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Translating by qhwdw
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6 Open Source AI Tools to Know
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In open source, no matter how original your own idea seems, it is always wise to see if someone else has already executed the concept. For organizations and individuals interested in leveraging the growing power of artificial intelligence (AI), many of the best tools are not only free and open source, but, in many cases, have already been hardened and tested.
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At leading companies and non-profit organizations, AI is a huge priority, and many of these companies and organizations are open sourcing valuable tools. Here is a sampling of free, open source AI tools available to anyone.
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**Acumos.** [Acumos AI][1] is a platform and open source framework that makes it easy to build, share, and deploy AI apps. It standardizes the infrastructure stack and components required to run an out-of-the-box general AI environment. This frees data scientists and model trainers to focus on their core competencies rather than endlessly customizing, modeling, and training an AI implementation.
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Acumos is part of the[LF Deep Learning Foundation][2], an organization within The Linux Foundation that supports open source innovation in artificial intelligence, machine learning, and deep learning. The goal is to make these critical new technologies available to developers and data scientists, including those who may have limited experience with deep learning and AI. The LF Deep Learning Foundation just [recently approved a project lifecycle and contribution process][3] and is now accepting proposals for the contribution of projects.
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**Facebook’s Framework.** Facebook[has open sourced][4] its central machine learning system designed for artificial intelligence tasks at large scale, and a series of other AI technologies. The tools are part of a proven platform in use at the company. Facebook has also open sourced a framework for deep learning and AI [called Caffe2][5].
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**Speaking of Caffe.** Yahoo also released its key AI software under an open source license. The[CaffeOnSpark tool][6] is based on deep learning, a branch of artificial intelligence particularly useful in helping machines recognize human speech or the contents of a photo or video. Similarly, IBM’s machine learning program known as [SystemML][7] is freely available to share and modify through the Apache Software Foundation.
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**Google’s Tools.** Google spent years developing its [TensorFlow][8] software framework to support its AI software and other predictive and analytics programs. TensorFlow is the engine behind several Google tools you may already use, including Google Photos and the speech recognition found in the Google app.
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Two [AIY kits][9] open sourced by Google let individuals easily get hands-on with artificial intelligence. Focused on computer vision and voice assistants, the two kits come as small self-assembly cardboard boxes with all the components needed for use. The kits are currently available at Target in the United States, and are based on the open source Raspberry Pi platform — more evidence of how much is happening at the intersection of open source and AI.
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**H2O.ai.** **** I[previously covered][10] H2O.ai, which has carved out a niche in the machine learning and artificial intelligence arena because its primary tools are free and open source. You can get the main H2O platform and Sparkling Water, which works with Apache Spark, simply by[downloading][11] them. These tools operate under the Apache 2.0 license, one of the most flexible open source licenses available, and you can even run them on clusters powered by Amazon Web Services (AWS) and others for just a few hundred dollars.
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**Microsoft Onboard.** “Our goal is to democratize AI to empower every person and every organization to achieve more,” Microsoft CEO Satya Nadella[has said][12]. With that in mind, Microsoft is continuing to iterate its[Microsoft Cognitive Toolkit][13]. It’s an open source software framework that competes with tools such as TensorFlow and Caffe. Cognitive Toolkit works with both Windows and Linux on 64-bit platforms.
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“Cognitive Toolkit enables enterprise-ready, production-grade AI by allowing users to create, train, and evaluate their own neural networks that can then scale efficiently across multiple GPUs and multiple machines on massive data sets,” reports the Cognitive Toolkit Team.
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Learn more about AI in this new ebook from The Linux Foundation. [Open Source AI: Projects, Insights, and Trends by Ibrahim Haddad][14] surveys 16 popular open source AI projects – looking in depth at their histories, codebases, and GitHub contributions. [Download the free ebook now.][14]
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--------------------------------------------------------------------------------
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via: https://www.linux.com/blog/2018/6/6-open-source-ai-tools-know
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作者：[Sam Dean][a]
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选题：[lujun9972](https://github.com/lujun9972)
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]:https://www.linux.com/users/sam-dean
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[1]:https://www.acumos.org/
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[2]:https://www.linuxfoundation.org/projects/deep-learning/
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[3]:https://www.linuxfoundation.org/blog/lf-deep-learning-foundation-announces-project-contribution-process/
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[4]:https://code.facebook.com/posts/1687861518126048/facebook-to-open-source-ai-hardware-design/
