It's hard to see anything other than artificial intelligence being the next frontier of the edtech front. More specifically, machine learning has quickly gained the potential to overhaul what educators can accomplish with technology due to how rapidly the field has advanced within the last two years. Using machine learning, truly "smart" education solutions that can perform intelligent, self-informed actions are poised to change how we facilitate learning inside and outside of K-12 and higher education classrooms. Here's four ways artificial intelligence is going to do just that: As edtech authority Eric Sheninger (1) has noted, digital tools can boost student collaboration while they grapple with class material. This increased and streamlined student engagement with other students, as well as the subject matter at hand, has been known to produce more holistic and deeper learning.

CES has turned into the first car show of the year, with major automakers choosing to show off upcoming features in Las Vegas. Microsoft wants to help make cars more intelligent, and it unveiled a new suite of services Thursday to do so. The Connected Vehicle Platform brings together a smorgasbord of services from Microsoft, including Azure IoT Hub, Cortana Intelligence Suite, Microsoft Dynamics and many others. In addition, Office 365, Skype for Business and Cortana can be integrated with the platform. Microsoft frequently packages cloud services as suites, then markets them for kick-starting particular applications.

New KDnuggets Cartoon examines what can happen when you combine a Self-Driving Car and Machine Learning technology such as Recommendations ... Car: "No, I will not take you to A-1 Steakhouse. I recommend you go to the gym first, and then to V8 vegetarian restaurant" This cartoon was ably drawn by Jon Carter. Here is KDnuggets Big Data, Data Mining, and Data Science Cartoon page Recent KDnuggets Cartoons: Cartoon: Thanksgiving, Big Data, and Turkey Data Science. Cartoon: Data Scientist - the sexiest job of the 21st century until ... Cartoon: Facebook data science experiments and Cats Cartoon: It all started with the iPhone answering my email Cartoon: Where humans are still ahead of Deep Learning Cartoon: A solution for Data Scientists allergies caused by Big Data Cartoon: Data Scientist - the sexiest job of the 21st century until ... Cartoon: Facebook data science experiments and Cats Cartoon: It all started with the iPhone answering my email Cartoon: Where humans are still ahead of Deep Learning Cartoon: A solution for Data Scientists allergies caused by Big Data

CASL, Canada's anti-spam legislation, has been with us since July 2014. In July 2017 a private right of action becomes effective that will allow anyone who receives spam as defined by CASL to sue the sender. CASL sets out statutory damages, so the complainant does not have to prove any damages. Class actions will no doubt be launched. The sad part is that breaches of CASL are to a large extent breaches of the very technical requirements of the statute, rather than the sending of what most people would call spam.

Artificial Intelligence: Upping the Ante," beginning Jan. 11 at Rivers Casino, poker pros will play a collective 120,000 hands of Heads-Up No-Limit Texas Hold'em over 20 days against a CMU computer program called Libratus.

The amount of data being collected by complex processing devices such as self-driving cars (Tesla and Uber) to simple devices such as shoes and watches, will allow devices to think on their own and only allow humans to pick the options (Sounds like the Terminator movie.

We are writing this blog to motivate all developer towards artificial intelligence and while reading this blog removes the complex that A.I. (Artificial Intelligence) is tough, difficult and blah, blah, blah. So now, we will explore the whole new world of artificial intelligence. So, what is artificial intelligence? Again, as we said we don't make things complicated, so, A.I. is just a piece of code which solves your real-life problem without getting any humans involved in it, or to be specific we can say that a software which is like a small child which needs some training and after that it is capable of solving your real-life problems. And if you want a specific definition by Alan Turing, Artificial Intelligence (AI) is usually defined as the science of making computers do things that require intelligence when done by humans.

Companies like Apple, Google, Samsung, and Microsoft have all vied for control over the phones we carry in our pockets, the computers at our desks, and even the watches on our wrists. But over the past year a new arms race has emerged in Silicon Valley: competing to develop the most sophisticated virtual assistant. That's the annual Las Vegas tech confab where gadget makers, auto manufacturers, and app developers gather to flaunt their latest and, they hope, greatest. Although Amazon didn't officially attend the conference, Alexa showed up in car infotainment systems, new smartphones, robots, lamps, and even laundry machines. Ford, Whirlpool, Lenovo, GE, and Dish are a few of the brands announcing plans to add Amazon's voice-activated helper to their hardware.

Summary: Which is the most critical element in data exploration, statistics or data visualization? The answer is a little like the lyric'love and marriage, you can't have one without the other'. It can be tempting to skip the data visualization but it's frequently the key to making sure we aren't heading down the completely wrong path. How much trouble can you get into with statistics? That old catch phrase has been applied to our broader society for a long time but even a data scientist can run afoul of this maxim. I admit, when talking about data visualization, I'm usually thinking about telling the story of the data and the analysis to my non-data scientist clients.

Intelligent machines are becoming a ubiquitous and essential part of business operations. Algorithms trained by faster, smarter data now play a key role in determining customer demand, increasing customer trust and delivering unprecedented productivity and intelligence to the enterprise. But despite the success of some early adopters, most machine learning projects fail. Regardless of their size, location or industry, businesses have been struggling to fully unlock and realize the value of their information. In fact, a recent Information Value Index from PwC and Iron Mountain found that 57 percent of businesses are unable to extract significant value from their data; 23 percent are unable to derive any real value at all.