In one vision of the future of transportation, humans are mere passengers, the ballistic baggage of all-knowing, all-seeing computers zipping about, safely and efficiently in fully autonomous cars. And the robot drivers are coming, no doubt. But if you want to retain any control of how you move through Tomorrowland--or just improve how you navigate today's world--you'll need a tool that upgrades your skill level. You'll need augmented reality, the oft-confused cousin of virtual reality that integrates digital intel into your natural view. Thanks to efforts like Mircosoft's HoloLens and secretive startup Magic Leap, the tech that many people know from Pokémon Go will be a $90 billion market by 2020, according to consulting firm Digi-Capital.

In 2016, we saw a wide range of breakthroughs having to do with artificial intelligence and deep learning in particular. Google, Facebook, and Baidu announced several breakthroughs using deep learning. Deep learning is one specific class of machine learning algorithms. It has a long history, taking its roots in the earlier days of computer science. However, not all of machine learning is deep learning.

Machine learning is a growing field, used in everything from the basics of anti-spam functions to the complexities of self-driving cars. As this is a constantly adapting technology, companies seeking to take advantage of the system for functions like analytics may have trouble finding the best place to begin. So what is the first step for a tech department that wants to start using machine learning to improve its data analytics? Even if you rely on outside expertise, it is important to understand what machine learning can and can't do with data. Stanford, Caltech and others offer online classes on Coursera that are very good.

Automated machine learning has become a topic of considerable interest over the past several months. A recent KDnuggets blog competition focused on this topic, and generated a handful of interesting ideas and projects. Of note, our readers were introduced to Auto-sklearn, an automated machine learning pipeline generator, via the competition, and learned more about the project in a follow-up interview with its developers. Prior to that competition, however, KDnuggets readers were introduced to TPOT, "your data science assistant," an open source Python tool that intelligently automates the entire machine learning process. For scikit-learn-compatible datasets, TPOT can automatically optimize a series of feature preprocessors and machine learning models that maximize the dataset's cross-validation accuracy, and outputs the optimal model as Python code leveraging scikit-learn.

The future of healthcare is already here. From automated insulin pumps to diagnostic instruments that can interpret their own results, today's medical devices are smarter and more sophisticated than ever. X-rays, MRIs, and ultrasounds produce diagnostic images that let doctors identify abnormalities that can't be seen from the outside. But what if the same machines that took the images could also interpret the results? Data analytics can be used to help imaging devices learn to recognize abnormal scans. Smart imagers could soon be connected to vast image libraries and patient health records.

Terah Lyons, discusses how Artificial Intelligence is currently handled by our government, and how this may change overtime. Terah Lyons is a Policy Advisor to the U.S. Chief Technology Officer at The White House Office of Science and Technology Policy. This talk was given at a TEDx event using the TED conference format but independently organized by a local community.

About three weeks ago, I was in a Pittsburgh casino for the beginning of a 20-day man-versus-machine poker battle. Four top human pros were beginning to take on a state-of-the-art artificial intelligence program running on a brand new supercomputer in a game called heads-up no-limit Texas Hold'em. The humans' spirits were high as they played during the day and dissected the bot's strategy over short ribs and glasses of wine late into the evening. On Monday evening, however, the match ended and the human pros were in the hole about $1.8 million. For some context, the players (four men and the machine, named Libratus) began each of the 120,000 hands with $20,000 in play money, and posted blinds of $50 and $100.

The Internet of Things (IoT) is conquering the world. At the center of networking the devices are Big Data, which brings in one of the challenges to IoT. It is becoming increasingly important to process data as early as possible. "The amount of data related to the Internet of Things is enormous – they have to be filtered. A processing directly in the device is already often more efficient. To achieve this, new analytics solutions are needed ", said Dr. Joachim Schaper, the Vice President & Head of AGT International, a pioneer in IoT and Social data management, Big Data integration and advanced analytics.

Email marketing of the future is exciting and fuelled by innovation, and for business willing to adapt, the opportunities are enormous. Here's a sneak peek at some of the changes email marketing is set to undergo. Chatbots are essentially artificial intelligence-powered software designed to streamline complex predefined tasks without the need for a human to be involved directly. They're also revolutionising the way email marketing is being carried out. Think of Siri, which is probably the most famous bot of all.

Years ago I had coffee with a friend who ran a startup. He had just turned 40. His father was ill, his back was sore, and he found himself overwhelmed by life. "Don't laugh at me," he said, "but I was counting on the singularity." My friend worked in technology; he'd seen the changes that faster microprocessors and networks had wrought. It wasn't that much of a step for him to believe that before he was beset by middle age, the intelligence of machines would exceed that of humans--a moment that futurists call the singularity.