Now that 2017 has officially started, we are looking forward to exploring the latest technologies and how they will impact our lives. It's clear that AI will infuse everything and it's expected to impact all industries including: healthcare, energy, transportation, and retail! One area expected to grow exponentially are virtual assistants. With the already established existence of Siri, Cortana and Google Now, we will see conversational agents becoming more and more personalized. The market is expected to grow drastically over the next few years and we'll be showcasing advances in virtual assistants and artificial intelligence from the world's leading innovators at the Virtual Assistant Summit on 26-27 January in San Francisco.

Last year was huge for advancements in artificial intelligence and machine learning. The idea has been around for decades, but combining it with large (or deep) neural networks provides the power needed to make it work on really complex problems (like the game of Go). Invented by Ian Goodfellow, now a research scientist at OpenAI, generative adversarial networks, or GANs, are systems consisting of one network that generates new data after learning from a training set, and another that tries to discriminate between real and fake data. The hope is that techniques that have produced spectacular progress in voice and image recognition, among other areas, may also help computers parse and generate language more effectively.

Healthcare is a ripe target for machine learning to both optimize processes and greatly improve care delivery. Take Mercy Health, for instance. When the health system was considering ways to improve care delivery, hospital executives looked at one of the most successful initiatives it had undertaken in the last decade: supply chain management. "We have a lot of experience with operational efficiency," said Todd Stewart, MD, vice president of clinical integrated solutions at Mercy. Using the operative suite as an example, he noted that all the supplies that go into and through it are very expensive.

Machine learning is becoming a buzzword--arguably an overused one--among companies that deal with networking. Recent announcements have touted machine learning capabilities at Google, Hewlett Packard Enterprise (HPE), and Nokia, for instance. But machine learning isn't being applied to networking itself. The intersection of machine learning and networking is where David Meyer, chief scientist at Brocade, has been working. After serving a term as the first chairman of the OpenDaylight Project's Technical Steering Committee (TSC), Meyer shifted his work into the realm of artificial intelligence.

This walkthrough uses HDInsight Spark to do data exploration and train binary classification and regression models using cross-validation and hyperparameter optimization on a sample of the NYC taxi trip and fare 2013 dataset. It walks you through the steps of the Data Science Process, end-to-end, using an HDInsight Spark cluster for processing and Azure blobs to store the data and the models. The process explores and visualizes data brought in from an Azure Storage Blob and then prepares the data to build predictive models. Python has been used to code the solution and to show the relevant plots. These models are build using the Spark MLlib toolkit to do binary classification and regression modeling tasks.

Humans use language to tap into the knowledge of others and learn skills faster. This helps us hone our intuition and go through our daily activities more efficiently. Inspired by this, Google Research, DeepMind (its UK artificial intelligence lab), and Google X have decided to allow their robots share their experiences. Sharing the learning process among multiple robots, the research team has considerably expedited general-purpose skill acquisition of robots. Using an artificial neural network, we can teach a robot to achieve a goal by analyzing the result of its previous experiences.

We have already started talking about robots and have even floated many pilot projects where robots are acting as assistants to help customers in trivial matters. Industry experts have already mulled loss of jobs in BPO sector because of introduction to chatbots. We have just scratched the tip of AI and as we make strides in technology and make advancements, it is likely that far-fetched prophecy comes true. The question here is what does the future hold for robot applications?

As 2016 passes, and we look forward to the year ahead, here is my fourth annual list of the ten best long-form stories about technology from last year. Andreessen Horowitz general partner, Chris Dixon, has written a number of compelling pieces on Medium this year, but my favorite is a great overview of "What's Next in Computing", in which he highlights how each product era has two phases, the gestation phase and the growth phase. He highlights how fast the growth is happening and why. As hardware becomes small, cheap, and ubiquitous, we all have access to sophisticated technology. He then offers predictions in a range of rising technologies such as artificial intelligence, the Internet of Things, wearables, virtual reality, augmented reality.

Google researchers got an eye-scanning algorithm to figure out on its own how to detect a common form of blindness, showing the potential for artificial intelligence to transform medicine remarkably soon. The algorithm can look at retinal images and detect diabetic retinopathy--which affects almost a third of diabetes patients--as well as a highly trained ophthalmologist can. It makes use of the same machine-learning technique that Google uses to label millions of Web images. Diabetic retinopathy is caused by damage to blood vessels in the eye and results in a gradual deterioration of vision. If caught early it can be treated, but a sufferer may experience no symptoms early on, making screening vital.

Artificial intelligence captured the world's attention last year when it defeated humanity's champion at the game of Go. It was a landmark event for AI, much like the moment 20 years earlier when IBM's Deep Blue defeated Garry Kasparov at chess in 1997. Starting next week an artificial intelligence system named Libratus, developed by a team at Carnegie Mellon University, will try to establish a new milestone: beating some of the best human players at Heads-Up No-Limit Texas Hold'em poker. While Libratus may one day be listed in the history books alongside Deep Blue and Alpha Go, it's actually attempting to solve a very different kind of problem. Go and chess are perfect-information games: each player knows exactly what moves have been made and what space is left on the board to consider.