6 Open Source AI Tools to Know


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.

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.

Acumos. Acumos AI 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.

Acumos is part of the LF Deep Learning Foundation, 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 and is now accepting proposals for the contribution of projects.

Facebook’s Framework. Facebook has open sourced 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.

Speaking of Caffe. Yahoo also released its key AI software under an open source license. The CaffeOnSpark tool 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 is freely available to share and modify through the Apache Software Foundation.

Google’s Tools. Google spent years developing its TensorFlow 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.

Two AIY kits 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.

H2O.ai. I previously covered 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 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.

Microsoft Onboard. “Our goal is to democratize AI to empower every person and every organization to achieve more,” Microsoft CEO Satya Nadella has said. With that in mind, Microsoft is continuing to iterate its Microsoft Cognitive Toolkit. 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.

“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.

Learn more about AI in this new ebook from The Linux Foundation. Open Source AI: Projects, Insights, and Trends by Ibrahim Haddad surveys 16 popular open source AI projects – looking in depth at their histories, codebases, and GitHub contributions. Download the free ebook now.