Today we can collect lots and lots of performance data. We build beautiful dashboards and even have fancy query languages to access and transform the data. Still performance data is a secret language only a couple of people understand. The more business becomes digital the more stakeholders are interested in this data including how it relates to business. Some of these people have never used a monitoring tool before.

The new Adobe Voco app is another example of how Digital data is different from the physical world. Digital information has no physical mass and can be edited, recoded, modified. It is this ambivalence feature that is so in contradictory to our everyday experience; we wish we can change our appearance, alter our skills or alter ego but our physical human bodies Can not yet be easily changed. This same property of digital data is what enables cyber security hackers and theft to be done remotely and unseen insider networks and software Or surveillance through web cams or now the growing internet of things of smart home devices that talk, recognise our faces, thumb prints and much more potentially. This is changing our boundaries of privacy and enabling new capabilities that could be for powerful good in areas of medicine to protection from car crashes to virtual reality entertainment.

Smartphone-based virtual reality headsets are great and all, but for the best games and experiences you need a dedicated facehugger tethered to a powerful PC like it's a diver's lifeline. Wireless hardware is one of the inevitable next steps for VR, and a company called TPCAST is already developing a cord-cutting peripheral for the Vive, supported by HTC's VR accelerator program. MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) is making headway in this area too, today releasing research into a wireless system that's both headset-agnostic and could address some unforeseen problems with peripherals like TPCAST's. MIT CSAIL's prototype system, known as MoViR, uses millimeter waves to send data from a transmitter that's hooked up to a computer to the headset's receiver. These high-frequency radio waves are capable of maintaining wireless connections at speeds over 6 Gbps -- enough bandwidth to stream the two, high-definition feeds required for VR -- but the signal doesn't penetrate objects well.

Bill Gates, Elon Musk and Stephen Hawking all have something in common: All three have gone on the record sharing their concerns and fears about artificial intelligence and robotics. While these technologies hold a great deal of promise, and will have a real impact on our future, it's important for us to understand the ramifications they could have for all of us, particularly in terms of labor. My first big concern about AI was recently highlighted in a New York Times piece by John Markoff, who wrote that while AI has great potential for good, it could also be abused by criminals who might use it for their nefarious goals. The growing sophistication of computer criminals can be seen in the evolution of attack tools like the widely used malicious program known as Blackshades, according to Mr. Goodman. The author of the program, a Swedish national, was convicted last year in the United States.

Tom Davenport: It's common to say that AI handles tasks that were previously only addressable by humans. But I think to exclude more traditional forms of automation, you also have to define it as performing tasks requiring a high level of expertise, insight or perception. Davenport: I found that while many companies are still doing a good bit of work in analytics and big data, they are less interested in hearing or reading about those topics. And to me, AI and cognitive technologies are a straightforward extension of analytics in most cases. Most of the models are statistical in nature, and analytical people are logical candidates to push AI forward in organizations.

A common challenge in Natural Language Processing (NLP) is Named Entity Recognition (NER) - this is the process of extracting specific pieces of data from a body of text, commonly people, places and organizations (for example trying to extract the name of all people mentioned in a wikipedia article). NER is a problem that has been tackled many times over the evolution of NLP, from dictionary-based, to rule-based, to statistical models and more recently using Neural Nets to solve the problem. Whilst there have been recent attempts to crack the problem without it, the crux of the issue is really that for approach to learn it needs a large corpus of marked up training data (there are some marked up corpora available, but the problem is still quite domain specific, so training on the WSJ data might not perform particularly well against your domain specific data) and finding a set of 100,000 marked up sentences is no easy feat. There are some approaches that can be used to tackle this by generating training data - but it can be hard to generate truly representative data and so this approach always risks over-fitting to the generated data. Having previously looked at Stanford's NLP library for some sentiment analysis, this time I am looking at using the OpenNLP library Further to this, the Stanford library is licensed under GPL which makes it harder to use in any kind of commercial/startup setting.

Ben Dickson is a software engineer and the founder of TechTalks. More posts by this contributor: Why it's so hard to create unbiased artificial intelligence How to facilitate the path to brownfield IoT development Why it's so hard to create unbiased artificial intelligence How to facilitate the path to brownfield IoT development Why it's so hard to create unbiased artificial intelligence While machine learning is introducing innovation and change to many sectors, it also is bringing trouble and worries to others. One of the most worrying aspects of emerging machine learning technologies is their invasiveness on user privacy. From rooting out your intimate and embarrassing secrets to imitating you, machine learning is making it hard to not only hide your identity but also keep ownership of it and prevent from being attributed to you words you haven't uttered and actions you haven't taken. Here are some of the technologies that might have been created with good-natured intent, but can also be used for evil deeds when put into the wrong hands.

Editors at the MIT Technology Review recently weighed in with their annual review of the world's 50 Smartest Companies. This list celebrates the most effective pairing of innovation and business across the globe. For the first time, more than 20% of MIT's picks rely on artificial intelligence to support their business at a fundamental level, somewhat redefining what it means to be a truly "smart" company today. How many of these 13 artificial intelligence leaders are you already using? It's working on speech recognition intelligence called Deep Speech 2. This reduces the chance of accidents on autopilot by 50% relative to the safety record of human drivers, according to CEO Elon Musk.

See this talk by Jeremy Howard: At Kaggle, It's a Disadvantage To Know Too Much and "Getting In Shape For The Sport Of Data Science" Start by practicing on toy datasets in MATLAB and walking through simple examples in Statistics and Neural Network Toolbox Below two books are standard introduction texts. They are complementary, the first one is written from a statistician perspective with lots of data analysis examples and the second one is focusing on algorithms. Both are graduate level texts requiring knowledge of algebra, statistics and calculus. It would also help to take a class on optimization but not strictly necessary. See this talk by Jeremy Howard: At Kaggle, It's a Disadvantage To Know Too Much and "Getting In Shape For The Sport Of Data Science" Below two books are standard introduction texts. They are complementary, the first one is written from a statistician perspective with lots of data analysis examples and the second one is focusing on algorithms.

Machine learning isn't confined to science fiction movie plots anymore. It has fueled the proliferation of technologies that touch our everyday lives, including voice recognition with Siri or Alexa, Facebook auto-tagging photos, and recommendations from Amazon and Spotify. And many enterprises are eager to leverage machine learning algorithms to increase the efficiency of their network. In fact, some are already using it to enhance their threat detection and optimize wide area networks. As with any technology, machine learning could wreak havoc on a network if improperly implemented.