The capabilities of artificial intelligence and machine learning are accelerating, and many cybersecurity tasks currently performed by humans will be automated. There will still be plenty of work to go around so job prospects should remain good, especially for those who keep up with technology, broaden their skill sets, and get a better understanding of their company's business needs. Cybersecurity jobs won't go the way of telephone operators. Take, for example, Spain-based antivirus company Panda Security. When the company first started, there were a number of people reverse-engineering malicious code and writing signatures.

Image recognition has progressed in leaps and bound over the years. Not too long ago, a challenging recognition task involved asking an AI "Is there a human in this image?" More recently, however, the bar has been raised -- and a new research project carried out at the University of Maryland and University of Colorado has another recognition task in its sights: whether or not an AI can read comic books. In some ways, this is deeply ironic. For a long time, comics were dismissed as a junk medium for kids and barely-literate adults.

On the heels of a report showing the inefficacy of government-run cyber security, it's imperative to understand the limitations of your system and model. As that article shows, in addition to bureaucratic risk the government also needs to worry about gaming-the-bureaucracy risk! Government snafus aside, data science has enjoyed considerable success in the past few years. Despite this success, models can fail in surprising ways. Last year we saw how deep neural nets for image recognition fail on noisy data.

What is the difference between AI, Machine Learning, NLP, and Deep Learning? AI (Artificial intelligence) is a subfield of computer science that was created in the 1960s, and it was/is concerned with solving tasks that are easy for humans but hard for computers. In particular, a so-called Strong AI would be a system that can do anything a human can (perhaps without purely physical things). This is fairly generic and includes all kinds of tasks such as planning, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (making art or poetry), etc. NLP (Natural language processing) is simply the part of AI that has to do with language (usually written). Machine learning is concerned with one aspect of this: given some AI problem that can be described in discrete terms (e.g.

The new British sci-fi film "Ex Machina," rolling into U.S. theaters over the next few weeks, is the kind of movie that discerning science fiction fans will want to seek out. Directed by Alex Garland (screenwriter of Sunshine and 28 Days Later), "Ex Machina" is a modern-day riff on the Frankenstein story, with high-tech labs, mad scientists and troublesome artificial intelligence (A.I.). It's got some thrilling twists, but "Ex Machina" is more about ideas than action, and it takes its science seriously. The setup: Computer coder Caleb (Domhnall Gleeson) is summoned to the remote research lab of his boss Nathan (Oscar Isaac), the reclusive genius founder of a ginormous tech company that doesn't rhyme with Google, but may as well. There, Caleb meets Ava -- a super-advanced A.I. housed in a super-advanced robotic body, played by Swedish actress Alicia Vikander.

November was all about testing for our article page group. We've been running A/B tests on a small percentage of the mobile audience; testing new commenting and site socialization features, variations on UX treatments and relevancy matching on ad units, as well as some improvements aimed at streamlining page flow and better surfacing of related content. We've also begun discovery on an overhaul of our registration and user account management experience, with an eye towards enhanced consumer identity management and a tighter platform alignment strategy. As we move towards the end of the year we'll be continuing and expanding our testing of new commenting and social engagement features, and planning a new and more scalable approach to prototyping and testing in 2017. This month was an exciting one for our new mobile products team.

Artificial Intelligence (AI) is changing everything from marketing to healthcare. And this holiday season is the beginning of the future for how marketers will leverage AI to better understand, connect with, and create superior experiences for consumers. To better appreciate the impact that AI is having on retailers, I connected with IBM's first CMO, Michelle Peluso. Peluso has a strong background in retail, having served at the CEO of Gilt as well as the Global Consumer Chief Marketing and Internet Officer at Citigroup. Peluso provides her thoughts below on how Watson's AI capability is changing the way retailers impact the consumer shopping experience.

NVIDIA GPUs are available in desktops, notebooks, servers, and supercomputers around the world, as well as in cloud services from Amazon, IBM, and Microsoft. You can choose a "plug-and-play" deep learning solution powered by NVIDIA GPUs or build your own. NVIDIA DGX-1 - The world's first purpose-built system for deep learning with fully integrated hardware and software that can be deployed quickly and easily NVIDIA Tesla P100 - The most advanced accelerator for deep learning training based on the NVIDIA Pascal architecture. NVIDIA DGX-1 - The world's first purpose-built system for deep learning with fully integrated hardware and software that can be deployed quickly and easily NVIDIA Tesla P100 - The most advanced accelerator for deep learning training based on the NVIDIA Pascal architecture.