A laser engine could someday allow tiny robots to pull 150 times their own weight. At least, that's the implication of new research that has shown how lasers can give tiny drops of water superpowers. The laser technology could someday be used by scientists to deliver drugs to specific areas of the body, scientists claim. Scientists coated millimetre-sized drops of water in a nanometre-scale powder of polypyrrole, a water-repelling plastic which heats up when illuminated. New Scientist reports that the powder coating turned the drop into a liquid marble, with the fluid contained inside.

In addition to accessing cutting edge data analytics tools, and allowing them to scale at speed, companies are looking to platform-as-a-service providers Google, Microsoft and Amazon to remove trade barriers in complex markets like China. Last week, Xero announced a strategic push into Asia with the opening of its new regional headquarters in Singapore. But it will bide its time on China until big platform-as-a-service providers pave the way. "As it works at the moment, we would have to give Chinese authorities access to our source code and host our data in China. I don't know that we'd feel very comfortable with that," Mr Drury told The Australian Financial Review at Xerocon last year when asked about the impact of the Trans-Pacific Partnership, a major trade deal involving Australia and 11 other countries.

Here is how I think about these issues. The Artificial in AI can sometimes mislead so let's start by getting rid of the A and asking instead whether more NI, Natural Intelligence, will decimate the middle class. As I said in my TED talk, the brainpower of China and India in the 20th century was essentially "offline". Instead of contributing to the world technological frontier the people of China and India were just barely feeding themselves. China and India are now coming online and I see the increase in natural intelligence as one of the most hopeful facts for the future.

Real-world artificial-intelligence applications are popping up in unexpected places--and much sooner than you might think. While winning a game of Go might be impressive, machine intelligence is also evolving to the point where it can be used by more people to do more things. That's how four engineers with almost zero knowledge of Japanese were able to create software, in just a few months, that can decipher handwriting in the language. The programmers at Reactive Inc. came up with an application that recognizes scrawled-out Japanese with 98.66 percent accuracy. The 18-month-old startup in Tokyo is part of a growing global community of coders and investors who are harnessing the power of neural networks to put AI to far more practical purposes than answering trivia or winning board games.

Facebook launched a concierge service called M through its Messenger app in 2015. M can purchase items, get gifts delivered, book restaurants, and make travel arrangements for the user. There is an element of the Mechanical Turk about it at the moment as it is powered by a combination of AI and real-life people. Siri has been around for a few years but has been upgraded in that time. It's now capable of showing the user specific photos he or she has taken, ordering things via ecommerce apps and giving directions.

In 2008, Max Yankelevich was in India, visiting the cubicle farms where big banks and insurance companies outsource business processes -- the invoices, memos, and other papers pushed to keep organizations humming. The employees were smart, says Yankelevich, who was running a cloud computing startup at the time. There was good money in doing this sort of back office work -- but it was mind numbing. Companies were trying to figure out the back office work with "the brute force of human power," says Yankelevich, who studied artificial intelligence while getting his MIT computer science degree in the 1990s. "I started thinking ... there's gotta be a way where artificial intelligence can be used generically enough to learn some of these things that these people are doing," he says.

With rotors and radios, we are witnessing the birth of a new sport. Aerial acrobatics, sharp turns and staggering climbs that NASCAR can only imagine, all done on the cheap with toys and remote controls, from woods to warehouses to special courses in Dubai. Welcome to the new age of drone racing, as it moves from backyards to broadcasts. Today, ESPN announced it will livestream the U.S. National Drone Racing Championships, a 3-day drone racing event held on New York City's Governor's Island in early August. And then, in October, ESPN will cover the 2016 World Drone Racing Championships at Kualoa Ranch, Hawaii. The drones are flown by pilots wearing goggles and watching through first person vision cameras.

Artificial consciousness[1] (AC), also known as machine consciousness (MC) or synthetic consciousness (Gamez 2008; Reggia 2013), is a field related to artificial intelligence and cognitive robotics. The aim of the theory of artificial consciousness is to "define that which would have to be synthesized were consciousness to be found in an engineered artifact" (Aleksander 1995). Neuroscience hypothesizes that consciousness is generated by the interoperation of various parts of the brain, called the neural correlates of consciousness or NCC, though there are challenges to that perspective. Proponents of AC believe it is possible to construct systems (e.g., computer systems) that can emulate this NCC interoperation.[2] Artificial consciousness concepts are also pondered in the philosophy of artificial intelligence through questions about mind, consciousness and mental states.[3] As there are many hypothesized types of consciousness, there are many potential implementations of artificial consciousness. In the philosophical literature, perhaps the most common taxonomy of consciousness is into "access" and "phenomenal" variants. Access consciousness concerns those aspects of experience that can be apprehended, while phenomenal consciousness concerns those aspects of experience that seemingly cannot be apprehended, instead being characterized qualitatively in terms of "raw feels", "what it is like" or qualia (Block 1997).

When it comes to human senses, we've found ways to reproduce the look and sound of the real world reasonably accurately. There are even technologies for reproducing the feel of certain experiences, such as flight and car simulators. But the problem of reproducing smell is much more intractable. The 1960 SmelloVision experiment is a case in point. This involved some 30 odors that were released into the cinema at certain times during a movie.

In 2008, Max Yankelevich was in India, visiting the cubicle farms where big banks and insurance companies outsource business processes -- the invoices, memos, and other papers pushed to keep organizations humming. The employees were smart, says Yankelevich, who was running a cloud computing startup at the time. There was good money in doing this sort of back office work -- but it was mind numbing. Companies were trying to figure out the back office work with "the brute force of human power," says Yankelevich, who studied artificial intelligence while getting his MIT computer science degree in the 1990s. "I started thinking ... there's gotta be a way where artificial intelligence can be used generically enough to learn some of these things that these people are doing," he says.