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.

In H. G. Wells's classic The War of the Worlds, the narrator pauses a moment to rue the fact that he didn't react sooner to the arrival of an "intelligence greater than man's"--in his case, Martians landing on earth. Comparing himself to a comfortable dodo in its nest, he imagined those ill-fated birds also dithering as hungry sailors invaded their island: "We will peck them to death tomorrow, my dear." As intelligent technologies take over more and more of the decision-making territory once occupied by humans, are you taking any action? Are you sufficiently aware of the signs that you should? To help you get the head start you may need, here are the signs that it's time to fly the nest.

For thousands of years humans believed that authority came from the gods. Then, during the modern era, humanism gradually shifted authority from deities to people. Jean-Jacques Rousseau summed up this revolution in Emile, his 1762 treatise on education. When looking for the rules of conduct in life, Rousseau found them "in the depths of my heart, traced by nature in characters which nothing can efface. I need only consult myself with regard to what I wish to do; what I feel to be good is good, what I feel to be bad is bad." Humanist thinkers such as Rousseau convinced us that our own feelings and desires were the ultimate source of meaning, and that our free will was, therefore, the highest authority of all.

Imagine the police arresting a bot and releasing it after months of custody and investigation. This is not a scenario from a futurist's blog -- it actually happened in Switzerland last year. What were the charges against the globe-trotting Swiss bot and its owners? Its name gives you an idea: Random Darknet Shopper. Created by a couple who are both artists, RDS shopped in the wrong places and bought illegal goods on the dark web, also called the "darknet", "deep web," and "darknet markets."

It has been dubbed the'quantum enigma machine' - and has been used for a groundbreaking new form of unbreakable encrypted messaging for the first time. The researchers proved a message could be sent with a key that's shorter than the message itself, breaking the conditions defined decades ago by the'father of information theory,' Claude Shannon. This encryption method, known as quantum data locking, could one day make for super-secure systems in which it is virtually impossible for a third party to obtain and translate the message. Using a device dubbed the'quantum enigma machine,' researchers have demonstrated a new form of unbreakable encrypted messaging for the first time. In an example explaining how this system works, a hypothetical'Alice' is sending an encrypted message to'Bob,' with'Eve' being the third party The work also taps into the fundamental uncertainty of quantum measurements, which states that the more we know about one property of a particle, the less we know about another.

This paper describes a new mechanism that might help with defining pattern sequences, by the fact that it can produce an upper bound on the ensemble value that can persistently oscillate with the actual values produced from each pattern. With every firing event, a node also receives an on/off feedback switch. If the node fires, then it sends a feedback result depending on the input signal strength. If the input signal is positive or larger, it can store an 'on' switch feedback for the next iteration. If the signal is negative or smaller, it can store an 'off' switch feedback for the next iteration. If the node does not fire, then it does not affect the current feedback situation and receives the switch command produced by the last active pattern event for the same neuron. The upper bound therefore also represents the largest or most enclosing pattern set and the lower value is for the actual set of firing patterns. If the pattern sequence repeats, it will oscillate between the two values, allowing them to be recognised and measured more easily, over time. Tests show that changing the sequence ordering produces different value sets, which can also be measured.

This paper extends the standard chaining technique (e.g., Pollard, 1990; Dudley, 1999; Györfi et al., 2002; Boucheron et al., 2012) to prove high-probability excess risk upper bounds for empirical risk minimization (ERM) for random design settings even if the magnitude of the noise and the estimates is unbounded. Our result (Theorem 1) covers bounded settings (Bartlett et al., 2005; Koltchinskii, 2011), extends to sub-Gaussian or even subexponential noise(van de Geer, 2000; Györfi and Wegkamp, 2008), and handles hypothesis classes with unbounded magnitude (Lecué and Mendelson, 2013; Mendelson, 2014; Liang et al., 2015). Furthermore, it applies to many loss functions besides the squared loss, and does not need additional statistical assumptions such as the bounded kurtosis of the transformed covariates over the hypothesis class, which prevent the latest developments to provide tight excess risk bounds for many sub-Gaussian cases (Section 1.2). To demonstrate the effectiveness of our method for such unbounded settings, we use our general excess risk bound (Theorem 1) to provide a detailed analysis for linear least squares estimators using quadratic slope constraint and penalty with sub-Gaussian noise and domain for the random design, nonrealizable setting(Section 3). Our result for the slope constrained case extends Theorem A of Lecué and Mendelson (2013) and nearly proves the conjecture of Shamir (2015), while our treatment for the penalized case (ridge regression) is comparable to the work of Hsu et al. (2014). The rest of this section introduces our notation through the formal definition of the regression problem and ERM estimators (Section 1.1), and discusses the limitations of 1 current excess risk upper bounds in the literature (Section 1.2). Then, we provide our main result in Section 2 to upper bound the excess risk of ERM estimators, and discuss its properties for various settings including many loss functions besides the squared loss. Next, Section 3 provides a detailed analysis for linear least squares estimators including the slope constrained case (Section 3.1) and ridge regression (Section 3.2). Finally, Section 4 proves our main result (Theorem 1).

At what point did we create an artificial intelligence? Was it when we first chiseled on rocks the memory of our debts? Was it that point when we enhanced reasoning by exploring possibilities in the arena of a game? Or when we solved a problem of inference beyond our merely fleshy ability to calculate? The dream of a fully autonomous artificial intelligence, stuff of infinite science-fiction prognostication, has blinded us to the incremental nature of artificial intelligence.

Volvo Cars today announced a partnership with Swedish-American vehicle safety systems supplier Autoliv Inc. to form a new jointly-owned company to develop autonomous driving software. The planned company, which has yet to be named, will have its headquarters in Gothenburg, Sweden and an initial workforce of about 200 taken from both firms. The new company is expected to start operations in early 2017, and then grow to more than 600 employees. Autoliv develops and sells automotive safety systems for all major automotive manufacturers around the globe; it has 80 facilities with 60,000 employees in 29 countries. The new Volvo/Autoliv company will develop advanced driver assistance systems (ADAS) and autonomous drive (AD) systems for use in Volvo cars and for sale exclusively by Autoliv to other car makers globally.

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.