo*/
What is open source
===========================

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translating by ynmlml
Tips for non-native English speakers working on open source projects
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![Tips for non-native English speakers working on open source projects](https://opensource.com/sites/default/files/styles/image-full-size/public/images/life/world_hands_diversity.png?itok=LMT5xbxJ "Tips for non-native English speakers working on open source projects")

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Translating by scoutydren
Be a force for good in your community
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Translating by SysTick
The decline of GPL?
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Why AlphaGo Is Not AI
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![null](http://spectrum.ieee.org/img/icub-1458246741752.jpg)
>Photo: RobotCub
>“There is no AI without robotics,” the author argues.
_This is a guest post. The views expressed here are solely those of the author and do not represent positions of _ IEEE Spectrum _ or the IEEE._
What is AI and what is not AI is, to some extent, a matter of definition. There is no denying that AlphaGo, the Go-playing artificial intelligence designed by Google DeepMind that [recently beat world champion Lee Sedol][1], and similar [deep learning approaches][2] have managed to solve quite hard computational problems in recent years. But is it going to get us to  _full AI_ , in the sense of an artificial general intelligence, or [AGI][3], machine? Not quite, and here is why.
One of the key issues when building an AGI is that it will have to make sense of the world for itself, to develop its own, internal meaning for everything it will encounter, hear, say, and do. Failing to do this, you end up with todays AI programs where all the meaning is actually provided by the designer of the application: the AI basically doesnt understand what is going on and has a narrow domain of expertise.
The problem of meaning is perhaps the most fundamental problem of AI and has still not been solved today. One of the first to express it was cognitive scientist Stevan Harnad, in his 1990 paper about “The Symbol Grounding Problem.” Even if you dont believe we are explicitly manipulating symbols, which is indeed questionable, the problem remains:  _the grounding of whatever representation exists inside the system into the real world outside_ .
To be more specific, the problem of meaning leads us to four sub-problems:
1. How do you structure the information the agent (human or AI) is receiving from the world?
2. How do you link this structured information to the world, or, taking the above definition, how do you build “meaning” for the agent?
3. How do you synchronize this meaning with other agents? (Otherwise, there is no communication possible and you get an incomprehensible, isolated form of intelligence.)
4. Why does the agent do something at all rather than nothing? How to set all this into motion?
The first problem, about structuring information, is very well addressed by deep learning and similar unsupervised learning algorithms, used for example in the [AlphaGo program][4]. We have made tremendous progress in this area, in part because of the recent gain in computing power and the use of GPUs that are especially good at parallelizing information processing. What these algorithms do is take a signal that is extremely redundant and expressed in a high dimensional space, and reduce it to a low dimensionality signal, minimizing the loss of information in the process. In other words, it “captures” what is important in the signal, from an information processing point of view.
“There is no AI without robotics . . . This realization is often called the embodiment problem and most researchers in AI now agree that intelligence and embodiment are tightly coupled issues. Every different body has a different form of intelligence, and you see that pretty clearly in the animal kingdom.”</aside>
The second problem, about linking information to the real world, or creating “meaning,” is fundamentally tied to robotics. Because you need a body to interact with the world, and you need to interact with the world to build this link. Thats why I often say that there is no AI without robotics (although there can be pretty good robotics without AI, but thats another story). This realization is often called the “embodiment problem” and most researchers in AI now agree that intelligence and embodiment are tightly coupled issues. Every different body has a different form of intelligence, and you see that pretty clearly in the animal kingdom.
It starts with simple things like making sense of your own body parts, and how you can control them to produce desired effects in the observed world around you, how you build your own notion of space, distance, color, etc. This has been studied extensively by researchers like [J. Kevin ORegan][5] and his “sensorimotor theory.” It is just a first step however, because then you have to build up more and more abstract concepts, on top of those grounded sensorimotor structures. We are not quite there yet, but thats the current state of research on that matter.
The third problem is fundamentally the question of the origin of culture. Some animals show some simple form of culture, even transgenerational acquired competencies, but it is very limited and only humans have reached the threshold of exponentially growing acquisition of knowledge that we call culture. Culture is the essential catalyst of intelligence and an AI without the capability to interact culturally would be nothing more than an academic curiosity.
However, culture can not be hand coded into a machine; it must be the result of a learning process. The best way to start looking to try to understand this process is in developmental psychology, with the work of Jean Piaget and Michael Tomasello, studying how children acquire cultural competencies. This approach gave birth to a new discipline in robotics called “developmental robotics,” which is taking the child as a model (as illustrated by the [iCub robot][6], pictured above).
