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Based on the disturbing number of successful data breaches over the past few years, it's pretty evident that organizations are being overwhelmed by the growing number of threats. However, a new breed of security solution has sprung up, offering to apply machine learning to enterprise security. These tools deliver the ability to analyze networks, learn about them, detect anomalies and protect enterprises from threats. So, is machine learning the answer to today's cybersecurity challenges? Industry analysts and companies offering these products say they're seeing increased demand, and the early reaction from users is positive.

Netflix spent 1 million for a machine learning and data mining competition called Netflix Prize to improve movie recommendations by crowdsourced solutions, but couldn't use the winning solution for their production system in the end. This white paper takes a closer look at the real-life issues Netflix faced and highlights key considerations when developing production machine learning systems.

"By developing a sophisticated molecular builder, using state-of-the-art quantum chemistry and machine learning, in addition to drawing on the expertise of experimentalists, we discovered a large set of high-performing blue OLED materials," said Aspuru-Guzik, Professor of Chemistry and Chemical Biology, who led the research. "Following that validation, I am extremely excited to see this platform adopted for commercial development, utilizing its capabilities for the rapid screening of TADF materials." The algorithms dramatically reduce the computational cost of testing candidate molecules for new technologies. In addition to Kyulux's licensing of the software, three key researchers who developed the system in Aspuru-Guzik's research group and were co-authors on the Nature Materials publication have chosen to join Kyulux's computational chemistry group in Boston. Professor Aspuru-Guzik will also join the company as a part-time scientific advisor.

The U.S. Defense Advanced Research Projects Agency (DARPA) has launched a program that will create the technology to make new generations of artificial intelligence (AI) systems "explainable." DARPA'S Explainable AI (XAI) program aims to create new machine learning methods to produce more explainable models and combine them with explanation techniques. And why the need to understand AI? That's because explainable AI -- especially explainable machine learning -- will be essential if future American warfighters are to understand, appropriately trust and effectively manage an emerging generation of AI "partners" such as battlefield robots and machines. XAI is vital because continued advances in AI promise to produce autonomous systems that will perceive, learn, decide and act on their own. The effectiveness of these AI systems, however, is limited by the machine's current inability to explain their decisions and actions to human users.

After we teach our kids how to survive with things like getting food into their mouths instead of all over their faces, using their words to communicate instead of pointing or grunting and wiping their own butts, we move on to social lessons. As we teach these lessons every day in the classroom or at the grocery store, it's inevitable that we slowly start to hear that faint echo of our parents' voices. It's as if their voices have been channeled through our own as we constantly remind our kids that it's not ok to burp without saying excuse me or to sneeze without covering their mouths. We hope they'll listen and learn because we know what happens to parents who have kids with bad manners. That judging glare we get at every event where parents and children are gathered in one place and we decide who the "bad" and "good" parents are simply by their obnoxious child's behavior.

In building automation, sensors such as motion detectors, photocells, temperature, and CO2 and smoke detectors are used primarily for energy savings and safety. Next-generation buildings, however, are intended to be significantly more intelligent, with the capability to analyze space utilization, monitor occupants' comfort, and generate business intelligence. To support such robust features, building-automation infrastructure requires considerably richer information that details what's happening across the building space. Since current sensing solutions are limited in their ability to address this need, a new generation of smart sensors (see figure below) is required to enhance the accuracy, reliability, flexibility, and granularity of the data they provide. Data Analytics at the Sensor Node In the new era of the Internet of Things (IoT), there arises the opportunity to introduce a new approach to building automation that decentralizes the architecture and pushes the analytics processing to the edge (the sensor unit) instead of the cloud or a central server.

Tapan Agarwal, Product Council Head at iGTB, has been cited in Global Trade Review in an article discussing the use of AI in the financial services industry. The article describes how financial services provides a fertile ground for AI applications because AI's strength comes from the quality of the data fed to it, and financial institutions themselves are data mines. In trade finance, AI applications can be found particularly in the field of compliance to prevent money laundering and fraud. Banks are currently facing the challenge of increased regulation in these areas, and keeping up with various requirements can be challenging for compliance departments. "Banks are failing to identify threats and fraudulent activities by relying solely on curated databases," commented Agarwal.

The chatbot lawyer that overturned hundreds and thousands of parking tickets is now tackling another problem: homelessness. London-born Stanford student Joshua Browder created DoNotPay initially to help people appeal against fines for unpaid parking tickets. Dubbed "the world's first robot lawyer", Browder later programmed it to deal with a wider range of legal issues, such as claiming for delayed flights and trains and payment protection insurance (PPI). Now, Browder, 19, wants his chatbot to provide free legal aid to people facing homelessness. He said: "I never could have imagined a parking ticket bot would appeal so much to people. Then I realised: this issue is bigger than a few parking tickets."

Apple CEO Tim Cook was the subject of an in-depth interview published over the weekend in The Washington Post in which he talked about a wide range of issues, including augmented reality (AR) and artificial intelligence (AI), the future of the iPhone, and the company's North Star. In the interview, Cook dismissed the idea of the iPhone accounting for two-thirds of Apple's revenues being problematic, calling the smartphone's dominance a privilege and expressing his belief that one day, every person on earth will own a smartphone. Cook also defended the company's progress in AI technology, pointing to the expanding capabilities of Siri, the digital assistant that Apple launched in 2011. Apple is opening up Siri to third-party developers so the technology can be used by other applications -- such as Uber or Lyft, as Cook pointed out -- to help users complete tasks faster and more efficiently. Earlier this month, the company reportedly bought Turi, a Seattle-based startup company and the latest purchase in a string of acquisitions aimed at bolstering its machine learning and AI capabilities.