When today's millennial generation has children of their own, they could live in a world filled with flying cars and robot workers while having the option to travel through space to distant planets. Science fiction buffs know the Star Trek universe takes place in the 23rd century when the Earth is at peace, humans have given up their desire for money, and mankind has developed colonies on other worlds. That's a mere 200 years from where we stand today, an insignificant time period in the history of the world. Looking at the technological achievements of humanity today, it's easy to see how such a world might become reality. While humans will always be a part of the Earth's labor pool, it's easy to see how smart robots will one day push large numbers of men and women out of the workforce.

It has been said that to give a man a fish is to feed him for a day, whereas to teach a man to fish is to feed him for life. Forward-looking financial service companies are similarly finding that giving computers instructions is not nearly as fruitful as teaching them to write their own. From assessing credit risks to beefing-up the security of their own networks, fintech startups, in particular, are turning to machine learning finance-based solutions in order to work smarter rather than harder. Considering that over 200 leading financial institutions will attend the upcoming October 2016 Machine Learning Fintech Conference, investment in this subset of artificial intelligence (AI) seems to be a wise move, indeed, for companies that don't want to be left behind. With leading banks starting to invest in AI, and machine learning in particular, fintech companies will be significantly disadvantaged if they fail to do likewise.

It seems only yesterday that we were lying on our friend's sofa vowing to abstain from all alcohol for the entirety of 2016 before being coerced into a pub trip by our pesky co-workers the first Friday back in the office. Yes, the start of the year seems no time ago at all, but 2016 is nearly over. There are under 9 weeks left of the year and this, coupled with the arrival of the colder weather, has got us feeling all nostalgic. Let's recap some of the biggest tech innovations of 2016 and our predictions for the tech world in 2017. Well Pokémon Go launched in July and thanks to the enormous number of nostalgic noughties kids roaming the streets with little to do, it took off at an astonishing rate.

We just came back from a week at an idyllic beach resort in Malaysia. Not having to wear shoes for a week felt liberating. Internet was non-existent in most parts of the island and sketchy at best at upmarket resorts. Even though it was utopia for a week, we could not wait to get back on the grid. Technology is a double-edged sword.

The deep learning community has devised a diverse set of methods to make gradient optimization, using large datasets, of large and highly complex models with deeply cascaded nonlinearities, practical. Taken as a whole, these methods constitute a breakthrough, allowing computational structures which are quite wide, very deep, and with an enormous number and variety of free parameters to be effectively optimized. The result now dominates much of practical machine learning, with applications in machine translation, computer vision, and speech recognition. Many of these methods, viewed through the lens of algorithmic differentiation (AD), can be seen as either addressing issues with the gradient itself, or finding ways of achieving increased efficiency using tricks that are AD-related, but not provided by current AD systems. The goal of this paper is to explain not just those methods of most relevance to AD, but also the technical constraints and mindset which led to their discovery. After explaining this context, we present a "laundry list" of methods developed by the deep learning community. Two of these are discussed in further mathematical detail: a way to dramatically reduce the size of the tape when performing reverse-mode AD on a (theoretically) time-reversible process like an ODE integrator; and a new mathematical insight that allows for the implementation of a stochastic Newton's method.

Social media services such as Twitter are a valuable source of information for decision support systems. Many studies have shown that this also holds for the medical domain, where Twitter is considered a viable tool for public health officials to sift through relevant information for the early detection, management, and control of epidemic outbreaks. This is possible due to the inherent capability of social media services to transmit information faster than traditional channels. However, the majority of current studies have limited their scope to the detection of common and seasonal health recurring events (e.g., Influenza-like Illness), partially due to the noisy nature of Twitter data, which makes outbreak detection and management very challenging. Within the European project M-Eco, we developed a Twitter-based Epidemic Intelligence (EI) system, which is designed to also handle a more general class of unexpected and aperiodic outbreaks. In particular, we faced three main research challenges in this endeavor: 1) dynamic classification to manage terminology evolution of Twitter messages, 2) alert generation to produce reliable outbreak alerts analyzing the (noisy) tweet time series, and 3) ranking and recommendation to support domain experts for better assessment of the generated alerts. In this paper, we empirically evaluate our proposed approach to these challenges using real-world outbreak datasets and a large collection of tweets. We validate our solution with domain experts, describe our experiences, and give a more realistic view on the benefits and issues of analyzing social media for public health.