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[5]:https://venturebeat.com/2017/04/18/facebook-open-sources-caffe2-a-new-deep-learning-framework/
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[6]:http://yahoohadoop.tumblr.com/post/139916563586/caffeonspark-open-sourced-for-distributed-deep
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[7]:https://systemml.apache.org/
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[8]:https://www.tensorflow.org/
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[9]:https://www.techradar.com/news/google-assistant-sweetens-raspberry-pi-with-ai-voice-control
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[10]:https://www.linux.com/news/sparkling-water-bridging-open-source-machine-learning-and-apache-spark
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[11]:http://www.h2o.ai/download
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[12]:https://blogs.msdn.microsoft.com/uk_faculty_connection/2017/02/10/microsoft-cognitive-toolkit-cntk/
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[13]:https://www.microsoft.com/en-us/cognitive-toolkit/
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[14]:https://www.linuxfoundation.org/publications/open-source-ai-projects-insights-and-trends/
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应该知道的 6 个开源 AI 工具
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======
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在开源领域，不管你的想法是多少的新颖独到，先去看一下别人是否已经做成了这个概念，总是一个很明智的做法。对于有兴趣借助不断成长的人工智能（AI）的力量的组织和个人来说，许多非常好的工具不仅是免费和开源的，而且在很多的情况下，它们都已经过测试和久经考验的。
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在领先的公司和非盈利组织中，AI 的优先级都非常高，并且这些公司和组织都开源了很有价值的工具。下面的样本是任何人都可以使用的免费的、开源的 AI 工具。
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**Acumos.** [Acumos AI][1] 是一个平台和开源框架，使用它可以很容易地去构建、共享和分发 AI 应用。它规范了需要的基础设施栈和组件，使其可以在一个“开箱即用的”通用 AI 环境中运行。这使得数据科学家和模型训练者可以专注于它们的核心竞争力，而不用在无止境的定制、建模、以及训练一个 AI 实现上浪费时间。
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Acumos 是 [LF 深度学习基金会][2] 的一部分，它是 Linux 基金会中的一个组织，它支持在人工智能、机器学习、以及深度学习方面的开源创新。它的目标是让这些重大的新技术可用于开发者和数据科学家，包括那些在深度学习和 AI 上经验有限的人。LF 深度学习基金会 [最近批准了一个项目生命周期和贡献流程][3]，并且它现在正接受项目贡献的建议。
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**Facebook 的框架.** Facebook 它自己 [有开源的][4] 中央机器学习系统，它设计用于做一些大规模的人工智能任务，以及一系列其它的 AI 技术。这个工具是经过他们公司验证的平台的一部分。Facebook 也开源了一个叫 [Caffe2][5] 的深度学习和人工智能的框架。
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**说到 Caffe.** Yahoo 也在开源许可证下发布了它自己的关键的 AI 软件。[CaffeOnSpark 工具][6] 是基于深度学习的，它是人工智能的一个分支，在帮助机器识别人类语言、或者照片、视频的内容方面非常有用。同样地，IBM 的机器学习程序 [SystemML][7] 可以通过 Apache 软件基金会免费共享和修改。
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**Google 的工具.** Google 花费了几年的时间开发了它自己的 [TensorFlow][8] 软件框架，用于去支持它的 AI 软件和其它预测和分析程序。TensorFlow 是你可能都已经在使用的一些 Google 工具背后的引擎，包括 Google Photos 和在 Google app 中使用的语言识别。
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Google 开源了两个 [AIY kits][9]，它可以让个人很容易地使用人工智能，它们专注于计算机视觉和语音助理。这两个工具包将用到的所有组件封装到一个盒子中。这个工具包目前在美国的 Target 中有售，并且它是基于开源的树莓派平台的 —— 有越来越多的证据表明，在开源和 AI 交集中将发生非常多的事情。
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**H2O.ai.** **** 我 [以前介绍过][10] H2O.ai，它在机器学习和人工智能领域中占有一席之地，因为它的主要工具是免费和开源的。你可以获取主要的 H2O 平台和 Sparkling Water，它与 Apache Spark 一起工作，只需要去 [下载][11] 它们即可。这些工具遵循 Apache 2.0 许可证，它是一个非常灵活的开源许可证，你甚至可以在 Amazon Web 服务（AWS）和其它的集群上运行它们，而这仅需要几百美元而已。
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**Microsoft Onboard.** “我们的目标是让 AI 大众化，让每个人和组织获得更大的成就，“ Microsoft CEO Satya Nadella [说][12]。因此，微软持续迭代它的 [Microsoft Cognitive Toolkit][13]。它是一个能够与 TensorFlow 和 Caffe 去竞争的一个开源软件框架。Cognitive 工具套件可以工作在 64 位的 Windows 和 Linux 平台上。
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Cognitive 工具套件团队的报告称，“Cognitive 工具套件通过允许用户去创建、训练、以及评估他们自己的神经网络，以使企业级的、生产系统级的 AI 成为可能，这些神经网络可能跨多个 GPU 以及多个机器在大量的数据集中高效伸缩。”
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从来自 Linux 基金会的新电子书中学习更多的有关 AI 知识。Ibrahim Haddad 的 [开源 AI：项目、洞察、和趋势][14] 调查了 16 个流行的开源 AI 项目—— 深入研究了他们的历史、代码库、以及 GitHub 的贡献。 [现在可以免费下载这个电子书][14]。
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--------------------------------------------------------------------------------
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via: https://www.linux.com/blog/2018/6/6-open-source-ai-tools-know
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作者：[Sam Dean][a]
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选题：[lujun9972](https://github.com/lujun9972)
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译者：[qhwdw](https://github.com/qhwdw)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]:https://www.linux.com/users/sam-dean
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[1]:https://www.acumos.org/
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[2]:https://www.linuxfoundation.org/projects/deep-learning/
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[3]:https://www.linuxfoundation.org/blog/lf-deep-learning-foundation-announces-project-contribution-process/
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[4]:https://code.facebook.com/posts/1687861518126048/facebook-to-open-source-ai-hardware-design/
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[5]:https://venturebeat.com/2017/04/18/facebook-open-sources-caffe2-a-new-deep-learning-framework/
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[6]:http://yahoohadoop.tumblr.com/post/139916563586/caffeonspark-open-sourced-for-distributed-deep
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[7]:https://systemml.apache.org/
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[8]:https://www.tensorflow.org/
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[9]:https://www.techradar.com/news/google-assistant-sweetens-raspberry-pi-with-ai-voice-control
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[10]:https://www.linux.com/news/sparkling-water-bridging-open-source-machine-learning-and-apache-spark
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[11]:http://www.h2o.ai/download
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[12]:https://blogs.msdn.microsoft.com/uk_faculty_connection/2017/02/10/microsoft-cognitive-toolkit-cntk/
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[13]:https://www.microsoft.com/en-us/cognitive-toolkit/
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[14]:https://www.linuxfoundation.org/publications/open-source-ai-projects-insights-and-trends/
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