Thanks to the sheer amount of data that machine learning technologies collect, end-user privacy will be more important than ever. But the data that desktop and mobile applications automatically collect, analyze using machine learning algorithms and act upon is a reality, and IT shops must be ready to handle this type and volume of information. In particular, thorny artificial intelligence data privacy issues can arise if employers can detect and view more -- and more personal -- data about their employees on devices or apps. Mobile devices are taking over the IT landscape, making Mobile Device Management (MDM) more important now than ever before; and at the top of MDM priority list should be is mobile data security. Access this complimentary 13-page editorial e-guide highlighting strategies you can take to boost your mobile data security.

"Someday AI will change the world. But it is important to realize that statistics knowledge, and that deep learning is much more superficial than people realize. We still have a long way to go", said Gary Marcus, Founder and CEO of Geometric Intelligence for Onalytica. From my perspective, this long way can become easier, on the one hand, with the help of researchers, and on the other hand, with the support of Artificial Influencers (AI) that spread the word and make people and organizations conscious about the Artificial Intelligence's benefits. The idea of artificial intelligence (AI) has been around since the 1950s and typically conjures up images of fully conscious robots.

There's no doubt that artificial intelligence is being used by more and more industries. As Information Age points out, Enhancing E-commerce, calculating valuations, inventory management and of course enabling driverless cars are among the many uses of artificial intelligence. With this in mind, which tech stocks should investors buy to cash in on this trend? Although Nvidia Corporation (NASDAQ:NVDA) now appears to be the prime beneficiary of the machine learning aspect of artificial intelligence, Nvidia stock has soared over 200% in the last year and likely already reflects a great deal of revenue from AI. Consequently, investors should wait for a better entry point before buying Nvidia stock. But four other tech stocks Intel Corporation (NASDAQ:INTC), Delphi Automotive PLC (NYSE:DLPH), Visteon Corp (NYSE:VC), and Baidu Inc (NASDAQ:BIDU) -- are definitely poised to get a meaningful boost from AI and should be bought at current levels.

In this piece Angela Logothetis, VP and CTO of Amdocs Open Network, argues that AI and automation are critical factors in the successful move to telco virtualization. Artificial Intelligence (AI) is the latest piece of the jigsaw telecom operators must put together as they evolve their networks from physical to cloud-based, virtualized infrastructures (NFV/SDN). It's easy to get carried away by the possibilities AI and technologies such as Machine Learning offer. Customer analytics, revenue assurance and network optimization are all ripe for this new technology. But the area where AI can make the biggest impact is operational automation: letting networks run themselves.

We all know that the growth of music distribution is moving fast from transaction to streaming subscriptions. The music value chain is adapting. With millions of songs streamed trillions of times, advanced technologies are necessary for consumers to find and discover their favorite songs and for music artists to find their fans and interact with them. Will the music industry be able to leverage digital technologies to adapt and be profitable? We can get some serious tips from Techstars, an accelerator with a current portfolio of about 7.8 billion. On Thursday, its new program, Techstars Music, partnered with major players including labels Warner Music Group and Sony Music Entertainment, to demo its darling music startups at the El Rey Theatre in Los Angeles.

How much maths do I need to learn to be a data scientist? Even though the question sounds simple, there is no simple answer to the the question. Usually, we say that you need to know basic descriptive and inferential statistics to start. That is good to start. But, once you have covered the basic concepts in machine learning, you will need to learn some more math. You need it to understand how these algorithms work. What are their limitations and in case they make any underlying assumptions. Now, there could be a lot of areas to study including algebra, calculus, statistics, 3-D geometry etc. If you get confused (like I did) and ask experts what should you learn at this stage, most of them would suggest / agree that you go ahead with Linear Algebra. But, the problem does not stop there. The next challenge is to figure out how to learn Linear Algebra. You can get lost in the detailed mathematics and derivation and learning them would not help as much! I went through that journey myself and hence decided to write this comprehensive guide. If you have faced this question about how to learn & what to learn in Linear Algebra – you are at the right place. I would like to present 4 scenarios to showcase why learning Linear Algebra is important, if you are learning Data Science and Machine Learning. What do you see when you look at the image above? You most likely said flower, leaves -not too difficult. But, if I ask you to write that logic so that a computer can do the same for you – it will be a very difficult task (to say the least).

R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required – this book will take you methodically through every stage of applying machine learning. Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R--a cross-platform, zero-cost statistical programming environment--there has never been a better time to start applying machine learning.

New Delhi, As artificial intelligence (AI)-powered smart devices and solutions gather momentum globally amid fears of "bots" taking over jobs soon, a top Adobe executive has allayed such fears, saying AI will actually assist people intelligently. "Saying AI will take over the creativity of humans is not right. It will take away a lot of stuff that you have to do in a mundane way. A human mind is a lot more creative than a machine," Shanmugh Natarajan, Executive Director and Vice President (Products) at Adobe, told IANS in an interview. "With AI, we are trying to make the work easier. It is not like self-driving cars where your driver is getting replaced. I think creativity is going to stay for a long time," Natarajan added.

By 2020, 85% of customer interactions will be managed without a human. In the years ahead, businesses will probably still be run by human beings, but in order to compete and succeed, they'll have to leverage the growing and ever-more-prevalent intelligence of machines.Forget for a moment about about geeking out on toys like hover boards and flying cars -- we're talking about leveraging real AI for hard business purposes. Business intelligence (BI) enables businesses to know more about their wider markets, internal process performance and progress over time. AI makes it possible to get this critical information faster and cheaper. Businesses using AI for customer-facing or employee-facing tasks can often free up resources that would have gone to paying a human for analysis tasks -- and reinvest those resources in humans executing on the insights produced by AI.

By 2020, 85% of customer interactions will be managed without a human. In the years ahead, businesses will probably still be run by human beings, but in order to compete and succeed, they'll have to leverage the growing and ever-more-prevalent intelligence of machines.Forget for a moment about about geeking out on toys like hover boards and flying cars -- we're talking about leveraging real AI for hard business purposes. Business intelligence (BI) enables businesses to know more about their wider markets, internal process performance and progress over time. AI makes it possible to get this critical information faster and cheaper. Businesses using AI for customer-facing or employee-facing tasks can often free up resources that would have gone to paying a human for analysis tasks -- and reinvest those resources in humans executing on the insights produced by AI.