If you had to invent the perfect place to roll out self-driving buses, Japan would be it. The country boasts an immaculate and extensive road network. Much of the aging population relies on public transport, especially in the countryside, to get around. And that customer base is shrinking; fewer passengers equals less fares. As a result, only a third of the country's bus companies are profitable, forcing regional governments to step in to support them.

If Japan, home to the world's largest public debt, wanted to save a bundle, it would close the Bank of Japan. Auctioning off its giant neo-baroque headquarter buildings around the nation and pink-slipping roughly 4,900 full-time employees would cheer Moody's and Standard & Poor's and plug holes in the national balance sheet. That's not going to happen, of course. But imagine if the BOJ had closed shop 17 years ago, right after it first cut interest rates to zero, and turned its function over to a computer program. Would the artificial-intelligence version of the BOJ be any closer to 2 percent inflation than the well-compensated humans occupying its buildings?

If you had to invent the perfect place to roll out self-driving buses, Japan would be it. The country boasts an immaculate and extensive road network. Much of the aging population relies on public transport to get around, especially in the countryside. And that customer base is shrinking, bringing in less fares. As a result, only a third of the country's bus companies are profitable, forcing regional governments to step in to support them.

Will smart automation, intelligent software bots and brainy robots take away our jobs anytime soon? Pose this question to any Indian working in a company where unions are strong, or to any Indian who has a government job, or to the majority of Indians who work in the unorganized sector--those who drive taxis, trucks pull handcarts, hawk goods on footpaths or are employed as maids--and you will, in all probability, be looked at askance or even dismissed as an uninformed prophet of doom. The reaction may not be surprising in emerging countries like India, given that a majority of such employees would never have heard about the Industrial Revolution, or terms like disguised unemployment, cloud computing, machine learning, deep learning, automation or artificial intelligence (AI)-driven software bots. They would perhaps have also never heard of drones taking photographs and doing surveillance; of robots delivering pizzas and packages; of assistive robots taking care of the elderly; of robots making hamburgers and others like the Roomba robots that mop floors; of software bots writing articles and movie scripts; of three-dimensional or 3D printing revolutionizing the manufacturing sector; of driverless cars and trucks--all of which would make it very hard for them to imagine the future impact of these technologies that have not yet directly touched their lives or their jobs. They would have surely seen humanoid robots in sci-fi films like actor Rajnikant's Enthiran in Tamil or Robot in English, or a movie like Terminator or Transformers.

It may be time to add'novelist' to the list of professions under threat from super-smart computer software, because a short story authored by artificial intelligence has made it through to the latter stages of a literary competition in Japan.

Before Chris White could help disrupt Jihadi finance networks, crush weapons markets, and bust up sex-slave rings with search tools that mine the dark Web, he first had to figure out how to stop himself from plummeting through the open gun door of a banking Black Hawk helicopter. White was on his way to a forward operating base outside Kabul headquarters, as part of a secret intelligence cell to help confront the Taliban and al-Qaida, smash their encrypted online money stream, and win over the hearts and minds of the Afghanistan population. Slight and lanky and 28, White felt Dukakis-ridiculous in his unwieldy body armor and bulbous helmet with "Dr. White" scrawled in marker on duct tape across the front, and with the dust from liftoff, he was finding it hard to breathe. He was still struggling with the unfamiliar seat straps when the pilot hit the stick, sending White sliding toward the hot square of the door and the desert 200 feet below. Down there, Afghanistan was a messy, dangerous place for pretty much everybody. After nearly a decade of U.S.-led war, the American body count had hit 1,000, and civilian casualties were beyond calculation, as President Obama's 30,000-troop surge intensified the fighting that spring. Many feared the situation was only going from bad to worse. The U.S. was escalating drone strikes across the border in Pakistan. And U.S. command was under assault after Gen. Stanley McChrystal, the surge's architect, found himself without a job after he and his staff made disparaging remarks about the commander in chief in some music magazine. It is hard to imagine that only a few weeks earlier, White had been just another impossibly young-looking Harvard postdoc in flip-flops looking forward to a Cambridge summer. Helicopter gunships and war zones weren't on the radar; there were lattes in the square and rock climbing, and on the other side of campus, a prestigious fellowship in the School of Engineering and Applied Sciences, where he was working at the intersection of big data, statistics, and machine learning. He had earned academic pole position and had every expectation it would continue that way forever -- becoming a professor, building a lab, and sniping out white papers from a tenured ivory tower.

We propose an expectation-maximization-like(EMlike) method to train Boltzmann machine with unconstrained connectivity. It adopts Monte Carlo approximation in the E-step, and replaces the intractable likelihood objective with efficiently computed objectives or directly approximates the gradient of likelihood objective in the M-step. The EM-like method is a modification of alternating minimization. We prove that EM-like method will be the exactly same with contrastive divergence in restricted Boltzmann machine if the M-step of this method adopts special approximation. We also propose a new measure to assess the performance of Boltzmann machine as generative models of data, and its computational complexity is O(Rmn). Finally, we demonstrate the performance of EM-like method using numerical experiments.

Conversational Commerce refers to using natural language within a messenger application (Messenger, WhatsApp, WeChat, and others) or using voice assistants (Siri, Amazon Alexa, and others) to interact with a business for an inquiry, purchase, or customer service. The term Conversational Commerce was coined by Chris Messina, Developer Experience Lead at Uber, in a brief post over a year ago. While Conversational Commerce has been around for a while (think IVR and SMS) the recent success is the culmination of three key factors – emergence of the mobile-first customer, domination of messaging apps, and the increasing maturity of Artificial Intelligence. In the IAB Nielsen's Mobile Ratings Report 2015, findings indicate that Australians live in a mobile-first world: While the total time spent on commercial activities is at an average of 3%, this trend is poised for rapid growth as organisations mature their Conversational Commerce capabilities. Messaging apps like WhatsApp and WeChat are no longer just for conversation between friends and family.

The car company announced last week that it would no longer use a vision system provided by MobileEye, an Israeli company that supplies technology to many automakers. This comes a few weeks after the National Highway Traffic Safety Administration announced that it was investigating a fatal accident that occurred while one of Tesla's cars was operating in Autopilot mode, a system designed to enable automated driving under a driver's supervision. It is unclear why Tesla is dropping MobileEye, but one reason may be the emergence of newer approaches to automated driving. MobileEye provides what amounts to an advanced image-recognition system, capable of identifying road signs or obstacles, such as other cars or pedestrians, on the road ahead. The company has said that it uses deep learning, a popular machine-learning technique based on training a many-layered network of simulated neurons to recognize input using a large number of training examples.

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?