Science fiction authors have always dreamed up cities of the future. When it comes to seeing those dreams realized, though, artists and filmmakers have heavily focused on what Western cities might look like. There are, however, exceptions to that rule. Olalekan Jeyifous is a Nigerian-born artist currently based in Brooklyn. With a background in architecture, much of his work is focused on urban environments and buildings.

Gartner tracks emerging technologies in its annual Hype Cycle reports that enable organisations to increase this interconnection, allowing for the real-time exchange of vast quantities of data, and for increased intelligence, complex analysis and decision making in real time. To help IT leaders understand significant technology trends, Gartner analysts looked across all 109 Hype Cycles and noted four high-level megatrends trends that track the evolution of digital business technologies, services and business models. "New digital business models require ecosystems of people, businesses and technologies that must scale beyond the enterprise," said Betsy Burton, vice president and distinguished analyst. Since 2008, cloud, data, mobile and social (the Nexus of Forces) have dramatically affected people's lives, business interactions, social change and global economies. These massive collections of technologies and the resulting changes in business models and human behaviour have accelerated the interconnection of technology, business and humans, and given rise to digital business.

After more than 10 years since the release of "Robocop" the dreams of film creators are likely to become true. Of course, the humanity has not devised hi-tech cyborgs like the main character of the film. However, today complicated robots maintain public order and security in different parts of the world. Here you can get yourself acquainted with "robocops" which help police in real world. This year after annual Republican Convention the police of Cleveland allowed a robot named Griffin to patrol the streets.

Data volumes are soaring to previously unimaginable heights. More data has been created in the past two years than in the entire history of humanity. It is predicted that, by 2020, each person on the planet will account for the creation of an average of 1.7 megabytes of new data every second. Scalable AI solutions could help address humanity's biggest challenges. Drawing meaningful insight from such vast amounts data is beyond our capabilities as humans, but perhaps not those of machines.

Timothy Amukele, an assistant professor of pathology at Johns Hopkins Medical School in Baltimore, and systems engineer Jeff Street are trying to figure out how to use drones to deliver blood samples. Timothy Amukele, an assistant professor of pathology at Johns Hopkins Medical School in Baltimore, and systems engineer Jeff Street are trying to figure out how to use drones to deliver blood samples. Three years ago, Geoff Baird bought a drone. A Seattle dad and hobby plane enthusiast, Baird used the 2.5-pound quadcopter to photograph the Hawaiian coastline and film his son's soccer and baseball games. But his big hope is that drones will soon fly tubes of blood and other specimens to Harborview Medical Center, where he works as a clinical pathologist running the hospital's chemistry and toxicology labs.

Sure, the autonomous era will wipe out a lot of jobs. Automakers, tech titans, and startups are racing to essentially put four million truckers, cabbies and other drivers out of work. But like all radical technological shifts, self-driving cars will provide opportunities, too--for those with the right skills. Working in the most compelling part of this field requires an understanding of deep learning, the branch of artificial intelligence that trains computers to do things like discern pedestrians from lamp posts. Universities can't crank out graduates fast enough.

Still think the Great Pyramid is an ancient tomb? Some experts in deep learning may beg to differ. They have discovered that a particular application of deep learning methodology applied to the ancient structures on the Giza Plateau, including the Great Pyramid, may in fact show that it is a three dimensional process diagram. "We were using a deep learning methodology that helps us identify and categorize unknown symbols and processes. We thought it would be fun to use it on something everyone knows. We were amazed to find that the Giza Plateau may in fact be a three dimensional process diagram."

Microsoft's attempt to use machine learning to improve on the Duckworth-Lewis method in cricket has been dismissed by the current custodian of the system. For the uninitiated, the Duckworth-Lewis method, or D/L method for short, is used to calculate the score that the team batting second in a limited overs cricket match needs to reach if the match is affected by rain. For example, if the team batting first scores 400 in 50 overs for five wickets, but rain reduces the second team's innings to 40 overs, the D/L method may put forward a score of 300. It was invented by statisticians Frank Duckworth and Tony Lewis and was first used in 1997 in a match between England and Zimbabwe. It is now regularly used at matches at all levels.

This is a tough task, I was in this precarious situation trying to explain to my younger son. I had a curated list of top 10 frequently used Machine Learning algorithms, but the key was to do a backward mapping of these Machine Learning techniques to solve problems which are of interest and relevance to my son. As a starting point to the conversation I asked him, list down your decision making points, meaning there may be many situations when you had to make decisions but you may not have all the information. I took those and matched it to the machine learning algorithms while explaining the core concept behind the problem solving. A classifier is a machine learning technique that takes a bunch of data and attempts to predict which class the new data belongs to. Sure, remember sometime back you were asking me who you want to invite to your birthday party and whether they will accept your invitation or not! Now, assume that you have got a data set about all the other 29 kids in your class. The information contains their hobby, kind of books they read, do they share their tiffin or not, are they friendly, in your last birthday did they come and did they bring nice gifts, etc. Now, given these information, you want to predict whether your classmates will accept your invitation or not.

WASHINGTON – CIA Director John Brennan warned on Sunday that Russia has "exceptionally capable and sophisticated" computer capabilities and that the U.S. must be on guard. When asked in a television interview whether Russia is trying to manipulate the American presidential election, Brennan didn't say. But he noted that the FBI is investigating the hacking of Democratic National Committee emails, and he cited Moscow's aggressive intelligence collection and its focus on high-tech snooping. "I think that we have to be very, very wary of what the Russians might be trying to do in terms of collecting information in a cyber realm, as well as what they might want to do with it," he told CBS' "Face the Nation" on the 15th anniversary of the Sept. 11 attacks. On the terrorism threat, Brennan said the U.S. government is much better now at sharing information.