On November 6 2014, Amazon announced Echo, a smart speaker featuring a voice-controlled assistant service called Alexa. The company didn't exactly go out of its way to whip up excitement for these newcomers. In sharp contrast to the rollout of its ill-fated Fire Phone--which kicked off with a press-conference extravaganza starring Jeff Bezos--it simply disclosed that Echo existed and would be sold, at first, on an invitation-only basis to Prime members. But Echo spoke for itself--and not just literally. People quickly saw that the pint-size cylinder was a genuinely new kind of consumer-electronics device.

Check out our Workology Podcast powered by Blogging4jobs. Click here to check out all our episodes. What Does Artificial Intelligence Mean in Human Resources and Recruiting? One of the most talked about trends in HR and recruiting in the second half of 2016 has been AI and artificial intelligence. Artificial intelligence is defined as "an ideal'intelligent' machine [that] is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal."

There is evidence all around us that humans are gradually becoming obsolete, the empty cashier lines in the grocery store while customers choose "self"-checkout, being just one example. There was a time when science fiction was just that--fiction--and the fantastic world of technology was just a dream. But reality is different today and the world of James Cameron's "Terminator" suddenly seems more realistic than ever. The most recent development has been the creation of automated automobiles, an idea that has been around for many years but has always seemed too far-fetched. Cruise control has a whole new meaning in these vehicles, as advanced computers use GPS and radar to electronically guide them through the streets. Innovation has occurred mostly in large transportation trucks, 18-wheelers.

Today on In Case You Missed It: MIT's Computer Science and AI Lab have cooked up another autonomously driving vehicle, but this one is a disability scooter. In this newly posted video, you can watch as the scooter navigates around human obstacles when taking a person on the way to their destination. In other AI news, Google and Blizzard Entertainment are teaming up to use Deepmind to train the system to autonomously play Starcraft II. If you, too, have a fondness for Big Mouth Billy Bass, the singing fish trophy, you need to see how one was hacked to be the voice of Alexa. And if you haven't yet played the New York Times' Voter Suppression Trail, you're missing out on both nostalgia and maybe sadness.

Facebook users will be able to record smartphone videos that ape the style of famous artworks with a new feature unveiled Tuesday. Using a technique called style transfer, the feature takes live video and turns it into something that resembles the work of Van Gogh, Picasso and other artists. That effect is probably familiar to people who have used the app Prisma, which uses similar techniques to change the look of photos. Prisma's app can't perform live filtering, and some filters require a connection to the internet. Facebook's system can work offline and render live.

The automotive market is seeing accelerated growth and rapid adoption of vision applications that will lead the way to autonomous vehicles. With the complexity of these systems, Tier-1 suppliers, OEMs, and the entire automotive industry are utilizing artificial intelligence and deep learning algorithms to identify objects, determine free space for vehicles and plan the vehicle movement. As companies explore these deep learning algorithms and shift from R&D labs to the realization and deployment of low power embedded solutions, it is important to have a sound foundation in the form of an efficient HW and SW platform that is optimized for CNN workloads and other deep learning approaches.

There are many good reasons why traditional security practices are becoming less effective at protecting against cyberattacks. There is too much security-related data flooding the network from an increasing number of users and devices. There is a lack of skilled personnel to watch over and analyze this data. And the security staff you have likely wastes too much time chasing down false positives. Valuable minutes -- or even hours -- can tick by before analysts and incident responders are aware of a threat.

Azure Machine Learning is a handy tool, absolutely. If I need to run some model quickly to justify gut feeling or to have a simple overview over data, it fits really well. Or, for example, set up a web service from a Machine Learning experiment is really easy, so kudos for that! But there are some things which annoy me time after time, which I really want to be implemented or done differently. Here is my top 3 "wish-list": 1. Navigation inside the experiment mean, honestly...

There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). There certainly is a massive uptick of articles about AI being a competitive game changer and that enterprises should begin to seriously explore the opportunities. The distinction between AI, ML and DL are very clear to practitioners in these fields. AI is the all encompassing umbrella that covers everything from Good Old Fashion AI (GOFAI) all the way to connectionist architectures like Deep Learning. ML is a sub-field of AI that covers anything that has to do with the study of learning algorithms by training with data.

Machine learning may once have been a topic of discussion only for computer scientists and researchers. Now, however, it is a technology businesses are eager to use. The need for machine learning and Artificial Intelligence (AI) is being driven by the massive amount of data being generated today. Statisticians can get insight from this data. But the volume is so large and growing at such a rate, the best way to tackle it is using the very same machines that are in part responsible for creating the data.