“Culture is the essential catalyst of intelligence and an AI without the capability to interact culturally would be nothing more than an academic curiosity. However, culture can not be hand coded into a machine; it must be the result of a learning process.”</aside>
It is also closely linked to the study of language learning, which is one of the topics that I mostly focused on as a researcher myself. The work of people like [Luc Steels][7] and many others have shown that we can see language acquisition as an evolutionary process: the agent creates new meanings by interacting with the world, use them to communicate with other agents, and select the most successful structures that help to communicate (that is, to achieve joint intentions, mostly). After hundreds of trial and error steps, just like with biological evolution, the system evolves the best meaning and their syntactic/grammatical translation.
This process has been tested experimentally and shows striking resemblance with how natural languages evolve and grow. Interestingly, it accounts for instantaneous learning, when a concept is acquired in one shot, something that heavily statistical models like deep learning are  _not_  capable to explain. Several research labs are now trying to go further into acquiring grammar, gestures, and more complex cultural conventions using this approach, in particular the [AI Lab][8] that I founded at [Aldebaran][9], the French robotics company—now part of the SoftBank Group—that created the robots [Nao][10], [Romeo][11], and [Pepper][12] (pictured below).
![img](http://spectrum.ieee.org/image/MjczMjg3Ng)
>Aldebarans humanoid robots: Nao, Romeo, and Pepper.</figcaption>
Finally, the fourth problem deals with what is called “intrinsic motivation.” Why does the agent do anything at all, rather than nothing. Survival requirements are not enough to explain human behavior. Even perfectly fed and secure, humans dont just sit idle until hunger comes back. There is more: they explore, they try, and all of that seems to be driven by some kind of intrinsic curiosity. Researchers like [Pierre-Yves Oudeyer][13] have shown that simple mathematical formulations of curiosity, as an expression of the tendency of the agent to maximize its rate of learning, are enough to account for incredibly complex and surprising behaviors (see, for example, [the Playground experiment][14] done at Sony CSL).
It seems that something similar is needed inside the system to drive its desire to go through the previous three steps: structure the information of the world, connect it to its body and create meaning, and then select the most “communicationally efficient” one to create a joint culture that enables cooperation. This is, in my view, the program of AGI.
Again, the rapid advances of deep learning and the recent success of this kind of AI at games like Go are very good news because they could lead to lots of really useful applications in medical research, industry, environmental preservation, and many other areas. But this is only one part of the problem, as Ive tried to show here. I dont believe deep learning is the silver bullet that will get us to true AI, in the sense of a machine that is able to learn to live in the world, interact naturally with us, understand deeply the complexity of our emotions and cultural biases, and ultimately help us to make a better world.
**[Jean-Christophe Baillie][15] is founder and president of [Novaquark][16], a Paris-based virtual reality startup developing [Dual Universe][17], a next-generation online world where participants will be able to create entire civilizations through fully emergent gameplay. A graduate from the École Polytechnique in Paris, Baillie received a PhD in AI from Paris IV University and founded the Cognitive Robotics Lab at ENSTA ParisTech and, later, Gostai, a robotics company acquired by the Aldebaran/SoftBank Group in 2012\. This article originally [appeared][18] in LinkedIn.**
--------------------------------------------------------------------------------
via: http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/why-alphago-is-not-ai
作者:[Jean-Christophe Baillie][a]
译者:[译者ID](https://github.com/译者ID)
校对:[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译,[Linux中国](https://linux.cn/) 荣誉推出
[a]:https://www.linkedin.com/in/jcbaillie
[1]:http://spectrum.ieee.org/tech-talk/computing/networks/alphago-wins-match-against-top-go-player
[2]:http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/facebook-ai-director-yann-lecun-on-deep-learning
[3]:https://en.wikipedia.org/wiki/Artificial_general_intelligence
[4]:http://spectrum.ieee.org/tech-talk/computing/software/monster-machine-defeats-prominent-pro-player
[5]:http://nivea.psycho.univ-paris5.fr/
[6]:http://www.icub.org/
[7]:https://ai.vub.ac.be/members/steels
[8]:http://a-labs.aldebaran.com/labs/ai-lab
[9]:https://www.aldebaran.com/en
[10]:http://spectrum.ieee.org/automaton/robotics/humanoids/aldebaran-new-nao-robot-demo
[11]:http://spectrum.ieee.org/automaton/robotics/humanoids/france-developing-advanced-humanoid-robot-romeo
[12]:http://spectrum.ieee.org/robotics/home-robots/how-aldebaran-robotics-built-its-friendly-humanoid-robot-pepper
[13]:http://www.pyoudeyer.com/
[14]:http://www.pyoudeyer.com/SS305OudeyerP-Y.pdf
[15]:https://www.linkedin.com/in/jcbaillie
[16]:http://www.dualthegame.com/novaquark
[17]:http://www.dualthegame.com/
[18]:https://www.linkedin.com/pulse/why-alphago-ai-jean-christophe-baillie

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#rusking translating
Why do you use Linux and open source software?
