Artificial intelligence is gaining traction faster than anybody imagined -- quickly knocking down the dominoes of complex tasks that computers have long struggled with. For example, in April this year Wired Magazine ran an article explaining why AI still sucked at transcribing speech. But just six months later, AI is now as good as humans at listening! And there have been similarly dramatic advances in other areas. Adding machine learning to Google Translate has improved error rates by up to 85% since it was introduced a month and a half ago! "It has improved more in one single leap than in 10 years combined" -- Barak Turovsky, product lead for Google Translate The list goes on: Google's AI can already lip read better than people, and the company's image service is now better than people at recognizing and tagging images.

Deep Learning is a technology that is as disruptive as mobile computing or the world wide web that came before it. Yet, most enterprises have no strategy on what to do. This is perplexing given that Deep Learning most hyped up slogan is "The Last Invention of Man". It boils down to one simple fact, enterprises don't understand Deep Learning. To make it even worse, they can't possibly understand the current wave of Artificial Intelligence (AI) developments if they don't understand Deep Learning.

Let's start with understanding "Predictive Analytics". This term originated as an evolution from "Descriptive Analytics", or just plain "Analytics". Descriptive analytics refers to the process of distilling large amounts of data into summary information that is more easily consumed by humans. Example techniques used in Descriptive Analytics include counts and averages to answer a question such as "What were my average sales by region last quarter?" By its nature, descriptive analytics is a backward looking view at "what happened."

Most of us already know that adopting new cloud applications can boost a business's productivity by enabling organizations to be more agile and ready to change course in our fast-moving and connected digital world. But the rapid adoption of cloud apps and services also brings with it profound security threats, including visibility and control challenges that aren't present in traditional on-premises environments. At the same time, the cloud - because of its interconnected, flexible and adaptable nature - can also provide new possibilities for addressing cloud security problems. By leveraging the power of the cloud with a data science and machine learning cloud-based solution, security and risk professionals can solve many of the traditional security challenges found in popular apps like Office 365, Google Drive, Salesforce and Box. In her session at 19th Cloud Expo, Deena Thomchick, Senior Director of Cloud Security at Symantec, detailed how cloud-based data science, machine learning, computational analysis and intelligent algorithms can work together to help to deliver truly intelligent and responsive security and compliance for the cloud.

India's patent laws allow for reverse-engineering of certain technologies. A prime example of this reverse-engineering is in the pharmaceutical space, where Indian pharma companies are allowed to reverse-engineer drugs, especially life-saving ones. These drugs may have been developed by pharma majors in other parts of the world--and then introduced into western markets--after India-based outsourcing firms had helped them out with clinical trials, data gathering and reporting to the US Food and Drug Administration (FDA) or its equivalent to get these drugs passed. Indian courts have continued to allow such reverse-engineering of drugs--famously prompting Bayer AG's then CEO Martin Dekkers to say at a conference a few years ago, "We did not develop this medicine for Indians. We developed it for western patients who can afford it."

In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics. As data science is a broad discipline, I start by describing the different types of data scientists that one may encounter in any business setting: you might even discover that you are a data scientist yourself, without knowing it. As in any scientific discipline, data scientists may borrow techniques from related disciplines, though we have developed our own arsenal, especially techniques and algorithms to handle very large data sets in automated ways, even without human interactions, to perform transactions in real-time or to make predictions. To get started and gain some historical perspective, you can read my article about 9 types of data scientists, published in 2014, or my article where I compare data science with 16 analytic disciplines, also published in 2014. I also wrote about the ABCD's of business processes optimization where D stands for data science, C for computer science, B for business science, and A for analytics science.

The Internet of Things (IoT) is touching every technology sector around the world, and it's having a significant impact on how enterprises and consumers interact with machines and devices. TechRepublic talked to IoT experts in a range of disciplines to find out what they think the biggest trends will be in 2017. Participants were Kevin Curran, IEEE senior member and senior lecturer in computer science at Ulster University; Francesco Cetraro, head of registrations, .cloud; Artificial intelligence, augmented reality, virtual reality, healthcare IoT, industrial IoT, and wearables are some of the topics of conversation about where the Internet of Things is headed in 2017. Diabetics have been waiting for years for better technology to manage their condition. Some got tired of waiting and hacked together an open source hardware and software solution.

With machine-learning technology, retailers can address the common--and costly--problem of having too much or too little fresh food in stock. Fresh food, already a fiercely competitive arena in grocery retail, is becoming an even more crowded battleground. Discounters, convenience-store chains, and online players are recognizing the power of fresh-food categories to drive store visits, basket size, and customer loyalty. With fresh products accounting for up to 40 percent of grocers' revenue and one-third of cost of goods sold, getting fresh-food retailing right is more important than ever.1 1.Raphael Buck and Arnaud Minvielle, "A fresh take on food retailing," Perspectives on retail and consumer goods, Winter 2013/14. Fresh food is perishable, demand is highly variable, and lead times are often uncertain.

The term "artificial intelligence" historically refers to systems that attempt to mimic or replicate human thought. This is not an accurate description of the actual science of artificial intelligence, and it implies a false choice between artificial and natural intelligences. That is why IBM and others have chosen to use different language to describe our work in this field. We feel that "cognitive computing" or "augmented intelligence" -- which describes systems designed to augment human thought, not replicate it -- are more representative of our approach. There is little commercial or societal imperative for creating "artificial intelligence."

Automation of jobs due to technology advancements is a well-known issue. In his 2016 State of the Union Address, U.S. President Barack Obama warned that technology "doesn't just replace jobs on the assembly line, but any job where work can be automated." Obama was not the first U.S. President to be concerned about automation, however. John F. Kennedy in 1962 said that the major domestic challenge of the decade was "to maintain full employment at a time when automation … is replacing men." Despite these concerns, automation did not lead to quick and total job losses -- neither in the 1960s, nor in the last century, nor even now.