Darktrace, a cybersecurity startup that uses machine learning to detect threats, announced Wednesday that it had raised 65 million to continue with its plans for global expansion. The company's software helps IT leaders see emerging threats on computer networks in real time. The company was founded in 2013, and has grown rapidly. At last count, Darktrace boasted 300 employees and more than 1,000 deployments of its Enterprise Immune System technology. What's more interesting, perhaps, is how it works.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Machine learning has advanced to the point where it more or less goes hand-in-hand with Big Data. Indeed, so popular is the technology that over a third of developers – some 36 percent – who're working on Big Data or advanced analytics projects use elements of machine learning, says a new study by Evans Data Corp. What with more Big Data being generated from sources like audio, the Internet of Things, social media, wearables and video, enterprises need more efficient ways to handle and process it to drive new business insights. Machine learning involves creating and improving complex algorithms that are able to analyze data automatically and identify patterns or predict outcomes based on the knowledge they have "learned". As such, it has great potential for helping companies to better understand what their data is telling them.

Use features and descriptors to track the car from the first frame as it moves from frame to frame. Store (ORB) descriptors in a Mat and match the features with those of the reference image as the video plays. Then multiply points by a homography matrix to create a bounding box around the identified object. The result isn't perfect, but try different filtering techniques and apply optical flow to improve on the sample implementation. Getting good at computer vision requires both parameter-tweaking and experimentation.

Artificial Intelligence (AI) is really big right now. Big news and big possibilities, but some big questions too, not least about how to get the best out of it. Could it really eliminate whole categories of jobs, as research by Oxford University suggests?[1] Or could it help make all of us smarter, more efficient, and more fulfilled in our work by doing the essential but repetitive tasks, and mundane activities that human beings do not excel at? Transformation is an overused word in the business world, but this is one case where it's justified.

Future historians of technology may look back at one week this March as a tipping point of a new era, and it all started with smooth black-and-white stones on a simple wooden board. It was a five-game match of Go, the ancient Chinese board game, pitting top-ranked world champion Lee Se-dol against an artificial intelligence system called AlphaGo from Google's DeepMind. Although Lee confidently predicted a shutout victory over AlphaGo, the system beat him a resounding 4-1. Games were live-streamed around the world, with a monumental ending reminiscent of Garry Kasparov's 1997 defeat against Deep Blue. But this AI victory goes far beyond the basic mathematical win that Deep Blue achieved. Among other factors, chess has fewer possible legal moves and a well-defined end state: checkmate.

"As we know, there are known knowns. There are things we know we know," famously explained former Defense Secretary Donald Rumsfeld. "We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns--the ones we don't know we don't know." Thanks to a technology innovation known as swarm AI the unknown unknown variables in politics and business may be evaporating.

Having herself held senior roles in IT at Wall Street companies including Deutsche Bank and Morgan Stanley Smith Barney, Oksana Sokolovsky is quite familiar with the challenge of Data Management and data discovery. As co-founder and CEO of ROKITT, her goal was "to build a product that solves that challenge," she says. The challenge exists across large enterprises in multiple industries, but is often especially acute in those dealing with regulatory pressures and compliance requirements – healthcare, for instance, and of course, the financial sector. Basel Committee on Banking Supervision (BCBS) 239 compliance for effective risk data aggregation and reporting, for example, is a big driver of improved Data Management for global systemically important banks. In fact, a McKinsey & Company and Institute of International Finance survey showed that more than half of the world's biggest banks faced significant challenges meeting the January 1, 2016 deadline for compliance, with the Global Association of Risk Professionals commenting that "many institutions continue to struggle to fully implement the requirements across the business under the most demanding interpretation of those requirements."

Machine Learning is a subfield within Artificial Intelligence that builds algorithms that allow computers to learn to perform tasks from data instead of being explicitly programmed. We can make machines learn to do things! The first time I heard that, it blew my mind. That means that we can program computers to learn things by themselves! The ability of learning is one of the most important aspects of intelligence. Translating that power to machines, sounds like a huge step towards making them more intelligent. And in fact, Machine Learning is the area that is making most of the progress in Artificial Intelligence today; being a trendy topic right now and pushing the possibility to have more intelligent machines.

Google has acquired Moodstocks, a company that develops machine-learning based object recognition tech for mobile phones. The Paris-based startup will shut down its object recognition Application Programming Interface (API) after its staff joins Mountain View's Parisan R&D team, reports PC World. The purchase was made for an unknown sum, and appears like an acquihire deal. The French technology startup builds photo and object recognition software by employing deep learning techniques. The company produced a visual search API and an Android app that could identify certain kinds of objects.