Deep learning is unlocking tremendous economic value across various market sectors. Individual data scientists can draw from several open source frameworks and basic hardware resources during the very initial investigative phases but quickly require significant hardware and software resources to build and deploy production models. Intel Nervana has built a competitive deep learning platform to make it easy for data scientists to start from the iterative, investigatory phase and take models all the way to deployment. Nervana's platform is designed for speed and scale, and serves as a catalyst for all types of organizations to benefit from the full potential of deep learning. Example of supported applications include but not limited to automotive speech interfaces, image search, language translation, agricultural robotics and genomics, financial document summarization, and finding anomalies in IoT data.

Intel has a disastrous history with smartphones. It fumbled a chance to be in Apple's first iPhone, and then quit making its Atom smartphone chip to focus on modems. But the company is now set to ship a groundbreaking modem that will deliver data transfer rates many times faster than most wired internet connections. The chipmaker will start shipping its first 5G modem for testing in the second half this year. Beyond mobile devices, the modem could also be used in autonomous cars, servers, base stations, networking equipment, drones, robots, and other internet-of-things devices.

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.

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.

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.

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.