Passengers' baggage is collected by robots, they relax in a luxurious waiting area complete with an indoor garden before getting a face scan and swiftly passing through security and immigration - this could be the airport of the future. It's a vision that planners hope will become reality as new technology is rolled out, transforming the exhausting experience of getting stuck in lengthy queues in ageing, overcrowded terminals into something far more pleasant. The changes also represent major challenges that could upend decades-old business models at major airports, with analysts warning operators may face a hit to their revenues to the tune of billions of dollars. Facial scanning in particular is generating a lot of buzz. Changi in the affluent city-state of Singapore, regarded as among the world's best airports, is set to roll out this biometric technology at a new terminal to open later this year.

Like me, I'm sure that many of you nerds have read the book "I, Robot." "I, Robot" is the seminal book written by Isaac Asimov (actually it was a series of books, but I only read the one) that explores the moral and ethical challenges posed by a world dominated by robots. But I read that book like 50 years ago, so the movie "I, Robot" with Will Smith is actually more relevant to me today. The movie does a nice job of discussing the ethical and moral challenges associated with a society where robots play such a dominant and crucial role in everyday life. Both the book and the movie revolve around the "Three Laws of Robotics," which are: It's like the "3 Commandments" of being a robot; adhere to these three laws and everything will be just fine. Unfortunately, that turned out not to be true (if 10 commandments can not effectively govern humans, how do we expect just 3 to govern robots?).

Although the quest for Artificial Intelligence (AI), equipping trading algorithms with human qualities such as self-learning, continues to fascinate, it will be the explosion of the Internet of Things that will soon re-energize trading in capital markets. The Internet of Things (IoT) is rapidly growing through the addition of sensors to machines that allow them to "feel." Once they are equipped with feelings-- particularly sight, sound and touch-- machines can behave more intelligently, for example optimizing operations to use less fuel or predicting when they need maintenance. However, an interesting side effect is that the data from the IoT could be a new source of "insider" data for trading firms. For example, if combine harvesters (accessorized with sensors) signal a bumper wheat cropin the U.S. grain belt, traders can take advantage of this information before the crop report is issued.

If you had to take a guess, what would you name as the two most prominent trends in technology right now? Like most people, I feel pretty confident in choosing artificial intelligence (AI) and the Internet of Things (IoT), not necessarily in that order. But in a rare convergence, in turns out these two trends are even hotter together. In fact, the new hotness is the combination of AI and IoT, manifesting itself in a wide variety of form and implementations in locations around the world. At IBM, for example, the company opened a Watson Internet of Things headquarters in Munich, Germany, earlier this year.

New studies show that a machine can understand what you are saying without hearing a sound. Researchers at Oxford University in the U.K. and Google have developed an algorithm that has outperformed professional human lip readers, a breakthrough they say could lead to surveillance video systems that can show the content of speech in addition to the actions of an individual. The researchers developed the algorithm by training Google's Deep Mind neural network on thousands of hours of subtitled BBC TV videos, showing a wide range of people speaking in a variety of poses, activities, and lighting. The neural network, dubbed Watch, Listen, Attend, and Spell (WLAS), learned to transcribe videos of mouth motion to characters, using more than 100,000 sentences from the videos. By translating mouth movements into individual characters, WLAS was able to spell out words.

This is a guest post by Ethan Ayer, CEO of Resilient Network Systems. With faceprints, voiceprints and iris scans beginning to replace passwords in everything from police work to amusement park admission, behavioral biometrics is becoming one of IT security's hottest trends. With promising contenders in Scandinavia to stateside biometrics companies being snapped up by the likes of MasterCard and others, the security race is on as organizations move to understand--and adopt--behavioral biometrics technology. IT security is a central concern for organizations as they seek to keep intellectual property and customer information secure. In today's threat climate, passwords and security questions are no longer enough to dissuade hackers.

In October 2013, Lauri Love was drinking coffee in his dressing gown in his bedroom at his parents' house in the village of Stradishall, Suffolk, when his mother called upstairs to say there was a deliveryman at the front door. Love, whose first name is pronounced "Lowry", like the English painter, clomped downstairs. In the front doorway was a man dressed in a UPS uniform. "Are you Lauri Love?" the man asked. In a single motion, the man grabbed Love's arm while presenting, not a package, but a pair of rattling handcuffs. For the next five hours, while dusk turned to evening outside, Love, then 28, and his parents sat in the front room as a dozen or so men from the National Crime Agency, which investigates organised crime and other serious offences, checked the computers in the house. In Love's bedroom, they found two laptops, and a PC tower humming on his desk. Among the bewildering Rolodex of open tabs in Love's internet browsers, the officers found accounts logged into several hacker forums and arcane internet chatrooms. Downstairs, Love, who knew that anything said in these limbo moments of investigation could be later used against him, kept the conversation to small talk about the weather and football. A little before midnight, Love was told that he was being arrested on suspicion of offences under the 1990 Computer Misuse Act, which covers, among other things, criminal hacking. He was not informed of what crimes he had allegedly committed, and was pressed into the back of an unmarked car, and driven to the police investigation centre in Bury St Edmunds. Love's computers, along with USB drives and old computing hardware, much of which belonged to his father, a computing enthusiast, left, too. Love, who was subsequently diagnosed with Asperger syndrome – a form of autism that causes him to fret and obsess – did press-ups in his cell until, in the early hours of the morning, he fell into a brief and fitful sleep.

Artificial intelligence can accurately guess whether people are gay or straight based on photos of their faces, according to new research suggesting that machines can have significantly better "gaydar" than humans. The study from Stanford University – which found that a computer algorithm could correctly distinguish between gay and straight men 81% of the time, and 74% for women – has raised questions about the biological origins of sexual orientation, the ethics of facial-detection technology and the potential for this kind of software to violate people's privacy or be abused for anti-LGBT purposes. The machine intelligence tested in the research, which was published in the Journal of Personality and Social Psychology and first reported in the Economist, was based on a sample of more than 35,000 facial images that men and women publicly posted on a US dating website. The researchers, Michal Kosinski and Yilun Wang, extracted features from the images using "deep neural networks", meaning a sophisticated mathematical system that learns to analyze visuals based on a large dataset. The research found that gay men and women tended to have "gender-atypical" features, expressions and "grooming styles", essentially meaning gay men appeared more feminine and vice versa.

Chatbots are about to change the world in more ways than we can imagine. Already, bots around the globe can complete a diverse set of varying tasks. From ordering pizza online to mashing faces together in Project Murphy, chatbots are about to become a normal element in everyday life. As the scope of chatbots becomes broader every day, there are new applications popping up constantly. Education has traditionally been known as a sector where innovation moves slowly.

In this paper, we propose an uncertainty-aware learning from demonstration method by presenting a novel uncertainty estimation method utilizing a mixture density network appropriate for modeling complex and noisy human behaviors. The proposed uncertainty acquisition can be done with a single forward path without Monte Carlo sampling and is suitable for real-time robotics applications. The properties of the proposed uncertainty measure are analyzed through three different synthetic examples, absence of data, heavy measurement noise, and composition of functions scenarios. We show that each case can be distinguished using the proposed uncertainty measure and presented an uncertainty-aware learn- ing from demonstration method of an autonomous driving using this property. The proposed uncertainty-aware learning from demonstration method outperforms other compared methods in terms of safety using a complex real-world driving dataset.