Machine Learning and Open Source Converge as 2016 Kicks Off
A few months back, OStatic caught up with Oleg Rogynskyy, who is shown here, VP of Marketing & Growth at H2O, for an interview. He discussed the renaissance going on in machine learning, and noted that open source trends are helping to drive the space forward. "We’re already seeing significant interest from the developer community regarding machine learning and its uses for app development," he told us. "Underlying tools and platforms will need to be able to abstract away a lot of data science and domain science and expose intelligent APIs that can be used to build smarter applications."
As 2016 begins, more bold predictions for the machine learning space are arriving, and there are some promising, newly open sourced tools to know about.
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 decistion 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. And, we reported on how H2O.ai, formerly known as Oxdata, has announced a new funding round that it is getting to the tune of $20 million. The money will go toward advancing its machine learning toolset.
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.