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朝鲜180局的网络战部门让西方国家忧虑。
Translating by hwlog
North Korea's Unit 180, the cyber warfare cell that worries the West
============================================================
[![在夜色的映衬下,部队的军车通过平壤市区,](http://www.abc.net.au/news/image/8545124-3x2-700x467.jpg "Military trucks through Pyongyang")][13] [**PHOTO:** 脱北者说, 平壤的网络战攻击目的在于一个叫做“180局”的部门来筹集资金。(Reuters: Damir Sagolj, file)][14]
据叛逃者官方和网络安全专家称朝鲜的情报机关有一个叫做180局的特殊部门 这个部门已经发起过多起勇敢且成功的网络战。
近几年朝鲜被美国,韩国,和周边几个国家指责对多数的金融网络发起过一系列在线袭击。
网络安全技术人员称他们找到了这个月感染了150多个国家30多万台计算机的全球想哭勒索病毒"ransomware"和朝鲜网络战有关联的技术证据。
平壤称该指控是“荒谬的”。
对朝鲜的关键指控是指朝鲜与一个叫做拉撒路的黑客组织有联系这个组织是在去年在孟加拉国中央银行网络抢劫8000万美元并在2014年攻击了索尼的好莱坞工作室的网路。
美国政府指责朝鲜对索尼公司的黑客袭击,同时美国政府对平壤在孟加拉国银行的盗窃行为提起公诉并要求立案。
由于没有确凿的证据没有犯罪指控并不能够立案。朝鲜之后也否认了Sony公司和银行的袭击与其有关。
朝鲜是世界上最封闭的国家之一,它秘密行动的一些细节很难获得。
但研究这个封闭的国家和流落到韩国和一些西方国家的的叛逃者已经给出了或多或少的提示。
### 黑客们喜欢用雇员来作为掩护
金恒光朝鲜前计算机教授2004叛逃到韩国他仍然有着韩国内部的消息他说平壤的网络战目的在于通过侦察总局下属的一个叫做180局来筹集资金这个局主要是负责海外的情报机构。
金教授称“180局负责入侵金融机构通过漏洞从银行账户提取资金”。
他之前也说过,他以前的一些学生已经加入了朝鲜的网络战略司令部-朝鲜的网络部队。
>"黑客们到海外寻找比朝鲜更好的互联网服务的地方,以免留下痕迹," 金教授补充说。
他说他们经常用贸易公司,朝鲜的海外分公司和在中国和东南亚合资企业的雇员来作为掩护
位于华盛顿的战略与国际研究中心的叫做James Lewis的朝鲜专家称平壤首先用黑客作为间谍活动的工具然后对韩国和美国的目的进行政治干扰。
索尼公司事件之后,他们改变方法,通过用黑客来支持犯罪活动来形成国内坚挺的货币经济政策。
“目前为止,网上毒品,假冒伪劣,走私,都是他们惯用的伎俩”。
Media player: 空格键播放“M”键静音“左击”和“右击”查看。
[**VIDEO:** 你遇到过勒索病毒吗? (ABC News)][16]
### 韩国声称拥有大量的“证据”
美国国防部称在去年提交给国会的一个报告中显示,朝鲜可能有作为有效成本的,不对称的,可拒绝的工具,它能够应付来自报复性袭击很小的风险,因为它的“网络”大部分是和因特网分离的。
> 报告中说," 它可能从第三方国家使用互联网基础设施"。
韩国政府称,他们拥有朝鲜网络战行动的大量证据。
“朝鲜进行网络战通过第三方国家来掩护网络袭击的来源并且使用他们的信息和通讯技术设施”Ahn Chong-ghee韩国外交部副部长在书面评论中告诉路透社。
除了孟加拉银行抢劫案,他说平壤也被怀疑与菲律宾,越南和波兰的银行袭击有关。
去年六月警察称朝鲜袭击了160个韩国公司和政府机构入侵了大约14万台计算机暗中在他的对手的计算机中植入恶意代码作为长期计划的一部分来进行大规模网络攻击。
朝鲜也被怀疑在2014年对韩国核反应堆操作系统进行阶段性网络攻击尽管朝鲜否认与其无关。
根据在一个韩国首尔的杀毒软件厂商“hauri”的高级安全研究员Simon Choi的说法网络袭击是来自于他在中国的一个基地。
Choi先生一个有着对朝鲜的黑客能力进行了广泛的研究的人称“他们在那里行动以至于不论他们做什么样的计划他们拥有中国的ip地址”。
--------------------------------------------------------------------------------
via: http://www.abc.net.au/news/2017-05-21/north-koreas-unit-180-cyber-warfare-cell-hacking/8545106
作者:[www.abc.net.au ][a]
译者:[译者ID](https://github.com/hwlog)
校对:[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译,[Linux中国](https://linux.cn/) 荣誉推出
[a]:http://www.abc.net.au
[1]:http://www.abc.net.au/news/2017-05-16/wannacry-ransomware-showing-up-in-obscure-places/8527060
[2]:http://www.abc.net.au/news/2015-08-05/why-we-should-care-about-cyber-crime/6673274
[3]:http://www.abc.net.au/news/2017-05-15/what-to-do-if-youve-been-hacked/8526118
[4]:http://www.abc.net.au/news/2017-05-16/researchers-link-wannacry-to-north-korea/8531110
[5]:http://www.abc.net.au/news/2017-05-18/adylkuzz-cyberattack-could-be-far-worse-than-wannacry:-expert/8537502
[6]:http://www.google.com/maps/place/Korea,%20Democratic%20People%20S%20Republic%20Of/@40,127,5z
[7]:http://www.abc.net.au/news/2017-05-16/wannacry-ransomware-showing-up-in-obscure-places/8527060
[8]:http://www.abc.net.au/news/2017-05-16/wannacry-ransomware-showing-up-in-obscure-places/8527060
[9]:http://www.abc.net.au/news/2015-08-05/why-we-should-care-about-cyber-crime/6673274
