In recent years Artificial Intelligence (AI) has rapidly gone from an obscure academic research field, to an ever more useful and ubiquitous applied discipline. We increasingly rely on AI for more and more of our everyday tasks, and whole lines of work are being thoroughly transformed by its advances. AI's increasing ubiquity is not making it any easier to understand. AI concepts and techniques are still domain of advanced undergraduate or graduate school level courses. There are a few popular AI books out there, but most of them don't get "under the hood" of how AI actually works.

Say what you will about civilian drones and drone pilots, but when the going gets tough, these drone pilots will get going. According to published Federal Aviation Administration (FAA) commercial drone exemption requests, a big chunk of drone pilots are willing to lend a hand for possible emergencies. A total of 19 percent mentioned emergency services--that's six times as many requests from the last quarter of 2015. With all these pilots willingly to help out in case of emergencies, this could mean that private pilots can easily aid officials during search and rescue missions and other equally critical emergency scenarios. Despite this magnanimous offer to lend a hand, it doesn't necessarily guarantee pilots flying time.

One prediction I have made about the future of service delivery automation (SDA) is that increasingly enterprise software will have the technology embedded. This is particularly true of intelligent and cognitive type of tools. I expect these to become a common feature of enterprise software in the next 5-7 years. We saw this kind of trend in the earlier days of business intelligence and reporting. The popularity of third-party tools saw the functionality built into enterprise software.

A fast-food chain with restaurants in 28 states is getting ready to start serving burgers and fries in Champaign-Urbana. Bruce Kim, director of franchise development for Checkers and Rally's Restaurants, said the company is in the process of awarding a franchise for up to three new Checkers restaurants in the area. "We are a quick-service restaurant," he said. "We are known for our seasoned, seared and grilled burgers; fries; grilled, all-meat hot dogs; crazy good chicken wings; golden fish sandwiches; and ice cream. "Our seasoned fries were named the best fries for 2015 by Yahoo."

Some time ago I started a journey into one of the most exciting fields in Computer Science -- Machine Learning. This is my subjective guide for anyone who would like to explore this topic, but don't know how to start. Your first steps should lead to Stanford Machine Learning class at Coursera by Andrew Ng. This course is simply brilliant! Along a way, you will be given everything you need to know, including algebra review.

Digital expertise will become increasingly critical in the insurance sector as digitization and machine learning leads to more highly'automatable' insurance according to management consultants McKinsey & Company. Meanwhile a separate piece of research by Accenture found that insurance companies are accelerating the shift to a radically different distribution model, where they say digital will play an increasingly important role in most interactions, and were agents' efforts are being refocused to add more value. And analysis by research outfit Ovum suggests strong investment in digital channels also. According to Ovum, " When it comes to investment, digital channels remains the top area for insurers. However, the significant majority of insurers will be increasing budgets across a broad range of functional areas with no single activity completely dominating spend.

We at Venture Scanner are tracking 957 Artificial Intelligence companies across 13 categories, with a combined funding amount of 4.8 Billion. The 15 visuals below summarize the current state of Artificial Intelligence. Deep Learning/Machine Learning (General): Companies that build computer algorithms that operate based on their learnings from existing data. Examples include predictive data models and software platforms that analyze behavioral data. Deep Learning/Machine Learning (Applications): Companies that utilize computer algorithms that operate based on existing data in vertically specific use cases.

"Really, the only game left after chess is Go," was how Demis Hassabis set the scene ahead of AlphaGo's match with world champion Lee Sedol earlier this month. Either Hassabis's copy of the latest Street Fighter didn't get delivered on time, or he was trying to be a little poetic to mark the occasion. Either way, you'd be forgiven for thinking there really were no games left to conquer after the media reaction to AlphaGo winning the first three games in a best-of-five against its human opponent. It's been a curious month to be an AI researcher. Watching the contest, which AlphaGo eventually won 4-1, I've learned a lot about Go and one of the most interesting things is how the spaces left empty on the board can often be as important and meaningful as the spaces where stones are played.

Humans have been taking a beating from computers lately. The 4-1 defeat of Go grandmaster Lee Se-Dol by Google's AlphaGo artificial intelligence (AI) is only the latest in a string of pursuits in which technology has triumphed over humanity. Self-driving cars are already less accident-prone than human drivers, the TV quiz show Jeopardy! is a lost cause, and in chess humans have fallen so woefully behind computers that a recent international tournament was won by a mobile phone. There is a real sense that this month's human vs AI Go match marks a turning point. Go has long been held up as requiring levels of human intuition and pattern recognition that should be beyond the powers of number-crunching computers.

Editor's note: This is the third in a series of articles about the effects of software and automation on the economy. You can read the other stories here and here. The way Hod Lipson describes his Creative Machines Lab captures his ambitions: "We are interested in robots that create and are creative." Lipson, an engineering professor at Cornell University (this July he's moving his lab to Columbia University), is one of the world's leading experts on artificial intelligence and robotics. His research projects provide a peek into the intriguing possibilities of machines and automation, from robots that "evolve" to ones that assemble themselves out of basic building blocks. A few years ago, Lipson demonstrated an algorithm that explained experimental data by formulating new scientific laws, which were consistent with ones known to be true. He had automated scientific discovery. Lipson's vision of the future is one in which machines and software possess abilities that were unthinkable until recently.