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.

When I'm walking around it would be wonderful to have access to street signs, maybe even just being able to get a perspective. Hey, what's around here, what am I looking at? What building is in front of me? What kind of car is this? As a blind person you don't really think about the things that you might be able to see because you aren't aware of them.

If you've been following the news recently, you've probably heard a few of the breaking stories about robots beating humans in fairly complex tasks. It started with game show contestants, evolved into outperforming human instinct, and now computers are even outperforming fighter pilots in tactical simulations. What does this all mean for you? Although Hollywood loves to depict artificial intelligence as robots out to destroy humans, that's far from the case in the real world. Chances are you've been using artificial intelligence for awhile and haven't even noticed.

Has A CEO BOT been BORN? Apparently, my thoughts on this subject have become "timely." With that said, I shall jump in with "both feet." That's generally my strategic plan once I make a decision. I wrote this and never posted it a few weeks ago, but when this article from Stan Choe came out, CEO Pay in 2015: When a 468,449 raise is typical, I decided to go ahead and hit the publish button. There are a lot of reports and links that provide numbers on the percentages and increases in executive salaries versus "the rest of the company."

This is a demonstration movie of AI, developed in Hitachi R & D, aiming to improve the performance of many kinds of business systems generically. In this movie, AI tries to improve the performance of "Swing Robot System", which even don't know how to swing.

Google DeepMind has announced its second collaboration with the NHS, as part of which it will work with Moorfields Eye Hospital in east London to build a machine learning system which will eventually be able to recognise sight-threatening conditions from just a digital scan of the eye. The five-year research project will draw on one million anonymous eye scans which are held on Moorfields' patient database, reports Ars Technica, with the aim to speed up the complex and time-consuming process of analysing eye scans. From the report:The hope is that this will allow diagnoses of common causes of sight loss, like diabetic retinopathy and age-related macular degeneration, to be spotted more rapidly and hence be treated more effectively. For example, Google says that up to 98 percent of sight loss resulting from diabetes can be prevented by early detection and treatment. Two million people are already living with sight loss in the UK, of whom around 360,000 are registered as blind or partially-sighted.

To learn more about the state of AI today and where we might be headed in coming years, download the free report "What is Artificial Intelligence?," by Mike Loukides and Ben Lorica. There are four basic ingredients for making AI: data, compute resources (i.e., hardware), algorithms (i.e., software), and the talent to put it all together. In this era of deep learning ascendancy, it has become conventional wisdom that data is the most differentiating and defensible of these resources; companies like Google and Facebook spend billions to develop and provide consumer services, largely in order to amass information about their users and the world they inhabit. While the original strategic motivation behind these services was to monetize that data via ad targeting, both of these companies--and others who are desperate to follow their lead--now view the creation of AI as an equally important justification for their massive collection efforts. While all four pieces are necessary to build modern AI systems, what we'll call their "scarcity" varies widely.

In this session, Vasyl Mylko - R&D Director of SoftServe, will explain how AI tools can be combined with the latest Big Data concepts to increase people productivity and build more human-like interactions with end users. The Second Machine Age is coming. We're now building thinking tools and machines to help us with mental tasks, in the same way that mechanical robots already help us with physical work. Older technologies are being combined with newly-created smart ones to meet the demands of the emerging experience economy. We are now in-between two computing ages: the older, transactional computing era and a new cognitive one.