Decades of research and speculative fiction have led to today's computerized assistants such as Apple's Siri. Advances in artificial intelligence (AI) have given the world computers that can beat people at chess and "Jeopardy!," as well as drive cars and manage calendars. But despite the progress, engineers are still years away from developing machines that are self-aware. Some believe the resulting technological singularity will eradicate poverty and disease, while others warn it could endanger human survival.

HBO's mysterious Westworld sent fans into a tweeting frenzy last week after the first sustained peek at the long-delayed sci-fi Western, which upgrades Michael Crichton's 1973 androids-run-amuck thriller for the new millennium, debuted on HBO. Totally reengineered by executive producers Jonathan Nolan (Person of Interest) and Lisa Joy (Pushing Daisies), Westworld tackles the promise and the threat of artificial intelligence (hey, even Stephen Hawking and Bill Gates say they're truly worried about it) in a lawless R-rated play-scape where a theme park's guests' darkest desires run wild. Only this time, you'll find yourself sympathizing with the sentient bots who are slave-laboring under the creepily apathetic gaze of Dr. Robert Ford (Anthony Hopkins). The resulting future-shock series resembles a mash-up of Blade Runner, Ex Machina, Black Mirror, and Crichton's own Jurassic Park; but its creators initially struggled to get their prime-time machine operational. The series was ordered two years ago, with a scheduled 2015 debut, then was delayed amid casting changes, story-retooling, and a production pause. Below, we were able to sneak a few questions to Nolan and Joy over the firewall of secrecy surrounding the drama, which debuts in October.

In this Big Data world, a major goal for businesses is to maximize the value of all their customer data. Most customer data, however, are housed in separate data silos. While each data silo contains important pieces of information about your customers, if you don't connect those pieces across those different data silos, you're only seeing parts of the entire customer puzzle. The integration of these disparate customer data silos helps your analytics team to identify the interrelationships among the different pieces of customer information, including their purchasing behavior, values, interests, attitudes about your brand, interactions with your brand and more. Integrating information/facts about your customers allows you to gain an understanding about how all the variables work together (i.e., are related to each other), driving deeper customer insight about why customers churn, recommend you and buy more from you.

Manufacturers often have to deal with up to 800 hours of downtime annually. On average an automotive manufacturer's TDC is 22,000 per minute; that is 1.3M per month! With the advance of predictive analytics, TDC can easily be reduced however only 14% of the manufacturing industry is taking advantage of its big data, according to a recent survey from MESA. Predictive maintenance is realized through the application of sophisticated machine learning techniques to equipment condition data collected in real-time or near real-time. It is now the new standard for reducing cost, risk and lost production in manufacturing facilities.

Israeli machine-learning radiology firm Zebra Medical Vision said today it raised 12 million to support the development of imaging algorithms being designed for automatic reading and diagnosis of medical imaging data. The round was led by InterMountain Healthcare, and joined by existing investors, Zebra Medical Vision said. As part of its investment, InterMountain Healthcare plans to work with Zebra to accelerate its development. "We are privileged that 1 of the top healthcare systems in the U.S. has placed such confidence in our team and our platform. In an environment where computing power and machine learning frameworks are becoming a commodity, the ability to quickly and efficiently curate large quantities of data from a world class integrated healthcare provider can make the difference between simplistic tools and insights that can truly add clinical value and positively impact patient care," CEO Elad Benjamin said in a prepared statement.

Imagine it's five minutes before a meeting. Your smartwatch, without prompting, sends you key points. While in the meeting, you take notes. Those notes are instantaneously absorbed by the system, then collated with relevant prior meetings, files and communications, in order to better prepare you for the next meeting. Born of the innovations of Big Data and possessed of a new net intelligence layer, self-learning software will have huge impacts on productivity across all departments of an enterprise.

Is AI ready for primetime? In a recent interview with Charlie Rose, he stated that machine learning showed great promise for cybersecurity, but that the necessary technology was probably five years out. If machine learning is currently so successful in other areas of society, why isn't it ready for cybersecurity? Machine learning is a subset of Artificial Intelligence, a field of computer science that started in 1958 when Marvin Minsky founded the Artificial Intelligence lab. Everyone, including DARPA, was pouring money into it.

Computers Don't Know When You Are Happy--Apple Is Adding a Previously Unseen Dimension To Your Device From the moment you are born, assuming normal eyesight, we open our eyes and fixate on the 43 muscles that control 1000s of nuances of facial expressions and emotion intent in the face of our parents. They inform a reaction to how to interpret the world, an extended sensor to help learn the basic emotions and reactions to the world around us. "Emotient is the leading authority on facial expression recognition and analysis technologies that are enabling a future of emotion aware computing." In the Spring of 2013 a team of scientists and researchers at the Machine Perception Lab at University of California, San Diego, was forming the technology and the basic elements of what was to become Emotient. The founding team were widely regarded as spearheading the use of machine learning for facial expression analysis with over 20 years of experience pioneering machine learning and computer vision technology for facial behavior analysis. The team has published hundreds of peer reviewed scientific publications, starting in 1995, which have been cited by thousands of other researchers in the field. Building around the work of Paul Ekman, Ph.D.[1] a pioneer in the study of emotions and facial expressions, and a professor emeritus of psychology in the Department of Psychiatry at the University of California Medical School (UCSF) where he has been active for 32 years, Emotient used AI to machine learn his ground breaking research in micro-emotions.

Justine Cassell has taken her virtual assistant Sara on a road trip. They're in Tianjin, China, where Carnegie Mellon University's associate dean of technology strategy and impact traveled to offer a glimpse of tomorrow at this week's Annual Meeting of New Champions. Sara, for "socially aware robot assistant," has spent the past several days greeting hundreds of people coming to the event, hosted by the World Economic Forum, at a station showcasing the office of the future. A life-size face and torso on a big-screen TV, Sara served as the front end to the event app. That presentation might make you think of Max Headroom, the stuttering AI character from the 1980s show.

A 750,000 grant will enable several engineers to employ the super-sensitive sense of smell in locusts to construct a bio-robotic nose of sorts. The problem is that biological systems possess a level of complexity that cannot be reached by their AI counterparts. While the sense of olfaction is a pretty primitive one, it extends across the board in all species. It is almost as if the field of biology made extra room for sensing chemicals in the air in order to warn the species of any danger or prey in the locality. A thorough understanding of the olfactory sense is very crucial for artificial intelligence.