人工智能开发语言 =Python


人工智能开发语言 =Python

谷歌AI击败了一位围棋大师,是一种衡量人工智能突然的快速发展的方式,也揭示了这些技术如何发展而来和将来可以如何发展。
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 香港云主机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”>

人工智能是一种未来性的技术,目前正在致力于研究自己的一套工具。一系列的进展在过去的几年中发生了:无事故驾驶超过300000英里并在三个州合法行驶迎来了自动驾驶的一个里程碑;IBM Waston击败了Jeopardy两届冠军;统计学习技术从对消费者兴趣到以万亿记的图像的复杂数据集进行模式识别。这些发展必然提高了科学家和巨匠们对人工智能的兴趣,这也使得开发者们了解创建人工智能应用的真实本质。开发这些需要注意的第一件事是:

哪一种编程语言适合人工智能?

你所熟练掌握的每一种编程语言都可以是人工智能的开发语言。

人工智能程序可以使用几乎所有的编程语言实现,最常见的有:Lisp,Prolog,C/C++,近来又有Java,最近还有Python.

LISP

LISP这样的高级语言在人工智能中备受青睐,因为在各高校多年的研究后选择了快速原型而舍弃了快速执行。垃圾收集,动态类型,数据函数,统一的语法,交互式环境和可扩展性等一些特性使得LIST非常适合人工智能编程。

PROLOG

这种语言有着LISP高层和传统优势有效结合,这对AI是非常有用的。它的优势是解决“基于逻辑的问题”。Prolog提供了针对于逻辑相关问题的解决方案,或者说它的解决方案有着简洁的逻辑特征。它的主要缺点(恕我直言)是学起来很难。

C/C++

就像猎豹一样,C/C++主要用于对执行速度要求很高的时候。它主要用于简单程序,统计人工智能,如神经网络就是一个常见的例子。Backpropagation 只用了几页的C/C++代码,但是要求速度,哪怕程序员只能提升一点点速度也是好的。

JAVA

新来者,Java使用了LISP中的几个理念,最明显的是垃圾收集。它的可移植性使它可以适用于任何程序,它还有一套内置类型。Java没有LISPProlog高级,又没有C那样快,但如果要求可移植性那它是最好的。

PYTHON

Python是一种用LISPJAVA编译的语言。按照Norvig文章中对LipsPython的比较,这两种语言彼此非常相似,仅有一些细小的差别。还有JPthon,提供了访问Java图像用户界面的途径。这是PeterNorvig选择用JPyhton翻译他人工智能书籍中程序的的原因。JPython可以让他使用可移植的GUI演示,和可移植的http/ftp/html库。因此,它非常适合作为人工智能语言的。

在人工智能上使用Python比其他编程语言的好处

优质的文档

平台无关,可以在现在每一个*nix版本上使用

和其他面向对象编程语言比学习更加简单快速

Python有许多图像加强库像Python Imaging
Libary,VTK
Maya 3D可视化工具包,Numeric
Python, Scientific Python
和其他很多可用工具可以于数值和科学应用。

Python设计非常好,快速,坚固,可移植,可扩展。很明显这些对于人工智能应用来说都是非常重要的因素。

对于科学用途的广泛编程任务都很有用,无论从小的shell脚本还是整个网站应用。

最后,它是开源的。可以得到相同的社区支持。

AIPython

总体的AI

AIMAPython实现了从RussellNorvigs的“人工智能:一种现代的方法”的算法

pyDatalogPython中的逻辑编程引擎

SimpleAIPython实现在“人工智能:一种现代的方法”这本书中描述过的人工智能的算法。它专注于提供一个易于使用,有良好文档和测试的库。

EasyAI:一个双人AI游戏的python引擎(负极大值,置换表、游戏解决)

机器学习库

PyBrain 一个灵活,简单而有效的针对机器学习任务的算法,它是模块化的Python机器学习库。它也提供了多种预定义好的环境来测试和比较你的算法。

PyML 一个用Python写的双边框架,重点研究SVM和其他内核方法。它支持LinuxMac
OS X

scikit-learn旨在提供简单而强大的解决方案,可以在不同的上下文中重用:机器学习作为科学和工程的一个多功能工具。它是python的一个模块,集成了经典的机器学习的算法,这些算法是和python科学包(numpy,scipy.matplotlib)紧密联系在一起的。

MDP-Toolkit这是一个Python数据处理的框架,可以很容易的进行扩展。它海收集了有监管和没有监管的学习算饭和其他数据处理单元,可以组合成数据处理序列或者更复杂的前馈网络结构。新算法的实现是简单和直观的。可用的算法是在不断的稳定增加的,包括信号处理方法(主成分分析、独立成分分析、慢特征分析),流型学习方法(局部线性嵌入),集中分类,概率方法(因子分析,RBM),数据预处理方法等等。

自然语言和文本处理库

NLTK 开源的Python模块,语言学数据和文档,用来研究和开发自然语言处理和文本分析。有windows,Mac OSXLinux版本。

案例

做了一个实验,一个使用人工智能和物联网做员工行为分析的软件。该软件通过员工情绪和行为的分心提供了一个有用的反馈给员工,从而提高了管理和工作习惯。

使用Python机器学习库,opencvhaarcascading概念来培训。建立了样品POC来检测通过安置在不同地点的无线摄像头传递回来基础情感像幸福,生气,悲伤,厌恶,怀疑,蔑视,讥讽和惊喜。收集到的数据会集中到云数据库中,甚至整个办公室都可以通过在Android设备或桌面点击一个按钮来取回。

开发者在深入分析脸部情感上复杂点和挖掘更多的细节中取得进步。在深入学习算法和机器学习的帮助下,可以帮助分析员工个人绩效和适当的员工/团队反馈。

结论

python因为提供像 scikit-learn的好的框架,在人工智能方面扮演了一个重要的角色:Python中的机器学习,实现了这一领域中大多的需求。D3.js JS中数据驱动文档时可视化最强大和易于使用的工具之一。处理框架,它的快速原型制造使得它成为一门不可忽视的重要语言。AI需要大量的研究,因此没有必要要求一个500KBJava样板代码去测试新的假说。python中几乎每一个想法都可以迅速通过20-30行代码来实现(JSLISP也是一样)。因此,它对于人工智能是一门非常有用的语言。

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