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The goal is to find a way to consistently produce data-driven insights at enterprise scale. This can be done with industrial machine learning (IML), which provides a scalable solution for ingesting data, building algorithms, deploying them into production, and generating continuous insights to ongoing business problems. IML is a modern take on a very old idea: the scientific method. Data scientists start with a hypothesis and collect data that could be useful in evaluating the hypothesis. They then generate a model and use it to explain the data.

Bain Capital Ventures announced today in a public filing that it has closed a 600 million investment fund. BCV, the venture arm for Boston-based firm Bain Capital, said it "also has money remaining in its [ 200 million] 2014 Coinvestment Fund for larger investments." The new fund, the firm said, will continue with previous BCV investing trends, pouring seed and growth capital into enterprise and infrastructure software startups, as well as in "industries transformed by data." BCV managing director Ajay Agarwal tells VentureBeat that specific areas of focus for the new fund include "SaaS, machine learning, financial and retail technology, cybersecurity, cloud infrastructure and open source." "We will also continue to balance investments geographically, including California which now represents half of our senior investment team and half of our investment dollars," said Agarwal.

The Air Force wants white papers that describe new ways quantum computing could help achieve its mission, according to an amended Broad Agency Announcement posted Friday. Eventually, the government could provide a test-bed where a contractor might install, develop and test a quantum computing system, according to the announcement. Last year, the Air Force announced it had about 40 million available to fund research into, and the eventual maintenance and installation of a quantum system -- a branch of emerging computing technology that relies on the mechanics of atomic particles to process complex equations. The Air Force Research Laboratory's Information Directorate, which focuses on processes such as signal processing, networking technology, cyber research and supercomputing, is collecting those white papers. Many problems that stump traditional computers can be re-framed as "optimization problems," the solicitation says; one of quantum computing's potential benefits is its ability to quickly find the optimum solution to a multidimensional problem, given certain constraints.

The three cutting-edge frameworks showcased in these talks from MesosCon North America demonstrate the amazing power and flexibility of Apache Mesos for solving large-scale problems. Perhaps you have noticed, in our Apache Mesos series, the importance of frameworks. Mesos frameworks are the essential glue that make everything work in a Mesos cluster, the layer between Mesos and your applications. They perform a multitude of tasks, including launching and scaling applications, monitoring and health checks, configuration management, and scheduling. In these talks, you'll learn how: Netflix uses Mesos to power their recommendation engines.

There's a lot of hype in the world at the moment about how algorithms are taking over the world. There's a lot of hype in the world at the moment about how machine learning is taking over the world. "Machine learning" is one of those things that is terribly complicated to the extent that most people don't really understand it. I've been reading a book recently, The Master Algorithm, which seems to make a pretty good stab at explaining both Algorithms and Machine Learning. I've no doubt that machine learning and algorithms will continue to change the world around us.

Cisco researchers have managed to spot malware in encrypted traffic, making a new development in the fight against malware. A group of Cisco researchers have managed to sport malicious traffic in encrypted traffic without any need to decrypt data. The discovery could pave the way for products that can secure networks while maintaining privacy. According to a paper published on Arxiv, malware within encrypted streams gives out enough clues to allow researchers to spot them. Traffic encrypted using TLS, is increasingly used by criminals to circumvent security products. "The use of TLS by malware poses new challenges to network threat detection because traditional pattern-matching techniques can no longer be applied to its messages," Blake Anderson, Subharthi Paul, and David McGrew said in their research paper.

Intel wants to take on Google's Tensor Processing Unit and Nvidia's GPUs in machine learning computing with improvements to its Xeon Phi mega-chips. The company will add new features to Xeon Phi to tune it for machine learning, said Nidhi Chappell, director of machine learning at Intel. Machine learning, a trendy technology, allows software to be trained to do tasks like image recognition or data analysis more efficiently. Intel didn't disclose when the new features will be added, but the next version of Xeon Phi will come by 2018. Intel's already behind chip rivals in machine learning, so it may have to speed up the next Xeon Phi release.

Scientists are closer to creating a computer with emotions. Researchers in Russia are expected to reveal an emotional computer within a year and a half, which will be able to think like a person and build up trust, its creators say. The system, called'Virtual Actor', is being created by the National Research Nuclear University in Moscow. Computers are machines used for practical reasons, without any emotion involved. The AI, called'Virtual Actor', is expected to be online within the next year and a half.

Late last year, I wrote about an interesting project from the US Defense Department to create a robotic jazz player. They developed a machine that is capable of performing a trumpet solo after picking up cues from fellow (human) musicians. "The goal of our research is to build a computer system and then hook it up to robots that can play instruments, and can play with human musicians in ways that we recognize as improvisational and adaptive," the researchers said. At the heart of this kind of project is a big data fueled pattern match, whereby machines learn what to do next by studying huge numbers of previous examples from human musicians. A similar project has been undertaken by researchers at Google.