Photographs used in Tsvetkov's study. You may think you do, but a recent experiment by a Russian photographer suggests otherwise. In a project entitled, "Your face is big data," Rodchenko Art School student, Egor Tsvetkov, began by photographing about 100 people who happened to sit across from him on the subway at some point. He then used FindFace, a facial-recognition app that taps neural-network technology, to try to track them down on Russian social media site VK. It was ridiculously easy to find 60 to 70 per cent of the subjects aged between 18 and 35 or so, he found, although for older people it was more difficult.

Omoju Miller, a self-described futurist (someone who studies the future's possibilities), enjoys picturing tomorrow. As a Nigerian woman who settled in the Bay Area, she's already torn down historical barriers to work as a software engineer in Silicon Valley, a white man's world. But in envisioning a new society, Miller isn't thinking only of contemporary struggles; she's pondering what humanity will need next. Take one of her projects: Hiphopathy, where she's using machine learning to parse rappers' metaphorical language, in the hopes of teaching a computer to think conceptually, developing, in the process, a form of artificial intelligence. Recently, NationSwell spoke with Miller about true visionaries that inspire her and the lessons we can all take away from their avant-garde thinking.

A pair of super-rich American technology gurus are planning to undergo surgery to install experimental implants directly into their brains. The two men are currently trying to find a doctor willing to perform this untested and highly risky procedure, The Mirror has learned. If they survive the operation, the men hope to be able to directly communicate with the primitive forms of artificial intelligence currently being developed in labs across the world. But critics and conspiracy theorists fear these pioneering implants are the first step towards creating a society where every human is plugged into "the matrix". Zoltan Istvan, a U.S. Presidential candidate, personally knows both of these would-be bionic men.

The glossy video on the website of Atieva, a Silicon Valley startup developing a self-driving electric vehicle, is a hard sell for the California dream. Applying the California state of mind to shape a new vision of what a car can be." Related: Inside Faraday Future: is it really a big player in the future of electric cars? But a Guardian investigation can reveal significant problems inside the troubled startup including concerns among staff after a major investor was revealed to be Chinese billionaire Jia Yueting – the owner of rival Californian EV startup Faraday Future. Sources inside Atieva fear that Jia's ultimate aim is to merge Atieva's battery expertise – and perhaps the whole company – into Faraday Future, a mysterious but well-funded startup that has yet to reveal much about its own plans. Atieva's problems started in late 2015 when its founder and CEO, Bernard Tse, left the company following friction with its largest shareholder, Beijing Automotive Industry Holding (BAIC), one of China's leading state-owned carmakers. The company has yet to publicly acknowledge this or announce a replacement. Related: Inside Faraday Future: is it really a big player in the future of electric cars? Then in early April 2016, BAIC sold its entire 25% stake in Atieva, according to sources close to the company, who say that Jia is the most likely purchaser.With Faraday Future breaking ground last week on a new 1bn assembly plant in Nevada, several Atieva staff now expect the company to be absorbed into Faraday Future before its own product even sees the light of day. The Guardian approached Atieva and LeEco, which has close links with Atieva, for comment but they did not respond. A spokesman for Faraday Future said that it operates independently. Previously a vice-president at Tesla, Tse founded Atieva in 2007 alongside former Oracle executive Sam Weng. "The car business is seeing a sea change," Tse told the Guardian. "This is the opportunity that Silicon Valley has been waiting for since 1945.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

ONE Saturday night last year, 11 people went looking for love. Like countless speed daters before them, they met in a room draped with curtains, the lights on low. In one hand they held traditional glasses of bubbly, but in the other were sheets of paper they had filled with their personal data. This twist on speed-dating was part of an experiment run by a team at Newcastle University in the UK. They wanted to know what would happen in a world where instead of vetting potential dates by their artfully posed selfies or carefully crafted dating-site profiles, we looked at data gathered by their computers and phones.

I wish we could derive the rest of the phænomena of nature by the same kind of reasoning from mechanical principles. LAW I: A body in motion will be kept in motion. A body at rest will be asked what its plans for the day are. The First Law deals primarily with inertia--which is often mistakenly identified as "relaxing"--and the different ways one body can affect another inert (and perfectly content) body. Conversely, it states that a body in motion will be kept in motion with a list of errands, written on the back of an envelope, before that body "becomes one with the couch for the rest of the day," which seems like an unnecessary characterization.

An AI created by scientists at the Massachusetts Institute of Technology (MIT) uses machine learning to detect suspicious activity - getting it right 85% of the time. The system uses an algorithm called "AI2", that detects anomalies, in conjunction with a human expert, because AI2 on its own can lead to false positives, according to MIT News. "The more attacks the system detects, the more analyst feedback it receives, which, in turn, improves the accuracy of future predictions," said one of the researchers behind the project, Kalyan Veeramachaneni. "That human-machine interaction creates a beautiful, cascading effect." The merging of artificial intelligence and what researchers call "analyst intuition" has allowed for this new system to be successful in its early development, Veeramachaneni and fellow scientist Ignacio Arnaldo said.

Business intelligence has gone from static reports that tell you what happened, to interactive dashboards where you can drill into information to try and understand why it happened. New big data sources, including Internet of Things (IoT) devices, are pushing businesses from those reactive analytics – whether you look back once a month to spot trends or once a day to check for problems – to proactive analytics that give you alerts and real-time dashboards. That makes better use of operational data, which is more useful while it's still current, before conditions change. "There's a demand for real-time dashboards," says Herain Oberoi from Microsoft's Cortana Analytics team. "A lot of businesses want to get the pulse of their business. But dashboards show things that have already happened."

Artificial Intelligence has made huge advances in recent years in many areas, including language processing, vision, and machine learning; we are also seeing the emergence of platforms that integrate different kinds of AI, such as IBM Watson (Arria is a Watson ecosystem partner). Within medicine, I see a lot of excitement about using many aspects of AI; of course Natural Language Generation (NLG), but also using predictive analytics to anticipate potential problems, using machine learning to build diagnostic algorithms, using natural language processing to identify relevant research findings, using computer vision to analyze scans, and using robotics to assist surgeons and other clinicians. Natural Language Generation (NLG) software systems generate narratives that summarize, explain, and communicate complex data sets to people. The huge amount of data available in the modern world can overwhelm people; NLG humanizes the flood of data so that it helps rather than overwhelms people. NLG systems use data analysis and artificial intelligence techniques to analyze complex data sets, and computational linguistic techniques to communicate the results of the analysis in a high-quality narrative text.