Machine Learning and Open Source: What You Need to Know Now
As 2016 begins, more bold predictions for the machine learning space are arriving, and there are very some promising, newly open sourced tools to know about. We've been covering these promising tools and conducting some relevant interviews with leaders in the machine learning space. Here are some must-see interviews and highlights from our coverage.
H2O.ai, formerly known as Oxdata, has steadily been carving out a niche with its open source software for big data analysis and machine learning. We've conducted a couple of recent interviews with leaders at the company:
Vinod Iyengar. In an interview, Vinod (seen in the photo above) discusses machine learning and open source with us, as well as what H2O.ai is working on. "Our machine learning platform H2O and Sparkling Water, our package for Spark, are completely open source and fully available for download at http://www.h2o.ai/download," he said. "We operate under the Apache 2.0 license, the most flexible open source license available. Code is truly getting commoditized and the only defensible asset is community. The relationships we have with our customers are also deepened due to the open source nature of our products. Because H2O and Sparkling Water are open source, our customers are also our community. They take part in H2O not just as consumers, but as developers as well."
Oleg Rogynskyy. We previously spoke with H2O.ai's Oleg Rogynskyy for an interview. He said: "All of our customers use our tools to make better predictions. Many, like Cisco and PayPal, already have extensive predictive models in place. For organizations like these our primary goal is to make the predictive process easier by offering them a way to score their models faster and more accurately."
Artificial intelligence and machine learning are much in the news right now, and some of the biggest tech companies are helping to drive the trend. Recently, I covered Google's decision to open source a program called TensorFlow. It’s based on the same internal toolset that Google has spent years developing to support its AI software and other predictive and analytics programs.
In addition, Facebook is open sourcing its machine learning system designed for artificial intelligence (AI) computing at a large scale. It's based on Nvidia hardware. And, IBM announced that its proprietary machine learning program known as SystemML will be freely available to share and modify through the Apache Software Foundation.
As The Wall Street Journal notes, regarding IBM's tool:
"The letters ML stand for 'machine learning,' a hot technology in Silicon Valley that enables computers to find common patterns in large amounts of data. Machine learning has been used to teach computers tasks such as predicting phrases entered into search engines, recognizing faces in photos and detecting unusual moves in stock prices. SystemML, which was first developed at IBM’s Almaden research lab nearly a decade ago, could make it easier for developers to create customized machine-learning software...."
Meanwhile, Facebook's Kevin Lee and Serkan Piantino wrote in a blog post that tthe company's open sourced machine learning hardware can be more efficient than off-the-shelf options because the servers can be operated within data centers based on Open Compute Project standards.
Google is also making a possibly hugely influential contribution to the field of machine learning. It is open sourcing a program called TensorFlow that will be freely available. It’s based on the same internal toolset that Google has spent years developing to support its AI software and other predictive and analytics programs.
You can find out more about TensorFlow at its site, and you might be surprised to learn that it is the engine behind several Google tools you may already use, including Google Photos and the speech recognition found in the Google app.
According to Google, TensorFlow could help speed up processes ranging from drug discovery to processing astronomy-related data sets.
"In 2016, every company will want to get on the machine-learning bandwagon," said Monte Zweben, co-founder and CEO of Splice Machine and executive chairman of RocketFuel, in a recent interview. "But without the right people, many won’t have the expertise to do it. Expect to see the development of turnkey databases that allow developers to build predictive models without having a Ph.D."
And, Ray Kurzweil's site has rounded up many of the top machine learning and artificial intelligence breathroughs of 2015 here.
The basic goal with most machine learning tools is to take a vast quantity of data and reduce it to manageable, actionable insights. Now, some of the biggest tech companies are putting the tools in place to let the community advance these efforts. Expect much more in this space as 2016 continues.