The evolution of artificial intelligence (AI) -- from artificial narrow intelligence (ANI), through artificial general intelligence (AGI), to artificial super intelligence (ASI) -- is on its way to changing everything. It's expected that soon, artificial intelligence will combine the intricacy and pattern recognition strength of human intelligence with the speed, memory and knowledge sharing of machine intelligence. As the rise of AI continues, AI is challenging and changing not only the way humans live, learn and work, but also how entities across nations: its government, industries, organizations and academia (NGIOA) construct their commercial and economic industries and markets. With this technology driven growth of artificial intelligence, the need to do most manual, mathematical and mundane work is already in decline and will likely be greatly diminished in the coming years. Moreover, with all these new digital assistants and decision-making algorithms assisting and directing humans, more complex day-to-day work for humans is being greatly lessened.

A coming milestone in the automobile world is the widespread rollout of Level 4 autonomy, where the car drives itself without supervision. Waymo, the company spun out of Google's self-driving car research, said it would start a commercial Level 4 taxi service by late 2018, although that hadn't happened as of press time. And GM Cruise, in San Francisco, is committed to do the same in 2019, using a Chevrolet Bolt that has neither a steering wheel nor pedals. These cars wouldn't work in all conditions and regions--that's why they're on rung 4 and not rung 5 of the autonomy ladder. But within some limited operational domain, they'll have the look and feel of a fully robotized car.

In one of the iconic scenes from the teen movie "Fast Times at Ridgemont High," sun-baked stoner Jeff Spicoli has a double cheese and sausage pizza delivered to his classroom, boldly interrupting his uncompromising instructor mid-lecture. Spicoli was considered a mischievous airhead for flouting early-1980s dining etiquette, but he may actually have been way ahead of his time. More than three decades later, a California campus is embracing a kind of food delivery -- via robot. On Wednesday, students at University of the Pacific in Stockton, Calif., will be able to order snacks and beverages for the first time from a bright-colored roving robot on wheels known as the "Snackbot." Its stout body perched atop six small wheels, the electric Snackbot resembles some combination of an Igloo cooler and a Volkswagen Microbus.

When Google announced that it would absorb DeepMind's health division, it sparked a major controversy over data privacy. Though DeepMind confirmed that the move wouldn't actually hand raw patient data to Google, just the idea of giving a tech giant intimate, identifying medical records made people queasy. This problem with obtaining lots of high-quality data has become the biggest obstacle to applying machine learning in medicine. To get around the issue, AI researchers have been advancing new techniques for training machine-learning models while keeping the data confidential. The latest method, out of MIT, is called a split neural network: it allows one person to start training a deep-learning model and another person to finish.

Amazon SageMaker, Microsoft Azure ML Services, Google Cloud ML Engine, IBM Watson Knowledge Studio are examples of ML PaaS in the cloud. If your business wants to bring agility into machine learning model development and deployment, consider ML PaaS. It combines the proven technique of CI/CD with ML model management.

At least 21 such attacks have been leveled at Waymo vans in Chandler, as first reported by The Arizona Republic. Some analysts say they expect more such behavior as the nation moves into a broader discussion about the potential for driverless cars to unleash colossal changes in American society. "People are lashing out justifiably," said Douglas Rushkoff, a media theorist at City University of New York and author of the book "Throwing Rocks at the Google Bus." He likened driverless cars to robotic incarnations of scabs -- workers who refuse to join strikes or who take the place of those on strike. "There's a growing sense that the giant corporations honing driverless technologies do not have our best interests at heart," Mr. Rushkoff said.

The UK government is investing millions in the development of micro-robots designed to work in underground pipe networks and dangerous sites such as decommissioned nuclear facilities. Airborne and underwater versions could also inspect and maintain difficult-to-reach locations such as offshore windfarms or oil and gas pressure vessels. Led by Prof Kirill Horoshenkov at the University of Sheffield and backed by a £7.2m government grant, the collaborative research programme will also involve scientists from Birmingham, Bristol and Leeds universities. It is hoped that the 1cm-long devices will use sensors and navigation systems to find and mend cracks in pipes, avoiding disruption from roadworks estimated to cost the economy £5bn a year. The remaining £19.4m will fund research into the use of robotics in hazardous environments, including drones for oil pipeline monitoring or artificial intelligence able to establish the need for repairs on satellites in orbit.

Computer scientist and professor Eugene Fiume talks about what science-fiction writer Isaac Asimov got right and wrong in his predictions of 2019.

CHELMSFORD, MASSACHUSETTS - The U.S. Army is looking for a few good robots. Not to fight -- not yet, at least -- but to help the men and women who do. These robots aren't taking up arms, but the companies making them have waged a different kind of battle. At stake is a contract worth almost half a billion dollars for 3,000 backpack-sized robots that can defuse bombs and scout enemy positions. Competition for the work has spilled over into Congress and federal court.

What jobs will AI probably not destroy? The jobs that are most susceptible to automation in the near term are those that are fundamentally routine or predictable in nature. If you have a boring job--where you come to work and do the same kinds of things again and again, you should probably worry. The tasks within jobs like this are likely to be encapsulated in the data that is collected by organizations. So it may only be a matter of time before a powerful machine learning algorithm comes along that can automate much of this work.