We hear a lot about the Internet of Things on the consumer side. The oft trotted out example of the'smart' refrigerator that tells consumers when they've run out of the milk, and so on. But more serious potential for IoT -- and potentially seriously big wins -- are likely to be on the enterprise side where connected sensors can be deployed to automate at scale. The company took in a 6 million Series A round in December, which it's just announcing now -- and which includes, on top of that, a 3 million convertible seed, bringing its total raised to date to 9 million. Investors in the business include Jabil Circuit, Stanford StartX Fund, Founder.org

"Science often follows technology, because inventions give us new ways to think about the world and new phenomena in need of explanation." Or so Aram Harrow, an MIT physics professor, counter-intuitively argues in "Why now is the right time to study quantum computing". He suggests that the scientific idea of entropy could not really be conceived until steam engine technology necessitated understanding of thermodynamics. Quantum computing similarly arose from attempts to simulate quantum mechanics on ordinary computers. So what does all this have to do with machine learning?

It's a weird feeling, cruising around Silicon Valley in a car driven by no one. I am in the back seat of one of Google's self-driving cars – a converted Lexus SUV with lasers, radar and low-res cameras strapped to the roof and fenders – as it maneuvers the streets of Mountain View, California, not far from Google's headquarters. I grew up about five miles from here and remember riding around on these same streets on a Schwinn Sting-Ray. Now, I am riding an algorithm, you might say – a mathematical equation, which, written as computer code, controls the Lexus. The car does not feel dangerous, nor does it feel like it is being driven by a human. It rolls to a full stop at stop signs (something no Californian ever does), veers too far away from a delivery van, taps the brakes for no apparent reason as we pass a line of parked cars. I wonder if the flaw is in me, not the car: Is it reacting to something I can't see? The car is capable of detecting the motion of a cat, or a car crossing the street hundreds of yards away in any direction, day or night (snow and fog can be another matter). "It sees much better than a human being," Dmitri Dolgov, the lead software engineer for Google's self-driving-car project, says proudly. He is sitting behind the wheel, his hands on his lap. As we stop at the intersection, waiting for a left turn, I glance over at a laptop in the passenger seat that provides a real-time look at how the car interprets its surroundings. On it, I see a gridlike world of colorful objects – cars, trucks, bicyclists, pedestrians – drifting by in a video-game-like tableau. Each sensor offers a different view – the lasers provide three-dimensional depth, the cameras identify road signs, turn signals, colors and lights. The computer in the back processes all this information in real time, gauging the speed of oncoming traffic, making a judgment about when it is OK to make a left turn.

The ability to access and use financial services is critical to managing day-to-day life, weathering unexpected events, and capturing opportunities. Yet, some 46 percent of working-age adults in developing countries remain excluded from the formal financial system. It means they use the age-old informal mechanisms such as the moneylender, the pawnbroker, or the rotating savings club that can be unreliable and very expensive. In developed countries, working families are more likely to be under- or badly served rather than outright excluded. In the US, for example, every year some 25 million households use alternative services such as payday lenders or check cashers.

Under the slogan "Be the First Mover in the Era of Knowledge and Information," South Korea has emerged as one of the world's IT powerhouses in just six years. Incheon International Airport, the epitome of IT convergence, has been named the world's best airport for 11th consecutive years. The S. Korean financial industry, which had finished 86th behind Uganda, has risen to the world's 7th largest, easing regulations on the separation of industrial and financial capital and granting Kakao and KT preliminary licenses to run the nation's first online-only banks. The Go-playing AI AlphaGo had the brand value of Google leapfrogging over that of Samsung Electronics instantly. It was all the work of software.

Until now, medicine has been a prestigious and often extremely lucrative career choice. But with intelligent machines now used to diagnose diseases, in the near future, will we need as many doctors as we have now? Are we going to see significant medical unemployment in the coming decade? Dr Saxon Smith, president of the Australian Medical Association NSW branch, said in a report late last year that the most common concerns he hears from doctors-in-training and medical students are, "what is the future of medicine?" The answers, he said, continue to elude him.

Fighting cyber attacks with artificial intelligence The next frontier of anti-virus software is leveraging artificial intelligence (AI) to not only predict what threats are out there, but to also actively fight back before they strike. This is according to American-based Cylance's chief marketing officer, Greg Fitzgerald, speaking at the NetEvents Press and Analyst Summit in Rome, Italy.The company says it is "revolutionising cyber security through the use of AI and machine learning to proactively prevent advanced persistent threats and malware". Cylance today announced it is expanding into the Europe, the Middle East and Africa (EMEA) with the establishment of a London-based team led by Evan Davidson, former enterprise sales director at FireEye. It also established a channel partnership with CoreSec Systems, which supplies cyber security and networking solutions in Sweden and Denmark.

It was hailed as the most significant test of machine intelligence since Deep Blue defeated Garry Kasparov in chess nearly 20 years ago. Google's AlphaGo has won two of the first three games against grandmaster Lee Sedol in a Go tournament, showing the dramatic extent to which AI has improved over the years. That fateful day when machines finally become smarter than humans has never appeared closer--yet we seem no closer in grasping the implications of this epochal event. Late last year, SpaceX co-founder Elon Musk warned that AI could take over the world, sparking a flurry of commentary both in condemnation and support. For such a monumental future event, there's a startling amount of disagreement about whether or not it'll even happen, or what form it will take.

Social networking giant Facebook is using its artificial intelligence (AI) technology and resources to map the entire Earth and launch the world's most detailed population maps that will help it beam cheap internet to remote areas. To begin with, the Facebook AI team crunched 14.6 billion images of maps from across 20 countries, including India, covering 21.6 million sq kms to come up with the first detailed map of human settlement for these countries. "This is an impressive project from our team developing solar-powered planes for beaming down internet connectivity and our AI research team. Many people live in remote communities and accurate data on where people live doesn't always exist," wrote Facebook CEO Mark Zuckerberg in a latest post. The 20 countries mapped were Algeria, Burkina Faso, Cameroon, Egypt, Ethiopia, Ghana, India, Ivory Coast, Kenya, Madagascar, Mexico, Mozambique, Nigeria, South Africa, Sri Lanka, Tanzania, Turkey, Uganda, Ukraine and Uzbekistan.

Drone images accumulate much faster than they can be analyzed. Researchers have developed a new approach that combines crowdsourcing and machine learning to speed up the process. Who would win in a real-life game of "Where's Waldo," humans or computers? A recent study suggests that when speed and accuracy are critical, an approach combing both human and machine intelligence would take the prize. With drones being used to monitor everything natural disaster sites, pollution, or wildlife populations, analyzing drone images in real-time has become a critically important big data challenge. Publishing in the journal Big Data, researchers, including Stéphane Joost from EPFL, present a new approach to rapidly interpret aerial images taken by camera drones that combines human crowdsourcing and machine learning.