[10]:http://www.abc.net.au/news/2015-08-05/why-we-should-care-about-cyber-crime/6673274
[11]:http://www.abc.net.au/news/2017-05-15/what-to-do-if-youve-been-hacked/8526118
[12]:http://www.abc.net.au/news/2017-05-15/what-to-do-if-youve-been-hacked/8526118
[13]:http://www.abc.net.au/news/2017-05-21/military-trucks-trhough-pyongyang/8545134
[14]:http://www.abc.net.au/news/2017-05-21/military-trucks-trhough-pyongyang/8545134
[15]:http://www.abc.net.au/news/2017-05-16/researchers-link-wannacry-to-north-korea/8531110
[16]:http://www.abc.net.au/news/2017-05-15/have-you-been-hit-by-ransomware/8527854

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Martin translating...
Network automation with Ansible
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translating by flankershen
# Network management with LXD (2.3+)
![LXD logo](https://linuxcontainers.org/static/img/containers.png)

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jayjay823 翻译中
User Editorial: Steam Machines & SteamOS after a year in the wild
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翻译中--by zky001
Top 8 systems operations and engineering trends for 2017
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vim-kakali translating
3 open source music players: Aqualung, Lollypop, and GogglesMM
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![3 open source music players: Aqualung, Lollypop, and GogglesMM](https://opensource.com/sites/default/files/styles/image-full-size/public/images/life/music-birds-recording-520.png?itok=wvh1g4Lw "3 open source music players: Aqualung, Lollypop, and GogglesMM")

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translating by cycoe
10 reasons to use Cinnamon as your Linux desktop environment
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geekrainy translating
A look at 6 iconic open source brands
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dongdongmian 翻译中
How to take screenshots on Linux using Scrot
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# rusking translating
An introduction to the Linux boot and startup processes
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translating by Flowsnow!
Many SQL Performance Problems Stem from “Unnecessary, Mandatory Work”
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Yoo-4x Translating
OpenGL & Go Tutorial Part 1: Hello, OpenGL
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【翻译中】
Getting started with Perl on the Raspberry Pi
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#rusking translating
An introduction to GRUB2 configuration for your Linux machine
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svtter tranlating...
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STUDY RUBY PROGRAMMING WITH OPEN-SOURCE BOOKS
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trnhoe translating~
Introduction to functional programming
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(翻译中 by runningwater)
FreeFileSync Compare and Synchronize Files in Ubuntu
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