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.

Experimentation with voice-activated assistants and text-triggered chatbots blossomed in 2016, enabling users to order from Taco Bell using Slack's messaging interface, check the status of UPS packages and order office supplies from Staples. In 2017, some of these tools won't make the cut while others will proliferate across consumer and enterprise sectors, creating new workflows, operational efficiencies and opportunities for improved customer service. Alexa, though positioned as a home product, is poised to become the go-to platform for voice-based assistants for consumers and businesses. Wynn Hotels plans to equip nearly 5,000 rooms with Amazon.com's Echo device, which will allow people to query Alexa for room and hotel information.

The wealth management industry is facing a wave of digital disruption. Developments in finance, science and technology have led to a new generation of financial technology start-ups. Most "fin-techs" focus on automated investment services, or so-called'robo-advisers'. Betterment, the pioneer of automated investing, recently surpassed $5 billion in assets under management, only 8 years after it was founded. And what can we expect from the automated advisers of the future?

Now with pillows, dog beds and even luxury pool floats, Casper is pushing itself as a business based around the entire culture of sleep rather than just a mattress startup. And for its latest project, Casper has even tapped into artificial intelligence to help us, well, stay awake. Casper recently launched insomnobot3000, a bot you can text to keep you entertained when you can't sleep. The project is the result of months of secretive development by a 10-person team spanning Casper's tech, design, data and creative departments, which actually looked to SmartChild, the infamous bot popular with tweens in the AOL Instant Messenger days, for inspiration. It's been a few months since insomnobot3000 officially launched, and while it doesn't use machine learning to grow smarter on its own, the development team is supposed to be constantly updating it based on the conversations it's having.

To assist a child we must provide him with an environment which will enable him to develop freely. There's a kindergarten I walk past on the way to work, and I can't help but peek inside everyday. The classroom -- packed with toys and puzzles, music and books, flower planters and even an occasional cat -- was obviously crafted to be a rich and bustling world for kids to interact and play in. Contrary to its meaning, child's play is far from simple. Playing in a diverse, exciting universe is how we nurture a child's budding intelligence.

From stopping cyberattacks to operating autonomous vehicles to visually searching through a wine database, 140 startups using AI as a core part of their products raised $1.05B in funding in Q3'16. Since 2012, deals and dollars to AI startups have been on a rise, and this year is extending that trend. Our AI category includes companies applying AI solutions to verticals like healthcare, security, advertising, and finance as well as those developing general-purpose AI tech. Our list excludes robotics (hardware-focused) and AR/VR startups, which we've analyzed separately here and here. Our analysis includes all equity funding rounds and convertible notes.