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.

There are data scientists that are doing truly new stuff. I've been in the analytics for over 20 years (PhD in stats, 1993), and what I do now - including the methodology used, not just the data - is radically different from what I did even 5 years ago. It involves a lot of automation, new algorithms (Jackknife regression, model-free confidence intervals, hidden decision trees, brand new random number generation, feature selection, predictive power) applied to all sorts of data, usually big data sets. It actually goes far beyond processing data: data processing is the tip of the iceberg, but the big picture of what I'm doing is making real-time systems (such as traffic generatiion for this very website) work automatically and smoothly, and in a scalable way. From scratch and/or using vendor platforms - I design, build, and deploy systems that work, with home-made metrics used to test performance and find areas of improvement.

Health Catalyst has created Healthcare.ai, a website that offers free open source predictive analytics software for hospitals and other healthcare organizations. "Wherever you have a data set that you pull together, you can create a model based on that by using these tools," said Levi Thatcher, director of data science at Health Catalyst. Machine learning and predictive analytics to improve health outcomes has so far been limited to an elite group of data scientists, mostly in the nation's top academic medical centers, he pointed out. Healthcare.ai – open source predictive analytics software – is part of a mission to make machine learning accessible to the thousands of healthcare professionals with only basic technical skills, but who share an interest in using the technology to improve patient care, Thatcher explained. By making its central repository of proven machine learning algorithms freely available, Healthcare.ai opens the doors to a large, diverse group of technical healthcare professionals to quickly use machine learning tools to build accurate models.

How can we programme a robot to behave morally when we don't have a working definition for morality ourselves? This is one of the many questions involved with the field of artificial intelligence and the development of advanced robots. While it might sound like something out of a science fiction novel, artificial intelligence has quickly become a facet of our daily lives. Take Siri or Google Now on our smartphones, and Amazon's Alexa. Rudimentary as they are today, these so-called "smart assistants" represent the future of artificial intelligence.

In statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data. For Big Data, it pays off to analyze the data upfront and then design the modeling pipeline accordingly. Statistical modeling is a lot like engineering. In engineering, there are various ways to build a key-value storage, and each design makes a different set of assumptions about the usage pattern. In statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data.

Yiling Chen (yiling@seas.harvard.edu) is Gordon McKay Professor of Computer Science at Harvard University, Cambridge, MA. Arpita Ghosh (arpitaghosh@cornell.edu) is an associate professor of information science at Cornell University, Ithaca, NY. Michael Kearns (mkearns@cis.upenn.edu) is a professor and National Center Chair of Computer and Information Science at the University of Pennsylvania, Philadelphia, PA. Tim Roughgarden (tim@cs.stanford.edu) is an associate professor of CS at Stanford University, Stanford, CA. Jennifer Wortman Vaughan (jenn@microsoft.com) is a senior researcher at Microsoft Research, New York, NY.