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?

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

Anonymised CT and MRI scans from 700 former University College London Hospital radiotherapy patients will be analysed by Google's artificial intelligence division, DeepMind. The aim is to develop an algorithm that can automatically differentiate between healthy and cancerous tissues. This "segmentation" is necessary in patients with head and neck cancers. And it is hoped the time it takes to design targeted radiotherapy treatments could be cut from four hours to one. "Clinicians will remain responsible for deciding radiotherapy treatment plans," UCLH said.

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.

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.

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."

Imagine it's 2018 and some scientists from NASA are at the White House to see President Clintrump. There's a piece of space coming toward us; it is rocky, and icy, and big, and the risk of it hitting the Earth is much larger than anyone is comfortable with. Even if there's time to act, there won't be much of it. Where did it come from? How come we didn't spot it until now? What's the best course of action to take?

I've just finished watching Entrepreneur First's sixth cohort's Demo Day Demo in London. The event, held at Facebook's UK HQ, saw 21 newly outed startups pitch their wares on stage to investors, press and other actors in the European tech scene. But before I give a run down of the presenting companies, including our top 3 picks, here's a quick reminder of how EF works and what has made it the new darling of the UK startup community. Founded back in 2011 by Alice Bentinck and Matt Clifford, the so-called "talent first" investor targets the best technical graduates in Europe and beyond to put them through a six-month program where they form teams and in turn found startups. This includes financial support in the form of a monthly stipend for living costs while founders find their co-founders and decide on an idea.

Impaired glucose tolerance (IGT) is a risk factor for the development of type 2 diabetes mellitus (T2DM) [1], and both IGT and T2DM are associated with increase in cardio-cerebrovascular related mortality [2, 3]. The Diabetes Epidemiology: Collaborative Analysis of Diagnostic Criteria in Europe (DECODE) [4] study showed a tight correlation between IGT and cardiovascular mortality, and IGT is a known risk factor for early-stage atherosclerosis [5]. In the Actos Now for Prevention of Diabetes (ACT NOW) study, it was shown that pharmacotherapy with pioglitazone in IGT subjects resulted in reduced development of T2DM [6] as well as reduced progression of atherosclerosis [7]. Therefore, identification of IGT subjects who are at risk for rapid atherosclerosis progression, and understanding the important characteristics that affect the identification process, may be beneficial in risk stratification and early intervention. Machine learning (ML) methods have been widely used to learn complex relationships or patterns from data to make accurate predictions [8] and are usually applied in the setting of massive datasets ("big data").

Imagine custom shirts and shoes at mass production prices with same day delivery; imagine turbine parts produced at the airport where and when they are needed; imagine a new tooth made while you're in the dentist chair. The age of smart local manufacturing is just around the corner. Often called Industry 4.0, this new wave manufacturing incorporated connected devices (internet of things: IoT), cloud computing and machine learning. The term Industry 4.0 originated in 2011 with German government-funded research on advanced manufacturing. The second industrial revolution was mass production, starting around 1870, but best known for the assembly lines of Henry Ford 1913.