Effective data governance consists of protocols, practices, and the people necessary for implementation to ensure trustworthy, consistent data. Its yields include regulatory compliance, improved data quality, and data's increased valuation as a monetary asset that organizations can bank on. Nonetheless, these aspects of governance would be impossible without what is arguably its most important component: the common terminologies and definitions that are sustainable throughout an entire organization, and which comprise the foundation for the aforementioned policy and governance outcomes. When intrinsically related to the technologies used to implement governance protocols, terminology systems (containing vocabularies and taxonomies) can unify terms and definitions at a granular level. The result is a greatly increased ability to tackle the most pervasive challenges associated with big data governance including recurring issues with unstructured and semi-structured data, integration efforts (such as mergers and acquisitions), and regulatory compliance.

Machine learning is generating a lot of chatter lately, making myriad marketers scramble to figure out to use it. Despite all the excitement surrounding machine learning, however, marketers still struggle with how to view it, what to expect from it, and how best to use this new technology. The volumes and rates of data captured by those systems have increased substantially each year. Marketers are seeking to augment and blend that data with other sources, such as in-store visitor data. The dizzying increase of data volume and growing number of data collection sources leave us marketers trying to figure out how to drive the right message at the right place, at the right time.

Tempting as it is to lay as much blame as possible at the feet of Philip Green – a man whose 63rd birthday cake involved an edible version of himself, topless, in bed with his chihuahua, silk sheets recreated in sugar paste, gold candles fringing the mattress like a flaming cage – the decline of British Home Stores was not entirely his fault. Like Austin Reed, the high-street tailor that went into administration the day after BHS, this was a business that collapsed in large part because it failed to be flexible in a changing marketplace. Both came to rely on the patronage of people who, either through a lack of access or an abundance of ethics, opted to shop physically rather than digitally. But as discussions on Twitter over what you'll miss about BHS (mostly: somewhere to go to the loo) also indicate, unless your shop serves a need that cannot be met online, it will now struggle to survive. Even Argos, whose current raison d'être is to trump Amazon by letting you get your mitts on a cheap toaster the same day, has seen a 36% year-on-year drop in profits. This its chief executive credits to "a continuation of the challenges we saw towards the back of last year with high street footfall and the move online".

Personal assistants and software bots have enjoyed significant buzz in recent months. New assistants are launched every week, and the Slack App Store already lists more than 40 bots. Working with Opus Research, we have published an update of the Intelligent Assistance Landscape that looks into the implications of this trend. This report features 110 companies (increased from the 70 participants in the initial report from October, 2105). A high-resolution version of this landscape is available here.

With reports that we may have reached'peak' phone and Apple sales falling for the first time, Google's boss has taken these claims a step further. CEO Sundar Pichai has predicted that the concept of the'device' will fade away and the computer will soon be replaced by intelligent assistants. He made the comments in the firm's annual Founder's Letter in which he discussed the firm's Cloud Platform and its plans for AI. CEO Sundar Pichai has predicted that the concept of the'device' will fade away and the computer will be replaced by intelligent assistants. He made the comments in the firm's annual Founder's Letter in which he discussed the company's Cloud Platform and its plans for AI Every year, the founders of Google's parent company Alphabet - Larry Page and Sergey Brin - write a Founders' Letter to stockholders updating them with recent highlights.

When Cheetah Mobile CEO Sheng Fu went to the US for the first time five years ago, he asked himself, "Why is that Chinese companies work so hard but the most important inventions and new technologies come out of America?" He concluded that the difference between China and the US is that Americans think bigger and dream bigger. The global service robotics market is set to boom, and the second half of 2015 should usher the first wave of these new helpers. Here are seven devices leading the charge. This week in Beijing, Fu announced his company is going to launch itself into AI and deep learning, create a dual headquarters in Beijing and Silicon Valley, and release robots for consumers on a global scale. No one is going to accuse Fu of not thinking big enough.

Artificial intelligence is becoming a reality and people are starting to wonder what the world will be like when'the robots' are advanced enough to undertake tasks that previously could only be done by humans. It is an uncomfortable thought to consider whether we will even be relevant in the future. Experts still disagree over the extent of the threat to the job market, but agree that change is coming. Computer scientist and futurist Jerry Kaplan insists the idea of AI creating a "jobs Armageddon" is ludicrous, whilst AI evangelist Ben Goertzel states "job losses are a feature not a bug of AI". During the British Agricultural Revolution farming jobs were lost to machinery, yet the approaching Industrial Revolution opened up many more roles.

Software major Infosys has developed a knowledge-based artificial intelligence (AI) platform to drive automation and innovation for its global clients. "The platform Mana brings machine learning with knowledge of an organisation to drive automation and innovation for enabling businesses reinvent their (computing) systems," the company said in a statement on Friday from San Francisco. Using the platform, the Indian bellwether improved productivity of a unspecified company with a fleet of field engineers by 50 percent by utilising its self-learning capabilities. "The platform enabled a global telecom firm to reduce 80 percent of its agents' entry effort by automating order validation and removing need for corrective processes," the statement asserted. Elaborating on multiple benefits of Mana, Infosys chief executive Vishal Sikka said, "We can automate the repetitive, mechanisable tasks; we can capture the knowledge and know-how across people and long-lived systems and bring this knowledge back inside the systems to drive more value; and in doing these things we can free people to put all of our creativity, passion, and imagination into thinking about the bigger opportunities ahead of us."

Deep learning could not have developed at the rapid pace it has over the last few years without companion work that has happened on the hardware side in high performance computing. While the applications and requirements for supercomputers versus neural network training are quite different (scalability, programming, etc.) without the rich base of GPU computing, high performance interconnect development, memory, storage, and other benefits from the HPC set, the boom around deep learning would be far quieter. In the midst of this convergence, Marc Hamilton has watched advancements on the HPC side over the years, beginning in the mid-1990s at Sun, where he spent 16 years, before becoming VP of high performance computing at HP. Now the VP of Solutions Architecture and Engineering, he says that there is indeed a perfect storm of technologies intermixing in both deep learning and HPC--and this bodes well for Nvidia's future business at both supercomputing sites and deep learning shops alike. "The reality is that every one of those supercomputing centers is producing data every year, more than they know what to do with, and they have problems they just can't sole with classic scientific computing and HPC approaches," Hamilton tells The Next Platform. "The number of people looking at how to apply deep learning to curing cancer or tackling weather prediction with deep learning is growing. What someone does to optimize a deep learning software package is different than what is needed in HPC, but at the end of the day, it's all matrix math. And that is what we do well; so HPC benefits and machine learning benefits."

The firm says it launched this is because progress made in reinforcement learning lags for a few reasons. Firstly, OpenAI notes that existing, open-source testing environments lack diversity and are difficult to set up and use. What's more, there's a dearth of standardization, which makes reproducing the tests -- key for any sort of academic research -- between different projects in an apples to apples way pretty hard to do.