In Part II of our year-ahead outlook, we explore the sleeper issues that will drive data management and the mainstreaming of AI in analytics. In the year ahead, we see the cloud, AI, and data management as the megaforces of the data and analytics agenda. And so, picking up where Big on Data bro Andrew Brust left off last week, we're looking at some of the underlying issues that are shaping adoption. In the world of data and analytics, you can't start a conversation today without bringing in cloud and AI. Yesterday in Part I, we hit the cloud checkbox: we explored how the upcoming generation change in enterprise applications will in turn shift the context of how enterprises are going to be evaluating cloud deployment.

The U.S. Postal Service plans to reduce wait times on about 80 million customer calls fielded annually through a partnership with Google Cloud announced Thursday. USPS awarded Carahsoft -- Google Cloud's authorized distributor for public sector clients -- a cloud contract with a $50 million ceiling covering customer experience and mail delivery solutions. Increased delivery competition from new carrier services like Amazon has the postal agency reinventing itself, and call center customer experience is "one of the bigger pain points," Mike Daniels, vice president of global public sector at Google Cloud, told FedScoop. "Their call wait times are very unacceptable. They're limited in what they can do with respect to staffing; you can't just throw more people at it," Daniels said.

Allowing machines to select and target humans sounds like something out of an apocalyptic sci-fi movie. But as we enter another decade, it is becoming increasingly obvious that we're teetering on the edge of that dangerous threshold. Countries including China, Israel, South Korea, Russia and the United States are already developing and deploying precursors to fully autonomous weapons, such as armed drones that are piloted remotely. These countries are investing heavily in military applications of artificial intelligence with the goal of gaining a technological advantage in next-generation preparedness for the battlefield. These killer robots, once activated, would select and engage targets without further human intervention.

Based on our interactions and the results of this study, we expect to see organizations not only adopt AI – but scale it across their enterprises, by building/developing their own AI, or putting ready-made AI applications to work. For example, according to the survey, 40% of respondents currently deploying AI said they are developing proof-of-concepts for specific AI-based or AI-assisted projects, and 40% are using pre-built AI applications, such as chatbots and virtual agents. I see the excitement building with clients every day. Consider just a couple of recent examples. Legal software developer LegalMation has leveraged IBM Watson and our natural language processing technology to help attorneys automate some of the most mundane litigation tasks, speeding, for example, the written discovery process from multiple hours to a few minutes.

Machine learning and AI are continually progressing. This year, we've seen this technology begin to exceed human performance across various narrow and specific domains, including certain medical diagnoses and image recognition software. There have been massive improvements in complex areas such as human-like content generation and vehicle autonomy. There are scores of businesses that increasingly depend on machine learning algorithms to make crucial decisions on a broad range of topics. This piece will explore predictions from eight thought leaders that are currently helping to shape the future of machine learning and AI.

According to The Wall Street Journal, virtual simulations incorporating artificial intelligence (AI) are being used to improve health care workers' bedside manner. Doctors, nurses and other caregivers in southern Maine are being trained in a virtual environment that simulates the conditions that patients with Alzheimer's disease, cancer and substance addictions experience. At the Hospice of Southern Maine in Scarborough, health care workers wear virtual reality headsets and are able to move their hands, pick up objects and get a panoramic view of the hospice setting as if they are the patient. The patient's family--in a simulated form--is present at the virtual bedside. Such examples demonstrate the remarkable advances in skills development and training, thanks to technology such as artificial intelligence--when machines learn based on experience--and virtual and augmented reality.

In 2016 Tom Clancy's The Division brought players together in the war-torn streets of Manhattan fighting against a variety of enemy factions, as well as one another. Ubisoft's RPG-shooter carries a diverse range of enemies to keep the player on their toes. But unlike other games I've covered to-date, The Division needs to deal with the practical issues of being an online co-operative game and how to manage all sorts of AI systems for thousands of players across the world. In this first of a two-part series, I'm going to be looking at how developers Massive Entertainment sought to create a variety of interesting and challenging encounters throughout the world of The Division; how they designed their enemy opponents and manage their behaviours as thousands of players rush to battle online every day in the mean streets of New York City. After an outbreak of weaponised smallpox, New York city breaks down into chaos and is quarantined from the outside world until the contamination is contained and order is restored.

That's some of what to expect from the tech industry's annual pilgrimage to the desert. CES, the mammoth tech trade show organized by the Consumer Technology Association (CTA), will draw some 170,000 people from around the world to Las Vegas to launch products and services – but also to make deals and schmooze with one another. For the consumer watching from afar (since the show isn't open to the public), the best part of CES often can be the range of what's there, from the weird to the wonderous to the stuff that makes us all ask just "why." Over the years, it has transitioned from just the gadgets and gizmos consumers can't wait to get their hands on, featuring many companies the average consumer might do a double take over. "Every company is becoming a tech company," says Gary Shapiro, CEO of the CTA.

Machine Learning, a subfield of computer science, involves the development of mathematical algorithms that discover knowledge from specific data sets, and then "learn" from the data in an iterative fashion that allows predictions to be made. Today, Machine Learning has a wide range of applications, including natural language processing, search engine optimization, medical diagnosis and treatment, financial fraud detection, and stock market analysis. This symposium, the fourteenth in an ongoing series presented by the Machine Learning Discussion Group at the New York Academy of Sciences, will feature Keynote Presentations from leading researchers in both applied and theoretical Machine Learning and Spotlight Talks, a series of short, early career investigator presentations across a variety of topics at the frontier of Machine Learning.

Retail has gotten incredibly competitive in the last several years. Online and brick and mortar retailers are continuing to innovate to grab consumers' attention, not just for the immediate sale, but for multiple sales over time. In this video, top Oracle experts in integration, machine learning, and blockchain discuss how new technologies and approaches to data management are empowering retailers to harness their data and use it to better reach customers. Retailers have historically struggled with how to take the massive amounts of information they have coming in from their customers and their suppliers and use it to their advantage. Cloud-based data lakes have become a real game-changer because they give the retailer the ability to combine different types of data, draw insights from that data, and scale with the data as it grows without having to pay for the pain of operating it.