Artificial intelligence (AI) is here and it is getting smarter all the time. For many, mention of AI immediately conjures up images of The Terminator. At the very least it makes many think of robots and machines disrupting the workforce and replacing jobs. While the latter may not be completely out of the realm of possibility, datapine is leaving that side of AI to Silicon Valley and John Connor. Instead we are excitedly monitoring and utilizing artificial intelligence in business intelligence.

Artificial intelligence (AI) has the power to change how our workforce operates, and if you want your business to stay competitive, you need to get ahead of the AI revolution. Don't let yourself feel daunted by it as a buzzword. At my current company, we've seen firsthand the renaissance of AI, neural networking, big data and machine learning over the past five years. Not only do we provide automation and analytics for clients, we've now started to incorporate AI into our product development and operational automation. A study by McKinsey found that 45% of all on-the-job activities could be automated by deploying AI. Google's Jeff Dean estimates about 10% of its engineers know machine learning, and he'd like that number to be 100%.

Artificial intelligence has a new job: setting a good example for your kids. It seems that children's behaviour can be influenced by the personality of a robot companion – playing with an enthusiastic or attentive robot, for instance, made them engage more and work harder. Researchers ran a series of experiments with Tega, a companion robot that looks like a cross between a Furby and a Teletubby. To test how the robot's personality could affect the children's behaviour, they programmed the robot with different responses. "The goal is to have a companion that has all of the behaviours that we want to instil and promote in the child," says team member Goren Gorden at Tel Aviv University in Israel.

These are just a few data-driven ways to overcome the everyday challenges of practical machine learning. Alyssa Frazee is a machine learning engineer at Stripe, where she builds models to detect fraud in online credit card payments. Before Stripe, she did a PhD in biostatistics and fell in love with programming at the Recurse Center. Find her on Twitter at @acfrazee. Machine learning has long powered many products we interact with daily–from "intelligent" assistants like Apple's Siri and Google Now, to recommendation engines like Amazon's that suggest new products to buy, to the ad ranking systems used by Google and Facebook.

Machine learning coupled with the explosion of data offers the very real possibility of addressing the most intractable problems in healthcare. For the first time, data is helping to answer healthcare's toughest questions. Like Google's Research Director Peter Norvig, I too believe it's not better algorithms that are fueling our advancement. Instead, it's the surge in data sources and the innate ability of machine learning to automatically apply complex calculations to vast stores of data and derive rules that help us to understand the correlations, patterns and predictions within the data. In 2015, our country spent $3.2 trillion a year on healthcare, or $9,990 per person, and the actuaries at the Centers for Medicare & Medicaid Services project the total to rise to nearly $5.6 trillion in 2025.

Suddenly, it seems, every application and cloud service has been fortified with machine learning or artificial intelligence. They now can do magic. Much of the marketing around machine learning and AI is misleading, making promises that aren't realistic--and often using the terms when they don't apply. In other words, there's a lot of BS being peddled. Artificial intelligence is a wide range of cognitive technologies to enable ad hoc or situational reasoning, planning, learning, communication, perception, and the ability manipulate objects to an intended purpose.

Are you willing to learn about Deep Learning for Cyber Security? Join the webinar to learn more! In this webinar, Steven Hutt, Consultant in Deep Learning and Financial Risk, will provide an overview of network anomaly detection. This webinar will be of interest to Data Scientists, Software Engineers and Entrepreneurs in the areas of Connected Cars, Internet of Things/Industrial Internet, Medical Devices, Financial Technology (blockchain) and predictive apps/APIs of all sorts. Steven Hutt is a consultant in Deep Learning and Financial Risk, currently working in Cyber Security and Algorithmic Trading.

Here's how science works: You have a question about some infinitesimal sliver of the universe. You form a hypothesis, test it, and eventually gather enough data to support or disprove what you thought was going on. The next bit is less glamorous: You write a manuscript, submit it to an academic journal, and endure the gauntlet of peer review, where a small group of anonymous experts in your field scrutinize the quality of your work. Peer review has its flaws. Human beings (even scientists) are biased, lazy, and self-interested.

To sell Millennials on new cars, personalized digital experiences using artificial intelligence and blockchain are on the horizon. According to a recent report on the automotive finance industry from the White Clarke Group, we've collectively entered what they call "Industry 4.0: Connected Intelligence." It's not only the pace of tech innovation that create a "connected intelligence" era, but also how rapidly young people--who will soon be the majority of the population and control a vast amount of purchasing power--are adopting new ideas. The ways in which this new industrial era affects the automotive finance industry are complicated and multifaceted. "We are experiencing an explosion of innovation, which has a profound impact on how we interact with each other, and how businesses build and maintain a relationship with their customers," said Brendan Gleeson, Group CEO of White Clarke Group.

When I started at O&M in 2004, I was working for an IT client. The split between offline advertising and online activities was 80% / 20%. By 2007, it was the opposite. With the rise of online, we started the long race of personalisation; digital marketing helped us to adapt the way we communicate on brands. We could segment better and send more specific communication for a lower budget!