That's where hallucinations, reflexes, Post Traumatic Stress, Phobias, and most importantly, dreams come from. You are right, the mind doesn't deal with much external data. Sense organs are all processed elsewhere, however, some sections of processing overlap, autonomic vs. reflex, etc. The conscious portion of human beings is very tiny compared with all the subconscious and unconscious/automatic processes going on.

And pretty soon, they'll come for us. That seems to be the story today, whether from Hollywood or in breathless articles in popular tech magazines about artificial intelligence and nanotechnology. Our biggest ever edition of TNW Conference is fast approaching! In a world where machines can learn, once humans push the "on" button, there's no stopping our robot overlords, right? When machines become more intelligent, humans are freed to become more creative.

College of Computing Professor Ashok Goel teaches Knowledge Based Artificial Intelligence (KBAI) every semester. And every time he offers it, Goel estimates, his 300 or so students post roughly 10,000 messages in the online forums -- far too many inquiries for him and his eight teaching assistants (TA) to handle. That's why Goel added a ninth TA this semester. Her name is Jill Watson, and she's unlike any other TA in the world. Jill is a computer -- a virtual TA --implemented on IBM's Watson platform.

Now WATSON HEALTH AI is being used in 16 cancer institutes across the country, helping to diagnose and treat patients. Meanwhile Google, not to be outdone, has launched DeepMind, which recently earned the title world champion of the complex game of Go. Now, DeepMind Health will create innovative new apps for healthcare professionals alerting them to patient emergencies, and the risk of complications when considering possible treatment options. Progenitors say that someday, it should even be able to predict a patient's needs down the pike. Other tech companies such as Dell, Hewlett-Packard, Apple, and Hitachi are also putting together AI programs for the healthcare field. Within the next five years, AI's use in medicine is expected to increase tenfold.

Emails won't literally get sucked down a vortex of analytics solutions. If you have ever bought something from an online store, chances are the store's used your email address with wanton disregard, bombarding you with email after email about its products and sales until you reach for the sweet oblivion of unsubscription. Stores and brands do this to keep customers engaged--but they don't know how many emails are too many. Adobe is releasing a tool today with the promise to help alleviate what they call "customer fatigue." By using machine learning algorithms to crunch the numbers of how often emails are opened and clicked on, marketers can see whether customers are tired of getting their emails.

Almost a year after it came out in the US, Facebook is releasing its facial recognition-powered photo app Moments in Europe. Except the new version won't actually include any facial recognition technology, thanks to the company's long-running fight with the Irish data protection commissioner over whether the technology is actually legal in the EU. Launched in June, Moments is Facebook's answer to dedicated photo management applications like Google Photos and Apple's Photos. The app bundles pictures together by the event they're taken at, and applies facial recognition technology to identify who's in each picture. Facebook takes the offering a step further than Apple or Google, by leveraging its social network: once you've created your "moments", you can share them with other people at the same event, to ensure that they have the photos of them, and you have the photos of you.

As you can see in the first video, some of the clips look no better than some of the images that come out of Google's DeepMind. Others, however, like the scenes from Cloud Atlas and the Jungle Book, seem significantly better-looking after being processed. It's still early days, but there are plenty of areas in both filmmaking and art where systems like this could be applied. Right now, it's computationally intensive, but not so much that you can't expect the cost and availability of such power to drop. As MIT Tech Review explains, it takes three minutes for each frame to be processed on a system using NVIDIA's 1,000-plus Titan X graphics card.

Editor's note: Register for the free webcast "How the machine learning wave is changing the way organizations look at analytics," hosted by Patrick Hall, senior machine learning scientist at SAS, and Andrew Pease, principal business solutions manager at SAS, to learn how different organizations are finding success with machine learning. The inherent trade-off between accuracy and interpretability in predictive modeling can be a catch-22 for analysts and data scientists working in regulated industries. Professionals in the regulated verticals of banking and insurance often feel locked into using traditional, linear modeling techniques to create their predictive models. This is mainly due to strenuous regulatory and documentation requirements. As machine learning becomes more mainstream, the forces of innovation and competition often drive these same analysts and data scientists to break out of the mold and try new algorithms with more predictive capacity.

One massive development is coming to define the year 2016 for the technology industry. It is not virtual reality, which most people say is between three and five years from significant adoption. Bots are experimental new forms of conversational user interfaces, at best. The real sea change in 2016 is coming from the democratization of machine learning and artificial intelligence. Significant contributions to machine learning and artificial intelligence come in the form of Google's DeepMind or TensorFlow, IBM's Watson, Intel's Xeon chips, Facebook's "M" personal assistant and its Wit.ai engine, Microsoft's Bot Framework and Cognitive Services which has 21 specific machine learning APIs for developers to access.

In a collaboration between Japan's Softbank Robotics and French-based Aldebaran Robotics, Pepper was officially put on the market for domestic use in June of 2015. Priced at about 1,600 with 200 in monthly data and insurance fees, the first thousand models available for launch sold out in under a minute. The company has stated that they aim to keep Pepper affordable, comparing the cost of the robot to that of a pet dog in Japan. Outside of personal ownership, the android has been used to assist Japanese citizens in banks and shops, including the promotion of Nescafe coffee machine sales. And in a limited run, large-scale experiment, Softbank opened a phone shop staffed exclusively by Pepper robots.