An Open Source AI Onslaught

Recently, in an article for TechCrunch, Spark Capital's John Melas-Kyriazi weighed in on how startups can leverage artificial intelligence to advance their businesses or even give birth to brand new ones. As a corollary avenue on that topic, it's worth noting that some very powerful artificial intelligence engines have recently been open sourced. Quite a few of them have been tested and hardened at Google, Facebook, Microsoft and other companies, and some of them may represent business opportunities.

Here are a few of these open tools to know about.

H2O.ai. In recent interviews here on OStatic, found here and here, we have explored the efforts of H2O.ai, formerly known as Oxdata, which has steadily been carving out a niche with its  open source software for big data analysis and machine learning. You can get the main H2O platform and Sparkling Water, a package that works with Apache Spark, by simply downloading them. You can run them on clusters powered by Amazon Web Services (AWS) and others for just a few hundred dollars. Find out more about the opportunity this company's tools can provide here. 

From Redmond. Microsoft CEO Satya Nadella, seen atop this post, has been very enthusiastic about AI. Microsoft has open sourced the artificial intelligence framework it uses to power speech recognition in its Cortana digital assistant and Skype Translate applications. The framework is called, CNTK, and can help machines do things like understand speech and determine logical connections between photos. Microsoft released its Computational Network Toolkit (CNTK) as an open source project on GitHub, and developers are likely to leverage it to advance deep learning networks.

Facebook On Board. In early 2015, Facebook open sourced modules for the Torch deep learning toolkit. According to Facebook leaders: "Torchnet provides a collection of boilerplate code, key abstractions, and reference implementations that can be snapped together or taken apart and then later reused, substantially speeding development. It encourages a modular programming approach, reducing the chance of bugs while making it easy to use asynchronous, parallel data loading and efficient multi-GPU computations."

Meanwhile, Facebook has open sourced its machine learning system designed for artificial intelligence (AI) computing at a large scale. It's based on Nvidia hardware. Facebook's Kevin Lee and Serkan Piantino wrote in a blog post that the open sourced AI hardware more efficient than off-the-shelf options because the servers can be operated within data centers based on Open Compute Project standards.

Google's TensorFlow. In numerous recent posts, we covered Google's decision to open source a program called TensorFlow and the related platform TensorFlow Serving. These are based on the same internal toolset that Google has spent years developing to support its AI software and other predictive and analytics programs. TensorFlow is rapidly gaining momentum. 

It is being leveraged by researchers who need to analyze very large sets of complex data, according to Google. According to a Google post:

"TensorFlow Serving....is a high performance, open source serving system for machine learning models, designed for production environments and optimized for TensorFlow.  TensorFlow Serving is ideal for running multiple models, at large scale, that change over time based on real-world data."

Are you hungry for even more open AI tools that can be leveraged for new ideas? InformationWeek has a good roundup of some of the other deep learning and AI tools open sourced recently. It's good to see some of the biggest tech companies contributing their deep learning and AI tools to the open source community. No doubt, these contributions will help AI advance over the next several years.