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Open source software enables Google to build things quickly and efficiently without reinventing the wheel, allowing us to focus on solving new problems. We stand on the shoulders of giants, and we know it. This is why we support open source and make it easy for Googlers to release the projects they're working on internally as open source. We've released more than 20-million lines of open source code to date, including projects such as Android, Angular, Chromium, Kubernetes, and TensorFlow. Our releases also include many projects you may not be familiar with, such as Cartographer, Omnitone, and Yeoman...
As reported in Forbes yesterday, Elon Musk announced the OpenAI research initiative, with the explicit goal to "advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return." Details are sparse at this time given its recent inception, but Musk has a history of being outspoken about the dangers of artificial intelligence, calling it the biggest existential threat that humanity may face in years to come.
Google says today it’s making the machine learning technology that powers a number of its products, including Google Photos search, speech recognition in the Google app, and the newly launched “Smart Reply” feature for its email app Inbox. Called TensorFlow, the technology helps makes apps smarter, and Google says it’s far more powerful than its first-generation system – allowing the company to build and train neural nets up to five times faster than before.
The development of smarter and more pervasive artificial intelligence (AI) is about to shift into overdrive with the announcement by Google this week that TensorFlow, its second-generation machine-learning system, will be made available free to anyone who wants to use it. Machine learning emulates the way the human brain learns about the world, recognising patterns and relationships, understanding language and coping with ambiguity. This is the technology that already provides the smarts for Google’s image and speech recognition, foreign language translation and various other applications. This is valuable technology, and it is now open source; the source code is freely available and can be modified, developed in new directions and redistributed in the same way that the Linux operating system is open.
We're only a few days into 2017, and it's already clear that one of the biggest tech categories of this year will be artificial intelligence. The good news is that open source AI tools are proliferating and making it easy for organizations to leverage them. AI is also driving acquisitions. As Computerworld is reporting, in the past year, at least 20 artificial intelligence companies have been acquired, according to CB Insights, a market analysis firm. MIT Technology Review is out with its five big predictions for AI this year. Here is a bit on what they expect, and some of the open source AI tools that you should know about...
Machine learning and artificial intelligence have quickly gained traction with the public through applications such as Apple’s Siri and Microsoft’s Cortana. The true promise of these disciplines, though, extends far beyond simple speech recognition performed on our smartphones. New, open source tools are arriving that can run on affordable hardware and allow individuals and small organizations to perform prodigious data crunching and predictive tasks.
No one has a crystal ball to see the future of technology. Even for projects developed out in the open, code alone can't tell us whether or not a project is destined for success—but there are hints along the way. For example, perhaps it's not unreasonable to assume that the projects that will help shape our future are those projects that have first seen rapid growth and popularity among the developer community. So which new projects should an open source developer watch in 2017? Let's take a look at a few projects that emerged in 2016 to achieve rapid notoriety in the GitHub community...
The O'Reilly AI Conference will cover all the most essential—and intriguing—topics in applied AI. You'll learn how to implement AI in your projects, uncover AI's limitations and untapped opportunities, and explore how AI will change the business landscape. And you'll meet the brightest minds in applied AI, including keynotes from Microsoft's Lili Cheng, Peter Norvig of Google, and Facebook's Yann LeCun. Urs Muller from NVIDIA will address learning for autonomous driving...