The statistically equivalent signature (SES) algorithm is a method for feature selection inspired by the principles of constrained-based learning of Bayesian Networks. Most of the currently available feature-selection methods return only a single subset of features, supposedly the one with the highest predictive power. We argue that in several domains multiple subsets can achieve close to maximal predictive accuracy, and that arbitrarily providing only one has several drawbacks. The SES method attempts to identify multiple, predictive feature subsets whose performances are statistically equivalent. Under that respect SES subsumes and extends previous feature selection algorithms, like the max-min parent children algorithm. SES is implemented in an homonym function included in the R package MXM, standing for mens ex machina, meaning 'mind from the machine' in Latin. The MXM implementation of SES handles several data-analysis tasks, namely classification, regression and survival analysis. In this paper we present the SES algorithm, its implementation, and provide examples of use of the SES function in R. Furthermore, we analyze three publicly available data sets to illustrate the equivalence of the signatures retrieved by SES and to contrast SES against the state-of-the-art feature selection method LASSO. Our results provide initial evidence that the two methods perform comparably well in terms of predictive accuracy and that multiple, equally predictive signatures are actually present in real world data.

Multi-hop inference is necessary for machine learning systems to successfully solve tasks such as Recognising Textual Entailment and Machine Reading. In this work, we demonstrate the effectiveness of adaptive computation for learning the number of inference steps required for examples of different complexity and that learning the correct number of inference steps is difficult. We introduce the first model involving Adaptive Computation Time which provides a small performance benefit on top of a similar model without an adaptive component as well as enabling considerable insight into the reasoning process of the model.

Jump to an update: • Waiting for Hillary on a gray morning • A few thoughts on the unthinkable • Palin speaks at the Trump party • Trump's path • Settling in for the night at Hillary Clinton's party • If Trump wins, he would likely also control all three branches of government • A new electoral map is upending the old one • The part of the night when Democrats start to freak out • Marco Rubio, again • The exit polls show a breakdown in demographics that is entirely predictable • A shooting near a polling place in Los Angeles • Early exit polls: No evidence Comey made a difference • Is the South still the conservative heartland? Clinton's motorcade arrived soon after. At campaign events and at her party last night, Clinton was permanently inside a huge bubble of safe space guarded by the Secret Service. At today's event, they were nowhere to be seen. Clinton arrived in a small caravan that stopped in a busy street. The only visible protection was provided by a handful of New York cops who hadn't received notice she was coming just then and halfheartedly tried to convince a crowd to move backward. Soon, Clinton's staff and the crowd and a few people who happened to have been walking down the street were mashed together for a panicky moment. A bicyclist, nearby, screamed, "Get out of the way, you fucking morons."--A. The executive branch of the United States government has grown in its power over the past eight years. After 9/11, George W. Bush built an aggressive national-security apparatus that Barack Obama only partially reined in. To cite just one of the powers that Commander-in-Chief Donald J. Trump would acquire, the American President has grown comfortable with killing alleged terrorists remotely with unmanned vehicles. Congress has done little in the way of oversight of this program, and it is just one of the many new powers Trump could inherit. Similarly, Congress has shown no interest in rewriting the overly broad war authorizations that Bush and Obama used to wage campaigns across the Middle East and Africa. As Congress and the White House became unable to pass legislation, Obama also pushed the boundaries with respect to the use of executive orders. These can be rescinded on day one of a Trump Presidency, but, just as important, Trump will undoubtedly push the boundaries of executive orders beyond what Obama did.

I find myself being summoned from various directions, as if I've just stepped into a party with my closest friends. Many pins I see are interesting to me -- it's a pleasant feeling. A house with a window lined with dark brown wooden shutters. A shelf made from the back of an iMac. A media console on wheels with iron legs and wooden slats.