In an unassuming building on the outskirts of Budapest engineers working for small Hungarian security firm BalaBit have spent the last three years working on technology its makers are convinced can contain one of cybersecurity's most intractable woes. In 2014 the relatively unknown firm launched a system called Blindspotter which, as its name suggests, gives its customers mostly in finance and telco sector buyers the ability to see things most networks barely acknowledge as existing let alone attempt to look for. Blindspotter is designed to watch what network users are doing in a lot of detail, a boon for organisations that worry about user credentials being abused, either deliberately from within by attackers who've somehow pilfered them. When used in conjunction with the firm's network proxy appliance, Shell Control Box (SCB), organisations suddenly have the ability to monitor their whole infrastructure using measurements of user behaviour rather than packets, ports and protocols. The system's real intrigue isn't what it does – cybersecurity is already chock full of network monitoring in different forms – so much as how it does it.

Filmmaker Oscar Sharp and technologist Ross Goodwin fed a machine learning algorithm with a bunch of Sci-Fi movie scripts to see what new script it would spit out. A script for Sunspring is the result, and this is the film, starring Thomas Middleditch. The thought of a machine tapping into emotion and creativity likely brings some sneers, but Goodwin argues that it's about assistance and augmentation rather than a replacement for the humans. The machine dictated that Middleditch's character should pull the camera. However, the reveal that he's holding nothing was a brilliant human interpretation, informed by the production team's many years of combined experience and education in the art of filmmaking.

I'm sure most people have heard of the dilemma of whether to design self-driving cars to reduce the number of deaths or to protect their driver. To those of you who haven't, picture this; you are sitting in your cars which is driving along in a partially blind curve. The car discovers a crowd of people in the lane without enough distance to stop the car. There are only two options, hit the crowd and potentially kill lots of people or drive the car off a cliff, sacrificing the driver while reducing the total number of deaths. On a macro level, the car should go off the cliff since the number of deaths is reduced.

If there's one thing software company Ebsta understands, it's customer relationship management. The San Diego- and London-based company sells a 10 per month per user Chrome browser extension that syncs customers' email accounts to the Salesforce database to streamline the onerous task of updating a CRM system. But finding new prospects for Ebsta is difficult and, as vice president of sales Bernhard Peters points out, expensive -- especially for a company with roughly 1 million in annual revenue. "We're still a tiny company," Peters says. "We have to be careful who we chase; we don't have a lot of money or manpower to spare. Buying lists never works; they're out of date. And data-mining companies charge at least 25,000 upfront, with no guarantee of ROI." Peters was in a San Diego restaurant when he overheard Olin Hyde, co-founder and CEO of Englue, explaining how his artificial intelligence product LeadCrunch could mine the web to uncover leads based on names of a company's best customers.

Utilities are significantly increasing data gathering and using external data sets for smarter capacity and investment planning. Everything from weather data to 3D modeling of networks and extraction sites is being gathered, combined with internal and historical data sets and used to inform, predict and plan a variety of business outcomes. While not cheap to obtain, data is being used to make informed decisions on everything from when to shut down power stations to avoid over-capacity in the market, to where to drill and how to manage fluctuating water supplies. For example, moves in several markets to deploy smart meters, capable of feeding back consumption data in near real time, provide a valuable data resource for energy companies. This flow of information--which can update as frequently as every 30 seconds--provides valuable insight into real-world energy consumption and can provide early warning of peaks in demand.

Kai Brunner is principal designer for continuous delivery enterprise software at Electric Cloud. Murphy's Law decrees: "Anything that can go wrong, will go wrong." For any of us whose livelihood depends on our labor, things going wrong could mean: "Anything that can be automated, will be automated." Our labor or skill in exchange for pay has undoubtedly caused us to seek security in the notion that we'll be forever needed. And yet time has shown that our ingenuity for efficiency orchestrates our removal from all forms of repetitive tasks.

Indian Angel Network (IAN), Indian angel investor network, announced undisclosed investment in Gurgaon-based Staqu, an Artificial Intelligence (AI) focused research startup working in automated image understanding technology. The funding will be used to further build and democratize technology and strengthen the team. Staqu, founded in 2015, comprises of researchers and engineers as a part of its core team. Atul Rai, co-founder and CEO said, "We plan to invest this round to expand the computational strength of our VGrep Lab (AI research lab at Staqu) and fuel it with clusters of GPUs and other technical resources. Currently, we are applying our research to solve pressing problems in the e-commerce sector. In the near future, we are planning on use it to address issues across other market segments, too. Throughout the funding process, our ideas were constantly cross-questioned, which helped us set a clear vision and goals."

We researched the necessary and desirable/nice-to-have skills the top companies look for when hiring engineers for Machine Learning, Data Science, Big Data etc. Our motive is to keep this list updated so it's relevant.

In the last decade, in the Semantic Web field, knowledge bases have attracted tremendous interest from both academia and industry and many large knowledge bases are now available. However, both generation of new knowledge and population of already existing knowledge bases with new facts face several challenges. Most of the time knowledge bases have been manually built, resulting in a highly specialistic and time consuming activity. Nevertheless, sources of unstructured and semi-structured data are still growing at a much faster rate than structured ones, as such it could be desirable to exploit such a large non-structured sources to populate structured knowledge bases. In the Semantic Web, a major cornerstone of knowledge bases are ontologies and schemas that play a key role for providing common vocabularies and for describing and constructing the Web of Data.

By the end of this article, you'll understand how Numerai is using advances in cryptography like homomorphic encryption to allow for open participation in the problem of stock market efficiency. Over the last few years, machine learning algorithms solved big problems in computer vision. One such problem was getting an algorithm to learn how to recognize handwritten digits in the MNIST dataset. Everyone writes digits differently, so the problem was difficult for computers to grasp. When the dataset first became available in 1998, machine learning algorithms for computer vision were not very accurate.