Machine learning is being used by companies such as Netflix, Facebook, and Spotify for automated data analysis. The results are then used to create recommendations based on past consumption habits. This approach is based on algorithms that learn and adapt to the usage data and patterns that emerge, not the hard-coded rules used in traditional analytics. Netflix in particular has successfully leveraged analytics for years, and their strategy serves as an example of how machine learning can help you gain a competitive advantage. Big Data analytics, being based on manual processes to search for patterns in data, has a major flaw--humans.

We all know that alcohol and tweeting is not always a good combination. Yet a surprising number of us indulge in this peculiar form of indiscretion. And this practice has given Nabil Hossain and pals at the University of Rochester an interesting idea. Today, these guys show how they've trained a machine to spot alcohol-related tweets. And they also show how to use this data to monitor alcohol-related activity and the way it is distributed throughout society.

Artificial intelligence--the mimicking of human cognition by computers--was once a fable in science fiction but is becoming reality in medicine. The combination of big data and artificial intelligence, referred to by some as the fourth industrial revolution,1 will change radiology and pathology along with other medical specialties. Although reports of radiologists and pathologists being replaced by computers seem exaggerated,2 these specialties must plan strategically for a future in which artificial intelligence is part of the health care workforce. Radiologists have always revered machines and technology. In 1960, Lusted predicted "an electronic scanner-computer to examine chest photofluorograms, to separate the clearly normal chest films from the abnormal chest films."3

Regardless of industry, the companies that win in the digital era are those that take the shortest paths to the best results. That means getting the right information in the right hands at the right time. These realities are why more organizations are turning to cognitive solutions. Our market report, "The cognitive advantage: Insights from early adopters on driving business value," reveals that early adopters employ cognitive computing for competitive differentiation. In fact, 65 percent say that cognitive adoption is very important to their strategy and success, and more than half regard cognitive computing as a must-have to remain competitive.

But what exactly is machine learning? And how is it different to what we call artificial intelligence and the term deep learning frequently used today? Artificial intelligence is the general term for a series of methods, tools and technologies that imitate human cognition, including recognition, learning, planning, reasoning and the ability to solve problems. One of these methods is machine learning, aimed at making the computer capable of performing these cognitive skills. Machine learning is based on data and expert knowledge, out of which computers extract significant patterns and transfer these patterns to other data in order to generate predictions and recommendations.

What if you could hire Google as an employee--or even your Chief Marketing Officer (CMO)? Though artificial intelligence (AI) is in its early phases, the real allure is not that it could, someday, be just like a human being, but that it could be more than a human. After all, even the smartest person in your company doesn't have the wealth of information that Google does, all available in a microsecond. While humankind will likely never reach the speed or accuracy of AI in performing certain tasks, employees of the future will need to function in similar ways. They will need to pull from a broad range of knowledge to find the right answers, deliver results quickly, offer a variety of solutions to a single problem, be adaptable, consider the whole market or subject before offering a suggestion, and be consistent in their performance. Is this too much to ask?

WiFi company Zenreach is used to winning at least 60% of the time when it goes head to head with competitors. But when the 150-person startup went back and studied the conversations its sales reps were having with prospects, CEO Jack Abraham was confronted with a challenger beating his team 3 times out of 4. "We hadn't even considered them a competitor," says Abraham. "But when you can scan through all the instances a competitor is mentioned, it's easy to scan through what you're missing." The Zenreach sales team got that wakeup call courtesy of a new technology that launched on Tuesday called TalkIQ. Incubated out of Abraham's venture fund Atomic, TalkIQ is coming out of the gate with $7 million in initial funding from name-brand investors and speech recognition software for sales, customer service and onboarding that the startup claims is 2x better than IBM Watson and 3x better than Google. TalkIQ formed more than a year ago along the premise that if someone (or a machine) were to study every call made in a company, she (or it) could uncover patterns and insights to make the business run better--especially if you accept, as TalkIQ did, research that suggests that as much as 68% of customer interactions with company contact centers still happen by phone.

This year, before the doors of the the Los Angeles Auto Show open to the public, the show held Automobility, an automotive tech showcase. As our cars become more like two-ton devices that we drive, auto shows are having to adjust their focus to include apps, AI, connected cars, and more. Here are a few of the most innovative tech stories from LA. Hyundai offered Blue Link, an app that allowed owners to unlock and start their cars via smartphone, in 2011. The second generation of Blue Link rolled out in 2014, adding smart watches to the app's repertoire. Now the service works with Amazon's Alexa in-home AI device.

It will not, if there is any certainty left in the world, top the charts this Christmas. But what it lacks in party hit potential, it more than makes up for with its unique, if vaguely unsettling, brand of festive cheer. To be fair, humans had very little hand in penning the song. Instead, scientists fed a Christmassy photograph into a computer and let it do its thing. A program analysed the image, whipped up some relevant lyrics, and then sang them to music it had composed along the way.

This summer, Riva-Melissa Tez was searching online for research that might help her father. He'd gone into a coma after suffering a stroke, and she wondered what the latest recommendations said--whether playing music to him in his native language could keep him connected to this world, or if giving him Prozac could boost his chances of recovery as it had done for mice in a study last year. Doctors are so busy saving lives, she thought, that they couldn't possibly keep up with all the papers published every day. Her concern is shared by doctors, who wonder what they could be missing in the 2.5 million scientific papers published every year. Popular sites like MedCalc and UptoDate are useful tools for doctors to consult diagnostic criteria and double check on treatment guidelines.