Fisher plays a recently divorced mother of two teens and out-of-work art critic determined to cook a traditional festive dinner with all the trimmings in her sunny Southern California home for her smartphone-addicted friends and extended family. But taming the turkey proves to be the least of her challenges when her neighborhood's cell reception suddenly goes dead, which proceeds to bring out the worst in some already less than exemplary behavior from her preoccupied houseguests. Unfortunately many viewers will have experienced their own connectivity issues long before those characters do. Although there's a genuinely cozy rapport between Fisher and Stevens, the other cast members, including Daphne Zuniga, Nick Court, Natasha Gregson Wagner and Michael Muhney, have a tougher time trying to make all the overwritten, self-consciously quirky dialogue believably their own. Filmmaker Russell Brown clearly had something pertinent he wished to say about our plugged-in, tuned-out obsession with the Internet and was obviously going for a Luis Buñuel-Robert Altman style of social commentary here.

Humanity may still be years if not decades away from producing sentient artificial intelligence. But with the rise of machine-learning services in our smartphones and other devices, one type of narrow, specialized AI has become all the rage. And the research on this branch of AI is only accelerating. In fact, as more industries and policymakers awaken to the benefits of machine learning, two countries appear to be pulling away in the research race. The results will probably have significant implications for the future of AI.

Using algorithms to help make better decisions has been the "next big thing in analytics" for over 25 years. It has been used in key areas such as fraud the entire time. But it's now become a full-throated mainstream business meme that features in every enterprise software keynote -- although the industry is battling with what to call it. It appears that terms like Data Mining, Predictive Analytics, and Advanced Analytics are considered too geeky or old for industry marketers and headline writers. The term Cognitive Computing seemed to be poised to win, but IBM's strong association with the term may have backfired -- journalists and analysts want to use language that is independent of any particular company.

Google needs to make "Parse for AI" to wedge themselves deeply into apps even when on other's platforms/cloud. I've been interested in this space for a while. A broad prediction I have for the coming years is that, as a developer, you won't need to be proficient in machine learning to take advantage of its power. The technology is becoming increasingly democratized and opening up access to millions of new developers. Eventually, you won't even need to know how to program to perform data analysis with ML.

At Data By The Bay in May, we saw a great talk by Netflix's Justin Basilico: Recommendations for Building Machine Learning Software. Justin describes some principles for effectively developing machine learning algorithms and integrating them into software products. We found ourselves nodding violently in agreement, and we wanted to recapitulate a few of his points that resonated most strongly with us, based on our experience working with data science teams in other organizations. Justin emphasizes that "developing models is iterative" and experimentation is important. He also suggests "avoiding dual implementations" so it's easy to use a model in production once it's been built, without a re-implementation step.

Plexure unlocks ML models for event driven decision making, or for live queries directly from customers that want immediate answers. Choose from an existing library or build your own custom model, and plug it into decision making live. Traditional CRM makes it hard to operationalize ML models, generally they can only be used for batch jobs to carry out items like segmentation.

In this video, NYU researchers describe their plans to advance deep learning with their new Nvidia DGX-1 AI supercomputer. New York University's Center for Data Science is at the cutting edge of fields with revolutionary implications such as machine learning, natural language processing, computer vision and intelligent machines. Because computing speed is critical to accelerating experimentation and advancing research, the center's Computational Intelligence, Learning, Vision and Robotics (CILVR) lab recently acquired a DGX-1 to fuel this work like never before. The CILVR lab has "unsupervised learning" as its focus. The lab's faculty, research scientists and graduate students are developing techniques that allow machines to learn from raw, unlabeled data by, for example, observing video, looking at images or listening to speech.

Francisco Webber offers a critical overview of current approaches to artificial intelligence using "brute force" (aka big data machine learning) as well as a practical demonstration of semantic folding, an alternative approach based on computational principles found in the human neocortex. Semantic folding is not just a research prototype--it's a production-grade enterprise technology. Francisco explores the theoretical underpinnings of semantic folding, which solves the representational problem and the semantic grounding problem--both well known by AI-researchers since the 1980s, and offers an introduction to the Retina Engine, an Apache Spark library for semantic processing of text. Along the way, Francisco demonstrates functional prototypes of semantic classification, semantic filtering, and semantic searching and explains the applications of semantic folding for the finance, media, automotive, legal, medical, and safety and security industries.

Artificial intelligence, intelligence as exhibited by machines, is not something that is new to this world. Nearly twenty years ago IBM's supercomputer Deep Blue beat world chess champion Gary Kasparov. The win was symbolically significant and a sign that artificial intelligence was catching up with human intelligence. Fast forward 20 years and the application of AI technologies is something we encounter on a regular basis. For example, manufacturing and the use of robots in assembly and packaging has revolutionised how our favourite products are made.

Two decades ago, Google was an Internet search engine company. Today, the Mountain View-based firm is one of the tech industry's biggest leaders with initiatives ranging from operating systems, apps, cloud services, and communications solutions to self-driving vehicles, artificial intelligence, virtual reality, wearables, and smartphones. To say that Google has diversified its portfolio would be a huge understatement. Furthermore, that strategy is seemingly paying off. The Alphabet-owned company currently has the second most valuable brand in the world and is a leading innovator in most branches of the tech industry.