This article was originally published by the World Economic Forum. The doomsday scenarios spun around this theme are so outlandish--like The Matrix, in which human-created artificial intelligence plugs humans into a simulated reality to harvest energy from their bodies--it's difficult to visualize them as serious threats. Meanwhile, artificially intelligent systems continue to develop apace. Self-driving cars are beginning to share our roads; pocket-sized devices respond to our queries and manage our schedules in real-time; algorithms beat us at Go; robots become better at getting up when they fall over. It's obvious how developing these technologies will benefit humanity. But, then, don't all the dystopian sci-fi stories start out this way?

Artificial intelligence (AI) is no longer the stuff of science fiction books and movies. It is a reality that is already permeating society and is affecting our daily lives. If you use facebook or google, artificial intelligence enables machines to virtually understand what you're looking for or to augment your network. In some countries and states in the US, self-driving cars have already taken to the streets. Elsewhere, companies are experimenting with bots that can act like lawyers, tellers, or doctors.

When IBM's Deep Blue beat chess Grandmaster Garry Kasparov in 1997 in a six-game chess match, Kasparov came to believe he was facing a machine that could experience human intuition. "The machine refused to move to a position that had a decisive short-term advantage," Kasparov wrote after the match. It was "showing a very human sense of danger."1 To Kasparov, Deep Blue seemed to be experiencing the game rather than just crunching numbers. Just a few years earlier, Kasparov had declared, "No computer will ever beat me."2

If your drone weighs more than 250 grams, airlines and pilots think you should get a drone pilots' license before you fly it in the European Union. They're worried about the number of near misses between drones and helicopters or fixed-wing aircraft, and they see greater regulation of drone use as the best way to improve safety. They also want more tests to be conducted to determine the damage that drones may cause to manned aircraft, much as is already done to reduce the threat of bird strikes. In a letter signed by 10 international associations for airlines, pilots, airports, and other organizations, they make little distinction between commercial and leisure uses of drones. All drones should be registered at the time of purchase or resale, they said, because knowing the devices can be traced is likely to make pilots behave more responsibly.

Around a year ago, a former embedded systems designer from the Japanese automobile industry named Makoto Koike, started helping out at his parents cucumber farm, and was amazed by the amount of work it takes to sort cucumbers by size, shape, colour and other attributes. In Japan, each farm has its own classification standard and there's no industry standard. There are some automatic sorters on the market, but they have limitations in terms of performance and cost, and small farms don't tend to use them. Makoto first got the idea to explore machine learning for sorting cucumbers from a wildly different source - Google AlphaGo - competing with the world's top professional Go player. "When I saw the Google's AlphaGo, I realized something really serious is happening here, said Makoto. That was the trigger for me to start developing the cucumber sorter with deep learning technology."

Sergul and Syed received their Ph.D.s in Electrical Engineering in 2014 from the University of Southern California, applying signal processing to neuroimaging data. They continue to use machine learning on brain imaging data as a pastime and sharing their knowledge with the community. They constantly challenge each other as good buddies and like to call themselves "signal learners." The views expressed in this article are of the authors and not of their employers or Domino Data Lab. In this post we will describe how we used PySpark, through Domino's data science platform, to analyze dominant components in high-dimensional neuroimaging data.

Is Artificial Intelligence (AI) the silver bullet that will make doctors all over the world unemployed? Will AI be able to outperform oncologist in creating treatment plans for cancer patients? Keep reading and get new perspectives on healthcare AI as I untangle opportunities and grand challenges within the field. "Too much information, too little time" is one of the big challenges in healthcare today. Patients, healthcare professionals and medical devices generate huge amounts of data.

With our growing reputation and a steady stream of successful and innovative projects the demand for Daden's services is steadily growing. Our current job opportunities are listed below. We are always keen to hear from anyone interested in working for us in a freelance capacity – particularly if Midlands based. We are looking to recruit a junior/graduate programmer, or programmer with 2-5 years experience, to join our team and work across the full range of our very varied and often highly innovative projects. Our largest projects at the moment are Fieldscapes, a Unity based virtual reality education environment being funded by InnovateUK, Datascape, a C#/WPF/DirectX based 3D/VR immersive data visualisation system, and a conversational AI project based around natural language processing and semantic web technologies.

Under, behind and inside many of the apps we use every day, a revolution is underway. It's a revolution that started decades ago but today is empowering companies to deliver better, smarter services with greater ease and on broader scales than ever before. At Singularity University's inaugural Global Summit, Neil Jacobstein, chair of Artificial Intelligence and Robotics, provided a primer showing how artificial intelligence literally transforms everything it touches. First of all, it's critical to define the scope of artificial intelligence (AI), which can be categorized into four areas: techniques in pattern recognition, software agency (that is, software that acts like real users), an exponential technology that is accelerating other exponential technologies, and a vision of a future superhuman intelligence (that fortunately hasn't happened yet). Anyone who has seen a science fiction film is likely familiar with this last area, but it's the other three areas where AI is making huge strides at a revolutionary pace.

IBM says machine learning necessary to process vast quantities of data produced by sensors. Artificial intelligence and machine learning will be an essential part of IoT systems as organisations struggle to make sense of the enormous amounts of data produced by the Internet of Things. In a keynote speech at the IFA trade show in Berlin, Germany, Harriet Green, global head of IBM Watson IoT said that "millions of sensors are giving appliances and devices eyes and ears, increasing their inbuilt intelligence and enabling them to interact with us better." "The challenge is that over next few years, the Internet of Things will become the biggest source of data on the planet," she said. "That's where IBM's Watson cognitive computing system comes in. Watson uses machine learning and other techniques to understand this data and turn it into insight, which can help automate tasks, enable manufacturers to design better products, innovate new services and enhance our overall quality of life – especially in the home. And with cognitive technologies, interactions with'things' through natural language and voice commands will dramatically improve."